{ // 获取包含Hugging Face文本的span元素 const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap'); spans.forEach(span => { if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) { span.textContent = 'AI快站'; } }); }); // 替换logo图片的alt属性 document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => { if (img.alt.match(/Hugging\s*Face/i)) { img.alt = 'AI快站 logo'; } }); } // 替换导航栏中的链接 function replaceNavigationLinks() { // 已替换标记,防止重复运行 if (window._navLinksReplaced) { return; } // 已经替换过的链接集合,防止重复替换 const replacedLinks = new Set(); // 只在导航栏区域查找和替换链接 const headerArea = document.querySelector('header') || document.querySelector('nav'); if (!headerArea) { return; } // 在导航区域内查找链接 const navLinks = headerArea.querySelectorAll('a'); navLinks.forEach(link => { // 如果已经替换过,跳过 if (replacedLinks.has(link)) return; const linkText = link.textContent.trim(); const linkHref = link.getAttribute('href') || ''; // 替换Spaces链接 - 仅替换一次 if ( (linkHref.includes('/spaces') || linkHref === '/spaces' || linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) && linkText !== 'OCR模型免费转Markdown' && linkText !== 'OCR模型免费转Markdown' ) { link.textContent = 'OCR模型免费转Markdown'; link.href = 'https://fast360.xyz'; link.setAttribute('target', '_blank'); link.setAttribute('rel', 'noopener noreferrer'); replacedLinks.add(link); } // 删除Posts链接 else if ( (linkHref.includes('/posts') || linkHref === '/posts' || linkText === 'Posts' || linkText.match(/^s*Postss*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } // 替换Docs链接 - 仅替换一次 else if ( (linkHref.includes('/docs') || linkHref === '/docs' || linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) && linkText !== '模型下载攻略' ) { link.textContent = '模型下载攻略'; link.href = '/'; replacedLinks.add(link); } // 删除Enterprise链接 else if ( (linkHref.includes('/enterprise') || linkHref === '/enterprise' || linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i)) ) { if (link.parentNode) { link.parentNode.removeChild(link); } replacedLinks.add(link); } }); // 查找可能嵌套的Spaces和Posts文本 const textNodes = []; function findTextNodes(element) { if (element.nodeType === Node.TEXT_NODE) { const text = element.textContent.trim(); if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') { textNodes.push(element); } } else { for (const child of element.childNodes) { findTextNodes(child); } } } // 只在导航区域内查找文本节点 findTextNodes(headerArea); // 替换找到的文本节点 textNodes.forEach(node => { const text = node.textContent.trim(); if (text === 'Spaces') { node.textContent = node.textContent.replace(/Spaces/g, 'OCR模型免费转Markdown'); } else if (text === 'Posts') { // 删除Posts文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } else if (text === 'Enterprise') { // 删除Enterprise文本节点 if (node.parentNode) { node.parentNode.removeChild(node); } } }); // 标记已替换完成 window._navLinksReplaced = true; } // 替换代码区域中的域名 function replaceCodeDomains() { // 特别处理span.hljs-string和span.njs-string元素 document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换hljs-string类的span中的域名(移除多余的转义符号) document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => { if (span.textContent && span.textContent.includes('huggingface.co')) { span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 替换pre和code标签中包含git clone命令的域名 document.querySelectorAll('pre, code').forEach(element => { if (element.textContent && element.textContent.includes('git clone')) { const text = element.innerHTML; if (text.includes('huggingface.co')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 处理特定的命令行示例 document.querySelectorAll('pre, code').forEach(element => { const text = element.innerHTML; if (text.includes('huggingface.co')) { // 针对git clone命令的专门处理 if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) { element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com'); } } }); // 特别处理模型下载页面上的代码片段 document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => { const content = container.innerHTML; if (content && content.includes('huggingface.co')) { container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com'); } }); // 特别处理模型仓库克隆对话框中的代码片段 try { // 查找包含"Clone this model repository"标题的对话框 const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]'); if (cloneDialog) { // 查找对话框中所有的代码片段和命令示例 const codeElements = cloneDialog.querySelectorAll('pre, code, span'); codeElements.forEach(element => { if (element.textContent && element.textContent.includes('huggingface.co')) { if (element.innerHTML.includes('huggingface.co')) { element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); } // 更精确地定位克隆命令中的域名 document.querySelectorAll('[data-target]').forEach(container => { const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string'); codeBlocks.forEach(block => { if (block.textContent && block.textContent.includes('huggingface.co')) { if (block.innerHTML.includes('huggingface.co')) { block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com'); } else { block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com'); } } }); }); } catch (e) { // 错误处理但不打印日志 } } // 当DOM加载完成后执行替换 if (document.readyState === 'loading') { document.addEventListener('DOMContentLoaded', () => { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); }); } else { replaceHeaderBranding(); replaceNavigationLinks(); replaceCodeDomains(); // 只在必要时执行替换 - 3秒后再次检查 setTimeout(() => { if (!window._navLinksReplaced) { console.log('[Client] 3秒后重新检查导航链接'); replaceNavigationLinks(); } }, 3000); } // 增加一个MutationObserver来处理可能的动态元素加载 const observer = new MutationObserver(mutations => { // 检查是否导航区域有变化 const hasNavChanges = mutations.some(mutation => { // 检查是否存在header或nav元素变化 return Array.from(mutation.addedNodes).some(node => { if (node.nodeType === Node.ELEMENT_NODE) { // 检查是否是导航元素或其子元素 if (node.tagName === 'HEADER' || node.tagName === 'NAV' || node.querySelector('header, nav')) { return true; } // 检查是否在导航元素内部 let parent = node.parentElement; while (parent) { if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') { return true; } parent = parent.parentElement; } } return false; }); }); // 只在导航区域有变化时执行替换 if (hasNavChanges) { // 重置替换状态,允许再次替换 window._navLinksReplaced = false; replaceHeaderBranding(); replaceNavigationLinks(); } }); // 开始观察document.body的变化,包括子节点 if (document.body) { observer.observe(document.body, { childList: true, subtree: true }); } else { document.addEventListener('DOMContentLoaded', () => { observer.observe(document.body, { childList: true, subtree: true }); }); } })(); \n\n"},"embedding":{"kind":"list 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\"mbart\",\n \"text2text-generation\",\n \"mbart-50\",\n \"multilingual\",\n \"ar\",\n \"cs\",\n \"de\",\n \"en\",\n \"es\",\n \"et\",\n \"fi\",\n \"fr\",\n \"gu\",\n \"hi\",\n \"it\",\n \"ja\",\n \"kk\",\n \"ko\",\n \"lt\",\n \"lv\",\n \"my\",\n \"ne\",\n \"nl\",\n \"ro\",\n \"ru\",\n \"si\",\n \"tr\",\n \"vi\",\n \"zh\",\n \"af\",\n \"az\",\n \"bn\",\n \"fa\",\n \"he\",\n \"hr\",\n \"id\",\n \"ka\",\n \"km\",\n \"mk\",\n \"ml\",\n \"mn\",\n \"mr\",\n \"pl\",\n \"ps\",\n \"pt\",\n \"sv\",\n \"sw\",\n \"ta\",\n \"te\",\n \"th\",\n \"tl\",\n \"uk\",\n \"ur\",\n \"xh\",\n \"gl\",\n \"sl\",\n \"arxiv:2008.00401\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text2text-generation"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: \n- multilingual\n- ar \n- cs\n- de\n- en\n- es\n- et\n- fi\n- fr\n- gu\n- hi\n- it\n- ja\n- kk\n- ko\n- lt\n- lv\n- my\n- ne\n- nl\n- ro\n- ru\n- si\n- tr\n- vi\n- zh\n- af\n- az\n- bn\n- fa\n- he\n- hr\n- id\n- ka\n- km\n- mk\n- ml\n- mn\n- mr\n- pl\n- ps\n- pt\n- sv\n- sw\n- ta\n- te\n- th\n- tl\n- uk\n- ur\n- xh\n- gl\n- sl\ntags:\n- mbart-50\n---\n\n# mBART-50 one to many multilingual machine translation\n\n\nThis model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-one-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper.\n\n\nThe model can translate English to other 49 languages mentioned below. \nTo translate into a target language, the target language id is forced as the first generated token. To force the\ntarget language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.\n\n```python\nfrom transformers import MBartForConditionalGeneration, MBart50TokenizerFast\narticle_en = \"The head of the United Nations says there is no military solution in Syria\"\nmodel = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50-one-to-many-mmt\")\ntokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50-one-to-many-mmt\", src_lang=\"en_XX\")\n\nmodel_inputs = tokenizer(article_en, return_tensors=\"pt\")\n\n# translate from English to Hindi\ngenerated_tokens = model.generate(\n **model_inputs,\n forced_bos_token_id=tokenizer.lang_code_to_id[\"hi_IN\"]\n)\ntokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n# => 'संयुक्त राष्ट्र के नेता कहते हैं कि सीरिया में कोई सैन्य समाधान नहीं है'\n\n# translate from English to Chinese\ngenerated_tokens = model.generate(\n **model_inputs,\n forced_bos_token_id=tokenizer.lang_code_to_id[\"zh_CN\"]\n)\ntokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n# => '联合国首脑说,叙利亚没有军事解决办法'\n```\n\nSee the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions.\n\n## Languages covered\nArabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)\n\n\n## BibTeX entry and citation info\n```\n@article{tang2020multilingual,\n title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},\n author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},\n year={2020},\n eprint={2008.00401},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"fill-mask"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"## GraphCodeBERT model\n\nGraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence length for the model is 512. The model is trained on the CodeSearchNet dataset, which includes 2.3M functions with document pairs for six programming languages. \n\nMore details can be found in the [paper](https://arxiv.org/abs/2009.08366) by Guo et. al.\n\n**Disclaimer:** The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face community members."},"embedding":{"kind":"list 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kandinsky-community/kandinsky-2-1-prior\ntags:\n- text-to-image\n- kandinsky\ninference: false\n---\n\n# Kandinsky 2.1\n\nKandinsky 2.1 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas.\n\nIt uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.\n\nThe Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov)\n\n## Usage\n\nKandinsky 2.1 is available in diffusers!\n\n```python\npip install diffusers transformers accelerate\n```\n### Text to image\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nimage = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0]\nimage.save(\"cheeseburger_monster.png\")\n```\n\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png)\n\n\n### Text Guided Image-to-Image Generation\n\n```python\nfrom diffusers import AutoPipelineForImage2Image\nimport torch\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nimport os\n\npipe = AutoPipelineForImage2Image.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\n\npipe.enable_model_cpu_offload()\n\nprompt = \"A fantasy landscape, Cinematic lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n \nresponse = requests.get(url)\noriginal_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\noriginal_image.thumbnail((768, 768))\n\nimage = pipe(prompt=prompt, image=original_image, strength=0.3).images[0]\nout.images[0].save(\"fantasy_land.png\")\n```\n\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png)\n\n\n### Interpolate \n\n```python\nfrom diffusers import KandinskyPriorPipeline, KandinskyPipeline\nfrom diffusers.utils import load_image\nimport PIL\n\nimport torch\n\npipe_prior = KandinskyPriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n)\npipe_prior.to(\"cuda\")\n\nimg1 = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n)\n\nimg2 = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/starry_night.jpeg\"\n)\n\n# add all the conditions we want to interpolate, can be either text or image\nimages_texts = [\"a cat\", img1, img2]\n\n# specify the weights for each condition in images_texts\nweights = [0.3, 0.3, 0.4]\n\n# We can leave the prompt empty\nprompt = \"\"\nprior_out = pipe_prior.interpolate(images_texts, weights)\n\npipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(prompt, **prior_out, height=768, width=768).images[0]\n\nimage.save(\"starry_cat.png\")\n```\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png)\n\n\n## Model Architecture\n\n### Overview\nKandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. \n\nThe model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. \n\n

\n \n

\n\nSpecifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [mCLIP model](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14). The trained image prior model is then used to generate mCLIP image embeddings for input text prompts. Both the input text prompts and its mCLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image.\n\n\n### Details\nThe image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution).\n\nThe main Text2Image diffusion model was trained on the basis of 170M text-image pairs from the [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) (an important condition was the presence of images with a resolution of at least 768x768). The use of 170M pairs is due to the fact that we kept the UNet diffusion block from Kandinsky 2.0, which allowed us not to train it from scratch. Further, at the stage of fine-tuning, a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources.\n\n\n### Evaluation\nWe quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID.\n\nFID metric values ​​for generative models on COCO_30k\n| | FID (30k)|\n|:------|----:|\n| eDiff-I (2022) | 6.95 | \n| Image (2022) | 7.27 | \n| Kandinsky 2.1 (2023) | 8.21|\n| Stable Diffusion 2.1 (2022) | 8.59 | \n| GigaGAN, 512x512 (2023) | 9.09 | \n| DALL-E 2 (2022) | 10.39 | \n| GLIDE (2022) | 12.24 | \n| Kandinsky 1.0 (2022) | 15.40 | \n| DALL-E (2021) | 17.89 | \n| Kandinsky 2.0 (2022) | 20.00 | \n| GLIGEN (2022) | 21.04 | \n\nFor more information, please refer to the upcoming technical report.\n\n## BibTex\nIf you find this repository useful in your research, please cite:\n```\n@misc{kandinsky 2.1,\n title = {kandinsky 2.1},\n author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov},\n year = {2023},\n howpublished = {},\n}\n```"},"embedding":{"kind":"list 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-0.1745699793100357\n]"}}},{"rowIdx":1105,"cells":{"modelId":{"kind":"string","value":"timm/eva_large_patch14_196.in22k_ft_in22k_in1k"},"author":{"kind":"string","value":"timm"},"last_modified":{"kind":"timestamp","value":"2023-03-31T06:13:42Z","string":"2023-03-31T06:13:42Z"},"downloads":{"kind":"number","value":20899,"string":"20,899"},"likes":{"kind":"number","value":0,"string":"0"},"library_name":{"kind":"string","value":"timm"},"tags":{"kind":"list like","value":["timm","pytorch","safetensors","image-classification","dataset:imagenet-1k","dataset:imagenet-22k","arxiv:2211.07636","license:mit","region:us"],"string":"[\n \"timm\",\n \"pytorch\",\n \"safetensors\",\n \"image-classification\",\n \"dataset:imagenet-1k\",\n \"dataset:imagenet-22k\",\n \"arxiv:2211.07636\",\n \"license:mit\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-classification"},"createdAt":{"kind":"timestamp","value":"2022-12-22T07:08:20Z","string":"2022-12-22T07:08:20Z"},"card":{"kind":"string","value":"---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\nlicense: mit\ndatasets:\n- imagenet-1k\n- imagenet-22k\n- imagenet-22k\n---\n# Model card for eva_large_patch14_196.in22k_ft_in22k_in1k\n\nAn EVA image classification model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-22k then on ImageNet-1k by paper authors.\n\nNOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred.\n\n\n## Model Details\n- **Model Type:** Image classification / feature backbone\n- **Model Stats:**\n - Params (M): 304.1\n - GMACs: 61.6\n - Activations (M): 63.5\n - Image size: 196 x 196\n- **Papers:**\n - EVA: Exploring the Limits of Masked Visual Representation Learning at Scale: https://arxiv.org/abs/2211.07636\n- **Pretrain Dataset:**\n - ImageNet-22k\n - ImageNet-22k\n- **Dataset:** ImageNet-1k\n- **Original:**\n - https://github.com/baaivision/EVA\n - https://huggingface.co/BAAI/EVA\n\n## Model Usage\n### Image Classification\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model('eva_large_patch14_196.in22k_ft_in22k_in1k', pretrained=True)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1\n\ntop5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)\n```\n\n### Image Embeddings\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model(\n 'eva_large_patch14_196.in22k_ft_in22k_in1k',\n pretrained=True,\n num_classes=0, # remove classifier nn.Linear\n)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor\n\n# or equivalently (without needing to set num_classes=0)\n\noutput = model.forward_features(transforms(img).unsqueeze(0))\n# output is unpooled, a (1, 197, 1024) shaped tensor\n\noutput = model.forward_head(output, pre_logits=True)\n# output is a (1, num_features) shaped tensor\n```\n\n## Model Comparison\nExplore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).\n\n|model |top1 |top5 |param_count|img_size|\n|-----------------------------------------------|------|------|-----------|--------|\n|eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08 |448 |\n|eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08 |448 |\n|eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 |\n|eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 |\n|eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 |\n|eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 |\n|eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 |\n|eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 |\n|eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 |\n|eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 |\n|eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 |\n|eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 |\n|eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 |\n|eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 |\n|eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 |\n|eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 |\n\n## Citation\n```bibtex\n@article{EVA,\n title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale},\n author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},\n journal={arXiv preprint arXiv:2211.07636},\n year={2022}\n}\n```\n```bibtex\n@misc{rw2019timm,\n author = {Ross Wightman},\n title = {PyTorch Image Models},\n year = {2019},\n publisher = {GitHub},\n journal = {GitHub repository},\n doi = {10.5281/zenodo.4414861},\n howpublished = {\\url{https://github.com/huggingface/pytorch-image-models}}\n}\n```\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-08-11T20:52:49Z","string":"2023-08-11T20:52:49Z"},"card":{"kind":"string","value":"---\nlicense: cc-by-nc-4.0\nlanguage:\n- en\n---\n\n---\n\n\n# UniNER-7B-all\n\n**Description**: This model is the best UniNER model. It is trained on the combinations of three data splits: (1) ChatGPT-generated [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type), (2) ChatGPT-generated [Pile-NER-definition data](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition), and (3) 40 supervised datasets in the Universal NER benchmark (see Fig. 4 in paper), where we randomly sample up to 10K instances from the train split of each dataset. Note that CrossNER and MIT datasets are excluded from training for OOD evaluation.\n\nCheck our [paper](https://arxiv.org/abs/2308.03279) for more information. Check our [repo](https://github.com/universal-ner/universal-ner) about how to use the model.\n\n## Inference\nThe template for inference instances is as follows:\n
\nPrompting template:
\nA virtual assistant answers questions from a user based on the provided text.
\nUSER: Text: {Fill the input text here}
\nASSISTANT: I’ve read this text.
\nUSER: What describes {Fill the entity type here} in the text?
\nASSISTANT: (model's predictions in JSON format)
\n
\n\n### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type.\n\n## License\n\nThis model and its associated data are released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes.\n\n## Citation\n\n```bibtex\n@article{zhou2023universalner,\n title={UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition}, \n author={Wenxuan Zhou and Sheng Zhang and Yu Gu and Muhao Chen and Hoifung Poon},\n year={2023},\n eprint={2308.03279},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"conversational"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nthumbnail: https://huggingface.co/front/thumbnails/dialogpt.png\ntags:\n- conversational\nlicense: mit\n---\n\n## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)\n\nDialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. \nThe [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.\nThe model is trained on 147M multi-turn dialogue from Reddit discussion thread. \n\n* Multi-turn generation examples from an interactive environment:\n\n|Role | Response |\n|---------|--------|\n|User | Does money buy happiness? |\n| Bot | Depends how much money you spend on it .|\n|User | What is the best way to buy happiness ? |\n| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |\n|User |This is so difficult ! |\n| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |\n\nPlease find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)\n\nArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)\n\n### How to use\n\nNow we are ready to try out how the model works as a chatting partner!\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\n\n\ntokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-large\")\nmodel = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-large\")\n\n# Let's chat for 5 lines\nfor step in range(5):\n\t# encode the new user input, add the eos_token and return a tensor in Pytorch\n\tnew_user_input_ids = tokenizer.encode(input(\">> User:\") + tokenizer.eos_token, return_tensors='pt')\n\n\t# append the new user input tokens to the chat history\n\tbot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n\n\t# generated a response while limiting the total chat history to 1000 tokens, \n\tchat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)\n\n\t# pretty print last ouput tokens from bot\n\tprint(\"DialoGPT: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))\n```\n"},"embedding":{"kind":"list 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-0.4686511158943176\n]"}}},{"rowIdx":1108,"cells":{"modelId":{"kind":"string","value":"BAAI/bge-base-en"},"author":{"kind":"string","value":"BAAI"},"last_modified":{"kind":"timestamp","value":"2023-10-12T03:37:19Z","string":"2023-10-12T03:37:19Z"},"downloads":{"kind":"number","value":20850,"string":"20,850"},"likes":{"kind":"number","value":41,"string":"41"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","bert","feature-extraction","mteb","en","arxiv:2310.07554","arxiv:2309.07597","license:mit","model-index","endpoints_compatible","has_space","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"bert\",\n \"feature-extraction\",\n \"mteb\",\n \"en\",\n \"arxiv:2310.07554\",\n \"arxiv:2309.07597\",\n \"license:mit\",\n \"model-index\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"feature-extraction"},"createdAt":{"kind":"timestamp","value":"2023-08-05T08:03:50Z","string":"2023-08-05T08:03:50Z"},"card":{"kind":"string","value":"---\ntags:\n- mteb\nmodel-index:\n- name: bge-base-en\n results:\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_counterfactual\n name: MTEB AmazonCounterfactualClassification (en)\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 75.73134328358209\n - type: ap\n value: 38.97277232632892\n - type: f1\n value: 69.81740361139785\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_polarity\n name: MTEB AmazonPolarityClassification\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 92.56522500000001\n - type: ap\n value: 88.88821771869553\n - type: f1\n value: 92.54817512659696\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_reviews_multi\n name: MTEB AmazonReviewsClassification (en)\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 46.91\n - type: f1\n value: 46.28536394320311\n - task:\n type: Retrieval\n dataset:\n type: arguana\n name: MTEB ArguAna\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 38.834\n - type: map_at_10\n value: 53.564\n - type: map_at_100\n value: 54.230000000000004\n - type: map_at_1000\n value: 54.235\n - type: map_at_3\n value: 49.49\n - type: map_at_5\n value: 51.784\n - type: mrr_at_1\n value: 39.26\n - type: mrr_at_10\n value: 53.744\n - type: mrr_at_100\n value: 54.410000000000004\n - type: mrr_at_1000\n value: 54.415\n - type: mrr_at_3\n value: 49.656\n - type: mrr_at_5\n value: 52.018\n - type: ndcg_at_1\n value: 38.834\n - type: ndcg_at_10\n value: 61.487\n - type: ndcg_at_100\n value: 64.303\n - type: ndcg_at_1000\n value: 64.408\n - type: ndcg_at_3\n value: 53.116\n - type: ndcg_at_5\n value: 57.248\n - type: precision_at_1\n value: 38.834\n - type: precision_at_10\n value: 8.663\n - type: precision_at_100\n value: 0.989\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 21.218999999999998\n - type: precision_at_5\n value: 14.737\n - type: recall_at_1\n value: 38.834\n - type: recall_at_10\n value: 86.629\n - type: recall_at_100\n value: 98.86200000000001\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 63.656\n - type: recall_at_5\n value: 73.68400000000001\n - task:\n type: Clustering\n dataset:\n type: mteb/arxiv-clustering-p2p\n name: MTEB ArxivClusteringP2P\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 48.88475477433035\n - task:\n type: Clustering\n dataset:\n type: mteb/arxiv-clustering-s2s\n name: MTEB ArxivClusteringS2S\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 42.85053138403176\n - task:\n type: Reranking\n dataset:\n type: mteb/askubuntudupquestions-reranking\n name: MTEB AskUbuntuDupQuestions\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 62.23221013208242\n - type: mrr\n value: 74.64857318735436\n - task:\n type: STS\n dataset:\n type: mteb/biosses-sts\n name: MTEB BIOSSES\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 87.4403443247284\n - type: cos_sim_spearman\n value: 85.5326718115169\n - type: euclidean_pearson\n value: 86.0114007449595\n - type: euclidean_spearman\n value: 86.05979225604875\n - type: manhattan_pearson\n value: 86.05423806568598\n - type: manhattan_spearman\n value: 86.02485170086835\n - task:\n type: Classification\n dataset:\n type: mteb/banking77\n name: MTEB Banking77Classification\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 86.44480519480518\n - type: f1\n value: 86.41301900941988\n - task:\n type: Clustering\n dataset:\n type: mteb/biorxiv-clustering-p2p\n name: MTEB BiorxivClusteringP2P\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 40.17547250880036\n - task:\n type: Clustering\n dataset:\n type: mteb/biorxiv-clustering-s2s\n name: MTEB BiorxivClusteringS2S\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 37.74514172687293\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackAndroidRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.096000000000004\n - type: map_at_10\n value: 43.345\n - type: map_at_100\n value: 44.73\n - type: map_at_1000\n value: 44.85\n - type: map_at_3\n value: 39.956\n - type: map_at_5\n value: 41.727\n - type: mrr_at_1\n value: 38.769999999999996\n - type: mrr_at_10\n value: 48.742000000000004\n - type: mrr_at_100\n value: 49.474000000000004\n - type: mrr_at_1000\n value: 49.513\n - type: mrr_at_3\n value: 46.161\n - type: mrr_at_5\n value: 47.721000000000004\n - type: ndcg_at_1\n value: 38.769999999999996\n - type: ndcg_at_10\n value: 49.464999999999996\n - type: ndcg_at_100\n value: 54.632000000000005\n - type: ndcg_at_1000\n value: 56.52\n - type: ndcg_at_3\n value: 44.687\n - type: ndcg_at_5\n value: 46.814\n - type: precision_at_1\n value: 38.769999999999996\n - type: precision_at_10\n value: 9.471\n - type: precision_at_100\n value: 1.4909999999999999\n - type: precision_at_1000\n value: 0.194\n - type: precision_at_3\n value: 21.268\n - type: precision_at_5\n value: 15.079\n - type: recall_at_1\n value: 32.096000000000004\n - type: recall_at_10\n value: 60.99099999999999\n - type: recall_at_100\n value: 83.075\n - type: recall_at_1000\n value: 95.178\n - type: recall_at_3\n value: 47.009\n - type: recall_at_5\n value: 53.348\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackEnglishRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.588\n - type: map_at_10\n value: 42.251\n - type: map_at_100\n value: 43.478\n - type: map_at_1000\n value: 43.617\n - type: map_at_3\n value: 39.381\n - type: map_at_5\n value: 41.141\n - type: mrr_at_1\n value: 41.21\n - type: mrr_at_10\n value: 48.765\n - type: mrr_at_100\n value: 49.403000000000006\n - type: mrr_at_1000\n value: 49.451\n - type: mrr_at_3\n value: 46.73\n - type: mrr_at_5\n value: 47.965999999999994\n - type: ndcg_at_1\n value: 41.21\n - type: ndcg_at_10\n value: 47.704\n - type: ndcg_at_100\n value: 51.916\n - type: ndcg_at_1000\n value: 54.013999999999996\n - type: ndcg_at_3\n value: 44.007000000000005\n - type: ndcg_at_5\n value: 45.936\n - type: precision_at_1\n value: 41.21\n - type: precision_at_10\n value: 8.885\n - type: precision_at_100\n value: 1.409\n - type: precision_at_1000\n value: 0.189\n - type: precision_at_3\n value: 21.274\n - type: precision_at_5\n value: 15.045\n - type: recall_at_1\n value: 32.588\n - type: recall_at_10\n value: 56.333\n - type: recall_at_100\n value: 74.251\n - type: recall_at_1000\n value: 87.518\n - type: recall_at_3\n value: 44.962\n - type: recall_at_5\n value: 50.609\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackGamingRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 40.308\n - type: map_at_10\n value: 53.12\n - type: map_at_100\n value: 54.123\n - type: map_at_1000\n value: 54.173\n - type: map_at_3\n value: 50.017999999999994\n - type: map_at_5\n value: 51.902\n - type: mrr_at_1\n value: 46.394999999999996\n - type: mrr_at_10\n value: 56.531\n - type: mrr_at_100\n value: 57.19800000000001\n - type: mrr_at_1000\n value: 57.225\n - type: mrr_at_3\n value: 54.368\n - type: mrr_at_5\n value: 55.713\n - type: ndcg_at_1\n value: 46.394999999999996\n - type: ndcg_at_10\n value: 58.811\n - type: ndcg_at_100\n value: 62.834\n - type: ndcg_at_1000\n value: 63.849999999999994\n - type: ndcg_at_3\n value: 53.88699999999999\n - type: ndcg_at_5\n value: 56.477999999999994\n - type: precision_at_1\n value: 46.394999999999996\n - type: precision_at_10\n value: 9.398\n - type: precision_at_100\n value: 1.2309999999999999\n - type: precision_at_1000\n value: 0.136\n - type: precision_at_3\n value: 24.221999999999998\n - type: precision_at_5\n value: 16.539\n - type: recall_at_1\n value: 40.308\n - type: recall_at_10\n value: 72.146\n - type: recall_at_100\n value: 89.60900000000001\n - type: recall_at_1000\n value: 96.733\n - type: recall_at_3\n value: 58.91499999999999\n - type: recall_at_5\n value: 65.34299999999999\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackGisRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 27.383000000000003\n - type: map_at_10\n value: 35.802\n - type: map_at_100\n value: 36.756\n - type: map_at_1000\n value: 36.826\n - type: map_at_3\n value: 32.923\n - type: map_at_5\n value: 34.577999999999996\n - type: mrr_at_1\n value: 29.604999999999997\n - type: mrr_at_10\n value: 37.918\n - type: mrr_at_100\n value: 38.732\n - type: mrr_at_1000\n value: 38.786\n - type: mrr_at_3\n value: 35.198\n - type: mrr_at_5\n value: 36.808\n - type: ndcg_at_1\n value: 29.604999999999997\n - type: ndcg_at_10\n value: 40.836\n - type: ndcg_at_100\n value: 45.622\n - type: ndcg_at_1000\n value: 47.427\n - type: ndcg_at_3\n value: 35.208\n - type: ndcg_at_5\n value: 38.066\n - type: precision_at_1\n value: 29.604999999999997\n - type: precision_at_10\n value: 6.226\n - type: precision_at_100\n value: 0.9079999999999999\n - type: precision_at_1000\n value: 0.11\n - type: precision_at_3\n value: 14.463000000000001\n - type: precision_at_5\n value: 10.35\n - type: recall_at_1\n value: 27.383000000000003\n - type: recall_at_10\n value: 54.434000000000005\n - type: recall_at_100\n value: 76.632\n - type: recall_at_1000\n value: 90.25\n - type: recall_at_3\n value: 39.275\n - type: recall_at_5\n value: 46.225\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackMathematicaRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 17.885\n - type: map_at_10\n value: 25.724000000000004\n - type: map_at_100\n value: 26.992\n - type: map_at_1000\n value: 27.107999999999997\n - type: map_at_3\n value: 23.04\n - type: map_at_5\n value: 24.529\n - type: mrr_at_1\n value: 22.264\n - type: mrr_at_10\n value: 30.548\n - type: mrr_at_100\n value: 31.593\n - type: mrr_at_1000\n value: 31.657999999999998\n - type: mrr_at_3\n value: 27.756999999999998\n - type: mrr_at_5\n value: 29.398999999999997\n - type: ndcg_at_1\n value: 22.264\n - type: ndcg_at_10\n value: 30.902\n - type: ndcg_at_100\n value: 36.918\n - type: ndcg_at_1000\n value: 39.735\n - type: ndcg_at_3\n value: 25.915\n - type: ndcg_at_5\n value: 28.255999999999997\n - type: precision_at_1\n value: 22.264\n - type: precision_at_10\n value: 5.634\n - type: precision_at_100\n value: 0.9939999999999999\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 12.396\n - type: precision_at_5\n value: 9.055\n - type: recall_at_1\n value: 17.885\n - type: recall_at_10\n value: 42.237\n - type: recall_at_100\n value: 68.489\n - type: recall_at_1000\n value: 88.721\n - type: recall_at_3\n value: 28.283\n - type: recall_at_5\n value: 34.300000000000004\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackPhysicsRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 29.737000000000002\n - type: map_at_10\n value: 39.757\n - type: map_at_100\n value: 40.992\n - type: map_at_1000\n value: 41.102\n - type: map_at_3\n value: 36.612\n - type: map_at_5\n value: 38.413000000000004\n - type: mrr_at_1\n value: 35.804\n - type: mrr_at_10\n value: 45.178000000000004\n - type: mrr_at_100\n value: 45.975\n - type: mrr_at_1000\n value: 46.021\n - type: mrr_at_3\n value: 42.541000000000004\n - type: mrr_at_5\n value: 44.167\n - type: ndcg_at_1\n value: 35.804\n - type: ndcg_at_10\n value: 45.608\n - type: ndcg_at_100\n value: 50.746\n - type: ndcg_at_1000\n value: 52.839999999999996\n - type: ndcg_at_3\n value: 40.52\n - type: ndcg_at_5\n value: 43.051\n - type: precision_at_1\n value: 35.804\n - type: precision_at_10\n value: 8.104\n - type: precision_at_100\n value: 1.256\n - type: precision_at_1000\n value: 0.161\n - type: precision_at_3\n value: 19.121\n - type: precision_at_5\n value: 13.532\n - type: recall_at_1\n value: 29.737000000000002\n - type: recall_at_10\n value: 57.66\n - type: recall_at_100\n value: 79.121\n - type: recall_at_1000\n value: 93.023\n - type: recall_at_3\n value: 43.13\n - type: recall_at_5\n value: 49.836000000000006\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackProgrammersRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 26.299\n - type: map_at_10\n value: 35.617\n - type: map_at_100\n value: 36.972\n - type: map_at_1000\n value: 37.096000000000004\n - type: map_at_3\n value: 32.653999999999996\n - type: map_at_5\n value: 34.363\n - type: mrr_at_1\n value: 32.877\n - type: mrr_at_10\n value: 41.423\n - type: mrr_at_100\n value: 42.333999999999996\n - type: mrr_at_1000\n value: 42.398\n - type: mrr_at_3\n value: 39.193\n - type: mrr_at_5\n value: 40.426\n - type: ndcg_at_1\n value: 32.877\n - type: ndcg_at_10\n value: 41.271\n - type: ndcg_at_100\n value: 46.843\n - type: ndcg_at_1000\n value: 49.366\n - type: ndcg_at_3\n value: 36.735\n - type: ndcg_at_5\n value: 38.775999999999996\n - type: precision_at_1\n value: 32.877\n - type: precision_at_10\n value: 7.580000000000001\n - type: precision_at_100\n value: 1.192\n - type: precision_at_1000\n value: 0.158\n - type: precision_at_3\n value: 17.541999999999998\n - type: precision_at_5\n value: 12.443\n - type: recall_at_1\n value: 26.299\n - type: recall_at_10\n value: 52.256\n - type: recall_at_100\n value: 75.919\n - type: recall_at_1000\n value: 93.185\n - type: recall_at_3\n value: 39.271\n - type: recall_at_5\n value: 44.901\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 27.05741666666667\n - type: map_at_10\n value: 36.086416666666665\n - type: map_at_100\n value: 37.26916666666667\n - type: map_at_1000\n value: 37.38191666666666\n - type: map_at_3\n value: 33.34225\n - type: map_at_5\n value: 34.86425\n - type: mrr_at_1\n value: 32.06008333333333\n - type: mrr_at_10\n value: 40.36658333333333\n - type: mrr_at_100\n value: 41.206500000000005\n - type: mrr_at_1000\n value: 41.261083333333325\n - type: mrr_at_3\n value: 38.01208333333334\n - type: mrr_at_5\n value: 39.36858333333333\n - type: ndcg_at_1\n value: 32.06008333333333\n - type: ndcg_at_10\n value: 41.3535\n - type: ndcg_at_100\n value: 46.42066666666666\n - type: ndcg_at_1000\n value: 48.655166666666666\n - type: ndcg_at_3\n value: 36.78041666666667\n - type: ndcg_at_5\n value: 38.91783333333334\n - type: precision_at_1\n value: 32.06008333333333\n - type: precision_at_10\n value: 7.169833333333332\n - type: precision_at_100\n value: 1.1395\n - type: precision_at_1000\n value: 0.15158333333333332\n - type: precision_at_3\n value: 16.852\n - type: precision_at_5\n value: 11.8645\n - type: recall_at_1\n value: 27.05741666666667\n - type: recall_at_10\n value: 52.64491666666666\n - type: recall_at_100\n value: 74.99791666666667\n - type: recall_at_1000\n value: 90.50524999999999\n - type: recall_at_3\n value: 39.684000000000005\n - type: recall_at_5\n value: 45.37225\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackStatsRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 25.607999999999997\n - type: map_at_10\n value: 32.28\n - type: map_at_100\n value: 33.261\n - type: map_at_1000\n value: 33.346\n - type: map_at_3\n value: 30.514999999999997\n - type: map_at_5\n value: 31.415\n - type: mrr_at_1\n value: 28.988000000000003\n - type: mrr_at_10\n value: 35.384\n - type: mrr_at_100\n value: 36.24\n - type: mrr_at_1000\n value: 36.299\n - type: mrr_at_3\n value: 33.717000000000006\n - type: mrr_at_5\n value: 34.507\n - type: ndcg_at_1\n value: 28.988000000000003\n - type: ndcg_at_10\n value: 36.248000000000005\n - type: ndcg_at_100\n value: 41.034\n - type: ndcg_at_1000\n value: 43.35\n - type: ndcg_at_3\n value: 32.987\n - type: ndcg_at_5\n value: 34.333999999999996\n - type: precision_at_1\n value: 28.988000000000003\n - type: precision_at_10\n value: 5.506\n - type: precision_at_100\n value: 0.853\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 14.11\n - type: precision_at_5\n value: 9.417\n - type: recall_at_1\n value: 25.607999999999997\n - type: recall_at_10\n value: 45.344\n - type: recall_at_100\n value: 67.132\n - type: recall_at_1000\n value: 84.676\n - type: recall_at_3\n value: 36.02\n - type: recall_at_5\n value: 39.613\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackTexRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 18.44\n - type: map_at_10\n value: 25.651000000000003\n - type: map_at_100\n value: 26.735\n - type: map_at_1000\n value: 26.86\n - type: map_at_3\n value: 23.409\n - type: map_at_5\n value: 24.604\n - type: mrr_at_1\n value: 22.195\n - type: mrr_at_10\n value: 29.482000000000003\n - type: mrr_at_100\n value: 30.395\n - type: mrr_at_1000\n value: 30.471999999999998\n - type: mrr_at_3\n value: 27.409\n - type: mrr_at_5\n value: 28.553\n - type: ndcg_at_1\n value: 22.195\n - type: ndcg_at_10\n value: 30.242\n - type: ndcg_at_100\n value: 35.397\n - type: ndcg_at_1000\n value: 38.287\n - type: ndcg_at_3\n value: 26.201\n - type: ndcg_at_5\n value: 28.008\n - type: precision_at_1\n value: 22.195\n - type: precision_at_10\n value: 5.372\n - type: precision_at_100\n value: 0.9259999999999999\n - type: precision_at_1000\n value: 0.135\n - type: precision_at_3\n value: 12.228\n - type: precision_at_5\n value: 8.727\n - type: recall_at_1\n value: 18.44\n - type: recall_at_10\n value: 40.325\n - type: recall_at_100\n value: 63.504000000000005\n - type: recall_at_1000\n value: 83.909\n - type: recall_at_3\n value: 28.925\n - type: recall_at_5\n value: 33.641\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackUnixRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 26.535999999999998\n - type: map_at_10\n value: 35.358000000000004\n - type: map_at_100\n value: 36.498999999999995\n - type: map_at_1000\n value: 36.597\n - type: map_at_3\n value: 32.598\n - type: map_at_5\n value: 34.185\n - type: mrr_at_1\n value: 31.25\n - type: mrr_at_10\n value: 39.593\n - type: mrr_at_100\n value: 40.443\n - type: mrr_at_1000\n value: 40.498\n - type: mrr_at_3\n value: 37.018\n - type: mrr_at_5\n value: 38.492\n - type: ndcg_at_1\n value: 31.25\n - type: ndcg_at_10\n value: 40.71\n - type: ndcg_at_100\n value: 46.079\n - type: ndcg_at_1000\n value: 48.287\n - type: ndcg_at_3\n value: 35.667\n - type: ndcg_at_5\n value: 38.080000000000005\n - type: precision_at_1\n value: 31.25\n - type: precision_at_10\n value: 6.847\n - type: precision_at_100\n value: 1.079\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 16.262\n - type: precision_at_5\n value: 11.455\n - type: recall_at_1\n value: 26.535999999999998\n - type: recall_at_10\n value: 52.92099999999999\n - type: recall_at_100\n value: 76.669\n - type: recall_at_1000\n value: 92.096\n - type: recall_at_3\n value: 38.956\n - type: recall_at_5\n value: 45.239000000000004\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackWebmastersRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 24.691\n - type: map_at_10\n value: 33.417\n - type: map_at_100\n value: 35.036\n - type: map_at_1000\n value: 35.251\n - type: map_at_3\n value: 30.646\n - type: map_at_5\n value: 32.177\n - type: mrr_at_1\n value: 30.04\n - type: mrr_at_10\n value: 37.905\n - type: mrr_at_100\n value: 38.929\n - type: mrr_at_1000\n value: 38.983000000000004\n - type: mrr_at_3\n value: 35.276999999999994\n - type: mrr_at_5\n value: 36.897000000000006\n - type: ndcg_at_1\n value: 30.04\n - type: ndcg_at_10\n value: 39.037\n - type: ndcg_at_100\n value: 44.944\n - type: ndcg_at_1000\n value: 47.644\n - type: ndcg_at_3\n value: 34.833999999999996\n - type: ndcg_at_5\n value: 36.83\n - type: precision_at_1\n value: 30.04\n - type: precision_at_10\n value: 7.4510000000000005\n - type: precision_at_100\n value: 1.492\n - type: precision_at_1000\n value: 0.234\n - type: precision_at_3\n value: 16.337\n - type: precision_at_5\n value: 11.897\n - type: recall_at_1\n value: 24.691\n - type: recall_at_10\n value: 49.303999999999995\n - type: recall_at_100\n value: 76.20400000000001\n - type: recall_at_1000\n value: 93.30000000000001\n - type: recall_at_3\n value: 36.594\n - type: recall_at_5\n value: 42.41\n - task:\n type: Retrieval\n dataset:\n type: BeIR/cqadupstack\n name: MTEB CQADupstackWordpressRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 23.118\n - type: map_at_10\n value: 30.714999999999996\n - type: map_at_100\n value: 31.656000000000002\n - type: map_at_1000\n value: 31.757\n - type: map_at_3\n value: 28.355000000000004\n - type: map_at_5\n value: 29.337000000000003\n - type: mrr_at_1\n value: 25.323\n - type: mrr_at_10\n value: 32.93\n - type: mrr_at_100\n value: 33.762\n - type: mrr_at_1000\n value: 33.829\n - type: mrr_at_3\n value: 30.775999999999996\n - type: mrr_at_5\n value: 31.774\n - type: ndcg_at_1\n value: 25.323\n - type: ndcg_at_10\n value: 35.408\n - type: ndcg_at_100\n value: 40.083\n - type: ndcg_at_1000\n value: 42.542\n - type: ndcg_at_3\n value: 30.717\n - type: ndcg_at_5\n value: 32.385000000000005\n - type: precision_at_1\n value: 25.323\n - type: precision_at_10\n value: 5.564\n - type: precision_at_100\n value: 0.843\n - type: precision_at_1000\n value: 0.116\n - type: precision_at_3\n value: 13.001\n - type: precision_at_5\n value: 8.834999999999999\n - type: recall_at_1\n value: 23.118\n - type: recall_at_10\n value: 47.788000000000004\n - type: recall_at_100\n value: 69.37\n - type: recall_at_1000\n value: 87.47399999999999\n - type: recall_at_3\n value: 34.868\n - type: recall_at_5\n value: 39.001999999999995\n - task:\n type: Retrieval\n dataset:\n type: climate-fever\n name: MTEB ClimateFEVER\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 14.288\n - type: map_at_10\n value: 23.256\n - type: map_at_100\n value: 25.115\n - type: map_at_1000\n value: 25.319000000000003\n - type: map_at_3\n value: 20.005\n - type: map_at_5\n value: 21.529999999999998\n - type: mrr_at_1\n value: 31.401\n - type: mrr_at_10\n value: 42.251\n - type: mrr_at_100\n value: 43.236999999999995\n - type: mrr_at_1000\n value: 43.272\n - type: mrr_at_3\n value: 39.164\n - type: mrr_at_5\n value: 40.881\n - type: ndcg_at_1\n value: 31.401\n - type: ndcg_at_10\n value: 31.615\n - type: ndcg_at_100\n value: 38.982\n - type: ndcg_at_1000\n value: 42.496\n - type: ndcg_at_3\n value: 26.608999999999998\n - type: ndcg_at_5\n value: 28.048000000000002\n - type: precision_at_1\n value: 31.401\n - type: precision_at_10\n value: 9.536999999999999\n - type: precision_at_100\n value: 1.763\n - type: precision_at_1000\n value: 0.241\n - type: precision_at_3\n value: 19.153000000000002\n - type: precision_at_5\n value: 14.228\n - type: recall_at_1\n value: 14.288\n - type: recall_at_10\n value: 36.717\n - type: recall_at_100\n value: 61.9\n - type: recall_at_1000\n value: 81.676\n - type: recall_at_3\n value: 24.203\n - type: recall_at_5\n value: 28.793999999999997\n - task:\n type: Retrieval\n dataset:\n type: dbpedia-entity\n name: MTEB DBPedia\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 9.019\n - type: map_at_10\n value: 19.963\n - type: map_at_100\n value: 28.834\n - type: map_at_1000\n value: 30.537999999999997\n - type: map_at_3\n value: 14.45\n - type: map_at_5\n value: 16.817999999999998\n - type: mrr_at_1\n value: 65.75\n - type: mrr_at_10\n value: 74.646\n - type: mrr_at_100\n value: 74.946\n - type: mrr_at_1000\n value: 74.95100000000001\n - type: mrr_at_3\n value: 72.625\n - type: mrr_at_5\n value: 74.012\n - type: ndcg_at_1\n value: 54\n - type: ndcg_at_10\n value: 42.014\n - type: ndcg_at_100\n value: 47.527\n - type: ndcg_at_1000\n value: 54.911\n - type: ndcg_at_3\n value: 46.586\n - type: ndcg_at_5\n value: 43.836999999999996\n - type: precision_at_1\n value: 65.75\n - type: precision_at_10\n value: 33.475\n - type: precision_at_100\n value: 11.16\n - type: precision_at_1000\n value: 2.145\n - type: precision_at_3\n value: 50.083\n - type: precision_at_5\n value: 42.55\n - type: recall_at_1\n value: 9.019\n - type: recall_at_10\n value: 25.558999999999997\n - type: recall_at_100\n value: 53.937999999999995\n - type: recall_at_1000\n value: 77.67399999999999\n - type: recall_at_3\n value: 15.456\n - type: recall_at_5\n value: 19.259\n - task:\n type: Classification\n dataset:\n type: mteb/emotion\n name: MTEB EmotionClassification\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 52.635\n - type: f1\n value: 47.692783881403926\n - task:\n type: Retrieval\n dataset:\n type: fever\n name: MTEB FEVER\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 76.893\n - type: map_at_10\n value: 84.897\n - type: map_at_100\n value: 85.122\n - type: map_at_1000\n value: 85.135\n - type: map_at_3\n value: 83.88\n - type: map_at_5\n value: 84.565\n - type: mrr_at_1\n value: 83.003\n - type: mrr_at_10\n value: 89.506\n - type: mrr_at_100\n value: 89.574\n - type: mrr_at_1000\n value: 89.575\n - type: mrr_at_3\n value: 88.991\n - type: mrr_at_5\n value: 89.349\n - type: ndcg_at_1\n value: 83.003\n - type: ndcg_at_10\n value: 88.351\n - type: ndcg_at_100\n value: 89.128\n - type: ndcg_at_1000\n value: 89.34100000000001\n - type: ndcg_at_3\n value: 86.92\n - type: ndcg_at_5\n value: 87.78200000000001\n - type: precision_at_1\n value: 83.003\n - type: precision_at_10\n value: 10.517999999999999\n - type: precision_at_100\n value: 1.115\n - type: precision_at_1000\n value: 0.11499999999999999\n - type: precision_at_3\n value: 33.062999999999995\n - type: precision_at_5\n value: 20.498\n - type: recall_at_1\n value: 76.893\n - type: recall_at_10\n value: 94.374\n - type: recall_at_100\n value: 97.409\n - type: recall_at_1000\n value: 98.687\n - type: recall_at_3\n value: 90.513\n - type: recall_at_5\n value: 92.709\n - task:\n type: Retrieval\n dataset:\n type: fiqa\n name: MTEB FiQA2018\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 20.829\n - type: map_at_10\n value: 32.86\n - type: map_at_100\n value: 34.838\n - type: map_at_1000\n value: 35.006\n - type: map_at_3\n value: 28.597\n - type: map_at_5\n value: 31.056\n - type: mrr_at_1\n value: 41.358\n - type: mrr_at_10\n value: 49.542\n - type: mrr_at_100\n value: 50.29900000000001\n - type: mrr_at_1000\n value: 50.334999999999994\n - type: mrr_at_3\n value: 46.579\n - type: mrr_at_5\n value: 48.408\n - type: ndcg_at_1\n value: 41.358\n - type: ndcg_at_10\n value: 40.758\n - type: ndcg_at_100\n value: 47.799\n - type: ndcg_at_1000\n value: 50.589\n - type: ndcg_at_3\n value: 36.695\n - type: ndcg_at_5\n value: 38.193\n - type: precision_at_1\n value: 41.358\n - type: precision_at_10\n value: 11.142000000000001\n - type: precision_at_100\n value: 1.8350000000000002\n - type: precision_at_1000\n value: 0.234\n - type: precision_at_3\n value: 24.023\n - type: precision_at_5\n value: 17.963\n - type: recall_at_1\n value: 20.829\n - type: recall_at_10\n value: 47.467999999999996\n - type: recall_at_100\n value: 73.593\n - type: recall_at_1000\n value: 90.122\n - type: recall_at_3\n value: 32.74\n - type: recall_at_5\n value: 39.608\n - task:\n type: Retrieval\n dataset:\n type: hotpotqa\n name: MTEB HotpotQA\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 40.324\n - type: map_at_10\n value: 64.183\n - type: map_at_100\n value: 65.037\n - type: map_at_1000\n value: 65.094\n - type: map_at_3\n value: 60.663\n - type: map_at_5\n value: 62.951\n - type: mrr_at_1\n value: 80.648\n - type: mrr_at_10\n value: 86.005\n - type: mrr_at_100\n value: 86.157\n - type: mrr_at_1000\n value: 86.162\n - type: mrr_at_3\n value: 85.116\n - type: mrr_at_5\n value: 85.703\n - type: ndcg_at_1\n value: 80.648\n - type: ndcg_at_10\n value: 72.351\n - type: ndcg_at_100\n value: 75.279\n - type: ndcg_at_1000\n value: 76.357\n - type: ndcg_at_3\n value: 67.484\n - type: ndcg_at_5\n value: 70.31500000000001\n - type: precision_at_1\n value: 80.648\n - type: precision_at_10\n value: 15.103\n - type: precision_at_100\n value: 1.7399999999999998\n - type: precision_at_1000\n value: 0.188\n - type: precision_at_3\n value: 43.232\n - type: precision_at_5\n value: 28.165000000000003\n - type: recall_at_1\n value: 40.324\n - type: recall_at_10\n value: 75.517\n - type: recall_at_100\n value: 86.982\n - type: recall_at_1000\n value: 94.072\n - type: recall_at_3\n value: 64.848\n - type: recall_at_5\n value: 70.41199999999999\n - task:\n type: Classification\n dataset:\n type: mteb/imdb\n name: MTEB ImdbClassification\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 91.4\n - type: ap\n value: 87.4422032289312\n - type: f1\n value: 91.39249564302281\n - task:\n type: Retrieval\n dataset:\n type: msmarco\n name: MTEB MSMARCO\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 22.03\n - type: map_at_10\n value: 34.402\n - type: map_at_100\n value: 35.599\n - type: map_at_1000\n value: 35.648\n - type: map_at_3\n value: 30.603\n - type: map_at_5\n value: 32.889\n - type: mrr_at_1\n value: 22.679\n - type: mrr_at_10\n value: 35.021\n - type: mrr_at_100\n value: 36.162\n - type: mrr_at_1000\n value: 36.205\n - type: mrr_at_3\n value: 31.319999999999997\n - type: mrr_at_5\n value: 33.562\n - type: ndcg_at_1\n value: 22.692999999999998\n - type: ndcg_at_10\n value: 41.258\n - type: ndcg_at_100\n value: 46.967\n - type: ndcg_at_1000\n value: 48.175000000000004\n - type: ndcg_at_3\n value: 33.611000000000004\n - type: ndcg_at_5\n value: 37.675\n - type: precision_at_1\n value: 22.692999999999998\n - type: precision_at_10\n value: 6.5089999999999995\n - type: precision_at_100\n value: 0.936\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 14.413\n - type: precision_at_5\n value: 10.702\n - type: recall_at_1\n value: 22.03\n - type: recall_at_10\n value: 62.248000000000005\n - type: recall_at_100\n value: 88.524\n - type: recall_at_1000\n value: 97.714\n - type: recall_at_3\n value: 41.617\n - type: recall_at_5\n value: 51.359\n - task:\n type: Classification\n dataset:\n type: mteb/mtop_domain\n name: MTEB MTOPDomainClassification (en)\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 94.36844505243957\n - type: f1\n value: 94.12408743818202\n - task:\n type: Classification\n dataset:\n type: mteb/mtop_intent\n name: MTEB MTOPIntentClassification (en)\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 76.43410852713177\n - type: f1\n value: 58.501855709435624\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_massive_intent\n name: MTEB MassiveIntentClassification (en)\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 76.04909213180902\n - type: f1\n value: 74.1800860395823\n - task:\n type: Classification\n dataset:\n type: mteb/amazon_massive_scenario\n name: MTEB MassiveScenarioClassification (en)\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 79.76126429051781\n - type: f1\n value: 79.85705217473232\n - task:\n type: Clustering\n dataset:\n type: mteb/medrxiv-clustering-p2p\n name: MTEB MedrxivClusteringP2P\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 34.70119520292863\n - task:\n type: Clustering\n dataset:\n type: mteb/medrxiv-clustering-s2s\n name: MTEB MedrxivClusteringS2S\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 32.33544316467486\n - task:\n type: Reranking\n dataset:\n type: mteb/mind_small\n name: MTEB MindSmallReranking\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 30.75499243990726\n - type: mrr\n value: 31.70602251821063\n - task:\n type: Retrieval\n dataset:\n type: nfcorpus\n name: MTEB NFCorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.451999999999999\n - type: map_at_10\n value: 13.918\n - type: map_at_100\n value: 17.316000000000003\n - type: map_at_1000\n value: 18.747\n - type: map_at_3\n value: 10.471\n - type: map_at_5\n value: 12.104\n - type: mrr_at_1\n value: 46.749\n - type: mrr_at_10\n value: 55.717000000000006\n - type: mrr_at_100\n value: 56.249\n - type: mrr_at_1000\n value: 56.288000000000004\n - type: mrr_at_3\n value: 53.818\n - type: mrr_at_5\n value: 55.103\n - type: ndcg_at_1\n value: 45.201\n - type: ndcg_at_10\n value: 35.539\n - type: ndcg_at_100\n value: 32.586\n - type: ndcg_at_1000\n value: 41.486000000000004\n - type: ndcg_at_3\n value: 41.174\n - type: ndcg_at_5\n value: 38.939\n - type: precision_at_1\n value: 46.749\n - type: precision_at_10\n value: 25.944\n - type: precision_at_100\n value: 8.084\n - type: precision_at_1000\n value: 2.076\n - type: precision_at_3\n value: 38.7\n - type: precision_at_5\n value: 33.56\n - type: recall_at_1\n value: 6.451999999999999\n - type: recall_at_10\n value: 17.302\n - type: recall_at_100\n value: 32.14\n - type: recall_at_1000\n value: 64.12\n - type: recall_at_3\n value: 11.219\n - type: recall_at_5\n value: 13.993\n - task:\n type: Retrieval\n dataset:\n type: nq\n name: MTEB NQ\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.037\n - type: map_at_10\n value: 46.565\n - type: map_at_100\n value: 47.606\n - type: map_at_1000\n value: 47.636\n - type: map_at_3\n value: 42.459\n - type: map_at_5\n value: 44.762\n - type: mrr_at_1\n value: 36.181999999999995\n - type: mrr_at_10\n value: 49.291000000000004\n - type: mrr_at_100\n value: 50.059\n - type: mrr_at_1000\n value: 50.078\n - type: mrr_at_3\n value: 45.829\n - type: mrr_at_5\n value: 47.797\n - type: ndcg_at_1\n value: 36.153\n - type: ndcg_at_10\n value: 53.983000000000004\n - type: ndcg_at_100\n value: 58.347\n - type: ndcg_at_1000\n value: 59.058\n - type: ndcg_at_3\n value: 46.198\n - type: ndcg_at_5\n value: 50.022\n - type: precision_at_1\n value: 36.153\n - type: precision_at_10\n value: 8.763\n - type: precision_at_100\n value: 1.123\n - type: precision_at_1000\n value: 0.11900000000000001\n - type: precision_at_3\n value: 20.751\n - type: precision_at_5\n value: 14.646999999999998\n - type: recall_at_1\n value: 32.037\n - type: recall_at_10\n value: 74.008\n - type: recall_at_100\n value: 92.893\n - type: recall_at_1000\n value: 98.16\n - type: recall_at_3\n value: 53.705999999999996\n - type: recall_at_5\n value: 62.495\n - task:\n type: Retrieval\n dataset:\n type: quora\n name: MTEB QuoraRetrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 71.152\n - type: map_at_10\n value: 85.104\n - type: map_at_100\n value: 85.745\n - type: map_at_1000\n value: 85.761\n - type: map_at_3\n value: 82.175\n - type: map_at_5\n value: 84.066\n - type: mrr_at_1\n value: 82.03\n - type: mrr_at_10\n value: 88.115\n - type: mrr_at_100\n value: 88.21\n - type: mrr_at_1000\n value: 88.211\n - type: mrr_at_3\n value: 87.19200000000001\n - type: mrr_at_5\n value: 87.85\n - type: ndcg_at_1\n value: 82.03\n - type: ndcg_at_10\n value: 88.78\n - type: ndcg_at_100\n value: 89.96300000000001\n - type: ndcg_at_1000\n value: 90.056\n - type: ndcg_at_3\n value: 86.051\n - type: ndcg_at_5\n value: 87.63499999999999\n - type: precision_at_1\n value: 82.03\n - type: precision_at_10\n value: 13.450000000000001\n - type: precision_at_100\n value: 1.5310000000000001\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.627\n - type: precision_at_5\n value: 24.784\n - type: recall_at_1\n value: 71.152\n - type: recall_at_10\n value: 95.649\n - type: recall_at_100\n value: 99.58200000000001\n - type: recall_at_1000\n value: 99.981\n - type: recall_at_3\n value: 87.767\n - type: recall_at_5\n value: 92.233\n - task:\n type: Clustering\n dataset:\n type: mteb/reddit-clustering\n name: MTEB RedditClustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 56.48713646277477\n - task:\n type: Clustering\n dataset:\n type: mteb/reddit-clustering-p2p\n name: MTEB RedditClusteringP2P\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 63.394940772438545\n - task:\n type: Retrieval\n dataset:\n type: scidocs\n name: MTEB SCIDOCS\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 5.043\n - type: map_at_10\n value: 12.949\n - type: map_at_100\n value: 15.146\n - type: map_at_1000\n value: 15.495000000000001\n - type: map_at_3\n value: 9.333\n - type: map_at_5\n value: 11.312999999999999\n - type: mrr_at_1\n value: 24.9\n - type: mrr_at_10\n value: 35.958\n - type: mrr_at_100\n value: 37.152\n - type: mrr_at_1000\n value: 37.201\n - type: mrr_at_3\n value: 32.667\n - type: mrr_at_5\n value: 34.567\n - type: ndcg_at_1\n value: 24.9\n - type: ndcg_at_10\n value: 21.298000000000002\n - type: ndcg_at_100\n value: 29.849999999999998\n - type: ndcg_at_1000\n value: 35.506\n - type: ndcg_at_3\n value: 20.548\n - type: ndcg_at_5\n value: 18.064\n - type: precision_at_1\n value: 24.9\n - type: precision_at_10\n value: 10.9\n - type: precision_at_100\n value: 2.331\n - type: precision_at_1000\n value: 0.367\n - type: precision_at_3\n value: 19.267\n - type: precision_at_5\n value: 15.939999999999998\n - type: recall_at_1\n value: 5.043\n - type: recall_at_10\n value: 22.092\n - type: recall_at_100\n value: 47.323\n - type: recall_at_1000\n value: 74.553\n - type: recall_at_3\n value: 11.728\n - type: recall_at_5\n value: 16.188\n - task:\n type: STS\n dataset:\n type: mteb/sickr-sts\n name: MTEB SICK-R\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 83.7007085938325\n - type: cos_sim_spearman\n value: 80.0171084446234\n - type: euclidean_pearson\n value: 81.28133218355893\n - type: euclidean_spearman\n value: 79.99291731740131\n - type: manhattan_pearson\n value: 81.22926922327846\n - type: manhattan_spearman\n value: 79.94444878127038\n - task:\n type: STS\n dataset:\n type: mteb/sts12-sts\n name: MTEB STS12\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 85.7411883252923\n - type: cos_sim_spearman\n value: 77.93462937801245\n - type: euclidean_pearson\n value: 83.00858563882404\n - type: euclidean_spearman\n value: 77.82717362433257\n - type: manhattan_pearson\n value: 82.92887645790769\n - type: manhattan_spearman\n value: 77.78807488222115\n - task:\n type: STS\n dataset:\n type: mteb/sts13-sts\n name: MTEB STS13\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 82.04222459361023\n - type: cos_sim_spearman\n value: 83.85931509330395\n - type: euclidean_pearson\n value: 83.26916063876055\n - type: euclidean_spearman\n value: 83.98621985648353\n - type: manhattan_pearson\n value: 83.14935679184327\n - type: manhattan_spearman\n value: 83.87938828586304\n - task:\n type: STS\n dataset:\n type: mteb/sts14-sts\n name: MTEB STS14\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 81.41136639535318\n - type: cos_sim_spearman\n value: 81.51200091040481\n - type: euclidean_pearson\n value: 81.45382456114775\n - type: euclidean_spearman\n value: 81.46201181707931\n - type: manhattan_pearson\n value: 81.37243088439584\n - type: manhattan_spearman\n value: 81.39828421893426\n - task:\n type: STS\n dataset:\n type: mteb/sts15-sts\n name: MTEB STS15\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 85.71942451732227\n - type: cos_sim_spearman\n value: 87.33044482064973\n - type: euclidean_pearson\n value: 86.58580899365178\n - type: euclidean_spearman\n value: 87.09206723832895\n - type: manhattan_pearson\n value: 86.47460784157013\n - type: manhattan_spearman\n value: 86.98367656583076\n - task:\n type: STS\n dataset:\n type: mteb/sts16-sts\n name: MTEB STS16\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 83.55868078863449\n - type: cos_sim_spearman\n value: 85.38299230074065\n - type: euclidean_pearson\n value: 84.64715256244595\n - type: euclidean_spearman\n value: 85.49112229604047\n - type: manhattan_pearson\n value: 84.60814346792462\n - type: manhattan_spearman\n value: 85.44886026766822\n - task:\n type: STS\n dataset:\n type: mteb/sts17-crosslingual-sts\n name: MTEB STS17 (en-en)\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 84.99292526370614\n - type: cos_sim_spearman\n value: 85.58139465695983\n - type: euclidean_pearson\n value: 86.51325066734084\n - type: euclidean_spearman\n value: 85.56736418284562\n - type: manhattan_pearson\n value: 86.48190836601357\n - type: manhattan_spearman\n value: 85.51616256224258\n - task:\n type: STS\n dataset:\n type: mteb/sts22-crosslingual-sts\n name: MTEB STS22 (en)\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 64.54124715078807\n - type: cos_sim_spearman\n value: 65.32134275948374\n - type: euclidean_pearson\n value: 67.09791698300816\n - type: euclidean_spearman\n value: 65.79468982468465\n - type: manhattan_pearson\n value: 67.13304723693966\n - type: manhattan_spearman\n value: 65.68439995849283\n - task:\n type: STS\n dataset:\n type: mteb/stsbenchmark-sts\n name: MTEB STSBenchmark\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 83.4231099581624\n - type: cos_sim_spearman\n value: 85.95475815226862\n - type: euclidean_pearson\n value: 85.00339401999706\n - type: euclidean_spearman\n value: 85.74133081802971\n - type: manhattan_pearson\n value: 85.00407987181666\n - type: manhattan_spearman\n value: 85.77509596397363\n - task:\n type: Reranking\n dataset:\n type: mteb/scidocs-reranking\n name: MTEB SciDocsRR\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 87.25666719585716\n - type: mrr\n value: 96.32769917083642\n - task:\n type: Retrieval\n dataset:\n type: scifact\n name: MTEB SciFact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 57.828\n - type: map_at_10\n value: 68.369\n - type: map_at_100\n value: 68.83399999999999\n - type: map_at_1000\n value: 68.856\n - type: map_at_3\n value: 65.38000000000001\n - type: map_at_5\n value: 67.06299999999999\n - type: mrr_at_1\n value: 61\n - type: mrr_at_10\n value: 69.45400000000001\n - type: mrr_at_100\n value: 69.785\n - type: mrr_at_1000\n value: 69.807\n - type: mrr_at_3\n value: 67\n - type: mrr_at_5\n value: 68.43299999999999\n - type: ndcg_at_1\n value: 61\n - type: ndcg_at_10\n value: 73.258\n - type: ndcg_at_100\n value: 75.173\n - type: ndcg_at_1000\n value: 75.696\n - type: ndcg_at_3\n value: 68.162\n - type: ndcg_at_5\n value: 70.53399999999999\n - type: precision_at_1\n value: 61\n - type: precision_at_10\n value: 9.8\n - type: precision_at_100\n value: 1.087\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 27\n - type: precision_at_5\n value: 17.666999999999998\n - type: recall_at_1\n value: 57.828\n - type: recall_at_10\n value: 87.122\n - type: recall_at_100\n value: 95.667\n - type: recall_at_1000\n value: 99.667\n - type: recall_at_3\n value: 73.139\n - type: recall_at_5\n value: 79.361\n - task:\n type: PairClassification\n dataset:\n type: mteb/sprintduplicatequestions-pairclassification\n name: MTEB SprintDuplicateQuestions\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.85247524752475\n - type: cos_sim_ap\n value: 96.25640197639723\n - type: cos_sim_f1\n value: 92.37851662404091\n - type: cos_sim_precision\n value: 94.55497382198953\n - type: cos_sim_recall\n value: 90.3\n - type: dot_accuracy\n value: 99.76138613861386\n - type: dot_ap\n value: 93.40295864389073\n - type: dot_f1\n value: 87.64267990074441\n - type: dot_precision\n value: 86.99507389162562\n - type: dot_recall\n value: 88.3\n - type: euclidean_accuracy\n value: 99.85049504950496\n - type: euclidean_ap\n value: 96.24254350525462\n - type: euclidean_f1\n value: 92.32323232323232\n - type: euclidean_precision\n value: 93.26530612244898\n - type: euclidean_recall\n value: 91.4\n - type: manhattan_accuracy\n value: 99.85346534653465\n - type: manhattan_ap\n value: 96.2635334753325\n - type: manhattan_f1\n value: 92.37899073120495\n - type: manhattan_precision\n value: 95.22292993630573\n - type: manhattan_recall\n value: 89.7\n - type: max_accuracy\n value: 99.85346534653465\n - type: max_ap\n value: 96.2635334753325\n - type: max_f1\n value: 92.37899073120495\n - task:\n type: Clustering\n dataset:\n type: mteb/stackexchange-clustering\n name: MTEB StackExchangeClustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 65.83905786483794\n - task:\n type: Clustering\n dataset:\n type: mteb/stackexchange-clustering-p2p\n name: MTEB StackExchangeClusteringP2P\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 35.031896152126436\n - task:\n type: Reranking\n dataset:\n type: mteb/stackoverflowdupquestions-reranking\n name: MTEB StackOverflowDupQuestions\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 54.551326709447146\n - type: mrr\n value: 55.43758222986165\n - task:\n type: Summarization\n dataset:\n type: mteb/summeval\n name: MTEB SummEval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 30.305688567308874\n - type: cos_sim_spearman\n value: 29.27135743434515\n - type: dot_pearson\n value: 30.336741878796563\n - type: dot_spearman\n value: 30.513365725895937\n - task:\n type: Retrieval\n dataset:\n type: trec-covid\n name: MTEB TRECCOVID\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.245\n - type: map_at_10\n value: 1.92\n - type: map_at_100\n value: 10.519\n - type: map_at_1000\n value: 23.874000000000002\n - type: map_at_3\n value: 0.629\n - type: map_at_5\n value: 1.0290000000000001\n - type: mrr_at_1\n value: 88\n - type: mrr_at_10\n value: 93.5\n - type: mrr_at_100\n value: 93.5\n - type: mrr_at_1000\n value: 93.5\n - type: mrr_at_3\n value: 93\n - type: mrr_at_5\n value: 93.5\n - type: ndcg_at_1\n value: 84\n - type: ndcg_at_10\n value: 76.447\n - type: ndcg_at_100\n value: 56.516\n - type: ndcg_at_1000\n value: 48.583999999999996\n - type: ndcg_at_3\n value: 78.877\n - type: ndcg_at_5\n value: 79.174\n - type: precision_at_1\n value: 88\n - type: precision_at_10\n value: 80.60000000000001\n - type: precision_at_100\n value: 57.64\n - type: precision_at_1000\n value: 21.227999999999998\n - type: precision_at_3\n value: 82\n - type: precision_at_5\n value: 83.6\n - type: recall_at_1\n value: 0.245\n - type: recall_at_10\n value: 2.128\n - type: recall_at_100\n value: 13.767\n - type: recall_at_1000\n value: 44.958\n - type: recall_at_3\n value: 0.654\n - type: recall_at_5\n value: 1.111\n - task:\n type: Retrieval\n dataset:\n type: webis-touche2020\n name: MTEB Touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 2.5170000000000003\n - type: map_at_10\n value: 10.915\n - type: map_at_100\n value: 17.535\n - type: map_at_1000\n value: 19.042\n - type: map_at_3\n value: 5.689\n - type: map_at_5\n value: 7.837\n - type: mrr_at_1\n value: 34.694\n - type: mrr_at_10\n value: 49.547999999999995\n - type: mrr_at_100\n value: 50.653000000000006\n - type: mrr_at_1000\n value: 50.653000000000006\n - type: mrr_at_3\n value: 44.558\n - type: mrr_at_5\n value: 48.333\n - type: ndcg_at_1\n value: 32.653\n - type: ndcg_at_10\n value: 26.543\n - type: ndcg_at_100\n value: 38.946\n - type: ndcg_at_1000\n value: 49.406\n - type: ndcg_at_3\n value: 29.903000000000002\n - type: ndcg_at_5\n value: 29.231\n - type: precision_at_1\n value: 34.694\n - type: precision_at_10\n value: 23.265\n - type: precision_at_100\n value: 8.102\n - type: precision_at_1000\n value: 1.5\n - type: precision_at_3\n value: 31.293\n - type: precision_at_5\n value: 29.796\n - type: recall_at_1\n value: 2.5170000000000003\n - type: recall_at_10\n value: 16.88\n - type: recall_at_100\n value: 49.381\n - type: recall_at_1000\n value: 81.23899999999999\n - type: recall_at_3\n value: 6.965000000000001\n - type: recall_at_5\n value: 10.847999999999999\n - task:\n type: Classification\n dataset:\n type: mteb/toxic_conversations_50k\n name: MTEB ToxicConversationsClassification\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 71.5942\n - type: ap\n value: 13.92074156956546\n - type: f1\n value: 54.671999698839066\n - task:\n type: Classification\n dataset:\n type: mteb/tweet_sentiment_extraction\n name: MTEB TweetSentimentExtractionClassification\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 59.39728353140916\n - type: f1\n value: 59.68980496759517\n - task:\n type: Clustering\n dataset:\n type: mteb/twentynewsgroups-clustering\n name: MTEB TwentyNewsgroupsClustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 52.11181870104935\n - task:\n type: PairClassification\n dataset:\n type: mteb/twittersemeval2015-pairclassification\n name: MTEB TwitterSemEval2015\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 86.46957143708649\n - type: cos_sim_ap\n value: 76.16120197845457\n - type: cos_sim_f1\n value: 69.69919295671315\n - type: cos_sim_precision\n value: 64.94986326344576\n - type: cos_sim_recall\n value: 75.19788918205805\n - type: dot_accuracy\n value: 83.0780234845324\n - type: dot_ap\n value: 64.21717343541934\n - type: dot_f1\n value: 59.48375497624245\n - type: dot_precision\n value: 57.94345759319489\n - type: dot_recall\n value: 61.108179419525065\n - type: euclidean_accuracy\n value: 86.6543482148179\n - type: euclidean_ap\n value: 76.4527555010203\n - type: euclidean_f1\n value: 70.10156056477584\n - type: euclidean_precision\n value: 66.05975723622782\n - type: euclidean_recall\n value: 74.67018469656992\n - type: manhattan_accuracy\n value: 86.66030875603504\n - type: manhattan_ap\n value: 76.40304567255436\n - type: manhattan_f1\n value: 70.05275426328058\n - type: manhattan_precision\n value: 65.4666360926393\n - type: manhattan_recall\n value: 75.32981530343008\n - type: max_accuracy\n value: 86.66030875603504\n - type: max_ap\n value: 76.4527555010203\n - type: max_f1\n value: 70.10156056477584\n - task:\n type: PairClassification\n dataset:\n type: mteb/twitterurlcorpus-pairclassification\n name: MTEB TwitterURLCorpus\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.42123646524624\n - type: cos_sim_ap\n value: 85.15431437761646\n - type: cos_sim_f1\n value: 76.98069301530742\n - type: cos_sim_precision\n value: 72.9314502239063\n - type: cos_sim_recall\n value: 81.50600554357868\n - type: dot_accuracy\n value: 86.70974502270346\n - type: dot_ap\n value: 80.77621563599457\n - type: dot_f1\n value: 73.87058697285117\n - type: dot_precision\n value: 68.98256396552877\n - type: dot_recall\n value: 79.50415768401602\n - type: euclidean_accuracy\n value: 88.46392672798541\n - type: euclidean_ap\n value: 85.20370297495491\n - type: euclidean_f1\n value: 77.01372369624886\n - type: euclidean_precision\n value: 73.39052800446397\n - type: euclidean_recall\n value: 81.01324299353249\n - type: manhattan_accuracy\n value: 88.43481973066325\n - type: manhattan_ap\n value: 85.16318289864545\n - type: manhattan_f1\n value: 76.90884877182597\n - type: manhattan_precision\n value: 74.01737396753062\n - type: manhattan_recall\n value: 80.03541730828458\n - type: max_accuracy\n value: 88.46392672798541\n - type: max_ap\n value: 85.20370297495491\n - type: max_f1\n value: 77.01372369624886\nlicense: mit\nlanguage:\n- en\n---\n\n\n**Recommend switching to newest [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5), which has more reasonable similarity distribution and same method of usage.**\n\n

FlagEmbedding

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\n Model List | \n FAQ |\n Usage |\n Evaluation |\n Train |\n Contact |\n Citation |\n License \n

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\n\nMore details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).\n\n\n[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)\n\nFlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.\nAnd it also can be used in vector databases for LLMs.\n\n************* 🌟**Updates**🌟 *************\n- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire: \n- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released \n- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released \n- 09/12/2023: New models: \n - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. \n - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.\n \n\n
\n More\n\n \n- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. \n- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). \n- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** \n- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: \n- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. \n \n
\n\n\n## Model List\n\n`bge` is short for `BAAI general embedding`.\n\n| Model | Language | | Description | query instruction for retrieval [1] |\n|:-------------------------------|:--------:| :--------:| :--------:|:--------:|\n| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |\n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |\n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |\n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |\n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |\n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |\n| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章:` |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章:` |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章:` |\n\n\n[1\\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.\n\n[2\\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. \nFor examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.\n\nAll models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. \nIf you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .\n\n\n## Frequently asked questions\n\n
\n 1. How to fine-tune bge embedding model?\n\n \nFollowing this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. \nSome suggestions:\n- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.\n- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.\n- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.\n\n \n
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\n 2. The similarity score between two dissimilar sentences is higher than 0.5\n\n \n**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** \n\nSince we finetune the models by contrastive learning with a temperature of 0.01, \nthe similarity distribution of the current BGE model is about in the interval \\[0.6, 1\\].\nSo a similarity score greater than 0.5 does not indicate that the two sentences are similar.\n\nFor downstream tasks, such as passage retrieval or semantic similarity, \n**what matters is the relative order of the scores, not the absolute value.**\nIf you need to filter similar sentences based on a similarity threshold, \nplease select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).\n\n
\n\n
\n 3. When does the query instruction need to be used\n\n \n\nFor the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. \nNo instruction only has a slight degradation in retrieval performance compared with using instruction. \nSo you can generate embedding without instruction in all cases for convenience.\n \nFor a retrieval task that uses short queries to find long related documents, \nit is recommended to add instructions for these short queries.\n**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**\nIn all cases, the documents/passages do not need to add the instruction. \n\n
\n\n\n## Usage \n\n### Usage for Embedding Model\n\nHere are some examples for using `bge` models with \n[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).\n\n#### Using FlagEmbedding\n```\npip install -U FlagEmbedding\n```\nIf it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.\n\n```python\nfrom FlagEmbedding import FlagModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagModel('BAAI/bge-large-zh-v1.5', \n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章:\",\n use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n\n# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query\n# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode(passages)\nscores = q_embeddings @ p_embeddings.T\n```\nFor the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). \n\nBy default, FlagModel will use all available GPUs when encoding. Please set `os.environ[\"CUDA_VISIBLE_DEVICES\"]` to select specific GPUs.\nYou also can set `os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"` to make all GPUs unavailable.\n\n\n#### Using Sentence-Transformers\n\nYou can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):\n\n```\npip install -U sentence-transformers\n```\n```python\nfrom sentence_transformers import SentenceTransformer\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = SentenceTransformer('BAAI/bge-large-zh-v1.5')\nembeddings_1 = model.encode(sentences_1, normalize_embeddings=True)\nembeddings_2 = model.encode(sentences_2, normalize_embeddings=True)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\nFor s2p(short query to long passage) retrieval task, \neach short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). \nBut the instruction is not needed for passages.\n```python\nfrom sentence_transformers import SentenceTransformer\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\ninstruction = \"为这个句子生成表示以用于检索相关文章:\"\n\nmodel = SentenceTransformer('BAAI/bge-large-zh-v1.5')\nq_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)\np_embeddings = model.encode(passages, normalize_embeddings=True)\nscores = q_embeddings @ p_embeddings.T\n```\n\n#### Using Langchain \n\nYou can use `bge` in langchain like this:\n```python\nfrom langchain.embeddings import HuggingFaceBgeEmbeddings\nmodel_name = \"BAAI/bge-large-en-v1.5\"\nmodel_kwargs = {'device': 'cuda'}\nencode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity\nmodel = HuggingFaceBgeEmbeddings(\n model_name=model_name,\n model_kwargs=model_kwargs,\n encode_kwargs=encode_kwargs,\n query_instruction=\"为这个句子生成表示以用于检索相关文章:\"\n)\nmodel.query_instruction = \"为这个句子生成表示以用于检索相关文章:\"\n```\n\n\n#### Using HuggingFace Transformers\n\nWith the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.\n\n```python\nfrom transformers import AutoTokenizer, AutoModel\nimport torch\n# Sentences we want sentence embeddings for\nsentences = [\"样例数据-1\", \"样例数据-2\"]\n\n# Load model from HuggingFace Hub\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')\nmodel = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')\nmodel.eval()\n\n# Tokenize sentences\nencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)\n# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')\n\n# Compute token embeddings\nwith torch.no_grad():\n model_output = model(**encoded_input)\n # Perform pooling. In this case, cls pooling.\n sentence_embeddings = model_output[0][:, 0]\n# normalize embeddings\nsentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)\nprint(\"Sentence embeddings:\", sentence_embeddings)\n```\n\n### Usage for Reranker\n\nDifferent from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. \nYou can get a relevance score by inputting query and passage to the reranker. \nThe reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.\n\n\n#### Using FlagEmbedding\n```\npip install -U FlagEmbedding\n```\n\nGet relevance scores (higher scores indicate more relevance):\n```python\nfrom FlagEmbedding import FlagReranker\nreranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nscore = reranker.compute_score(['query', 'passage'])\nprint(score)\n\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])\nprint(scores)\n```\n\n\n#### Using Huggingface transformers\n\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')\nmodel = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')\nmodel.eval()\n\npairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]\nwith torch.no_grad():\n inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)\n scores = model(**inputs, return_dict=True).logits.view(-1, ).float()\n print(scores)\n```\n\n## Evaluation \n\n`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**\nFor more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). \n\n- **MTEB**: \n\n| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |\n|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | \n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | \n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | \n| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | \n| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | \n| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |\n| [gte-base](https://huggingface.co/thenlper/gte-base) \t| 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |\n| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |\n| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | \n| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |\n| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |\n| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |\n| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |\n| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |\n| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |\n| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \t| 768 | 514 \t| 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |\n| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \t| 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |\n\n\n\n- **C-MTEB**: \nWe create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. \nPlease refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.\n \n| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |\n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | \n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | \n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | \n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |\n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |\n| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |\n| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |\n| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |\n| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |\n| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |\n| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |\n| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |\n| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |\n| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |\n\n\n- **Reranking**:\nSee [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.\n\n| Model | T2Reranking | T2RerankingZh2En\\* | T2RerankingEn2Zh\\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| \n| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | \n| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | \n| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | \n| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | \n| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | \n| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | \n| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | \n| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | \n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | \n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \n\n\\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks\n\n## Train\n\n### BAAI Embedding \n\nWe pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. \n**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**\nWe also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).\nNote that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.\nMore training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).\n\n\n\n### BGE Reranker\n\nCross-encoder will perform full-attention over the input pair, \nwhich is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.\nTherefore, it can be used to re-rank the top-k documents returned by embedding model.\nWe train the cross-encoder on a multilingual pair data, \nThe data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). \nMore details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)\n\n\n## Contact\nIf you have any question or suggestion related to this project, feel free to open an issue or pull request.\nYou also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). \n\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{bge_embedding,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n## License\nFlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.\n\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"fill-mask"},"createdAt":{"kind":"timestamp","value":"2022-04-29T14:55:54Z","string":"2022-04-29T14:55:54Z"},"card":{"kind":"string","value":"---\nlanguage: \n - de\n - fr\n - en\n - ro\n - zh\nthumbnail: \ntags:\n- sentence alignment\nlicense: bsd-3-clause\n---\n\n# AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders\n\nThis model comes from the following GitHub repository: [https://github.com/neulab/awesome-align](https://github.com/neulab/awesome-align)\n\nIt corresponds to this paper: [https://arxiv.org/abs/2101.08231](https://arxiv.org/abs/2101.08231)\n\nPlease cite the original paper if you decide to use the model: \n\n```\n@inproceedings{dou2021word,\n title={Word Alignment by Fine-tuning Embeddings on Parallel Corpora},\n author={Dou, Zi-Yi and Neubig, Graham},\n booktitle={Conference of the European Chapter of the Association for Computational Linguistics (EACL)},\n year={2021}\n}\n```\n\n\n`awesome-align` is a tool that can extract word alignments from multilingual BERT (mBERT) [Demo](https://colab.research.google.com/drive/1205ubqebM0OsZa1nRgbGJBtitgHqIVv6?usp=sharing) and allows you to fine-tune mBERT on parallel corpora for better alignment quality (see our paper for more details).\n\n## Usage (copied from this [DEMO](https://colab.research.google.com/drive/1205ubqebM0OsZa1nRgbGJBtitgHqIVv6?usp=sharing) )\n\n```python\nfrom transformers import AutoModel, AutoTokenizer\nimport itertools\nimport torch\n\n# load model\nmodel = AutoModel.from_pretrained(\"aneuraz/awesome-align-with-co\")\ntokenizer = AutoTokenizer.from_pretrained(\"aneuraz/awesome-align-with-co\")\n\n# model parameters\nalign_layer = 8\nthreshold = 1e-3\n\n# define inputs\nsrc = 'awesome-align is awesome !'\ntgt = '牛对齐 是 牛 !'\n\n# pre-processing\nsent_src, sent_tgt = src.strip().split(), tgt.strip().split()\ntoken_src, token_tgt = [tokenizer.tokenize(word) for word in sent_src], [tokenizer.tokenize(word) for word in sent_tgt]\nwid_src, wid_tgt = [tokenizer.convert_tokens_to_ids(x) for x in token_src], [tokenizer.convert_tokens_to_ids(x) for x in token_tgt]\nids_src, ids_tgt = tokenizer.prepare_for_model(list(itertools.chain(*wid_src)), return_tensors='pt', model_max_length=tokenizer.model_max_length, truncation=True)['input_ids'], tokenizer.prepare_for_model(list(itertools.chain(*wid_tgt)), return_tensors='pt', truncation=True, model_max_length=tokenizer.model_max_length)['input_ids']\nsub2word_map_src = []\nfor i, word_list in enumerate(token_src):\n sub2word_map_src += [i for x in word_list]\nsub2word_map_tgt = []\nfor i, word_list in enumerate(token_tgt):\n sub2word_map_tgt += [i for x in word_list]\n \n# alignment\nalign_layer = 8\nthreshold = 1e-3\nmodel.eval()\nwith torch.no_grad():\n out_src = model(ids_src.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]\n out_tgt = model(ids_tgt.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]\n\n dot_prod = torch.matmul(out_src, out_tgt.transpose(-1, -2))\n\n softmax_srctgt = torch.nn.Softmax(dim=-1)(dot_prod)\n softmax_tgtsrc = torch.nn.Softmax(dim=-2)(dot_prod)\n\n softmax_inter = (softmax_srctgt > threshold)*(softmax_tgtsrc > threshold)\n\nalign_subwords = torch.nonzero(softmax_inter, as_tuple=False)\nalign_words = set()\nfor i, j in align_subwords:\n align_words.add( (sub2word_map_src[i], sub2word_map_tgt[j]) )\n \nprint(align_words)\n```\n"},"embedding":{"kind":"list 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model for masked language modeling (MLM) pretrained on BookCorpus and English Wikipedia for sequence length 512.\n\n## About Nyströmformer\n\nThe Nyströmformer model was proposed in [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, and Vikas Singh.\n\nThe abstract from the paper is the following:\n\nTransformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of other tokens on each specific token. While beneficial, the quadratic complexity of self-attention on the input sequence length has limited its application to longer sequences — a topic being actively studied in the community. To address this limitation, we propose Nyströmformer — a model that exhibits favorable scalability as a function of sequence length. Our idea is based on adapting the Nyström method to approximate standard self-attention with O(n) complexity. The scalability of Nyströmformer enables application to longer sequences with thousands of tokens. We perform evaluations on multiple downstream tasks on the GLUE benchmark and IMDB reviews with standard sequence length, and find that our Nyströmformer performs comparably, or in a few cases, even slightly better, than standard self-attention. On longer sequence tasks in the Long Range Arena (LRA) benchmark, Nyströmformer performs favorably relative to other efficient self-attention methods. Our code is available at this https URL.\n\n## Usage\n\n```python\n>>> from transformers import pipeline\n>>> unmasker = pipeline('fill-mask', model='uw-madison/nystromformer-512')\n>>> unmasker(\"Paris is the [MASK] of France.\")\n\n[{'score': 0.829957902431488,\n 'token': 1030,\n 'token_str': 'capital',\n 'sequence': 'paris is the capital of france.'},\n {'score': 0.022157637402415276,\n 'token': 16081,\n 'token_str': 'birthplace',\n 'sequence': 'paris is the birthplace of france.'},\n {'score': 0.01904447190463543,\n 'token': 197,\n 'token_str': 'name',\n 'sequence': 'paris is the name of france.'},\n {'score': 0.017583081498742104,\n 'token': 1107,\n 'token_str': 'kingdom',\n 'sequence': 'paris is the kingdom of france.'},\n {'score': 0.005948934704065323,\n 'token': 148,\n 'token_str': 'city',\n 'sequence': 'paris is the city of france.'}]\n```"},"embedding":{"kind":"list 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playing music, playing sports\n example_title: Cat & Dog\n---\n# Model Card for CLIP ViT-g/14 - LAION-2B\n\n# Table of Contents\n\n1. [Model Details](#model-details)\n2. [Uses](#uses)\n3. [Training Details](#training-details)\n4. [Evaluation](#evaluation)\n5. [Acknowledgements](#acknowledgements) \n6. [Citation](#citation)\n7. [How To Get Started With the Model](#how-to-get-started-with-the-model)\n\n\n# Model Details\n\n## Model Description\n\nA CLIP ViT-g/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).\n\nModel training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. \n\n# Uses\n\nAs per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. \n\nThe OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. \n\n## Direct Use\n\nZero-shot image classification, image and text retrieval, among others.\n\n## Downstream Use\n\nImage classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.\n\n## Out-of-Scope Use\n\nAs per the OpenAI models,\n\n**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. \n\nCertain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.\n\nSince the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.\n\nFurther the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.\n\n# Training Details\n\n## Training Data\n\nThis model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).\n\n**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.\n\n## Training Procedure\n\nPlease see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/slow-g-14--VmlldzoyNTMwMjg5).\n\n# Evaluation\n\nEvaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).\n\n## Testing Data, Factors & Metrics\n\n### Testing Data\n\nThe testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.\n\n**TODO** - more detail\n\n## Results\n\nThe model achieves a 76.6 zero-shot top-1 accuracy on ImageNet-1k.\n\nAn initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb\n\n**TODO** - create table for just this model's metrics.\n\n# Acknowledgements\n\nAcknowledging [stability.ai](https://stability.ai/) for the compute used to train this model.\n\n# Citation\n\n**BibTeX:**\n\nIn addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite:\n\nOpenAI CLIP paper\n```\n@inproceedings{Radford2021LearningTV,\n title={Learning Transferable Visual Models From Natural Language Supervision},\n author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},\n booktitle={ICML},\n year={2021}\n}\n```\n\nOpenCLIP software\n```\n@software{ilharco_gabriel_2021_5143773,\n author = {Ilharco, Gabriel and\n Wortsman, Mitchell and\n Wightman, Ross and\n Gordon, Cade and\n Carlini, Nicholas and\n Taori, Rohan and\n Dave, Achal and\n Shankar, Vaishaal and\n Namkoong, Hongseok and\n Miller, John and\n Hajishirzi, Hannaneh and\n Farhadi, Ali and\n Schmidt, Ludwig},\n title = {OpenCLIP},\n month = jul,\n year = 2021,\n note = {If you use this software, please cite it as below.},\n publisher = {Zenodo},\n version = {0.1},\n doi = {10.5281/zenodo.5143773},\n url = {https://doi.org/10.5281/zenodo.5143773}\n}\n```\n\n# How to Get Started with the Model\n\nUse the code below to get started with the model.\n\n** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\r\npipeline_tag: sentence-similarity\r\nlanguage: en\r\nlicense: apache-2.0\r\ntags:\r\n- sentence-transformers\r\n- feature-extraction\r\n- sentence-similarity\r\n- transformers\r\n---\r\n\r\n# sentence-transformers/sentence-t5-large\r\n\r\nThis is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.\r\n\r\nThis model was converted from the Tensorflow model [st5-large-1](https://tfhub.dev/google/sentence-t5/st5-large/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.\r\n\r\nThe model uses only the encoder from a T5-large model. The weights are stored in FP16. \r\n\r\n\r\n## Usage (Sentence-Transformers)\r\n\r\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\r\n\r\n```\r\npip install -U sentence-transformers\r\n```\r\n\r\nThen you can use the model like this:\r\n\r\n```python\r\nfrom sentence_transformers import SentenceTransformer\r\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\r\n\r\nmodel = SentenceTransformer('sentence-transformers/sentence-t5-large')\r\nembeddings = model.encode(sentences)\r\nprint(embeddings)\r\n```\r\n\r\nThe model requires sentence-transformers version 2.2.0 or newer.\r\n\r\n## Evaluation Results\r\n\r\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-large)\r\n\r\n\r\n\r\n## Citing & Authors\r\n\r\nIf you find this model helpful, please cite the respective publication:\r\n[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)\r\n"},"embedding":{"kind":"list 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-0.1956893801689148\n]"}}},{"rowIdx":1113,"cells":{"modelId":{"kind":"string","value":"Minej/bert-base-personality"},"author":{"kind":"string","value":"Minej"},"last_modified":{"kind":"timestamp","value":"2023-07-13T13:11:50Z","string":"2023-07-13T13:11:50Z"},"downloads":{"kind":"number","value":20597,"string":"20,597"},"likes":{"kind":"number","value":5,"string":"5"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","bert","text-classification","en","arxiv:1810.04805","license:mit","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"bert\",\n \"text-classification\",\n \"en\",\n \"arxiv:1810.04805\",\n \"license:mit\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2023-06-06T19:17:08Z","string":"2023-06-06T19:17:08Z"},"card":{"kind":"string","value":"---\nlicense: mit\nlanguage:\n- en\nlibrary_name: transformers\npipeline_tag: text-classification\n---\n\n## How to Get Started with the Model\n\nTo use the model through Hosted inference API, follow the code snippet provided below:\n\n```python\nfrom transformers import BertTokenizer, BertForSequenceClassification\n\ndef personality_detection(text):\n tokenizer = BertTokenizer.from_pretrained(\"Minej/bert-base-personality\")\n model = BertForSequenceClassification.from_pretrained(\"Minej/bert-base-personality\")\n\n inputs = tokenizer(text, truncation=True, padding=True, return_tensors=\"pt\")\n outputs = model(**inputs)\n predictions = outputs.logits.squeeze().detach().numpy()\n\n label_names = ['Extroversion', 'Neuroticism', 'Agreeableness', 'Conscientiousness', 'Openness']\n result = {label_names[i]: predictions[i] for i in range(len(label_names))}\n\n return result\n```\n\n#### Result Format\n\nThe personality_detection function returns a dictionary containing the predicted personality traits based on the given input text.\n\nThe dictionary contains the following personality traits with their corresponding predicted values:\n\n Extroversion: A value between 0 and 1 representing the predicted extroversion trait.\n Neuroticism: A value between 0 and 1 representing the predicted neuroticism trait.\n Agreeableness: A value between 0 and 1 representing the predicted agreeableness trait.\n Conscientiousness: A value between 0 and 1 representing the predicted conscientiousness trait.\n Openness: A value between 0 and 1 representing the predicted openness trait.\n\n```python\ntext_input = \"I am feeling excited about the upcoming event.\"\npersonality_prediction = personality_detection(text_input)\n\nprint(personality_prediction)\n```\n###### Output:\n```python\n{\n \"Extroversion\": 0.535,\n \"Neuroticism\": 0.576,\n \"Agreeableness\": 0.399,\n \"Conscientiousness\": 0.253,\n \"Openness\": 0.563\n}\n```\nNote: The values in the example output are just placeholders and may not reflect the actual predictions.\n\nYou can modify the example code and the result format to match your specific use case and desired output format.\n\n### Model Description\n\nTransfer Learning for Big Five Personality Prediction\n\nIn machine learning, training accurate models can be challenging when labeled data is limited. Transfer learning offers a solution by leveraging pre-existing labeled data from a similar task or domain. By transferring knowledge learned from one task to another, we can overcome data scarcity and train more effective models.\n\nIn this project, we used transfer learning with the BERT BASE UNCASED model to predict Big Five personality traits. The model was fine-tuned on a curated dataset for personality traits, learning patterns between input text and personality characteristics. By applying transfer learning, we improved the accuracy of personality trait predictions.\n\nBy leveraging transfer learning and fine-tuning BERT BASE UNCASED, we accurately predict an individual's Big Five personality traits based on their input text. This approach addresses the challenges of limited labeled data in personality prediction, providing insights into individuals' personalities.\n\nThis project showcases the power of transfer learning in machine learning and highlights the effectiveness of BERT BASE UNCASED for predicting Big Five personality traits.\n\n- **Model type:** BERT BASE UNCASED\n- **Language(s) (NLP):** English\n- **License:** MIT\n- **Finetuned from model [optional]:** https://huggingface.co/bert-base-uncased\n\n\n## Uses\n\n\n\n### Direct Use\n\nThe personality prediction model can be used directly by individuals who are interested in gaining insights into their own personality traits based on their input text. Users can input text and receive predictions for the Big Five personality traits.\n\n### Downstream Use\n\nThis model is not intended for downstream use or fine-tuning for specific tasks. It is designed as a standalone personality prediction model.\n\n### Out-of-Scope Use\n\nThis model is not suitable for uses beyond personality prediction. It should not be used for making critical decisions or judgments about individuals in areas such as employment, education, or legal matters.\n\n## Bias, Risks, and Limitations\n\nThe personality prediction model, like any machine learning model, has certain limitations and potential biases that should be taken into account:\n\n Limited Context: \n The model makes predictions based on input text alone and may not capture the full context of an individual's personality. It is important to consider that personality traits are influenced by various factors beyond textual expression.\n \n Generalization: \n The model predicts personality traits based on patterns learned from a specific dataset. Its performance may vary when applied to individuals from different demographic or cultural backgrounds not well represented in the training data.\n \n Ethical Considerations: \n Personality prediction models should be used responsibly, with an understanding that personality traits do not determine a person's worth or abilities. It is important to avoid making unfair judgments or discriminating against individuals based on predicted personality traits.\n \n Privacy Concerns: \n The model relies on user-provided input text, which may contain sensitive or personal information. Users should exercise caution when sharing personal details and ensure the security of their data.\n \n False Positives/Negatives: \n The model's predictions may not always align perfectly with an individual's actual personality traits. It is possible for the model to generate false positives (predicting a trait that is not present) or false negatives (missing a trait that is present).\n\n### Recommendations\n\nTo mitigate risks and limitations associated with personality prediction models, the following recommendations are suggested:\n\n Awareness and Education: \n Users should be informed about the limitations and potential biases of the model. Promote understanding that personality traits are complex and cannot be fully captured by a single model or text analysis.\n \n Avoid Stereotyping and Discrimination: \n Users should be cautious about making judgments or decisions solely based on predicted personality traits. Personality predictions should not be used to discriminate against individuals or perpetuate stereotypes.\n \n Interpret with Context: \n Interpret the model's predictions in the appropriate context and consider additional information about an individual beyond their input text.\n \n Data Privacy and Security: \n Ensure that user data is handled securely and with respect to privacy regulations. Users should be aware of the information they provide and exercise caution when sharing personal details.\n \n Promote Ethical Use: \n Encourage responsible use of personality prediction models and discourage misuse or harmful applications.\n\nIt is important to note that the above recommendations are general guidelines, and further context-specific recommendations should be developed based on the particular use case and ethical considerations.\n\n## How to Download the Model\n\nIf you would like to download the model files and use them instead of the Hosted inference API, then you can follow the code snippet provided below:\n\n```python\nfrom transformers import BertForSequenceClassification, BertTokenizer\nimport torch\n\n# Initialization of the model values\nmodel = BertForSequenceClassification.from_pretrained(\".\", num_labels=5)\ntokenizer = BertTokenizer.from_pretrained('.', do_lower_case=True)\nmodel.config.label2id = {\n \"Extroversion\": 0,\n \"Neuroticism\": 1,\n \"Agreeableness\": 2,\n \"Conscientiousness\": 3,\n \"Openness\": 4,\n}\nmodel.config.id2label = {\n \"0\": \"Extroversion\",\n \"1\": \"Neuroticism\",\n \"2\": \"Agreeableness\",\n \"3\": \"Conscientiousness\",\n \"4\": \"Openness\",\n}\n\ndef personality_detection(model_input: str) -> dict:\n '''\n Performs personality prediction on the given input text\n\n Args: \n model_input (str): The text conversation \n\n Returns:\n dict: A dictionary where keys are speaker labels and values are their personality predictions\n '''\n\n if len(model_input) == 0:\n ret = {\n \"Extroversion\": float(0),\n \"Neuroticism\": float(0),\n \"Agreeableness\": float(0),\n \"Conscientiousness\": float(0),\n \"Openness\": float(0),\n }\n return ret\n else:\n dict_custom = {}\n preprocess_part1 = model_input[:len(model_input)]\n dict1 = tokenizer.encode_plus(preprocess_part1, max_length=1024, padding=True, truncation=True)\n dict_custom['input_ids'] = [dict1['input_ids'], dict1['input_ids']]\n dict_custom['token_type_ids'] = [dict1['token_type_ids'], dict1['token_type_ids']]\n dict_custom['attention_mask'] = [dict1['attention_mask'], dict1['attention_mask']]\n outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask']))\n b_logit_pred = outs[0]\n pred_label = torch.sigmoid(b_logit_pred)\n ret = {\n \"Extroversion\": float(pred_label[0][0]),\n \"Neuroticism\": float(pred_label[0][1]),\n \"Agreeableness\": float(pred_label[0][2]),\n \"Conscientiousness\": float(pred_label[0][3]),\n \"Openness\": float(pred_label[0][4]),\n }\n return ret\n\npersonality_prediction = personality_detection(text_input)\n```\n\nMake sure you have the required dependencies installed (transformers and torch). This code snippet initializes the model, tokenizer, and configuration. It then defines the personality_detection function, which takes a text conversation as input and returns a dictionary with personality predictions for each speaker.\n\nYou can call the personality_detection function with your input text to obtain the personality predictions. The personality_prediction variable will hold the resulting dictionary.\n\nPlease note that this code assumes you have already downloaded the necessary model files (config.json, pytorch_model.bin, special_tokens_map.json, tokenizer_config.json, vocab.txt\n) and placed them in the current directory (indicated by \".\"). Adjust the paths and filenames accordingly if needed.\n\n\n\n## Citation\n\n @article{DBLP:journals/corr/abs-1810-04805,\n author = {Jacob Devlin and\n Ming{-}Wei Chang and\n Kenton Lee and\n Kristina Toutanova},\n title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language\n Understanding},\n journal = {CoRR},\n volume = {abs/1810.04805},\n year = {2018},\n url = {http://arxiv.org/abs/1810.04805},\n archivePrefix = {arXiv},\n eprint = {1810.04805},\n timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},\n biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n }\n\n\n## More Information\n\nTBA\n\n\n\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"feature-extraction"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:04Z","string":"2022-03-02T23:29:04Z"},"card":{"kind":"string","value":"---\nlanguage:\n- ru\n---\n\n# rubert-base-cased-sentence\n\nSentence RuBERT \\(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\\[1\\] google-translated to russian and on russian part of XNLI dev set\\[2\\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\\[3\\].\n\n\n\\[1\\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \\(2015\\) A large annotated corpus for learning natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)\n\n\\[2\\]: Williams A., Bowman S. \\(2018\\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)\n\n\\[3\\]: N. Reimers, I. Gurevych \\(2019\\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"null"},"createdAt":{"kind":"timestamp","value":"2022-04-17T15:54:03Z","string":"2022-04-17T15:54:03Z"},"card":{"kind":"string","value":"---\nlanguage: en\ndatasets:\n- librispeech_asr\ntags:\n- speech\nlicense: apache-2.0\n---\n\n# Wav2Vec2-Conformer-Large with Relative Position Embeddings\n\nWav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\n**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.\n\n**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)\n\n**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino\n\nThe results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).\n\nThe original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.\n\n# Usage\n\nSee [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model."},"embedding":{"kind":"list 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results: []\npipeline_tag: text-classification\nlanguage:\n- en\n---\n# cardiffnlp/twitter-roberta-base-hate-latest\n\nThis model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification. \nA combination of 13 different hate-speech datasets in the English language were used to fine-tune the model. \nMore details in the [reference paper](https://aclanthology.org/2023.woah-1.25/).\n\n| **Dataset** | **Accuracy** | **Macro-F1** | **Weighted-F1** |\n|:----------|-----------:|-----------:|--------------:|\n| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5831 | 0.5646 | 0.548 |\n| ucberkeley-dlab/measuring-hate-speech | 0.9273 | 0.9193 | 0.928 |\n| Detecting East Asian Prejudice on Social Media | 0.9231 | 0.6623 | 0.9428 |\n| Call me sexist, but | 0.9686 | 0.9203 | 0.9696 |\n| Predicting the Type and Target of Offensive Posts in Social Media | 0.9164 | 0.6847 | 0.9098 |\n| HateXplain | 0.8653 | 0.845 | 0.8662 |\n| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.7801 | 0.7446 | 0.7614 |\n| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9944 | 0.4986 | 0.9972 |\n| Hate speech and offensive content identification in indo-european languages | 0.8779 | 0.6904 | 0.8706 |\n| Are You a Racist or Am I Seeing Things? | 0.921 | 0.8935 | 0.9216 |\n| Automated Hate Speech Detection | 0.9423 | 0.9249 | 0.9429 |\n| Hate Towards the Political Opponent | 0.8783 | 0.6595 | 0.8788 |\n| Hateful Symbols or Hateful People? | 0.8187 | 0.7833 | 0.8323 |\n| **Overall** | **0.8766** | **0.7531** | **0.8745** |\n\n\n\n### Usage\nInstall tweetnlp via pip.\n```shell\npip install tweetnlp\n```\nLoad the model in python.\n```python\nimport tweetnlp\nmodel = tweetnlp.Classifier(\"cardiffnlp/twitter-roberta-base-hate-latest\")\nmodel.predict('I love everybody :)')\n>> {'label': 'NOT-HATE'}\n\n```\n\n\n### Reference paper - Model based on:\n```\n@inproceedings{antypas-camacho-collados-2023-robust,\n title = \"Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation\",\n author = \"Antypas, Dimosthenis and\n Camacho-Collados, Jose\",\n booktitle = \"The 7th Workshop on Online Abuse and Harms (WOAH)\",\n month = jul,\n year = \"2023\",\n address = \"Toronto, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2023.woah-1.25\",\n pages = \"231--242\"\n}\n\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-classification"},"createdAt":{"kind":"timestamp","value":"2023-02-19T07:24:50Z","string":"2023-02-19T07:24:50Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- vision\n- image-classification\ndatasets:\n- imagenet-22k\nwidget:\n- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg\n example_title: Tiger\n- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg\n example_title: Teapot\n- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg\n example_title: Palace\n---\n\n# ConvNeXt V2 (tiny-sized model) \n\nConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 384x384. It was introduced in the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Woo et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt-V2). \n\nDisclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team.\n\n## Model description\n\nConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various recognition benchmarks.\n\n![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png)\n\n## Intended uses & limitations\n\nYou can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnextv2) to look for\nfine-tuned versions on a task that interests you.\n\n### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n```python\nfrom transformers import AutoImageProcessor, ConvNextV2ForImageClassification\nimport torch\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"huggingface/cats-image\")\nimage = dataset[\"test\"][\"image\"][0]\n\npreprocessor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-22k-384\")\nmodel = ConvNextV2ForImageClassification.from_pretrained(\"facebook/convnextv2-tiny-22k-384\")\n\ninputs = preprocessor(image, return_tensors=\"pt\")\n\nwith torch.no_grad():\n logits = model(**inputs).logits\n\n# model predicts one of the 1000 ImageNet classes\npredicted_label = logits.argmax(-1).item()\nprint(model.config.id2label[predicted_label]),\n```\n\nFor more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnextv2).\n\n### BibTeX entry and citation info\n\n```bibtex\n@article{DBLP:journals/corr/abs-2301-00808,\n author = {Sanghyun Woo and\n Shoubhik Debnath and\n Ronghang Hu and\n Xinlei Chen and\n Zhuang Liu and\n In So Kweon and\n Saining Xie},\n title = {ConvNeXt {V2:} Co-designing and Scaling ConvNets with Masked Autoencoders},\n journal = {CoRR},\n volume = {abs/2301.00808},\n year = {2023},\n url = {https://doi.org/10.48550/arXiv.2301.00808},\n doi = {10.48550/arXiv.2301.00808},\n eprinttype = {arXiv},\n eprint = {2301.00808},\n timestamp = {Tue, 10 Jan 2023 15:10:12 +0100},\n biburl = {https://dblp.org/rec/journals/corr/abs-2301-00808.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```"},"embedding":{"kind":"list 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-0.4467354416847229\n]"}}},{"rowIdx":1118,"cells":{"modelId":{"kind":"string","value":"microsoft/swin-tiny-patch4-window7-224"},"author":{"kind":"string","value":"microsoft"},"last_modified":{"kind":"timestamp","value":"2023-09-15T19:59:37Z","string":"2023-09-15T19:59:37Z"},"downloads":{"kind":"number","value":20451,"string":"20,451"},"likes":{"kind":"number","value":23,"string":"23"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","pytorch","tf","safetensors","swin","image-classification","vision","dataset:imagenet-1k","arxiv:2103.14030","license:apache-2.0","autotrain_compatible","endpoints_compatible","has_space","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"tf\",\n \"safetensors\",\n \"swin\",\n \"image-classification\",\n \"vision\",\n \"dataset:imagenet-1k\",\n \"arxiv:2103.14030\",\n \"license:apache-2.0\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- vision\n- image-classification\ndatasets:\n- imagenet-1k\nwidget:\n- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg\n example_title: Tiger\n- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg\n example_title: Teapot\n- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg\n example_title: Palace\n---\n\n# Swin Transformer (tiny-sized model) \n\nSwin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). \n\nDisclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.\n\n## Model description\n\nThe Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.\n\n![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png)\n\n[Source](https://paperswithcode.com/method/swin-transformer)\n\n## Intended uses & limitations\n\nYou can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for\nfine-tuned versions on a task that interests you.\n\n### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n```python\nfrom transformers import AutoImageProcessor, AutoModelForImageClassification\nfrom PIL import Image\nimport requests\n\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\nimage = Image.open(requests.get(url, stream=True).raw)\n\nprocessor = AutoImageProcessor.from_pretrained(\"microsoft/swin-tiny-patch4-window7-224\")\nmodel = AutoModelForImageClassification.from_pretrained(\"microsoft/swin-tiny-patch4-window7-224\")\n\ninputs = processor(images=image, return_tensors=\"pt\")\noutputs = model(**inputs)\nlogits = outputs.logits\n# model predicts one of the 1000 ImageNet classes\npredicted_class_idx = logits.argmax(-1).item()\nprint(\"Predicted class:\", model.config.id2label[predicted_class_idx])\n```\n\nFor more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#).\n\n### BibTeX entry and citation info\n\n```bibtex\n@article{DBLP:journals/corr/abs-2103-14030,\n author = {Ze Liu and\n Yutong Lin and\n Yue Cao and\n Han Hu and\n Yixuan Wei and\n Zheng Zhang and\n Stephen Lin and\n Baining Guo},\n title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},\n journal = {CoRR},\n volume = {abs/2103.14030},\n year = {2021},\n url = {https://arxiv.org/abs/2103.14030},\n eprinttype = {arXiv},\n eprint = {2103.14030},\n timestamp = {Thu, 08 Apr 2021 07:53:26 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: zh\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\nlicense: apache-2.0\nwidget:\n - source_sentence: \"那个人很开心\"\n sentences:\n - \"那个人非常开心\"\n - \"那只猫很开心\"\n - \"那个人在吃东西\"\n---\n\n# Chinese Sentence BERT\n\n## Model description\n\nThis is the sentence embedding model pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.\n\n## How to use\n\nYou can use this model to extract sentence embeddings for sentence similarity task. We use cosine distance to calculate the embedding similarity here: \n\n```python\n>>> from sentence_transformers import SentenceTransformer\n>>> model = SentenceTransformer('uer/sbert-base-chinese-nli')\n>>> sentences = ['那个人很开心', '那个人非常开心']\n>>> sentence_embeddings = model.encode(sentences)\n>>> from sklearn.metrics.pairwise import paired_cosine_distances\n>>> cosine_score = 1 - paired_cosine_distances([sentence_embeddings[0]],[sentence_embeddings[1]])\n```\n\n## Training data\n\n[ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) is used as training data. \n\n## Training procedure\n\nThe model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune five epochs with a sequence length of 128 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved.\n\n```\npython3 finetune/run_classifier_siamese.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \\\n --vocab_path models/google_zh_vocab.txt \\\n --config_path models/sbert/base_config.json \\\n --train_path datasets/ChineseTextualInference/train.tsv \\\n --dev_path datasets/ChineseTextualInference/dev.tsv \\\n --learning_rate 5e-5 --epochs_num 5 --batch_size 64\n```\n\nFinally, we convert the pre-trained model into Huggingface's format:\n\n```\npython3 scripts/convert_sbert_from_uer_to_huggingface.py --input_model_path models/finetuned_model.bin \\ \n --output_model_path pytorch_model.bin \\ \n --layers_num 12\n```\n\n### BibTeX entry and citation info\n\n```\n@article{reimers2019sentence,\n title={Sentence-bert: Sentence embeddings using siamese bert-networks},\n author={Reimers, Nils and Gurevych, Iryna},\n journal={arXiv preprint arXiv:1908.10084},\n year={2019}\n}\n\n@article{zhao2019uer,\n title={UER: An Open-Source Toolkit for Pre-training Models},\n author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},\n journal={EMNLP-IJCNLP 2019},\n pages={241},\n year={2019}\n}\n\n@article{zhao2023tencentpretrain,\n title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},\n author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},\n journal={ACL 2023},\n pages={217},\n year={2023}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"translation"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:04Z","string":"2022-03-02T23:29:04Z"},"card":{"kind":"string","value":"---\ntags:\n- translation\nlicense: apache-2.0\n---\n\n### opus-mt-ca-en\n\n* source languages: ca\n* target languages: en\n* OPUS readme: [ca-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ca-en/README.md)\n\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.zip)\n* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.test.txt)\n* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.eval.txt)\n\n## Benchmarks\n\n| testset | BLEU | chr-F |\n|-----------------------|-------|-------|\n| Tatoeba.ca.en \t| 51.4 \t| 0.678 |\n\n"},"embedding":{"kind":"list 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llama2\n---\n# LLaMA2-13B-Tiefighter\nTiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge.\nTo achieve this the following recipe was used:\n\n* We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merged, contrary to the name this model does not have a strong NSFW bias.\n* Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model.\n* After applying this lora we merged the new model with PocketDoc/Dans-RetroRodeo-13b at 5% to weaken the newly introduced adventure bias.\n* The resulting merge was used as a new basemodel to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%.\n\nThis means this model contains the following ingredients from their upstream models for as far as we can track them:\n- Undi95/Xwin-MLewd-13B-V0.2\n- - Undi95/ReMM-S-Light\n - Undi95/CreativeEngine\n - Brouz/Slerpeno\n - - elinas/chronos-13b-v2\n - jondurbin/airoboros-l2-13b-2.1\n - NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2\n - CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2\n - - KoboldAI/LLAMA2-13B-Holodeck-1\n - NousResearch/Nous-Hermes-13b\n - OpenAssistant/llama2-13b-orca-8k-3319\n - ehartford/WizardLM-1.0-Uncensored-Llama2-13b\n - Henk717/spring-dragon\n - The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible)\n - - SuperCOT (Undisclosed version)\n - elinas/chronos-13b-v2 (Version assumed)\n - NousResearch/Nous-Hermes-Llama2-13b\n - stabilityai/StableBeluga-13B (Version assumed)\n - zattio770/120-Days-of-LORA-v2-13B\n - PygmalionAI/pygmalion-2-13b\n - Undi95/Storytelling-v1-13B-lora\n - TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter\n - nRuaif/Kimiko-v2-13B\n - The-Face-Of-Goonery/Huginn-13b-FP16\n - - \"a lot of different models, like hermes, beluga, airoboros, chronos.. limarp\"\n - lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT\n - Xwin-LM/Xwin-LM-13B-V0.2\n- PocketDoc/Dans-RetroRodeo-13b\n- Blackroot/Llama-2-13B-Storywriter-LORA\n\nWhile we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible!\nThanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible.\n\n# Usage\nThis model is meant to be creative, If you let it improvise you get better results than if you drown it in details.\n\n## Story Writing\nRegular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.\n\n### Generate a story on demand\nTo generate stories on demand you can use an instruction (tested in the Alpaca format) such as \"Write a novel about X, use chapters and dialogue\" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.\n\n## Chatbots and persona's\nThis model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further.\n\nFor example, you can put this in memory in regular chat mode: \n``` \n### Instruction: \nGenerate a conversation between Alice and Henk where they discuss language models.\nIn this conversation Henk is excited to teach Alice about Tiefigther. \n### Response: \n```\n\nBecause the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.\n\n## Instruct Prompting\nThis model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.\n\nDuring instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.\n\nKeep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.\n\n## Adventuring and Adventure Games\nThis model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).\n\nIt is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.\n\n## Discovered something cool and want to engage with us? \nJoin our community at https://koboldai.org/discord !"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"summarization"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: en\ntags:\n- summarization\n---\n\n### Pegasus Models\nSee Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)\n\nOriginal TF 1 code [here](https://github.com/google-research/pegasus)\n\nAuthors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019\n\nMaintained by: [@sshleifer](https://twitter.com/sam_shleifer)\n\nTask: Summarization\n\nThe following is copied from the authors' README.\n\n# Mixed & Stochastic Checkpoints\n\nWe train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.\n\n| dataset | C4 | HugeNews | Mixed & Stochastic|\n| ---- | ---- | ---- | ----|\n| xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64|\n| cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30|\n| newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18|\n| multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95|\n| gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76|\n| wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *|\n| reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94|\n| big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *|\n| arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67|\n| pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25|\n| aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51|\n| billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59|\n\nThe \"Mixed & Stochastic\" model has the following changes:\n- trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). \n- trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).\n- the model uniformly sample a gap sentence ratio between 15% and 45%.\n- importance sentences are sampled using a 20% uniform noise to importance scores.\n- the sentencepiece tokenizer is updated to be able to encode newline character.\n\n\n(*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data:\n- wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information.\n- we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.\n\n\nThe \"Mixed & Stochastic\" model has the following changes (from pegasus-large in the paper):\n\n\ntrained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).\ntrained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).\nthe model uniformly sample a gap sentence ratio between 15% and 45%.\nimportance sentences are sampled using a 20% uniform noise to importance scores.\nthe sentencepiece tokenizer is updated to be able to encode newline character.\n\n\nCitation\n```\n\n\n@misc{zhang2019pegasus,\n title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},\n author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},\n year={2019},\n eprint={1912.08777},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"translation"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:04Z","string":"2022-03-02T23:29:04Z"},"card":{"kind":"string","value":"---\ntags:\n- translation\nlicense: apache-2.0\n---\n\n### opus-mt-cs-en\n\n* source languages: cs\n* target languages: en\n* OPUS readme: [cs-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cs-en/README.md)\n\n* dataset: opus\n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/cs-en/opus-2019-12-18.zip)\n* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/cs-en/opus-2019-12-18.test.txt)\n* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/cs-en/opus-2019-12-18.eval.txt)\n\n## Benchmarks\n\n| testset | BLEU | chr-F |\n|-----------------------|-------|-------|\n| newstest2014-csen.cs.en \t| 34.1 \t| 0.612 |\n| newstest2015-encs.cs.en \t| 30.4 \t| 0.565 |\n| newstest2016-encs.cs.en \t| 31.8 \t| 0.584 |\n| newstest2017-encs.cs.en \t| 28.7 \t| 0.556 |\n| newstest2018-encs.cs.en \t| 30.3 \t| 0.566 |\n| Tatoeba.cs.en \t| 58.0 \t| 0.721 |\n\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ndatasets:\n- go_emotions\n\nmetrics:\n- accuracy\nmodel-index:\n- name: xtremedistil-emotion\n results:\n - task:\n name: Multi Label Text Classification\n type: multi_label_classification\n dataset:\n name: go_emotions\n type: emotion\n args: default\n metrics:\n - name: Accuracy\n type: accuracy\n value: NaN\n \n---\n# xtremedistil-l6-h384-go-emotion\nThis model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the \n[go_emotions dataset](https://huggingface.co/datasets/go_emotions). \n\nSee notebook for how the model was trained and converted to ONNX format [![Training Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jobergum/emotion/blob/main/TrainGoEmotions.ipynb)\n\nThis model is deployed to [aiserv.cloud](https://aiserv.cloud/) for live demo of the model. \n\nSee [https://github.com/jobergum/browser-ml-inference](https://github.com/jobergum/browser-ml-inference) for how to reproduce. \n\n\n### Training hyperparameters\n- batch size 128 \n- learning_rate=3e-05\n- epocs 4 \n
\n  Num examples = 211225\n  Num Epochs = 4\n  Instantaneous batch size per device = 128\n  Total train batch size (w. parallel, distributed & accumulation) = 128\n  Gradient Accumulation steps = 1\n  Total optimization steps = 6604\n [6604/6604 53:23, Epoch 4/4]\nStep\tTraining Loss\n500\t0.263200\n1000\t0.156900\n1500\t0.152500\n2000\t0.145400\n2500\t0.140500\n3000\t0.135900\n3500\t0.132800\n4000\t0.129400\n4500\t0.127200\n5000\t0.125700\n5500\t0.124400\n6000\t0.124100\n6500\t0.123400\n
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verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhZTdkNzYzMjhjZjc4MTAxNWZiYjgzMjhhNjRiZWRmYjc5YTA0NTQ1MzllMTYxMTVkMDk4OTE0ZGEyMTNhMiIsInZlcnNpb24iOjF9.YIZfj2Eo1nMX2GVSfqJy-Cp7VBubfUh2LuOnU60sG5Lci8FdlNbAanS1IzAyxU3U29lqiTasxfS_yrwAj5cmBQ\n - type: recall\n value: 0.8743657671177089\n name: Recall Macro\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y2YTcyNzMwYzZiMmM1Yzc4YWZhNDM3ZDQyMjI1NWZhMjQyNmU5NTA0YmE2ZDBiZmY1MmUyZWRlMjRhMjFmYSIsInZlcnNpb24iOjF9.XKlFy_Cx4T4l7Otd8aAwWcI-fJ_dJ6V1Kp3uZm6OWjwCb1Do6mSdPFfwiMeBZZyfEIsNBnguegssZvHsOfTSAQ\n - type: recall\n value: 0.931\n name: Recall Micro\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzgzN2JkNzAzZDRjNjJmZjNkY2RmYzVkMWEzYTMzZDU4NzJlYzBmOWE4MTU0MGU0MTJhM2JjZDdjODhlZDExOCIsInZlcnNpb24iOjF9.9tSVB4yNBdFXpH3equwo1ZaEnVUktO6lm93UEJ-luKhxo6wgS54OLjgDq7IpJYwa3lvYyjy-sxzQEe9ri31WAg\n - type: recall\n value: 0.931\n name: Recall Weighted\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGVhZTIyMmVmOTU1YWNjMmZiZjNmOTNlNzlhZTk3NjhlZmMwZGFkZWQxZTlhZWUwZGQyN2JhOWQyNWQ3MTVhOCIsInZlcnNpb24iOjF9.2odv2fK7zH0_S_7wC3obONzjxOipDdjWvddhnGdMnrIN6CiZwLp7XgizpqcWbwAQ_9YJwjC-6wXpbq2jTvN0Bw\n - type: f1\n value: 0.8821236522209227\n name: F1 Macro\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDI0YTUxOTA2M2ZjNGM1OTJlZDAzZTAxNTg4YjY3OWNmMjNmMTk0YWRjZTE2Y2ZmYWI1ZmU3ZmJmNzNjMjBlOCIsInZlcnNpb24iOjF9.P5-TbuEUrCtX9H7F-tKn8LI1RBPhoJwjJm_l853WTSzdLioThAtIK5HBG0xgXT2uB0Q8v94qH2b8cz1j_WonDg\n - type: f1\n value: 0.931\n name: F1 Micro\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjNmNDgyMmFjODYwNjcwOTJiOGM2N2YwYjUyMDk5Yjk2Y2I3NmFmZGFhYjU0NGM2OGUwZmRjNjcxYTU3YzgzNSIsInZlcnNpb24iOjF9.2ZoRJwQWVIcl_Ykxce1MnZ3mSxBGxGeNYFPxt9mivo9yTi3gUE7ua6JRpVEOnOUbevlWxVkUUNnmOPFqBN1sCQ\n - type: f1\n value: 0.9300782840205046\n name: F1 Weighted\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGE1OTcxNmNmMjQ3ZDAzYzk0N2Q1MGFjM2VhNWMyYmRjY2E3ZThjODExOTNlNWMxYzdlMWM2MDBiMTZhY2M2OSIsInZlcnNpb24iOjF9.r63SEArCiFB5m0ccV2q_t5uSOtjVnWdz4PfvCYUchm0JlrRC9YAm5oWKeO419wdyFY4rZFe014yv7sRcV-CgBQ\n - type: loss\n value: 0.15155883133411407\n name: loss\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2M4MmVlNjAzZjhiMWJlNWQxMDg5ZTRiYjFlZGYyMGMyYzU4M2IwY2E1M2E2MzA5NmU5ZjgwZTZmMDI5YjgzMyIsInZlcnNpb24iOjF9.kjgFJohkTxLKtzHJDlBvd6qolGQDSZLbrDE7C07xNGmarhTLc_A3MmLeC4MmQGOl1DxfnHflImIkdqPylyylDA\n---\n# robert-base-emotion\n\n## Model description:\n[roberta](https://arxiv.org/abs/1907.11692) is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining.\n\n[roberta-base](https://huggingface.co/roberta-base) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters\n```\n learning rate 2e-5, \n batch size 64,\n num_train_epochs=8,\n```\n\n## Model Performance Comparision on Emotion Dataset from Twitter:\n\n| Model | Accuracy | F1 Score | Test Sample per Second |\n| --- | --- | --- | --- |\n| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 |\n| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 |\n| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 |\n| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 |\n\n## How to Use the model:\n```python\nfrom transformers import pipeline\nclassifier = pipeline(\"text-classification\",model='bhadresh-savani/roberta-base-emotion', return_all_scores=True)\nprediction = classifier(\"I love using transformers. The best part is wide range of support and its easy to use\", )\nprint(prediction)\n\n\"\"\"\nOutput:\n[[\n{'label': 'sadness', 'score': 0.002281982684507966}, \n{'label': 'joy', 'score': 0.9726489186286926}, \n{'label': 'love', 'score': 0.021365027874708176}, \n{'label': 'anger', 'score': 0.0026395076420158148}, \n{'label': 'fear', 'score': 0.0007162453257478774}, \n{'label': 'surprise', 'score': 0.0003483477921690792}\n]]\n\"\"\"\n```\n\n## Dataset:\n[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).\n\n## Training procedure\n[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)\nfollow the above notebook by changing the model name to roberta\n\n## Eval results\n```json\n{\n 'test_accuracy': 0.9395,\n 'test_f1': 0.9397328860104454,\n 'test_loss': 0.14367154240608215,\n 'test_runtime': 10.2229,\n 'test_samples_per_second': 195.639,\n 'test_steps_per_second': 3.13\n }\n```\n\n## Reference:\n* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-to-image"},"createdAt":{"kind":"timestamp","value":"2023-03-21T13:09:58Z","string":"2023-03-21T13:09:58Z"},"card":{"kind":"string","value":"---\nlicense: deepfloyd-if-license\nextra_gated_prompt: \"DeepFloyd LICENSE AGREEMENT\\nThis License Agreement (as may be amended in accordance with this License Agreement, “License”), between you, or your employer or other entity (if you are entering into this agreement on behalf of your employer or other entity) (“Licensee” or “you”) and Stability AI Ltd.. 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RESTRICTIONS\\n You will not, and will not permit, assist or cause any third party to:\\n a. use, modify, copy, reproduce, create derivative works of, or distribute the Software Products (or any derivative works thereof, works incorporating the Software Products, or any data produced by the Software), in whole or in part, for (i) any commercial or production purposes, (ii) military purposes or in the service of nuclear technology, (iii) purposes of surveillance, including any research or development relating to surveillance, (iv) biometric processing, (v) in any manner that infringes, misappropriates, or otherwise violates any third-party rights, or (vi) in any manner that violates any applicable law and violating any privacy or security laws, rules, regulations, directives, or governmental requirements (including the General Data Privacy Regulation (Regulation (EU) 2016/679), the California Consumer Privacy Act, and any and all laws governing the processing of biometric information), as well as all amendments and successor laws to any of the foregoing;\\n b. alter or remove copyright and other proprietary notices which appear on or in the Software Products;\\n c. utilize any equipment, device, software, or other means to circumvent or remove any security or protection used by Stability AI in connection with the Software, or to circumvent or remove any usage restrictions, or to enable functionality disabled by Stability AI; or\\n d. offer or impose any terms on the Software Products that alter, restrict, or are inconsistent with the terms of this License.\\n e. 1) violate any applicable U.S. and non-U.S. export control and trade sanctions laws (“Export Laws”); 2) directly or indirectly export, re-export, provide, or otherwise transfer Software Products: (a) to any individual, entity, or country prohibited by Export Laws; (b) to anyone on U.S. or non-U.S. government restricted parties lists; or (c) for any purpose prohibited by Export Laws, including nuclear, chemical or biological weapons, or missile technology applications; 3) use or download Software Products if you or they are: (a) located in a comprehensively sanctioned jurisdiction, (b) currently listed on any U.S. or non-U.S. restricted parties list, or (c) for any purpose prohibited by Export Laws; and (4) will not disguise your location through IP proxying or other methods.\\L\\n3. ATTRIBUTION\\n Together with any copies of the Software Products (as well as derivative works thereof or works incorporating the Software Products) that you distribute, you must provide (i) a copy of this License, and (ii) the following attribution notice: “DeepFloyd is licensed under the DeepFloyd License, Copyright (c) Stability AI Ltd. All Rights Reserved.”\\L\\n4. DISCLAIMERS\\n THE SOFTWARE PRODUCTS ARE PROVIDED “AS IS” and “WITH ALL FAULTS” WITH NO WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. STABILITY AIEXPRESSLY DISCLAIMS ALL REPRESENTATIONS AND WARRANTIES, EXPRESS OR IMPLIED, WHETHER BY STATUTE, CUSTOM, USAGE OR OTHERWISE AS TO ANY MATTERS RELATED TO THE SOFTWARE PRODUCTS, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, SATISFACTORY QUALITY, OR NON-INFRINGEMENT. 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You will promptly notify the Stability AI Parties of any such Claims, and cooperate with Stability AI Parties in defending such Claims. You will also grant the Stability AI Parties sole control of the defense or settlement, at Stability AI’s sole option, of any Claims. This indemnity is in addition to, and not in lieu of, any other indemnities or remedies set forth in a written agreement between you and Stability AI or the other Stability AI Parties.\\L\\n7. TERMINATION; SURVIVAL\\n a. This License will automatically terminate upon any breach by you of the terms of this License.\\L\\Lb. We may terminate this License, in whole or in part, at any time upon notice (including electronic) to you.\\L\\Lc. The following sections survive termination of this License: 2 (Restrictions), 3 (Attribution), 4 (Disclaimers), 5 (Limitation on Liability), 6 (Indemnification) 7 (Termination; Survival), 8 (Third Party Materials), 9 (Trademarks), 10 (Applicable Law; Dispute Resolution), and 11 (Miscellaneous).\\L\\n8. THIRD PARTY MATERIALS\\n The Software Products may contain third-party software or other components (including free and open source software) (all of the foregoing, “Third Party Materials”), which are subject to the license terms of the respective third-party licensors. Your dealings or correspondence with third parties and your use of or interaction with any Third Party Materials are solely between you and the third party. Stability AI does not control or endorse, and makes no representations or warranties regarding, any Third Party Materials, and your access to and use of such Third Party Materials are at your own risk.\\L\\n9. TRADEMARKS\\n Licensee has not been granted any trademark license as part of this License and may not use any name or mark associated with Stability AI without the prior written permission of Stability AI, except to the extent necessary to make the reference required by the “ATTRIBUTION” section of this Agreement.\\L\\n10. APPLICABLE LAW; DISPUTE RESOLUTION\\n This License will be governed and construed under the laws of the State of California without regard to conflicts of law provisions. Any suit or proceeding arising out of or relating to this License will be brought in the federal or state courts, as applicable, in San Mateo County, California, and each party irrevocably submits to the jurisdiction and venue of such courts.\\L\\n11. MISCELLANEOUS\\n If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Stability AI to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Stability AI regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Stability AI regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Stability AI.\"\nextra_gated_fields:\n \"Organization /\\_Affiliation\": text\n Previously related publications: text\n I accept the above license agreement, and will use the Software non-commercially and for research purposes only: checkbox\ntags:\n- if\n- text-to-image\ninference: false\n---\n\n# IF-II-L-v1.0\n\nDeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of `6.66` on the COCO dataset.\n\n*Inspired by* [*Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding*](https://arxiv.org/pdf/2205.11487.pdf)\n\n![](./pics/deepfloyd_if_scheme.jpg)\n\n## Model Details\n\n- **Developed by:** DeepFloyd, StabilityAI\n- **Model type:** pixel-based text-to-image cascaded diffusion model\n- **Cascade Stage:** II\n- **Num Parameters:** 1.2B\n- **Language(s):** primarily English and, to a lesser extent, other Romance languages\n- **License:** DeepFloyd IF License Agreement\n- **Model Description:** DeepFloyd-IF is modular composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024. All stages of the model utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling \n- **Resources for more information:** [GitHub](https://github.com/deep-floyd/IF), [Website](https://deepfloyd.ai/), [All Links](https://linktr.ee/deepfloyd)\n\n## Using with `diffusers`\n\nIF is integrated with the 🤗 Hugging Face [🧨 diffusers library](https://github.com/huggingface/diffusers/), which is optimized to run on GPUs with as little as 14 GB of VRAM.\n\nBefore you can use IF, you need to accept its usage conditions. To do so:\n1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be loggin in\n2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)\n3. Make sure to login locally. Install `huggingface_hub`\n```sh\npip install huggingface_hub --upgrade\n```\n\nrun the login function in a Python shell\n\n```py\nfrom huggingface_hub import login\n\nlogin()\n```\n\nand enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens).\n\nNext we install `diffusers` and dependencies:\n\n```sh\npip install diffusers accelerate transformers safetensors sentencepiece\n```\n\nAnd we can now run the model locally.\n\nBy default `diffusers` makes use of [model cpu offloading](https://huggingface.co/docs/diffusers/optimization/fp16#model-offloading-for-fast-inference-and-memory-savings) to run the whole IF pipeline with as little as 14 GB of VRAM.\n\nIf you are using `torch>=2.0.0`, make sure to **remove all** `enable_xformers_memory_efficient_attention()` functions.\n\n* **Load all stages and offload to CPU**\n\n```py\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import pt_to_pil\nimport torch\n\n# stage 1\nstage_1 = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\nstage_1.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0\nstage_1.enable_model_cpu_offload()\n\n# stage 2\nstage_2 = DiffusionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n)\nstage_2.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0\nstage_2.enable_model_cpu_offload()\n\n# stage 3\nsafety_modules = {\"feature_extractor\": stage_1.feature_extractor, \"safety_checker\": stage_1.safety_checker, \"watermarker\": stage_1.watermarker}\nstage_3 = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-x4-upscaler\", **safety_modules, torch_dtype=torch.float16)\nstage_3.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0\nstage_3.enable_model_cpu_offload()\n```\n\n* **Retrieve Text Embeddings**\n\n```py\nprompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says \"very deep learning\"'\n\n# text embeds\nprompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)\n```\n\n* **Run stage 1**\n\n```py\ngenerator = torch.manual_seed(0)\n\nimage = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type=\"pt\").images\npt_to_pil(image)[0].save(\"./if_stage_I.png\")\n```\n\n* **Run stage 2**\n\n```py\nimage = stage_2(\n image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type=\"pt\"\n).images\npt_to_pil(image)[0].save(\"./if_stage_II.png\")\n```\n\n* **Run stage 3**\n\n```py\nimage = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images\nimage[0].save(\"./if_stage_III.png\")\n```\n\n There are multiple ways to speed up the inference time and lower the memory consumption even more with `diffusers`. To do so, please have a look at the Diffusers docs:\n\n- 🚀 [Optimizing for inference time](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-speed)\n- ⚙️ [Optimizing for low memory during inference](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-memory)\n\nFor more in-detail information about how to use IF, please have a look at [the IF blog post](https://huggingface.co/blog/if) and the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/if) 📖.\n\nDiffusers dreambooth scripts also supports fine-tuning 🎨 [IF](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#if).\nWith parameter efficient finetuning, you can add new concepts to IF with a single GPU and ~28 GB VRAM.\n\n## Training\n\n**Training Data:** \n\n1.2B text-image pairs (based on LAION-A and few additional internal datasets)\n\nTest/Valid parts of datasets are not used at any cascade and stage of training. Valid part of COCO helps to demonstrate \"online\" loss behaviour during training (to catch incident and other problems), but dataset is never used for train.\n\n**Training Procedure:** IF-II-L-v1.0 is a pixel-based diffusion cascade which uses T5-Encoder embeddings (hidden states) to upscale image from 64px to 256px. During training,\n\n- Images are cropped to square via shifted-center-crop augmentation (randomly shift from center up to 0.1 of size) and resized to 64px (low-res) and 256px (ground-truth) using `Pillow==9.2.0` BICUBIC resampling with reducing_gap=None (it helps to avoid aliasing) and processed to tensor BxCxHxW\n\n- Low-res images are extra augmented by noise (q-sample methods) with the same diffusion configuration for cascade-I series. Uniform distributed randomised augmentation noising param (aug-level) is added to Unet as condition to process by trainable layers timestep embedding and linear projection with activation.\n\n- Text prompts are encoded through open-sourced frozen T5-v1_1-xxl text-encoder (that completely was trained by Google team), random 10% of texts are dropped to empty string to add ability for classifier free guidance (CFG)\n\n- The non-pooled output of the text encoder is fed into the projection (linear layer without activation) and is used in UNet backbone of the diffusion model via controlled hybrid self- and cross- attention\n\n- Also, the output of the text encode is pooled via attention-pooling (64 heads) and is used in time embed as additional features\n\n- Diffusion process is limited by 1000 discrete steps, with cosine beta schedule of noising image\n\n- The loss is a reconstruction objective between the noise that was added to the image and the prediction made by the UNet\n\n- The training process for checkpoint IF-II-L-v1.0 has 2_500_000 steps at resolution 256x256 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder\n\n![](./pics/loss.jpg)\n\n**Hardware:** 32 x 8 x A100 GPUs\n\n**Optimizer:** [AdamW8bit](https://arxiv.org/abs/2110.02861) + [DeepSpeed ZeRO-1](https://www.deepspeed.ai/tutorials/zero/)\n\n**Batch:** 1536\n\n**Learning rate**: [one-cycle](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.OneCycleLR.html) cosine strategy, warmup 10000 steps, start_lr=4e-6, max_lr=1e-4, final_lr=1e-8\n\n\n![](./pics/lr.jpg)\n\n\n## Evaluation Results\n\n`FID-30K: 6.66`\n\n![](./pics/fid30k_if.jpg)\n\n# Uses\n\n## Direct Use \nThe model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model.\n\nPossible research areas and tasks include:\n- Generation of artistic imagery and use in design and other artistic processes.\n- Safe deployment of models which have the potential to generate harmful content.\n- Probing and understanding the limitations and biases of generative models.\n- Applications in educational or creative tools.\n- Research on generative models.\n\nExcluded uses are described below.\n\n ### Misuse, Malicious Use, and Out-of-Scope Use\n_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion but applies in the same way for IF_.\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.\n\n#### Out-of-Scope Use\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.\n\n#### Misuse and Malicious Use\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:\n\n- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n- Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n- Impersonating individuals without their consent.\n- Sexual content without consent of the people who might see it.\n- Mis- and disinformation\n- Representations of egregious violence and gore\n- Sharing of copyrighted or licensed material in violation of its terms of use.\n- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.\n\n## Limitations and Bias\n\n### Limitations\n\n- The model does not achieve perfect photorealism\n- The model was trained mainly with English captions and will not work as well in other languages.\n- The model was trained on a subset of the large-scale dataset\n [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section).\n\n### Bias\nWhile the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. \nIF was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), \nwhich consists of images that are limited to English descriptions. \nTexts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. \nThis affects the overall output of the model, as white and western cultures are often set as the default. Further, the \nability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.\nIF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.\n\n\n\n*This model card was written by: DeepFloyd-Team and is based on the [StableDiffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).*"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage:\n- en\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n---\n\n# msmarco-MiniLM-L6-cos-v5\nThis is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)\n\n\n## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\n\n```\npip install -U sentence-transformers\n```\n\nThen you can use the model like this:\n```python\nfrom sentence_transformers import SentenceTransformer, util\n\nquery = \"How many people live in London?\"\ndocs = [\"Around 9 Million people live in London\", \"London is known for its financial district\"]\n\n#Load the model\nmodel = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L6-cos-v5')\n\n#Encode query and documents\nquery_emb = model.encode(query)\ndoc_emb = model.encode(docs)\n\n#Compute dot score between query and all document embeddings\nscores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()\n\n#Combine docs & scores\ndoc_score_pairs = list(zip(docs, scores))\n\n#Sort by decreasing score\ndoc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\n\n#Output passages & scores\nfor doc, score in doc_score_pairs:\n print(score, doc)\n```\n\n\n## Usage (HuggingFace Transformers)\nWithout [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.\n\n```python\nfrom transformers import AutoTokenizer, AutoModel\nimport torch\nimport torch.nn.functional as F\n\n#Mean Pooling - Take average of all tokens\ndef mean_pooling(model_output, attention_mask):\n token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings\n input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n\n\n#Encode text\ndef encode(texts):\n # Tokenize sentences\n encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')\n\n # Compute token embeddings\n with torch.no_grad():\n model_output = model(**encoded_input, return_dict=True)\n\n # Perform pooling\n embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n\n # Normalize embeddings\n embeddings = F.normalize(embeddings, p=2, dim=1)\n\t\n return embeddings\n\n\n# Sentences we want sentence embeddings for\nquery = \"How many people live in London?\"\ndocs = [\"Around 9 Million people live in London\", \"London is known for its financial district\"]\n\n# Load model from HuggingFace Hub\ntokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/msmarco-MiniLM-L6-cos-v5\")\nmodel = AutoModel.from_pretrained(\"sentence-transformers/msmarco-MiniLM-L6-cos-v5\")\n\n#Encode query and docs\nquery_emb = encode(query)\ndoc_emb = encode(docs)\n\n#Compute dot score between query and all document embeddings\nscores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()\n\n#Combine docs & scores\ndoc_score_pairs = list(zip(docs, scores))\n\n#Sort by decreasing score\ndoc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\n\n#Output passages & scores\nfor doc, score in doc_score_pairs:\n print(score, doc)\n```\n\n## Technical Details\n\nIn the following some technical details how this model must be used:\n\n| Setting | Value |\n| --- | :---: |\n| Dimensions | 384 |\n| Produces normalized embeddings | Yes |\n| Pooling-Method | Mean pooling |\n| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |\n\nNote: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.\n\n## Citing & Authors\n\nThis model was trained by [sentence-transformers](https://www.sbert.net/). \n \nIf you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):\n```bibtex \n@inproceedings{reimers-2019-sentence-bert,\n title = \"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\",\n author = \"Reimers, Nils and Gurevych, Iryna\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing\",\n month = \"11\",\n year = \"2019\",\n publisher = \"Association for Computational Linguistics\",\n url = \"http://arxiv.org/abs/1908.10084\",\n}\n```"},"embedding":{"kind":"list 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sequence-tagger-model\nlanguage: en\ndatasets:\n- conll2000\nwidget:\n- text: \"The happy man has been eating at the diner\"\n---\n\n## English Chunking in Flair (default model)\n\nThis is the standard phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).\n\nF1-Score: **96,48** (CoNLL-2000)\n\nPredicts 4 tags:\n\n| **tag** | **meaning** |\n|---------------------------------|-----------|\n| ADJP | adjectival |\n| ADVP | adverbial |\n| CONJP | conjunction |\n| INTJ | interjection |\n| LST | list marker |\n| NP | noun phrase |\n| PP | prepositional |\n| PRT | particle |\n| SBAR | subordinate clause |\n| VP | verb phrase |\n\nBased on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.\n\n---\n\n### Demo: How to use in Flair\n\nRequires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)\n\n```python\nfrom flair.data import Sentence\nfrom flair.models import SequenceTagger\n\n# load tagger\ntagger = SequenceTagger.load(\"flair/chunk-english\")\n\n# make example sentence\nsentence = Sentence(\"The happy man has been eating at the diner\")\n\n# predict NER tags\ntagger.predict(sentence)\n\n# print sentence\nprint(sentence)\n\n# print predicted NER spans\nprint('The following NER tags are found:')\n# iterate over entities and print\nfor entity in sentence.get_spans('np'):\n print(entity)\n\n```\n\nThis yields the following output:\n```\nSpan [1,2,3]: \"The happy man\" [− Labels: NP (0.9958)]\nSpan [4,5,6]: \"has been eating\" [− Labels: VP (0.8759)]\nSpan [7]: \"at\" [− Labels: PP (1.0)]\nSpan [8,9]: \"the diner\" [− Labels: NP (0.9991)]\n\n```\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as **noun phrases** (NP) and \"*has been eating*\" is labeled as a **verb phrase** (VP) in the sentence \"*The happy man has been eating at the diner*\". \n\n\n---\n\n### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n```python\nfrom flair.data import Corpus\nfrom flair.datasets import CONLL_2000\nfrom flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings\n\n# 1. get the corpus\ncorpus: Corpus = CONLL_2000()\n\n# 2. what tag do we want to predict?\ntag_type = 'np'\n\n# 3. make the tag dictionary from the corpus\ntag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)\n\n# 4. initialize each embedding we use\nembedding_types = [\n\n # contextual string embeddings, forward\n FlairEmbeddings('news-forward'),\n\n # contextual string embeddings, backward\n FlairEmbeddings('news-backward'),\n]\n\n# embedding stack consists of Flair and GloVe embeddings\nembeddings = StackedEmbeddings(embeddings=embedding_types)\n\n# 5. initialize sequence tagger\nfrom flair.models import SequenceTagger\n\ntagger = SequenceTagger(hidden_size=256,\n embeddings=embeddings,\n tag_dictionary=tag_dictionary,\n tag_type=tag_type)\n\n# 6. initialize trainer\nfrom flair.trainers import ModelTrainer\n\ntrainer = ModelTrainer(tagger, corpus)\n\n# 7. run training\ntrainer.train('resources/taggers/chunk-english',\n train_with_dev=True,\n max_epochs=150)\n```\n\n\n\n---\n\n### Cite\n\nPlease cite the following paper when using this model.\n\n```\n@inproceedings{akbik2018coling,\n title={Contextual String Embeddings for Sequence Labeling},\n author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},\n booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},\n pages = {1638--1649},\n year = {2018}\n}\n```\n\n---\n\n### Issues?\n\nThe Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).\n"},"embedding":{"kind":"list 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bigscience-bloom-rail-1.0\nlanguage:\n- ak\n- ar\n- as\n- bm\n- bn\n- ca\n- code\n- en\n- es\n- eu\n- fon\n- fr\n- gu\n- hi\n- id\n- ig\n- ki\n- kn\n- lg\n- ln\n- ml\n- mr\n- ne\n- nso\n- ny\n- or\n- pa\n- pt\n- rn\n- rw\n- sn\n- st\n- sw\n- ta\n- te\n- tn\n- ts\n- tum\n- tw\n- ur\n- vi\n- wo\n- xh\n- yo\n- zh\n- zhs\n- zht\n- zu\npipeline_tag: text-generation\n---\n\n

BLOOM LM

\n

BigScience Large Open-science Open-access Multilingual Language Model

\n

Model Card

\n\"BigScience\n\n\nVersion 1.0 / 26.May.2022\n\n\n# Model Card for Bloom-1b7\n\n\n\n## Table of Contents\n1. [Model Details](#model-details)\n2. [Uses](#uses)\n3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)\n 4. [Recommendations](#recommendations)\n5. [Training Data](#training-data)\n6. [Evaluation](#evaluation)\n7. [Environmental Impact](#environmental-impact)\n8. [Technical Specifications](#techincal-specifications)\n9. [Citation](#citation)\n10. [Glossary and Calculations](#glossary-and-calculations)\n11. [More Information](#more-information)\n12. [Model Card Authors](#model-card-authors)\n13. [Model Card Contact](#model-card-contact)\n\n## Model Details\n\n### Model Description\n*This section provides information for anyone who wants to know about the model.*\n \n- **Developed by:** BigScience ([website](https://bigscience.huggingface.co))\n\n * All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n \n- **Model Type:** Transformer-based Language Model\n- **Version:** 1.0.0\n- **Languages:** Multiple; see [training data](#training-data)\n- **License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))\n- **Release Date Estimate:** Monday, 11.July.2022\n- **Funded by:** \n \n * The French government.\n\n * Hugging Face ([website](https://huggingface.co)).\n\n * Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n## Uses\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. \nIt provides information for anyone considering using the model or who is affected by the model.*\n \n### Intended Use\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.\n\n#### **Direct Use**\n\n- Text generation\n\n- Exploring characteristics of language generated by a language model\n\n - Examples: Cloze tests, counterfactuals, generations with reframings\n\n#### **Downstream Use**\n\n- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization\n\n### Misuse and Out-of-scope Use\n*This section addresses what users ought not do with the model.*\n\nSee the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.\n\n#### **Out-of-scope Uses**\n\nUsing the model in [high-stakes](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct. \n\n##### Out-of-scope Uses Include:\n\n- Usage in biomedical domains, political and legal domains, or finance domains\n\n- Usage for evaluating or scoring individuals, such as for employment, education, or credit\n\n- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct\n\n#### **Misuse**\n\nIntentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:\n\n- Spam generation\n\n- Disinformation and influence operations\n\n- Disparagement and defamation\n\n- Harassment and abuse\n \n- [Deception](#deception)\n\n- Unconsented impersonation and imitation\n\n- Unconsented surveillance \n\n- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)\n\n### Intended Users\n\n#### **Direct Users**\n\n- General Public\n\n- Researchers\n\n- Students\n\n- Educators\n\n- Engineers/developers\n\n- Non-commercial entities\n\n- Community advocates, including human and civil rights groups\n\n#### Indirect Users\n\n- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)\n\n- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)\n\n#### Others Affected (Parties Prenantes)\n\n- People and groups referred to by the LLM\n\n- People and groups exposed to outputs of, or decisions based on, the LLM\n\n- People and groups whose original work is included in the LLM\n\n\n\n## Bias, Risks, and Limitations\n*This section identifies foreseeable harms and misunderstandings.*\n \nModel may:\n\n- Overrepresent some viewpoints and underrepresent others\n\n- Contain stereotypes\n \n- Contain [personal information](#personal-data-and-information)\n\n- Generate:\n\n - Hateful, abusive, or violent language\n\n - Discriminatory or prejudicial language\n\n - Content that may not be appropriate for all settings, including sexual content\n\n- Make errors, including producing incorrect information as if it were factual\n\n- Generate irrelevant or repetitive outputs\n\n\n### Recommendations\n\n\n*This section provides information on warnings and potential mitigations.*\n\n- Indirect users should be made aware when the content they're working with is created by the LLM.\n\n- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.\n\n- Models pretrained with the LLM should include an updated Model Card.\n\n- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n\n\n## Training Data\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n\n \nDetails for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).\n\nTraining data includes:\n\n- 45 natural languages\n \n- 12 programming languages\n\n- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)\n\n\n#### **Languages**\n \nThe pie chart shows the distribution of languages in training data.\n \n![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_chart.svg?raw=true)\n\n\n**The following table shows the further distribution of Niger-Congo and Indic languages in the training data.**\n\n \n| Niger Congo | Percentage | | Indic | Percentage |\n|----------------|------------ |------ |-----------|------------|\n| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |\n| Kikuyu | 0.00004 | | Odia | 0.04 |\n| Bambara | 0.00004 | | Gujarati | 0.04 |\n| Akan | 0.00007 | | Marathi | 0.05 |\n| Xitsonga | 0.00007 | | Punjabi | 0.05 |\n| Sesotho | 0.00007 | | Kannada | 0.06 |\n| Chi Chewa | 0.0001 | | Nepali | 0.07 |\n| Setswana | 0.0002 | | Telugu | 0.09 |\n| Northern Sotho | 0.0002 | | Malayalam | 0.10 |\n| Fon | 0.0002 | | Urdu | 0.10 |\n| Kirundi | 0.0003 | | Tamil | 0.20 |\n| Wolof | 0.0004 | | Bengali | 0.50 |\n| Kuganda | 0.0004 | | Hindi | 0.70 |\n| Chi Shona | 0.001 |\n| Isi Zulu | 0.001 |\n| Igbo | 0.001 |\n| Xhosa | 0.001 |\n| Kinyarwanda | 0.003 |\n| Yoruba | 0.006 |\n| Swahili | 0.02 |\n\n\n**The following table shows the distribution of programming languages.**\n\n \n| Extension | Language | Number of files |\n|----------------|------------|-----------------|\n| java | Java | 5,407,724 |\n| php | PHP | 4,942,186 |\n| cpp | C++ | 2,503,930 |\n| py | Python | 2,435,072 |\n| js | JavaScript | 1,905,518 |\n| cs | C# | 1,577,347 |\n| rb | Ruby | 6,78,413 |\n| cc | C++ | 443,054 |\n| hpp | C++ | 391,048 |\n| lua | Lua | 352,317 |\n| go | GO | 227,763 |\n| ts | TypeScript | 195,254 |\n| C | C | 134,537 |\n| scala | Scala | 92,052 |\n| hh | C++ | 67,161 |\n| H | C++ | 55,899 |\n| tsx | TypeScript | 33,107 |\n| rs | Rust | 29,693 |\n| phpt | PHP | 9,702 |\n| c++ | C++ | 1,342 |\n| h++ | C++ | 791 |\n| php3 | PHP | 540 |\n| phps | PHP | 270 |\n| php5 | PHP | 166 |\n| php4 | PHP | 29 |\n \n\n## Evaluation\n*This section describes the evaluation protocols and provides the results.*\n\n\n### Metrics \n*This section describes the different ways performance is calculated and why.*\n \nIncludes:\n\n| Metric | Why chosen |\n|--------------------|--------------------------------------------------------------------|\n| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |\n| Cross Entropy [Loss](#loss) | Standard objective for language models. |\n\nAnd multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_\n\n### Factors \n*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*\n\n- Language, such as English or Yoruba\n\n- Domain, such as newswire or stories\n \n- Demographic characteristics, such as gender or nationality\n\n### Results\n*Results are based on the [Factors](#factors) and [Metrics](#metrics).*\n\n**Train-time Evaluation:**\n\nAs of 25.May.2022, 15:00 PST:\n\n- Training Loss: 2.0\n\n- Validation Loss: 2.2\n\n- Perplexity: 8.9\n\n(More evaluation scores forthcoming at the end of model training.)\n\n- [BLOOM Book](https://huggingface.co/spaces/bigscience/bloom-book): Read generations from BLOOM based on prompts provided by the community\n\n\n\n## Environmental Impact\n\nThe training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n \n**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*\n \n**Estimated electricity usage:** *(Forthcoming upon completion of training.)*\n\n\n\n## Technical Specifications\n*This section provides information for people who work on model development.*\n\n\nPlease see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.\n\n**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):\n\n* Decoder-only architecture\n\n* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))\n\n* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions\n\n* 1,722,408,960 parameters:\n\n * 513,802,240 embedding parameters\n\n * 24 layers, 16 attention heads\n\n * Hidden layers are 2048-dimensional\n\n * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))\n\n**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).\n \n**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).\n\n* Hardware: 64 V100 16/32GB GPUs (16 nodes):\n\n * 4 GPUs per node\n \n * 40 CPUs per task\n \n * 1 task per node\n \n * CPU: AMD\n\n * CPU memory: 160GB per node\n\n * GPU memory: 64GB or 128GB (depending on node availability during training) per node\n\n * Inter-node connect: Omni-Path Architecture (OPA)\n\n * NCCL-communications network: a fully dedicated subnet\n\n * Disc IO network: shared network with other types of nodes\n\n* Software:\n \n * Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))\n\n * DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))\n\n * PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))\n\n * apex ([Github link](https://github.com/NVIDIA/apex))\n \n### **Training**\n\n- Checkpoint size:\n \n - Fp16 weights: 2.6GB (# params * 2)\n \n - Full checkpoint with optimizer states: --\n\n- Training throughput: --\n\n- Number of epochs: 1\n\n- Dates:\n \n - Start: 11th March, 2022 11:42am PST\n\n - End: 20 May, 2022\n\n- Server training location: Île-de-France, France\n\n### **Tokenization**\n \nThe BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:\n \n- A byte-level Byte Pair Encoding (BPE) algorithm \n\n- A simple pre-tokenization rule, no normalization\n\n- A vocabulary size of 250,680\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. \n \n\n\n## Citation\n\n**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022\n\n## Glossary and Calculations\n\n*This section defines common terms and how metrics are calculated.*\n\n- **Loss:** A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss. \n\n- **Perplexity:** This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. \n\n- **High-stakes settings:** Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).\n\n- **Critical decisions:** Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).\n\n- **Human rights:** Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).\n\n- **Personal Data and Personal Information:** Personal data and information is defined in multiple data protection regulations, such as \"[personal data](https://gdpr-info.eu/issues/personal-data/)\" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and \"personal information\" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).\n \n- **Sensitive characteristics:** This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))\n\n- **Deception:** Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n## More Information\n\n \n### Dataset Creation\n\nBlog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling\n\n### Technical Specifications\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours\n\nMore details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml\n\nBlog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model\n\nDetails on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml\n\nTensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss\n\nInsights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md\n\n### Initial Results\n\nInitial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book\n \n## Model Card Authors\n*Ordered roughly chronologically and by amount of time spent.*\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n## Model Card Contact\n\n**Send Questions to:** bigscience-contact@googlegroups.com\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-07-23T00:39:34Z","string":"2023-07-23T00:39:34Z"},"card":{"kind":"string","value":"---\nlicense: other\nlanguage:\n- en\npipeline_tag: text-generation\ninference: false\ntags:\n- pytorch\n- llama\n- llama-2\n- qCammel-70\nlibrary_name: transformers\n---\n# qCammel-70\nqCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities.\n\n## Model Details\n*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept their License before downloading this model .*\n\nThe fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA, \n\n\n**Variations** The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 70B model.\n\n**Input** Models input text only.\n\n**Output** Models generate text only.\n\n**Model Architecture** qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture.\n\n**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)\nLlama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved\n\n**Research Papers** \n- [Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://arxiv.org/abs/2305.12031)\n- [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)\n- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.70971)\n\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-11-01T07:40:30Z","string":"2023-11-01T07:40:30Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\nlanguage:\n- ja\n- en\ntags:\n- japanese\n- causal-lm\ninference: false\n---\n# CyberAgentLM2-7B-Chat (CALM2-7B-Chat)\n\n## Model Description\n\nCyberAgentLM2-Chat is a fine-tuned model of [CyberAgentLM2](https://huggingface.co/cyberagent/calm2-7b) for dialogue use cases.\n\n## Requirements\n- transformers >= 4.34.1\n- accelerate\n\n## Usage\n\n```python\nimport transformers\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer\n\nassert transformers.__version__ >= \"4.34.1\"\n\nmodel = AutoModelForCausalLM.from_pretrained(\"cyberagent/calm2-7b-chat\", device_map=\"auto\", torch_dtype=\"auto\")\ntokenizer = AutoTokenizer.from_pretrained(\"cyberagent/calm2-7b-chat\")\nstreamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n\nprompt = \"\"\"USER: AIによって私達の暮らしはどのように変わりますか?\nASSISTANT: \"\"\"\n\ntoken_ids = tokenizer.encode(prompt, return_tensors=\"pt\")\noutput_ids = model.generate(\n input_ids=token_ids.to(model.device),\n max_new_tokens=300,\n do_sample=True,\n temperature=0.8,\n streamer=streamer,\n)\n```\n\n## Chat Template\n```\nUSER: {user_message1}\nASSISTANT: {assistant_message1}<|endoftext|>\nUSER: {user_message2}\nASSISTANT: {assistant_message2}<|endoftext|>\nUSER: {user_message3}\nASSISTANT: {assistant_message3}<|endoftext|>\n```\n\n## Model Details\n\n* **Model size**: 7B\n* **Context length**: 32768\n* **Model type**: Transformer-based Language Model\n* **Language(s)**: Japanese, English\n* **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/)\n* **License**: Apache-2.0\n\n## Author\n\n[Ryosuke Ishigami](https://huggingface.co/rishigami)\n\n## Citations\n```tex\n@article{touvron2023llama,\n title={LLaMA: Open and Efficient Foundation Language Models},\n author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\\'e}e and Rozi{\\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},\n journal={arXiv preprint arXiv:2302.13971},\n year={2023}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"question-answering"},"createdAt":{"kind":"timestamp","value":"2022-09-18T15:46:12Z","string":"2022-09-18T15:46:12Z"},"card":{"kind":"string","value":"## Persian XLM-RoBERTA Large For Question Answering Task\n\nXLM-RoBERTA is a multilingual language model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116v2) by Conneau et al. .\n\nMultilingual [XLM-RoBERTa large for QA on various languages](https://huggingface.co/deepset/xlm-roberta-large-squad2) is fine-tuned on various QA datasets but PQuAD, which is the biggest persian QA dataset so far. This second model is our base model to be fine-tuned.\n\nPaper presenting PQuAD dataset: [arXiv:2202.06219](https://arxiv.org/abs/2202.06219)\n\n---\n\n## Introduction\n\nThis model is fine-tuned on PQuAD Train set and is easily ready to use.\nIts very long training time encouraged me to publish this model in order to make life easier for those who need.\n\n\n## Hyperparameters of training\nI set batch size to 4 due to the limitations of GPU memory in Google Colab.\n```\nbatch_size = 4\nn_epochs = 1\nbase_LM_model = \"deepset/xlm-roberta-large-squad2\"\nmax_seq_len = 256\nlearning_rate = 3e-5\nevaluation_strategy = \"epoch\",\nsave_strategy = \"epoch\",\nlearning_rate = 3e-5,\nwarmup_ratio = 0.1,\ngradient_accumulation_steps = 8,\nweight_decay = 0.01,\n``` \n## Performance\nEvaluated on the PQuAD Persian test set with the [official PQuAD link](https://huggingface.co/datasets/newsha/PQuAD).\nI trained for more than 1 epoch as well, but I get worse results.\n Our XLM-Roberta outperforms [our ParsBert on PQuAD](https://huggingface.co/pedramyazdipoor/parsbert_question_answering_PQuAD), but the former is more than 3 times bigger than the latter one; so comparing these two is not fair.\n### Question Answering On Test Set of PQuAD Dataset\n\n| Metric | Our XLM-Roberta Large| Our ParsBert |\n|:----------------:|:--------------------:|:-------------:|\n| Exact Match | 66.56* | 47.44 |\n| F1 | 87.31* | 81.96 |\n\n\n\n## How to use\n\n## Pytorch\n```python\nfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering\npath = 'pedramyazdipoor/persian_xlm_roberta_large'\ntokenizer = AutoTokenizer.from_pretrained(path)\nmodel = AutoModelForQuestionAnswering.from_pretrained(path)\n```\n## Inference \nThere are some considerations for inference:\n1) Start index of answer must be smaller than end index.\n2) The span of answer must be within the context.\n3) The selected span must be the most probable choice among N pairs of candidates.\n\n```python\ndef generate_indexes(start_logits, end_logits, N, min_index):\n \n output_start = start_logits\n output_end = end_logits\n\n start_indexes = np.arange(len(start_logits))\n start_probs = output_start\n list_start = dict(zip(start_indexes, start_probs.tolist()))\n end_indexes = np.arange(len(end_logits))\n end_probs = output_end\n list_end = dict(zip(end_indexes, end_probs.tolist()))\n\n sorted_start_list = sorted(list_start.items(), key=lambda x: x[1], reverse=True) #Descending sort by probability\n sorted_end_list = sorted(list_end.items(), key=lambda x: x[1], reverse=True)\n\n final_start_idx, final_end_idx = [[] for l in range(2)]\n\n start_idx, end_idx, prob = 0, 0, (start_probs.tolist()[0] + end_probs.tolist()[0])\n for a in range(0,N):\n for b in range(0,N):\n if (sorted_start_list[a][1] + sorted_end_list[b][1]) > prob :\n if (sorted_start_list[a][0] <= sorted_end_list[b][0]) and (sorted_start_list[a][0] > min_index) :\n prob = sorted_start_list[a][1] + sorted_end_list[b][1]\n start_idx = sorted_start_list[a][0]\n end_idx = sorted_end_list[b][0]\n final_start_idx.append(start_idx) \n final_end_idx.append(end_idx) \n\n return final_start_idx[0], final_end_idx[0]\n```\n\n```python\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nmodel.eval().to(device)\ntext = 'سلام من پدرامم 26 سالمه'\nquestion = 'چند سالمه؟'\nencoding = tokenizer(question,text,add_special_tokens = True,\n return_token_type_ids = True,\n return_tensors = 'pt',\n padding = True,\n return_offsets_mapping = True,\n truncation = 'only_first',\n max_length = 32)\nout = model(encoding['input_ids'].to(device),encoding['attention_mask'].to(device), encoding['token_type_ids'].to(device))\n#we had to change some pieces of code to make it compatible with one answer generation at a time\n#If you have unanswerable questions, use out['start_logits'][0][0:] and out['end_logits'][0][0:] because (the 1st token) is for this situation and must be compared with other tokens.\n#you can initialize min_index in generate_indexes() to put force on tokens being chosen to be within the context(startindex must be greater than seperator token).\nanswer_start_index, answer_end_index = generate_indexes(out['start_logits'][0][1:], out['end_logits'][0][1:], 5, 0)\nprint(tokenizer.tokenize(text + question))\nprint(tokenizer.tokenize(text + question)[answer_start_index : (answer_end_index + 1)])\n>>> ['▁سلام', '▁من', '▁پدر', 'ام', 'م', '▁26', '▁سالم', 'ه', 'نام', 'م', '▁چیست', '؟']\n>>> ['▁26']\n```\n\n## Acknowledgments\nWe hereby, express our gratitude to the [Newsha Shahbodaghkhan](https://huggingface.co/datasets/newsha/PQuAD/tree/main) for facilitating dataset gathering.\n## Contributors\n- Pedram Yazdipoor : [Linkedin](https://www.linkedin.com/in/pedram-yazdipour/)\n## Releases\n### Release v0.2 (Sep 18, 2022)\nThis is the second version of our Persian XLM-Roberta-Large.\nThere were some problems using the previous version."},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-to-image"},"createdAt":{"kind":"timestamp","value":"2023-05-04T18:42:24Z","string":"2023-05-04T18:42:24Z"},"card":{"kind":"string","value":"---\nlicense: openrail\nbase_model: runwayml/stable-diffusion-v1-5\ntags:\n- art\n- controlnet\n- stable-diffusion\n- controlnet-v1-1\n- image-to-image\nduplicated_from: ControlNet-1-1-preview/control_v11f1e_sd15_tile\n---\n\n# Controlnet - v1.1 - *Tile Version*\n\n**Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel).\n\nThis checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11f1e_sd15_tile.pth) into `diffusers` format.\nIt can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).\n\n\nFor more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet).\n\n\nControlNet is a neural network structure to control diffusion models by adding extra conditions. \n\n![img](./sd.png)\n\nThis checkpoint corresponds to the ControlNet conditioned on **tiled image**. Conceptually, it is similar to a super-resolution model, but its usage is not limited to that. It is also possible to generate details at the same size as the input (conditione) image.\n\n**This model was contributed by [*takuma104*](https://huggingface.co/takuma104)**\n\n## Model Details\n- **Developed by:** Lvmin Zhang, Maneesh Agrawala\n- **Model type:** Diffusion-based text-to-image generation model\n- **Language(s):** English\n- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.\n- **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543).\n- **Cite as:**\n\n @misc{zhang2023adding,\n title={Adding Conditional Control to Text-to-Image Diffusion Models}, \n author={Lvmin Zhang and Maneesh Agrawala},\n year={2023},\n eprint={2302.05543},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n }\n## Introduction\n\nControlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by \nLvmin Zhang, Maneesh Agrawala.\n\nThe abstract reads as follows:\n\n*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. \nThe ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). \nMoreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. \nAlternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. \nWe report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. \nThis may enrich the methods to control large diffusion models and further facilitate related applications.*\n\n## Example\n\nIt is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint has been trained on it.\nExperimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.\n\n\n1. Let's install `diffusers` and related packages:\n```\n$ pip install diffusers transformers accelerate\n```\n2. Run code:\n```python\n\nimport torch\nfrom PIL import Image\nfrom diffusers import ControlNetModel, DiffusionPipeline\nfrom diffusers.utils import load_image\n\ndef resize_for_condition_image(input_image: Image, resolution: int):\n input_image = input_image.convert(\"RGB\")\n W, H = input_image.size\n k = float(resolution) / min(H, W)\n H *= k\n W *= k\n H = int(round(H / 64.0)) * 64\n W = int(round(W / 64.0)) * 64\n img = input_image.resize((W, H), resample=Image.LANCZOS)\n return img\n\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1e_sd15_tile', \n torch_dtype=torch.float16)\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"stable_diffusion_controlnet_img2img\",\n controlnet=controlnet,\n torch_dtype=torch.float16).to('cuda')\npipe.enable_xformers_memory_efficient_attention()\n\nsource_image = load_image('https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png')\n\ncondition_image = resize_for_condition_image(source_image, 1024)\nimage = pipe(prompt=\"best quality\", \n negative_prompt=\"blur, lowres, bad anatomy, bad hands, cropped, worst quality\", \n image=condition_image, \n controlnet_conditioning_image=condition_image, \n width=condition_image.size[0],\n height=condition_image.size[1],\n strength=1.0,\n generator=torch.manual_seed(0),\n num_inference_steps=32,\n ).images[0]\n\nimage.save('output.png')\n```\n\n![original](./images/original.png)\n![tile_output](./images/output.png)\n\n## Other released checkpoints v1-1\nThe authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) \non a different type of conditioning:\n\n| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |\n|---|---|---|---|---|\n|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)
| *Trained with canny edge detection* | A monochrome image with white edges on a black background.|||\n|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)
| *Trained with pixel to pixel instruction* | No condition .|||\n|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)
| Trained with image inpainting | No condition.|||\n|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)
| Trained with multi-level line segment detection | An image with annotated line segments.|||\n|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)
| Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|||\n|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)
| Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|||\n|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)
| Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|||\n|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)
| Trained with line art generation | An image with line art, usually black lines on a white background.|||\n|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)
| Trained with anime line art generation | An image with anime-style line art.|||\n|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)
| Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|||\n|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)
| Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|||\n|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)
| Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|||\n|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)
| Trained with image shuffling | An image with shuffled patches or regions.|||\n|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)
| Trained with image tiling | A blurry image or part of an image .|||\n\n## More information\n\nFor more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly)."},"embedding":{"kind":"list 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apache-2.0\n---\n\nBERT Miniatures\n===\n\nThis is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).\n\nWe have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.\n\nOur goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.\n\nYou can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below:\n\n| |H=128|H=256|H=512|H=768|\n|---|:---:|:---:|:---:|:---:|\n| **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]|\n| **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]|\n| **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]|\n| **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]|\n| **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]|\n| **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]|\n\nNote that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model.\n\nHere are the corresponding GLUE scores on the test set:\n\n|Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX|\n|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0|\n|BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1|\n|BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6|\n|BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5|\n\nFor each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs:\n- batch sizes: 8, 16, 32, 64, 128\n- learning rates: 3e-4, 1e-4, 5e-5, 3e-5\n\nIf you use these models, please cite the following paper:\n\n```\n@article{turc2019,\n title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},\n author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},\n journal={arXiv preprint arXiv:1908.08962v2 },\n year={2019}\n}\n```\n\n[2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2\n[2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4\n[2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8\n[2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12\n[4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2\n[4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4\n[4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8\n[4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12\n[6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2\n[6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4\n[6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8\n[6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12\n[8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2\n[8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4\n[8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8\n[8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12\n[10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2\n[10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4\n[10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8\n[10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12\n[12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2\n[12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4\n[12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8\n[12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12\n"},"embedding":{"kind":"list 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\"wav2vec2\",\n \"automatic-speech-recognition\",\n \"audio\",\n \"icelandic\",\n \"xlrs-53-icelandic\",\n \"iceland\",\n \"reykjavik\",\n \"samromur\",\n \"is\",\n \"dataset:language-and-voice-lab/samromur_asr\",\n \"dataset:language-and-voice-lab/samromur_children\",\n \"dataset:language-and-voice-lab/malromur_asr\",\n \"dataset:language-and-voice-lab/althingi_asr\",\n \"license:cc-by-4.0\",\n \"model-index\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"automatic-speech-recognition"},"createdAt":{"kind":"timestamp","value":"2022-12-08T01:25:56Z","string":"2022-12-08T01:25:56Z"},"card":{"kind":"string","value":"---\nlanguage: is\ndatasets:\n- language-and-voice-lab/samromur_asr\n- language-and-voice-lab/samromur_children\n- language-and-voice-lab/malromur_asr\n- language-and-voice-lab/althingi_asr\ntags:\n- audio\n- automatic-speech-recognition\n- icelandic\n- xlrs-53-icelandic\n- iceland\n- reykjavik\n- samromur\nlicense: cc-by-4.0\nmodel-index:\n- name: wav2vec2-large-xlsr-53-icelandic-ep10-1000h\n results:\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Samrómur (Test)\n type: language-and-voice-lab/samromur_asr\n split: test\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 9.847\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Samrómur (Dev)\n type: language-and-voice-lab/samromur_asr\n split: validation\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 8.736\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Samrómur Children (Test)\n type: language-and-voice-lab/samromur_children\n split: test\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 9.391\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Samrómur Children (Dev)\n type: language-and-voice-lab/samromur_children\n split: validation\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 6.055\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Malrómur (Test)\n type: language-and-voice-lab/malromur_asr\n split: test\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 5.643\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Malrómur (Dev)\n type: language-and-voice-lab/malromur_asr\n split: validation\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 6.156\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Althingi (Test)\n type: language-and-voice-lab/althingi_asr\n split: test\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 11.437\n - task:\n name: Automatic Speech Recognition\n type: automatic-speech-recognition\n dataset:\n name: Althingi (Dev)\n type: language-and-voice-lab/althingi_asr\n split: validation\n args: \n language: is\n metrics:\n - name: WER\n type: wer\n value: 11.093\n---\n# wav2vec2-large-xlsr-53-icelandic-ep10-1000h\n\nThe \"wav2vec2-large-xlsr-53-icelandic-ep10-1000h\" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model \"facebook/wav2vec2-large-xlsr-53\" for 10 epochs with around 1000 hours of Icelandic data developed by the [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab). Most of the data is available at public repositories such as [LDC](https://www.ldc.upenn.edu/), [OpenSLR](https://openslr.org/) or [Clarin.is](https://clarin.is/)\n\nThe specific list of corpora used to fine-tune the model is:\n\n- [Samrómur 21.05 (114h34m)](http://www.openslr.org/112/)\n- [Samrómur Children (127h25m)](https://catalog.ldc.upenn.edu/LDC2022S11)\n- [Malrómur (119hh03m)](https://clarin.is/en/resources/malromur/)\n- [Althingi Parliamentary Speech (514h29m)](https://catalog.ldc.upenn.edu/LDC2021S01)\n- L2-Speakers Data (125h55m) **Unpublished material**\n\t\nThe fine-tuning process was performed during December (2022) in the servers of the Language and Voice Laboratory (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.\n\n# Evaluation\n```python\nimport torch\nfrom transformers import Wav2Vec2Processor\nfrom transformers import Wav2Vec2ForCTC\n\n#Load the processor and model.\nMODEL_NAME=\"carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h\"\nprocessor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)\nmodel = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)\n\n#Load the dataset\nfrom datasets import load_dataset, load_metric, Audio\nds=load_dataset(\"language-and-voice-lab/samromur_children\", split=\"test\")\n\n#Downsample to 16kHz\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\n\n#Process the dataset\ndef prepare_dataset(batch):\n audio = batch[\"audio\"]\n #Batched output is \"un-batched\" to ensure mapping is correct\n batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n with processor.as_target_processor():\n batch[\"labels\"] = processor(batch[\"normalized_text\"]).input_ids\n return batch\nds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)\n\n#Define the evaluation metric\nimport numpy as np\nwer_metric = load_metric(\"wer\")\ndef compute_metrics(pred):\n pred_logits = pred.predictions\n pred_ids = np.argmax(pred_logits, axis=-1)\n pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n pred_str = processor.batch_decode(pred_ids)\n #We do not want to group tokens when computing the metrics\n label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n wer = wer_metric.compute(predictions=pred_str, references=label_str)\n return {\"wer\": wer}\n\n#Do the evaluation (with batch_size=1)\nmodel = model.to(torch.device(\"cuda\"))\ndef map_to_result(batch):\n with torch.no_grad():\n input_values = torch.tensor(batch[\"input_values\"], device=\"cuda\").unsqueeze(0)\n logits = model(input_values).logits\n pred_ids = torch.argmax(logits, dim=-1)\n batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n batch[\"sentence\"] = processor.decode(batch[\"labels\"], group_tokens=False)\n return batch\nresults = ds.map(map_to_result,remove_columns=ds.column_names)\n\n#Compute the overall WER now.\nprint(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references=results[\"sentence\"])))\n```\n**Test Result**: 0.094\n\n# BibTeX entry and citation info\n*When publishing results based on these models please refer to:*\n```bibtex\n@misc{mena2022xlrs53icelandic,\n title={Acoustic Model in Icelandic: wav2vec2-large-xlsr-53-icelandic-ep10-1000h.}, \n author={Hernandez Mena, Carlos Daniel},\n url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h},\n year={2022}\n}\n```\n\n# Acknowledgements\n\nSpecial thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the \"Language Technology Programme for Icelandic 2019-2023\" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\npipeline_tag: sentence-similarity\nlicense: apache-2.0\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n---\n\n**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**\n\n\n# sentence-transformers/distilbert-base-nli-stsb-mean-tokens\n\nThis is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\n\n\n## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\n\n```\npip install -U sentence-transformers\n```\n\nThen you can use the model like this:\n\n```python\nfrom sentence_transformers import SentenceTransformer\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\n\nmodel = SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\nembeddings = model.encode(sentences)\nprint(embeddings)\n```\n\n\n\n## Usage (HuggingFace Transformers)\nWithout [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.\n\n```python\nfrom transformers import AutoTokenizer, AutoModel\nimport torch\n\n\n#Mean Pooling - Take attention mask into account for correct averaging\ndef mean_pooling(model_output, attention_mask):\n token_embeddings = model_output[0] #First element of model_output contains all token embeddings\n input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n\n\n# Sentences we want sentence embeddings for\nsentences = ['This is an example sentence', 'Each sentence is converted']\n\n# Load model from HuggingFace Hub\ntokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\nmodel = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\n\n# Tokenize sentences\nencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n\n# Compute token embeddings\nwith torch.no_grad():\n model_output = model(**encoded_input)\n\n# Perform pooling. In this case, max pooling.\nsentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n\nprint(\"Sentence embeddings:\")\nprint(sentence_embeddings)\n```\n\n\n\n## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distilbert-base-nli-stsb-mean-tokens)\n\n\n\n## Full Model Architecture\n```\nSentenceTransformer(\n (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel \n (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n)\n```\n\n## Citing & Authors\n\nThis model was trained by [sentence-transformers](https://www.sbert.net/). \n \nIf you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):\n```bibtex \n@inproceedings{reimers-2019-sentence-bert,\n title = \"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\",\n author = \"Reimers, Nils and Gurevych, Iryna\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing\",\n month = \"11\",\n year = \"2019\",\n publisher = \"Association for Computational Linguistics\",\n url = \"http://arxiv.org/abs/1908.10084\",\n}\n```"},"embedding":{"kind":"list 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\"Composer\",\n \"MosaicML\",\n \"llm-foundry\",\n \"StreamingDatasets\",\n \"custom_code\",\n \"dataset:allenai/c4\",\n \"dataset:mc4\",\n \"dataset:togethercomputer/RedPajama-Data-1T\",\n \"dataset:bigcode/the-stack-dedup\",\n \"dataset:allenai/s2orc\",\n \"arxiv:2108.12409\",\n \"arxiv:2302.13971\",\n \"arxiv:2205.14135\",\n \"arxiv:2010.04245\",\n \"arxiv:1909.08053\",\n \"arxiv:2302.06675\",\n \"license:apache-2.0\",\n \"has_space\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-06-20T16:29:39Z","string":"2023-06-20T16:29:39Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- Composer\n- MosaicML\n- llm-foundry\n- StreamingDatasets\ndatasets:\n- allenai/c4\n- mc4\n- togethercomputer/RedPajama-Data-1T\n- bigcode/the-stack-dedup\n- allenai/s2orc\ninference: false\n---\n\n# MPT-30B\n\nMPT-30B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code.\nThis model was trained by [MosaicML](https://www.mosaicml.com).\n\nMPT-30B is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.\n\nMPT-30B comes with special features that differentiate it from other LLMs, including an 8k token context window (which can be further extended via finetuning; see [MPT-7B-StoryWriter](https://huggingface.co/mosaicml/mpt-7b-storywriter)), support for context-length extrapolation via [ALiBi](https://arxiv.org/abs/2108.12409), and efficient inference + training via FlashAttention. It also has strong coding abilities thanks to its pretraining mix. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer).\nThe size of MPT-30B was also specifically chosen to make it easy to deploy on a single GPU—either 1xA100-80GB in 16-bit precision or 1xA100-40GB in 8-bit precision.\n\nThis model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference.\n\n\n### How is this model different?\n\nMPT-30B is:\n* **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)).\n* **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)).\n* **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409).\n* **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer))\n* **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry)\n\n### Models finetuned off MPT-30B:\n\nThe following models are finetuned on MPT-30B:\n\n* [MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct): a model for long-form instruction following (especially summarization and question-answering).\nBuilt by finetuning MPT-30B on several carefully curated datasets.\n * License: _CC-BY-SA-3.0_\n\n* [MPT-30B-Chat](https://huggingface.co/mosaicml/mpt-30b-chat): a chatbot-like model for dialogue generation.\nBuilt by finetuning MPT-30B on [ShareGPT-Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered), [Camel-AI](https://huggingface.co/camel-ai),\n [GPTeacher](https://github.com/teknium1/GPTeacher), [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Baize](https://github.com/project-baize/baize-chatbot) and some generated datasets.\n * License: _CC-By-NC-SA-4.0_\n * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-30b-chat)\n\n## Model Date\n\nJune 22, 2023\n\n## Model License\n\nApache-2.0\n\n## Documentation\n\n* [Blog post: MPT-30B: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b)\n* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)\n* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!\n\n\n## How to Use\n\nThis model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning.\n\n```python\nimport transformers\nmodel = transformers.AutoModelForCausalLM.from_pretrained(\n 'mosaicml/mpt-30b',\n trust_remote_code=True\n)\n```\nNote: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.\nThis is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.\n`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.\n\nTo use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:\n```python\nimport torch\nimport transformers\n\nname = 'mosaicml/mpt-30b'\n\nconfig = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)\nconfig.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention\nconfig.init_device = 'cuda:0' # For fast initialization directly on GPU!\n\nmodel = transformers.AutoModelForCausalLM.from_pretrained(\n name,\n config=config,\n torch_dtype=torch.bfloat16, # Load model weights in bfloat16\n trust_remote_code=True\n)\n```\n\nThe model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example:\n\n```python\nimport transformers\n\nname = 'mosaicml/mpt-30b'\n\nconfig = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)\nconfig.max_seq_len = 16384 # (input + output) tokens can now be up to 16384\n\nmodel = transformers.AutoModelForCausalLM.from_pretrained(\n name,\n config=config,\n trust_remote_code=True\n)\n```\n\nThis model was trained with the MPT-30B tokenizer which is identical to the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.\n\n```python\nfrom transformers import AutoTokenizer\ntokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b')\n```\n\nThe model can then be used, for example, within a text-generation pipeline. \nNote: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).\n\n```python\nfrom transformers import pipeline\n\nwith torch.autocast('cuda', dtype=torch.bfloat16):\n inputs = tokenizer('Here is a recipe for vegan banana bread:\\n', return_tensors=\"pt\").to('cuda')\n outputs = model.generate(**inputs, max_new_tokens=100)\n print(tokenizer.batch_decode(outputs, skip_special_tokens=True))\n\n# or using the HF pipeline\npipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')\nwith torch.autocast('cuda', dtype=torch.bfloat16):\n print(\n pipe('Here is a recipe for vegan banana bread:\\n',\n max_new_tokens=100,\n do_sample=True,\n use_cache=True))\n```\n\n## Model Description\n\nThe architecture is a modification of a standard decoder-only transformer.\n\nThe model has been modified from a standard transformer in the following ways:\n* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)\n* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings\n* It does not use biases\n\n\n| Hyperparameter | Value |\n|----------------|-------|\n|n_parameters | 29.95B |\n|n_layers | 48 |\n| n_heads | 64 |\n| d_model | 7168 |\n| vocab size | 50432 |\n| sequence length | 8192 |\n\n\n\n## Training Data\n\n### Streaming Datasets\n\nData was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training.\nStreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset.\n\n\n### Data Mix\n\nThe model was trained for 1T tokens on the following data mix:\n\n| Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs |\n|-------------|----------------------------|------------|----------------------------|--------|\n| mC4 3.1.0 - English (200+ words) | 2417.99 B | 33.50% | 335 B | 0.14 |\n| c4 - English - SemDedup 80% | 100.42 B | 29.90% | 299 B | 2.98 |\n| RedPajama - CommonCrawl | 878.45 B | 8.50% | 85 B | 0.097 |\n| The Stack - Selected Languages | 463.78 B | 10.00% | 100 B | 0.22 |\n| RedPajama - Wikipedia | 4.87 B | 4.00% | 40 B | 8.21 |\n| The Stack - Markdown | 107.07 B | 4.50% | 45 B | 0.42 |\n| Semantic Scholar ORC | 48.95 B | 3.30% | 33 B | 0.67 |\n| RedPajama - Books | 26.02 B | 3.00% | 30 B | 1.15 |\n| RedPajama - arXiv | 28.10 B | 1.90% | 19 B | 0.68 |\n| RedPajama - StackExchange | 20.54 B | 1.40% | 14 B |0.68 |\n\nSamples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the sequence length. To build 8k support into MPT-30B efficiently, we first pre-trained on 1T tokens using sequences that were 2k tokens long, and then trained for an additional 50B tokens using sequences that were 8k tokens long.\n\nThe data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics,\nmost of which are relevant for tokenizing code:\n(1) It was trained on a diverse mix of data that includes code (The Pile)\n(2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces\n(3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters.\n\nThe model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)).\n\n### Training Configuration\n\nThe model was trained in three stages using the [MosaicML Platform](https://www.mosaicml.com/platform):\n(i) First it was trained on 440 A100-40GBs with a batch size of 1760.\n(ii) Then, on 216 A100-40GBs with a batch size of 1728.\n(iii) Training was completed on 256 H100-80GBs with a batch size of 512 with 8k context length and 50B tokens.\nThe model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer.\n\n## Limitations and Biases\n\n_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_\n\nMPT-30B (Base) is **not** intended for deployment without finetuning.\nIt should not be used for human-facing interactions without further guardrails and user consent.\n\nMPT-30B can produce factually incorrect output, and should not be relied on to produce factually accurate information.\nMPT-30B was trained on various public datasets.\nWhile great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.\n\n\n## MosaicML Platform\n\nIf you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-30b).\n\n## Disclaimer\n\nThe license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.\n\n## Citation\n\nPlease cite this model using the following format:\n\n```\n@online{MosaicML2023Introducing,\n author = {MosaicML NLP Team},\n title = {Introducing MPT-30B: Raising the bar\nfor open-source foundation models},\n year = {2023},\n url = {www.mosaicml.com/blog/mpt-30b},\n note = {Accessed: 2023-06-22},\n urldate = {2023-06-22}\n}\n```"},"embedding":{"kind":"list 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text-generation\n---\n\nFor testing purposes.\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2023-03-07T03:32:14Z","string":"2023-03-07T03:32:14Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\nlanguage:\n- zh\npipeline_tag: sentence-similarity\ntags:\n- text2vec\n- feature-extraction\n- sentence-similarity\n- transformers\n---\n\nBased on the derivative model of https://huggingface.co/shibing624/text2vec-base-chinese, replace MacBERT with LERT, and keep other training conditions unchanged。\n\nRefer to the following items for usage:\n\nhttps://github.com/shibing624/text2vec\n\nTalk to me: https://twitter.com/GanymedeNil"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2023-01-03T16:50:53Z","string":"2023-01-03T16:50:53Z"},"card":{"kind":"string","value":"---\ntags:\n- autotrain\n- text-classification\nlanguage:\n- unk\nwidget:\n- text: \"I love AutoTrain 🤗\"\ndatasets:\n- dafex/autotrain-data-indobert-sentiment-analysis\nco2_eq_emissions:\n emissions: 1.3428141985163928\n---\n\n# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 2713480683\n- CO2 Emissions (in grams): 1.3428\n\n## Validation Metrics\n\n- Loss: 0.132\n- Accuracy: 0.960\n- Precision: 0.966\n- Recall: 0.973\n- AUC: 0.993\n- F1: 0.969\n\n## Usage\n\nYou can use cURL to access this model:\n\n```\n$ curl -X POST -H \"Authorization: Bearer YOUR_API_KEY\" -H \"Content-Type: application/json\" -d '{\"inputs\": \"I love AutoTrain\"}' https://api-inference.huggingface.co/models/dafex/autotrain-indobert-sentiment-analysis-2713480683\n```\n\nOr Python API:\n\n```\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\nmodel = AutoModelForSequenceClassification.from_pretrained(\"dafex/autotrain-indobert-sentiment-analysis-2713480683\", use_auth_token=True)\n\ntokenizer = AutoTokenizer.from_pretrained(\"dafex/autotrain-indobert-sentiment-analysis-2713480683\", use_auth_token=True)\n\ninputs = tokenizer(\"I love AutoTrain\", return_tensors=\"pt\")\n\noutputs = model(**inputs)\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-segmentation"},"createdAt":{"kind":"timestamp","value":"2023-01-02T17:36:46Z","string":"2023-01-02T17:36:46Z"},"card":{"kind":"string","value":"---\nlicense: other\ntags:\n- vision\n- image-segmentation\ndatasets:\n- coco\nwidget:\n- src: http://images.cocodataset.org/val2017/000000039769.jpg\n example_title: Cats\n- src: http://images.cocodataset.org/val2017/000000039770.jpg\n example_title: Castle\n---\n\n# Mask2Former\n\nMask2Former model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation\n](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). \n\nDisclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team.\n\n## Model description\n\nMask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, \n[MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without\nwithout introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.\n\n![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png)\n\n## Intended uses & limitations\n\nYou can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other\nfine-tuned versions on a task that interests you.\n\n### How to use\n\nHere is how to use this model:\n\n```python\nimport requests\nimport torch\nfrom PIL import Image\nfrom transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation\n\n\n# load Mask2Former fine-tuned on COCO panoptic segmentation\nprocessor = AutoImageProcessor.from_pretrained(\"facebook/mask2former-swin-tiny-coco-panoptic\")\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained(\"facebook/mask2former-swin-tiny-coco-panoptic\")\n\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\nimage = Image.open(requests.get(url, stream=True).raw)\ninputs = processor(images=image, return_tensors=\"pt\")\n\nwith torch.no_grad():\n outputs = model(**inputs)\n\n# model predicts class_queries_logits of shape `(batch_size, num_queries)`\n# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`\nclass_queries_logits = outputs.class_queries_logits\nmasks_queries_logits = outputs.masks_queries_logits\n\n# you can pass them to processor for postprocessing\nresult = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\n# we refer to the demo notebooks for visualization (see \"Resources\" section in the Mask2Former docs)\npredicted_panoptic_map = result[\"segmentation\"]\n```\n\nFor more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former)."},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-to-image"},"createdAt":{"kind":"timestamp","value":"2023-01-08T03:47:50Z","string":"2023-01-08T03:47:50Z"},"card":{"kind":"string","value":"---\nlanguage:\n- en\nlicense: other\ntags:\n- stable-diffusion\n- stable-diffusion-diffusers\n- text-to-image\n- image-to-image\n- diffusers\n- anime\ninference: false\n---\n\n# Dreamlike Anime 1.0 is a high quality anime model, made by [dreamlike.art](https://dreamlike.art/).\n\n# If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! \n\nAdd **anime** to your prompt to make your gens look more anime. \nAdd **photo** to your prompt to make your gens look more photorealistic and have better anatomy. \nThis model was trained on 768x768px images, so use 768x768px, 704x832px, 832x704px, etc. Higher resolution or non-square aspect ratios may produce artifacts. \n\nAdd this to the start of your prompts for best results:\n```\nphoto anime, masterpiece, high quality, absurdres\n```\n\nUse negative prompts for best results, for example:\n```\nsimple background, duplicate, retro style, low quality, lowest quality, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013, bad anatomy,\nbad proportions, extra digits, lowres, username, artist name, error, duplicate, watermark, signature, text, extra digit, fewer digits, worst quality,\njpeg artifacts, blurry\n```\n\n**1girl**, **girl**, etc. give a bit different results, feel free to experiment and see which one you like more!\n\n### Examples\n\n\n\n\n\n# dreamlike.art\n\nUse this model as well as [Dreamlike Diffusion 1.0](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0) and [Dreamlike Photoreal 2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0) for free on [dreamlike.art](https://dreamlike.art/)!\n\n\n\n### CKPT\n\n[Download dreamlike-anime-1.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/dreamlike-anime-1.0.ckpt)\n\n### Safetensors\n[Download dreamlike-anime-1.0.safetensors (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/dreamlike-anime-1.0.safetensors)\n\n### 🧨 Diffusers\n\nThis model can be used just like any other Stable Diffusion model. For more information,\nplease have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"dreamlike-art/dreamlike-anime-1.0\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\n\nprompt = \"anime, masterpiece, high quality, 1girl, solo, long hair, looking at viewer, blush, smile, bangs, blue eyes, skirt, medium breasts, iridescent, gradient, colorful, besides a cottage, in the country\"\nnegative_prompt = 'simple background, duplicate, retro style, low quality, lowest quality, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013, bad anatomy, bad proportions, extra digits, lowres, username, artist name, error, duplicate, watermark, signature, text, extra digit, fewer digits, worst quality, jpeg artifacts, blurry'\nimage = pipe(prompt, negative_prompt=negative_prompt).images[0]\n\nimage.save(\"./result.jpg\")\n```\n\n\n\n# License\n\nThis model is licesed under a **modified** CreativeML OpenRAIL-M license.\n\n- **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at contact@dreamlike.art**\n- **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Anime 1.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-anime-1.0)** \n- **You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less**\n- You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n- You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/blob/main/LICENSE.md"},"embedding":{"kind":"list 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-0.1293242871761322\n]"}}},{"rowIdx":1147,"cells":{"modelId":{"kind":"string","value":"SRDdev/QABERT-small"},"author":{"kind":"string","value":"SRDdev"},"last_modified":{"kind":"timestamp","value":"2023-06-21T15:00:00Z","string":"2023-06-21T15:00:00Z"},"downloads":{"kind":"number","value":19615,"string":"19,615"},"likes":{"kind":"number","value":0,"string":"0"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","distilbert","question-answering","en","dataset:squad_v2","autotrain_compatible","endpoints_compatible","has_space","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"distilbert\",\n \"question-answering\",\n \"en\",\n \"dataset:squad_v2\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"question-answering"},"createdAt":{"kind":"timestamp","value":"2023-02-08T12:40:31Z","string":"2023-02-08T12:40:31Z"},"card":{"kind":"string","value":"---\ndatasets:\n- squad_v2\nlanguage:\n- en\nmetrics:\n- accuracy\nlibrary_name: transformers\npipeline_tag: question-answering\ntags:\n- question-answering\n---\n# QA-BERT\nQA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there.\n\n## Dataset\nThe Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike.\nDue to GPU limitations, this version is trained on `30k samples` from the Stanford Question Answering Dataset.\n\n
\n Structure of the Data Dictonary\n\n\n {\n \"data\":[\n {\n \"title\":\"Article Title\",\n \"paragraphs\":[\n {\n \"context\":\"The context text of the paragraph\",\n \"qas\":[\n {\n \"question\":\"The question asked about the context\",\n \"id\":\"A unique identifier for the question\",\n \"answers\":[\n {\n \"text\":\"The answer to the question\",\n \"answer_start\":\"The starting index of the answer in the context\"\n }\n ]\n }\n ]\n }\n ]\n }\n ],\n \"version\":\"The version of the SQuAD dataset\"\n }\n
\n\n## Model\nBERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language processing tasks such as question answering. BERT is fine-tuned for question answering by adding a linear layer on top of the pre-trained BERT representations to predict the start and end of the answer in the input context. BERT has achieved state-of-the-art results on multiple benchmark datasets, including the Stanford Question Answering Dataset (SQuAD). The fine-tuning process allows BERT to effectively capture the relationships between questions and answers and generate accurate answers.\n\nFor more detail about this read [Understanding QABERT](https://github.com/SRDdev/AnswerMind)\n\n\n## Inference\n_Load model_\n```python\nfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering\n\nQAtokenizer = AutoTokenizer.from_pretrained(\"SRDdev/QABERT-small\")\n\nQAmodel = AutoModelForQuestionAnswering.from_pretrained(\"SRDdev/QABERT-small\")\n```\n\n_context_\n```text\nExtractive Question Answering is the task of extracting an answer from a text given a question. An example of a\nquestion-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune\na model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.\n```\n\n\n_Build Pipeline_\n```python\nfrom transformers import pipeline\n\nask = pipeline(\"question-answering\", model= QAmodel , tokenizer = QAtokenizer)\n\nresult = ask(question=\"What is a good example of a question answering dataset?\", context=context)\n\nprint(f\"Answer: '{result['answer']}'\")\n```\n\n## Contributing\n\nPull requests are welcome. For major changes, please open an issue first\nto discuss what you would like to change.\nPlease make sure to update tests as appropriate.\n\n## Citations\n```\n@citation{ QA-BERT-small,\n author = {Shreyas Dixit},\n year = {2023},\n url = {https://huggingface.co/SRDdev/QA-BERT-small}\n}\n```\n"},"embedding":{"kind":"list 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\"stablelm_epoch\",\n \"text-generation\",\n \"causal-lm\",\n \"custom_code\",\n \"en\",\n \"dataset:tiiuae/falcon-refinedweb\",\n \"dataset:togethercomputer/RedPajama-Data-1T\",\n \"dataset:CarperAI/pilev2-dev\",\n \"dataset:bigcode/starcoderdata\",\n \"dataset:allenai/peS2o\",\n \"arxiv:2307.09288\",\n \"arxiv:2104.09864\",\n \"arxiv:2204.06745\",\n \"arxiv:1607.06450\",\n \"arxiv:1910.07467\",\n \"arxiv:2101.00027\",\n \"arxiv:2305.06161\",\n \"arxiv:1910.02054\",\n \"license:cc-by-sa-4.0\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-09-29T06:05:21Z","string":"2023-09-29T06:05:21Z"},"card":{"kind":"string","value":"---\nlicense: cc-by-sa-4.0\ndatasets:\n- tiiuae/falcon-refinedweb\n- togethercomputer/RedPajama-Data-1T\n- CarperAI/pilev2-dev\n- bigcode/starcoderdata\n- allenai/peS2o\nlanguage:\n- en\ntags:\n- causal-lm\nextra_gated_fields:\n Name: text\n Email: text\n Country: text\n Organization or Affiliation: text\n I ALLOW Stability AI to email me about new model releases: checkbox\n---\n# `StableLM-3B-4E1T`\n\n## Model Description\n\n`StableLM-3B-4E1T` is a 3 billion parameter decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs.\n\n## Usage\n\nGet started generating text with `StableLM-3B-4E1T` by using the following code snippet:\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\ntokenizer = AutoTokenizer.from_pretrained(\"stabilityai/stablelm-3b-4e1t\")\nmodel = AutoModelForCausalLM.from_pretrained(\n \"stabilityai/stablelm-3b-4e1t\",\n trust_remote_code=True,\n torch_dtype=\"auto\",\n)\nmodel.cuda()\ninputs = tokenizer(\"The weather is always wonderful\", return_tensors=\"pt\").to(\"cuda\")\ntokens = model.generate(\n **inputs,\n max_new_tokens=64,\n temperature=0.75,\n top_p=0.95,\n do_sample=True,\n)\nprint(tokenizer.decode(tokens[0], skip_special_tokens=True))\n```\n\n## Model Details\n\n* **Developed by**: [Stability AI](https://stability.ai/)\n* **Model type**: `StableLM-3B-4E1T` models are auto-regressive language models based on the transformer decoder architecture.\n* **Language(s)**: English\n* **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)\n* **License**: Model checkpoints are licensed under the Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)). Under this license, you must give [credit](https://creativecommons.org/licenses/by/4.0/#) to Stability AI, provide a link to the license, and [indicate if changes were made](https://creativecommons.org/licenses/by/4.0/#). You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use.\n* **Contact**: For questions and comments about the model, please email `lm@stability.ai`\n\n### Model Architecture\n\nThe model is a decoder-only transformer similar to the LLaMA ([Touvron et al., 2023](https://arxiv.org/abs/2307.09288)) architecture with the following modifications:\n\n| Parameters | Hidden Size | Layers | Heads | Sequence Length |\n|----------------|-------------|--------|-------|-----------------|\n| 2,795,443,200 | 2560 | 32 | 32 | 4096 |\n\n* **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf).\n* **Normalization**: LayerNorm ([Ba et al., 2016](https://arxiv.org/abs/1607.06450)) with learned bias terms as opposed to RMSNorm ([Zhang & Sennrich, 2019](https://arxiv.org/abs/1910.07467)).\n* **Tokenizer**: GPT-NeoX ([Black et al., 2022](https://arxiv.org/abs/2204.06745)).\n\n## Training\n\nFor complete dataset and training details, please see the [StableLM-3B-4E1T Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo).\n\n### Training Dataset\n\nThe dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), RedPajama-Data ([Together Computer., 2023](https://github.com/togethercomputer/RedPajama-Data)) and The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)) both without the *Books3* subset, and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)).\n\n* Given the large amount of web data, we recommend fine-tuning the base StableLM-3B-4E1T for your downstream tasks.\n\n### Training Procedure\n\nThe model is pre-trained on the aforementioned datasets in `bfloat16` precision, optimized with AdamW, and trained using the NeoX tokenizer with a vocabulary size of 50,257. We outline the complete hyperparameters choices in the project's [GitHub repository - config](https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-3b-4e1t.yml).\n\n### Training Infrastructure\n\n* **Hardware**: `StableLM-3B-4E1T` was trained on the Stability AI cluster across 256 NVIDIA A100 40GB GPUs (AWS P4d instances). Training began on August 23, 2023, and took approximately 30 days to complete.\n\n* **Software**: We use a fork of `gpt-neox` ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf))\n\n## Use and Limitations\n\n### Intended Use\n\nThe model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications.\n\n### Limitations and Bias\n​\nAs a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.\n\n## How to Cite\n\n```bibtex\n@misc{StableLM-3B-4E1T,\n url={[https://huggingface.co/stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)},\n title={StableLM 3B 4E1T},\n author={Tow, Jonathan and Bellagente, Marco and Mahan, Dakota and Riquelme, Carlos}\n}\n```\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"# Twitter-roBERTa-base for Hate Speech Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark. \nThis model is specialized to detect hate speech against women and immigrants.\n\n**NEW!** We have made available a more recent and robust hate speech detection model here: [https://huggingface.co/cardiffnlp/twitter-roberta-base-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-hate-latest)\n\n- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). \n- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).\n\n## Example of classification\n\n```python\nfrom transformers import AutoModelForSequenceClassification\nfrom transformers import TFAutoModelForSequenceClassification\nfrom transformers import AutoTokenizer\nimport numpy as np\nfrom scipy.special import softmax\nimport csv\nimport urllib.request\n\n# Preprocess text (username and link placeholders)\ndef preprocess(text):\n new_text = []\n for t in text.split(\" \"):\n t = '@user' if t.startswith('@') and len(t) > 1 else t\n t = 'http' if t.startswith('http') else t\n new_text.append(t)\n return \" \".join(new_text)\n\n# Tasks:\n# emoji, emotion, hate, irony, offensive, sentiment\n# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary\n\ntask='hate'\nMODEL = f\"cardiffnlp/twitter-roberta-base-{task}\"\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL)\n\n# download label mapping\nlabels=[]\nmapping_link = f\"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt\"\nwith urllib.request.urlopen(mapping_link) as f:\n html = f.read().decode('utf-8').split(\"\\n\")\n csvreader = csv.reader(html, delimiter='\\t')\nlabels = [row[1] for row in csvreader if len(row) > 1]\n\n# PT\nmodel = AutoModelForSequenceClassification.from_pretrained(MODEL)\nmodel.save_pretrained(MODEL)\n\ntext = \"Good night 😊\"\ntext = preprocess(text)\nencoded_input = tokenizer(text, return_tensors='pt')\noutput = model(**encoded_input)\nscores = output[0][0].detach().numpy()\nscores = softmax(scores)\n\n# # TF\n# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)\n# model.save_pretrained(MODEL)\n\n# text = \"Good night 😊\"\n# encoded_input = tokenizer(text, return_tensors='tf')\n# output = model(encoded_input)\n# scores = output[0][0].numpy()\n# scores = softmax(scores)\n\nranking = np.argsort(scores)\nranking = ranking[::-1]\nfor i in range(scores.shape[0]):\n l = labels[ranking[i]]\n s = scores[ranking[i]]\n print(f\"{i+1}) {l} {np.round(float(s), 4)}\")\n\n```\n\nOutput: \n\n```\n1) not-hate 0.9168\n2) hate 0.0832\n```\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"graph-ml"},"createdAt":{"kind":"timestamp","value":"2023-01-05T10:12:34Z","string":"2023-01-05T10:12:34Z"},"card":{"kind":"string","value":"---\nlicense: mit\ntags:\n- graphs\npipeline_tag: graph-ml\n---\n\n# Model Card for pcqm4mv1_graphormer_base\n\nThe Graphormer is a graph classification model.\n\n# Model Details\n\n## Model Description\n\nThe Graphormer is a graph Transformer model, pretrained on PCQM4M-LSC, and which got 1st place on the KDD CUP 2021 (quantum prediction track).\n\n\n- **Developed by:** Microsoft\n- **Model type:** Graphormer\n- **License:** MIT\n\n## Model Sources\n\n\n\n- **Repository:** [Github](https://github.com/microsoft/Graphormer)\n- **Paper:** [Paper](https://arxiv.org/abs/2106.05234)\n- **Documentation:** [Link](https://graphormer.readthedocs.io/en/latest/)\n\n# Uses\n\n## Direct Use\n\nThis model should be used for graph classification tasks or graph representation tasks; the most likely associated task is molecule modeling. It can either be used as such, or finetuned on downstream tasks.\n\n# Bias, Risks, and Limitations\n\nThe Graphormer model is ressource intensive for large graphs, and might lead to OOM errors.\n\n## How to Get Started with the Model\n\nSee the Graph Classification with Transformers tutorial.\n\n# Citation [optional]\n\n\n\n**BibTeX:**\n```\n@article{DBLP:journals/corr/abs-2106-05234,\n author = {Chengxuan Ying and\n Tianle Cai and\n Shengjie Luo and\n Shuxin Zheng and\n Guolin Ke and\n Di He and\n Yanming Shen and\n Tie{-}Yan Liu},\n title = {Do Transformers Really Perform Bad for Graph Representation?},\n journal = {CoRR},\n volume = {abs/2106.05234},\n year = {2021},\n url = {https://arxiv.org/abs/2106.05234},\n eprinttype = {arXiv},\n eprint = {2106.05234},\n timestamp = {Tue, 15 Jun 2021 16:35:15 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2106-05234.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlicense: other\ntags:\n- vision\ndatasets:\n- imagenet_1k\nwidget:\n- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg\n example_title: House\n- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg\n example_title: Castle\n---\n\n# SegFormer (b2-sized) encoder pre-trained-only\n\nSegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). \n\nDisclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.\n\n## Model description\n\nSegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.\n\nThis repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes.\n\n## Intended uses & limitations\n\nYou can use the model for fine-tuning of semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.\n\n### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n```python\nfrom transformers import SegformerFeatureExtractor, SegformerForImageClassification\nfrom PIL import Image\nimport requests\n\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\nimage = Image.open(requests.get(url, stream=True).raw)\n\nfeature_extractor = SegformerFeatureExtractor.from_pretrained(\"nvidia/mit-b2\")\nmodel = SegformerForImageClassification.from_pretrained(\"nvidia/mit-b2\")\n\ninputs = feature_extractor(images=image, return_tensors=\"pt\")\noutputs = model(**inputs)\nlogits = outputs.logits\n# model predicts one of the 1000 ImageNet classes\npredicted_class_idx = logits.argmax(-1).item()\nprint(\"Predicted class:\", model.config.id2label[predicted_class_idx])\n```\n\nFor more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#).\n\n### License\n\nThe license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE).\n\n### BibTeX entry and citation info\n\n```bibtex\n@article{DBLP:journals/corr/abs-2105-15203,\n author = {Enze Xie and\n Wenhai Wang and\n Zhiding Yu and\n Anima Anandkumar and\n Jose M. Alvarez and\n Ping Luo},\n title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers},\n journal = {CoRR},\n volume = {abs/2105.15203},\n year = {2021},\n url = {https://arxiv.org/abs/2105.15203},\n eprinttype = {arXiv},\n eprint = {2105.15203},\n timestamp = {Wed, 02 Jun 2021 11:46:42 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```\n"},"embedding":{"kind":"list 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-0.25917983055114746\n]"}}},{"rowIdx":1152,"cells":{"modelId":{"kind":"string","value":"beomi/llama-2-ko-7b"},"author":{"kind":"string","value":"beomi"},"last_modified":{"kind":"timestamp","value":"2023-11-24T15:54:53Z","string":"2023-11-24T15:54:53Z"},"downloads":{"kind":"number","value":19502,"string":"19,502"},"likes":{"kind":"number","value":105,"string":"105"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","llama","text-generation","facebook","meta","llama-2","kollama","llama-2-ko","en","ko","doi:10.57967/hf/1098","has_space","text-generation-inference","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"llama\",\n \"text-generation\",\n \"facebook\",\n \"meta\",\n \"llama-2\",\n \"kollama\",\n \"llama-2-ko\",\n \"en\",\n \"ko\",\n \"doi:10.57967/hf/1098\",\n \"has_space\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-07-20T03:25:25Z","string":"2023-07-20T03:25:25Z"},"card":{"kind":"string","value":"---\nlanguage:\n- en\n- ko\npipeline_tag: text-generation\ninference: false\ntags:\n- facebook\n- meta\n- pytorch\n- llama\n- llama-2\n- kollama\n- llama-2-ko\n---\n\n> 🚧 Note: this repo is under construction 🚧\n\n**Update Log**\n\n- 2023.10.19\n - Fix Tokenizer bug(space not applied when decoding) after `transforemrs>=4.34.0`\n\n\n# **Llama-2-Ko** 🦙🇰🇷\n\nLlama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below.\n\n## Model Details\n\n**Model Developers** Junbum Lee (Beomi)\n\n**Variations** Llama-2-Ko will come in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.\n\n**Input** Models input text only.\n\n**Output** Models generate text only.\n\n**Model Architecture** \n\nLlama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2.\n\n||Training Data|Params|Content Length|GQA|Tokens|LR|\n|---|---|---|---|---|---|---|\n|Llama 2|*A new mix of Korean online data*|7B|4k|&#10007;|>40B*|1e-5|\n*Plan to train upto 200B tokens\n\n**Vocab Expansion**\n\n| Model Name | Vocabulary Size | Description | \n| --- | --- | --- |\n| Original Llama-2 | 32000 | Sentencepiece BPE |\n| **Expanded Llama-2-Ko** | 46336 | Sentencepiece BPE. Added Korean vocab and merges |\n\n**Tokenizing \"안녕하세요, 오늘은 날씨가 좋네요.\"**\n\n| Model | Tokens |\n| --- | --- |\n| Llama-2 | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요']` |\n| Llama-2-Ko | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요']` |\n\n**Tokenizing \"Llama 2: Open Foundation and Fine-Tuned Chat Models\"**\n\n| Model | Tokens |\n| --- | --- |\n| Llama-2 | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` |\n| Llama-2-Ko | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` |\n\n# **Model Benchmark**\n\n## LM Eval Harness - Korean (polyglot branch)\n\n- Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot\n\n### NSMC (Acc) - 50000 full test\n\nTBD\n\n### COPA (F1)\n\n\n\n| Model | 0-shot | 5-shot | 10-shot | 50-shot |\n| --- | --- | --- | --- | --- |\n| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6696 | 0.6477 | 0.6419 | 0.6514 |\n| https://huggingface.co/kakaobrain/kogpt | 0.7345 | 0.7287 | 0.7277 | 0.7479 |\n| https://huggingface.co/facebook/xglm-7.5B | 0.6723 | 0.6731 | 0.6769 | 0.7119 |\n| https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.7196 | 0.7193 | 0.7204 | 0.7206 |\n| https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.7595 | 0.7608 | 0.7638 | 0.7788 |\n| https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.7745 | 0.7676 | 0.7775 | 0.7887 |\n| https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.7937 | 0.8108 | 0.8037 | 0.8369 |\n| Llama-2 Original 7B* | 0.562033 | 0.575982 | 0.576216 | 0.595532 |\n| Llama-2-Ko-7b 20B (10k) | 0.738780 | 0.762639 | 0.780761 | 0.797863 |\n| Llama-2-Ko-7b 40B (20k) | 0.743630 | 0.792716 | 0.803746 | 0.825944 |\n*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated)\n\n### HellaSwag (F1)\n\n\n\n| Model | 0-shot | 5-shot | 10-shot | 50-shot |\n| --- | --- | --- | --- | --- |\n| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.5243 | 0.5272 | 0.5166 | 0.5352 |\n| https://huggingface.co/kakaobrain/kogpt | 0.5590 | 0.5833 | 0.5828 | 0.5907 |\n| https://huggingface.co/facebook/xglm-7.5B | 0.5665 | 0.5689 | 0.5565 | 0.5622 |\n| https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.5247 | 0.5260 | 0.5278 | 0.5427 |\n| https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.5707 | 0.5830 | 0.5670 | 0.5787 |\n| https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.5976 | 0.5998 | 0.5979 | 0.6208 |\n| https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.5954 | 0.6306 | 0.6098 | 0.6118 |\n| Llama-2 Original 7B* | 0.415390 | 0.431382 | 0.421342 | 0.442003 |\n| Llama-2-Ko-7b 20B (10k) | 0.451757 | 0.466751 | 0.472607 | 0.482776 |\n| Llama-2-Ko-7b 40B (20k) | 0.456246 | 0.465665 | 0.469810 | 0.477374 |\n*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated)\n\n### BoolQ (F1)\n\n\n\n| Model | 0-shot | 5-shot | 10-shot | 50-shot |\n| --- | --- | --- | --- | --- |\n| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.3356 | 0.4014 | 0.3640 | 0.3560 |\n| https://huggingface.co/kakaobrain/kogpt | 0.4514 | 0.5981 | 0.5499 | 0.5202 |\n| https://huggingface.co/facebook/xglm-7.5B | 0.4464 | 0.3324 | 0.3324 | 0.3324 |\n| https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.3552 | 0.4751 | 0.4109 | 0.4038 |\n| https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4320 | 0.5263 | 0.4930 | 0.4038 |\n| https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.4356 | 0.5698 | 0.5187 | 0.5236 |\n| https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.4818 | 0.6041 | 0.6289 | 0.6448 |\n| Llama-2 Original 7B* | 0.352050 | 0.563238 | 0.474788 | 0.419222 |\n| Llama-2-Ko-7b 20B (10k) | 0.360656 | 0.679743 | 0.680109 | 0.662152 |\n| Llama-2-Ko-7b 40B (20k) | 0.578640 | 0.697747 | 0.708358 | 0.714423 |\n*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated)\n\n### SentiNeg (F1)\n\n\n\n| Model | 0-shot | 5-shot | 10-shot | 50-shot |\n| --- | --- | --- | --- | --- |\n| https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6065 | 0.6878 | 0.7280 | 0.8413 |\n| https://huggingface.co/kakaobrain/kogpt | 0.3747 | 0.8942 | 0.9294 | 0.9698 |\n| https://huggingface.co/facebook/xglm-7.5B | 0.3578 | 0.4471 | 0.3964 | 0.5271 |\n| https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.6790 | 0.6257 | 0.5514 | 0.7851 |\n| https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4858 | 0.7950 | 0.7320 | 0.7851 |\n| https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.3394 | 0.8841 | 0.8808 | 0.9521 |\n| https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.9117 | 0.9015 | 0.9345 | 0.9723 |\n| Llama-2 Original 7B* | 0.347502 | 0.529124 | 0.480641 | 0.788457 |\n| Llama-2-Ko-7b 20B (10k) | 0.485546 | 0.829503 | 0.871141 | 0.851253 |\n| Llama-2-Ko-7b 40B (20k) | 0.459447 | 0.761079 | 0.727611 | 0.936988 |\n*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated)\n\n\n## Note for oobabooga/text-generation-webui\n\nRemove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`.\n\n```python\ndiff --git a/modules/models.py b/modules/models.py\nindex 232d5fa..de5b7a0 100644\n--- a/modules/models.py\n+++ b/modules/models.py\n@@ -106,7 +106,7 @@ def load_tokenizer(model_name, model):\n trust_remote_code=shared.args.trust_remote_code,\n use_fast=False\n )\n- except ValueError:\n+ except:\n tokenizer = AutoTokenizer.from_pretrained(\n path_to_model,\n trust_remote_code=shared.args.trust_remote_code,\n```\n\nSince Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package, \nit is required to use `use_fast=True` option when initialize tokenizer.\n\nApple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU)\n\n## Citation\n\n```\n@misc {l._junbum_2023,\n\tauthor = { {L. Junbum} },\n\ttitle = { llama-2-ko-7b (Revision 4a9993e) },\n\tyear = 2023,\n\turl = { https://huggingface.co/beomi/llama-2-ko-7b },\n\tdoi = { 10.57967/hf/1098 },\n\tpublisher = { Hugging Face }\n}\n```\n\n## Acknowledgement\n\nThe training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.\n\n\n# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\nDetailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b)\n\n| Metric | Value |\n|-----------------------|---------------------------|\n| Avg. | 39.43 |\n| ARC (25-shot) | 48.46 |\n| HellaSwag (10-shot) | 75.28 |\n| MMLU (5-shot) | 39.56 |\n| TruthfulQA (0-shot) | 34.49 |\n| Winogrande (5-shot) | 72.14 |\n| GSM8K (5-shot) | 1.97 |\n| DROP (3-shot) | 4.1 |\n"},"embedding":{"kind":"list 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image-classification\n- timm\nlibrary_name: timm\nlicense: apache-2.0\ndatasets:\n- imagenet-21k\n---\n# Model card for tf_efficientnetv2_s.in21k\n\nA EfficientNet-v2 image classification model. Trained on ImageNet-21k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.\n\n\n## Model Details\n- **Model Type:** Image classification / feature backbone\n- **Model Stats:**\n - Params (M): 48.2\n - GMACs: 5.4\n - Activations (M): 22.8\n - Image size: train = 300 x 300, test = 384 x 384\n- **Papers:**\n - EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298\n- **Dataset:** ImageNet-21k\n- **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet\n\n## Model Usage\n### Image Classification\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model('tf_efficientnetv2_s.in21k', pretrained=True)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1\n\ntop5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)\n```\n\n### Feature Map Extraction\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model(\n 'tf_efficientnetv2_s.in21k',\n pretrained=True,\n features_only=True,\n)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1\n\nfor o in output:\n # print shape of each feature map in output\n # e.g.:\n # torch.Size([1, 24, 150, 150])\n # torch.Size([1, 48, 75, 75])\n # torch.Size([1, 64, 38, 38])\n # torch.Size([1, 160, 19, 19])\n # torch.Size([1, 256, 10, 10])\n\n print(o.shape)\n```\n\n### Image Embeddings\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model(\n 'tf_efficientnetv2_s.in21k',\n pretrained=True,\n num_classes=0, # remove classifier nn.Linear\n)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor\n\n# or equivalently (without needing to set num_classes=0)\n\noutput = model.forward_features(transforms(img).unsqueeze(0))\n# output is unpooled, a (1, 1280, 10, 10) shaped tensor\n\noutput = model.forward_head(output, pre_logits=True)\n# output is a (1, num_features) shaped tensor\n```\n\n## Model Comparison\nExplore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).\n\n## Citation\n```bibtex\n@inproceedings{tan2021efficientnetv2,\n title={Efficientnetv2: Smaller models and faster training},\n author={Tan, Mingxing and Le, Quoc},\n booktitle={International conference on machine learning},\n pages={10096--10106},\n year={2021},\n organization={PMLR}\n}\n```\n```bibtex\n@misc{rw2019timm,\n author = {Ross Wightman},\n title = {PyTorch Image Models},\n year = {2019},\n publisher = {GitHub},\n journal = {GitHub repository},\n doi = {10.5281/zenodo.4414861},\n howpublished = {\\url{https://github.com/huggingface/pytorch-image-models}}\n}\n```\n"},"embedding":{"kind":"list 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\"endpoints_compatible\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"question-answering"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: tr\ndatasets:\n- TQUAD\ntags: \n- question-answering\n- question-generation\n- multitask-model\n\nlicense: apache-2.0\n---\n\n# mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System\n\n[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset) for three downstream task **Answer extraction, Question Generation and Question Answering** served in this single model. mT5 model was also trained for multiple text2text NLP tasks. \n\nAll data processing, training and pipeline codes can be found on my [Github](https://github.com/ozcangundes/multitask-question-generation). I will share the training details in the repo as soon as possible. \n\nmT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tune it. \n\n8 epoch and 1e-4 learning rate with 0 warmup steps was applied during training. These hparams and the others can be fine-tuned for much more better results.\n\n## Requirements ❗❗❗\n```\n!pip install transformers==4.4.2\n!pip install sentencepiece==0.1.95\n!git clone https://github.com/ozcangundes/multitask-question-generation.git\n%cd multitask-question-generation/\n```\n\n## Usage 🚀🚀\n```\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\ntokenizer = AutoTokenizer.from_pretrained(\"ozcangundes/mt5-multitask-qa-qg-turkish\") \nmodel = AutoModelForSeq2SeqLM.from_pretrained(\"ozcangundes/mt5-multitask-qa-qg-turkish\")\n\nfrom pipelines import pipeline #pipelines.py script in the cloned repo\nmultimodel = pipeline(\"multitask-qa-qg\",tokenizer=tokenizer,model=model)\n\n#sample text\ntext=\"Özcan Gündeş, 1993 yılı Tarsus doğumludur. Orta Doğu Teknik Üniversitesi \\\\\\\\\nEndüstri Mühendisliği bölümünde 2011 2016 yılları arasında lisans eğitimi görmüştür. \\\\\\\\\nYüksek lisansını ise 2020 Aralık ayında, 4.00 genel not ortalaması ile \\\\\\\\\nBoğaziçi Üniversitesi, Yönetim Bilişim Sistemleri bölümünde tamamlamıştır.\\\\\\\\\nFutbolla yakından ilgilenmekle birlikte, Galatasaray kulübü taraftarıdır.\"\n```\n\n## Example - Both Question Generation and Question Answering 💬💬\n```\nmultimodel(text)\n\n#output\n=> [{'answer': 'Tarsus', 'question': 'Özcan Gündeş nerede doğmuştur?'},\n {'answer': '1993', 'question': 'Özcan Gündeş kaç yılında doğmuştur?'},\n {'answer': '2011 2016',\n 'question': 'Özcan Gündeş lisans eğitimini hangi yıllar arasında tamamlamıştır?'},\n {'answer': 'Boğaziçi Üniversitesi, Yönetim Bilişim Sistemleri',\n 'question': 'Özcan Gündeş yüksek lisansını hangi bölümde tamamlamıştır?'},\n {'answer': 'Galatasaray kulübü',\n 'question': 'Özcan Gündeş futbolla yakından ilgilenmekle birlikte hangi kulübü taraftarıdır?'}]\n```\nFrom this text, 5 questions are generated and they are answered by the model. \n\n## Example - Question Answering 💭💭\n\nBoth text and also, related question should be passed into pipeline.\n```\nmultimodel({\"context\":text,\"question\":\"Özcan hangi takımı tutmaktadır?\"})\n\n#output\n=> Galatasaray\n\nmultimodel({\"context\":text,\"question\":\"Özcan, yüksek lisanstan ne zaman mezun oldu?\"})\n\n#output\n=> 2020 Aralık ayında\n\n\nmultimodel({\"context\":text,\"question\":\"Özcan'ın yüksek lisans bitirme notu kaçtır?\"})\n\n#output\n=> 4.00 \n\n#Sorry for being cocky 😝😝\n```\n\n## ACKNOWLEDGEMENT\n\nThis work is inspired from [Suraj Patil's great repo](https://github.com/patil-suraj/question_generation). I would like to thank him for the clean codes and also,[Okan Çiftçi](https://github.com/okanvk) for the Turkish dataset 🙏\n\n"},"embedding":{"kind":"list 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en\ntags:\n- exbert\n\nlicense: mit\n---\n\n\n# GPT-2\n\nTest the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large\n\nPretrained model on English language using a causal language modeling (CLM) objective. It was introduced in\n[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)\nand first released at [this page](https://openai.com/blog/better-language-models/).\n\nDisclaimer: The team releasing GPT-2 also wrote a\n[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card\nhas been written by the Hugging Face team to complete the information they provided and give specific examples of bias.\n\n## Model description\n\nGPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This\nmeans it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots\nof publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,\nit was trained to guess the next word in sentences.\n\nMore precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,\nshifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the\npredictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a\nprompt.\n\n## Intended uses & limitations\n\nYou can use the raw model for text generation or fine-tune it to a downstream task. See the\n[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.\n\n### How to use\n\nHere is how to use the ONNX models of gpt2 to get the features of a given text:\n\nExample using transformers.pipelines:\n\n```python\nfrom transformers import AutoTokenizer, pipeline\nfrom optimum.onnxruntime import ORTModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\nmodel = ORTModelForCausalLM.from_pretrained(\"gpt2\", from_transformers=True)\nonnx_gen = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n\ntext = \"My name is Philipp and I live in Germany.\"\ngen = onnx_gen(text)\n```\n\nExample of text generation:\n\n```python\nfrom transformers import AutoTokenizer\nfrom optimum.onnxruntime import ORTModelForCausalLM\nimport torch\n\ntokenizer = AutoTokenizer.from_pretrained(\"optimum/gpt2\")\nmodel = ORTModelForCausalLM.from_pretrained(\"optimum/gpt2\")\n\ninputs = tokenizer(\"My name is Arthur and I live in\", return_tensors=\"pt\")\n\ngen_tokens = model.generate(**inputs,do_sample=True,temperature=0.9, min_length=20,max_length=20)\ntokenizer.batch_decode(gen_tokens)\n```\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"document-question-answering"},"createdAt":{"kind":"timestamp","value":"2022-07-19T13:58:22Z","string":"2022-07-19T13:58:22Z"},"card":{"kind":"string","value":"---\nlicense: mit\npipeline_tag: document-question-answering\ntags:\n- donut\n- image-to-text\n- vision\nwidget:\n- text: \"What is the invoice number?\"\n src: \"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png\"\n- text: \"What is the purchase amount?\"\n src: \"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg\"\n---\n\n# Donut (base-sized model, fine-tuned on DocVQA) \n\nDonut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team.\n\n## Model description\n\nDonut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder. \n\n![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/donut_architecture.jpg)\n\n## Intended uses & limitations\n\nThis model is fine-tuned on DocVQA, a document visual question answering dataset.\n\nWe refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.\n\n### BibTeX entry and citation info\n\n```bibtex\n@article{DBLP:journals/corr/abs-2111-15664,\n author = {Geewook Kim and\n Teakgyu Hong and\n Moonbin Yim and\n Jinyoung Park and\n Jinyeong Yim and\n Wonseok Hwang and\n Sangdoo Yun and\n Dongyoon Han and\n Seunghyun Park},\n title = {Donut: Document Understanding Transformer without {OCR}},\n journal = {CoRR},\n volume = {abs/2111.15664},\n year = {2021},\n url = {https://arxiv.org/abs/2111.15664},\n eprinttype = {arXiv},\n eprint = {2111.15664},\n timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},\n biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"null"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: \n- multilingual\n- af\n- am\n- ar\n- az\n- be\n- bg\n- bn\n- bs\n- ca\n- ceb\n- co\n- cs\n- cy\n- da\n- de\n- el\n- en\n- eo\n- es\n- et\n- eu\n- fa\n- fi\n- fil\n- fr\n- fy\n- ga\n- gd\n- gl\n- gu\n- ha\n- haw\n- hi\n- hmn\n- hr\n- ht\n- hu\n- hy\n- id\n- ig\n- is\n- it\n- iw\n- ja\n- jv\n- ka\n- kk\n- km\n- kn\n- ko\n- ku\n- ky\n- la\n- lb\n- lo\n- lt\n- lv\n- mg\n- mi\n- mk\n- ml\n- mn\n- mr\n- ms\n- mt\n- my\n- ne\n- nl\n- no\n- ny\n- pa\n- pl\n- ps\n- pt\n- ro\n- ru\n- sd\n- si\n- sk\n- sl\n- sm\n- sn\n- so\n- sq\n- sr\n- st\n- su\n- sv\n- sw\n- ta\n- te\n- tg\n- th\n- tr\n- uk\n- ur\n- uz\n- vi\n- xh\n- yi\n- yo\n- zh\n- zu\nlicense: apache-2.0\ndatasets:\n- wikipedia\n---\n\n# RemBERT (for classification) \n\nPretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821). A direct export of the model checkpoint was first made available in [this repository](https://github.com/google-research/google-research/tree/master/rembert). This version of the checkpoint is lightweight since it is meant to be finetuned for classification and excludes the output embedding weights.\n\n## Model description\n\nRemBERT's main difference with mBERT is that the input and output embeddings are not tied. Instead, RemBERT uses small input embeddings and larger output embeddings. This makes the model more efficient since the output embeddings are discarded during fine-tuning. It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT. \n\n## Intended uses & limitations\n\nYou should fine-tune this model for your downstream task. It is meant to be a general-purpose model, similar to mBERT. In our [paper](https://arxiv.org/abs/2010.12821), we have successfully applied this model to tasks such as classification, question answering, NER, POS-tagging. For tasks such as text generation you should look at models like GPT2.\n\n\n## Training data\n\nThe RemBERT model was pretrained on multilingual Wikipedia data over 110 languages. The full language list is on [this repository](https://github.com/google-research/google-research/tree/master/rembert)\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{DBLP:conf/iclr/ChungFTJR21,\n author = {Hyung Won Chung and\n Thibault F{\\'{e}}vry and\n Henry Tsai and\n Melvin Johnson and\n Sebastian Ruder},\n title = {Rethinking Embedding Coupling in Pre-trained Language Models},\n booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,\n Virtual Event, Austria, May 3-7, 2021},\n publisher = {OpenReview.net},\n year = {2021},\n url = {https://openreview.net/forum?id=xpFFI\\_NtgpW},\n timestamp = {Wed, 23 Jun 2021 17:36:39 +0200},\n biburl = {https://dblp.org/rec/conf/iclr/ChungFTJR21.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n```"},"embedding":{"kind":"list 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-0.19979815185070038\n]"}}},{"rowIdx":1159,"cells":{"modelId":{"kind":"string","value":"TheBloke/zephyr-7B-beta-GPTQ"},"author":{"kind":"string","value":"TheBloke"},"last_modified":{"kind":"timestamp","value":"2023-10-27T14:56:24Z","string":"2023-10-27T14:56:24Z"},"downloads":{"kind":"number","value":19378,"string":"19,378"},"likes":{"kind":"number","value":42,"string":"42"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","safetensors","mistral","text-generation","generated_from_trainer","en","dataset:HuggingFaceH4/ultrachat_200k","dataset:HuggingFaceH4/ultrafeedback_binarized","arxiv:2305.18290","arxiv:2310.16944","base_model:HuggingFaceH4/zephyr-7b-beta","license:mit","text-generation-inference","4-bit","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"mistral\",\n \"text-generation\",\n \"generated_from_trainer\",\n \"en\",\n \"dataset:HuggingFaceH4/ultrachat_200k\",\n \"dataset:HuggingFaceH4/ultrafeedback_binarized\",\n \"arxiv:2305.18290\",\n \"arxiv:2310.16944\",\n \"base_model:HuggingFaceH4/zephyr-7b-beta\",\n \"license:mit\",\n \"text-generation-inference\",\n \"4-bit\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-10-27T13:16:11Z","string":"2023-10-27T13:16:11Z"},"card":{"kind":"string","value":"---\nbase_model: HuggingFaceH4/zephyr-7b-beta\ndatasets:\n- HuggingFaceH4/ultrachat_200k\n- HuggingFaceH4/ultrafeedback_binarized\ninference: false\nlanguage:\n- en\nlicense: mit\nmodel-index:\n- name: zephyr-7b-beta\n results: []\nmodel_creator: Hugging Face H4\nmodel_name: Zephyr 7B Beta\nmodel_type: mistral\nprompt_template: '<|system|>\n\n
\n\n <|user|>\n\n {prompt}\n\n <|assistant|>\n\n '\nquantized_by: TheBloke\ntags:\n- generated_from_trainer\n---\n\n\n\n\n
\n\"TheBlokeAI\"\n
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Chat & support: TheBloke's Discord server

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Want to contribute? TheBloke's Patreon page

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TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

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\n\n\n# Zephyr 7B Beta - GPTQ\n- Model creator: [Hugging Face H4](https://huggingface.co/HuggingFaceH4)\n- Original model: [Zephyr 7B Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\n\n\n## Description\n\nThis repo contains GPTQ model files for [Hugging Face H4's Zephyr 7B Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta).\n\nMultiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.\n\nThese files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).\n\n\n\n## Repositories available\n\n* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/zephyr-7B-beta-AWQ)\n* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF)\n* [Hugging Face H4's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\n\n\n\n## Prompt template: Zephyr\n\n```\n<|system|>\n\n<|user|>\n{prompt}\n<|assistant|>\n\n```\n\n\n\n\n\n\n## Known compatible clients / servers\n\nThese GPTQ models are known to work in the following inference servers/webuis.\n\n- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n- [KobaldAI United](https://github.com/henk717/koboldai)\n- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)\n\nThis may not be a complete list; if you know of others, please let me know!\n\n\n\n## Provided files, and GPTQ parameters\n\nMultiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.\n\nEach separate quant is in a different branch. See below for instructions on fetching from different branches.\n\nMost GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.\n\n
\n Explanation of GPTQ parameters\n\n- Bits: The bit size of the quantised model.\n- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. \"None\" is the lowest possible value.\n- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.\n- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.\n- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).\n- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.\n- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.\n\n
\n\n| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |\n| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |\n| [main](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | \n| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | \n| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | \n| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | \n| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | \n| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |\n\n\n\n\n## How to download, including from branches\n\n### In text-generation-webui\n\nTo download from the `main` branch, enter `TheBloke/zephyr-7B-beta-GPTQ` in the \"Download model\" box.\n\nTo download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/zephyr-7B-beta-GPTQ:gptq-4bit-32g-actorder_True`\n\n### From the command line\n\nI recommend using the `huggingface-hub` Python library:\n\n```shell\npip3 install huggingface-hub\n```\n\nTo download the `main` branch to a folder called `zephyr-7B-beta-GPTQ`:\n\n```shell\nmkdir zephyr-7B-beta-GPTQ\nhuggingface-cli download TheBloke/zephyr-7B-beta-GPTQ --local-dir zephyr-7B-beta-GPTQ --local-dir-use-symlinks False\n```\n\nTo download from a different branch, add the `--revision` parameter:\n\n```shell\nmkdir zephyr-7B-beta-GPTQ\nhuggingface-cli download TheBloke/zephyr-7B-beta-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir zephyr-7B-beta-GPTQ --local-dir-use-symlinks False\n```\n\n
\n More advanced huggingface-cli download usage\n\nIf you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\nThe cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.\n\nFor more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:\n\n```shell\npip3 install hf_transfer\n```\n\nAnd set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:\n\n```shell\nmkdir zephyr-7B-beta-GPTQ\nHF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/zephyr-7B-beta-GPTQ --local-dir zephyr-7B-beta-GPTQ --local-dir-use-symlinks False\n```\n\nWindows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.\n
\n\n### With `git` (**not** recommended)\n\nTo clone a specific branch with `git`, use a command like this:\n\n```shell\ngit clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ\n```\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)\n\n\n\n## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n\nPlease make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n1. Click the **Model tab**.\n2. Under **Download custom model or LoRA**, enter `TheBloke/zephyr-7B-beta-GPTQ`.\n\n - To download from a specific branch, enter for example `TheBloke/zephyr-7B-beta-GPTQ:gptq-4bit-32g-actorder_True`\n - see Provided Files above for the list of branches for each option.\n\n3. Click **Download**.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to **Model**.\n6. In the **Model** dropdown, choose the model you just downloaded: `zephyr-7B-beta-GPTQ`\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.\n\n - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.\n\n9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!\n\n\n\n\n## Serving this model from Text Generation Inference (TGI)\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`\n\nExample Docker parameters:\n\n```shell\n--model-id TheBloke/zephyr-7B-beta-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096\n```\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n```shell\npip3 install huggingface-hub\n```\n\n```python\nfrom huggingface_hub import InferenceClient\n\nendpoint_url = \"https://your-endpoint-url-here\"\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''<|system|>\n\n<|user|>\n{prompt}\n<|assistant|>\n'''\n\nclient = InferenceClient(endpoint_url)\nresponse = client.text_generation(prompt,\n max_new_tokens=128,\n do_sample=True,\n temperature=0.7,\n top_p=0.95,\n top_k=40,\n repetition_penalty=1.1)\n\nprint(f\"Model output: {response}\")\n```\n\n\n## How to use this GPTQ model from Python code\n\n### Install the necessary packages\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n```shell\npip3 install transformers optimum\npip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7\n```\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:\n\n```shell\npip3 uninstall -y auto-gptq\ngit clone https://github.com/PanQiWei/AutoGPTQ\ncd AutoGPTQ\ngit checkout v0.4.2\npip3 install .\n```\n\n### You can then use the following code\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n\nmodel_name_or_path = \"TheBloke/zephyr-7B-beta-GPTQ\"\n# To use a different branch, change revision\n# For example: revision=\"gptq-4bit-32g-actorder_True\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name_or_path,\n device_map=\"auto\",\n trust_remote_code=False,\n revision=\"main\")\n\ntokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''<|system|>\n\n<|user|>\n{prompt}\n<|assistant|>\n'''\n\nprint(\"\\n\\n*** Generate:\")\n\ninput_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()\noutput = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)\nprint(tokenizer.decode(output[0]))\n\n# Inference can also be done using transformers' pipeline\n\nprint(\"*** Pipeline:\")\npipe = pipeline(\n \"text-generation\",\n model=model,\n tokenizer=tokenizer,\n max_new_tokens=512,\n do_sample=True,\n temperature=0.7,\n top_p=0.95,\n top_k=40,\n repetition_penalty=1.1\n)\n\nprint(pipe(prompt_template)[0]['generated_text'])\n```\n\n\n\n## Compatibility\n\nThe files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.\n\n[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.\n\nFor a list of clients/servers, please see \"Known compatible clients / servers\", above.\n\n\n\n\n## Discord\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n[TheBloke AI's Discord server](https://discord.gg/theblokeai)\n\n## Thanks, and how to contribute\n\nThanks to the [chirper.ai](https://chirper.ai) team!\n\nThanks to Clay from [gpus.llm-utils.org](llm-utils)!\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n* Patreon: https://patreon.com/TheBlokeAI\n* Ko-Fi: https://ko-fi.com/TheBlokeAI\n\n**Special thanks to**: Aemon Algiz.\n\n**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski\n\n\nThank you to all my generous patrons and donaters!\n\nAnd thank you again to a16z for their generous grant.\n\n\n\n# Original model card: Hugging Face H4's Zephyr 7B Beta\n\n\n\n\n\"Zephyr\n\n\n# Model Card for Zephyr 7B β\n\nZephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944).\n\n\n## Model description\n\n- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.\n- **Language(s) (NLP):** Primarily English\n- **License:** MIT\n- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)\n\n### Model Sources\n\n\n\n- **Repository:** https://github.com/huggingface/alignment-handbook\n- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat\n- **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org\n\n## Performance\n\nAt the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks:\n\n| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |\n|-------------|-----|----|---------------|--------------|\n| StableLM-Tuned-α | 7B| dSFT |2.75| -|\n| MPT-Chat | 7B |dSFT |5.42| -|\n| Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|\n| Mistral-Instructv0.1 | 7B| - | 6.84 |-|\n| Zephyr-7b-α |7B| dDPO| 6.88| -|\n| **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** |\n| Falcon-Instruct | 40B |dSFT |5.17 |45.71|\n| Guanaco | 65B | SFT |6.41| 71.80|\n| Llama2-Chat | 70B |RLHF |6.86| 92.66|\n| Vicuna v1.3 | 33B |dSFT |7.12 |88.99|\n| WizardLM v1.0 | 70B |dSFT |7.71 |-|\n| Xwin-LM v0.1 | 70B |dPPO |- |95.57|\n| GPT-3.5-turbo | - |RLHF |7.94 |89.37|\n| Claude 2 | - |RLHF |8.06| 91.36|\n| GPT-4 | -| RLHF |8.99| 95.28|\n\nIn particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png)\n\nHowever, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.\n\n\n## Intended uses & limitations\n\nThe model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. \n\nYou can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66)\n\nHere's how you can run the model using the `pipeline()` function from 🤗 Transformers:\n\n```python\n# Install transformers from source - only needed for versions <= v4.34\n# pip install git+https://github.com/huggingface/transformers.git\n# pip install accelerate\n\nimport torch\nfrom transformers import pipeline\n\npipe = pipeline(\"text-generation\", model=\"HuggingFaceH4/zephyr-7b-beta\", torch_dtype=torch.bfloat16, device_map=\"auto\")\n\n# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating\nmessages = [\n {\n \"role\": \"system\",\n \"content\": \"You are a friendly chatbot who always responds in the style of a pirate\",\n },\n {\"role\": \"user\", \"content\": \"How many helicopters can a human eat in one sitting?\"},\n]\nprompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\noutputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\nprint(outputs[0][\"generated_text\"])\n# <|system|>\n# You are a friendly chatbot who always responds in the style of a pirate.\n# <|user|>\n# How many helicopters can a human eat in one sitting?\n# <|assistant|>\n# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!\n```\n\n## Bias, Risks, and Limitations\n\n\n\nZephyr-7B-β has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). \nIt is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.\n\n\n## Training and evaluation data\n\nDuring DPO training, this model achieves the following results on the evaluation set:\n\n- Loss: 0.7496\n- Rewards/chosen: -4.5221\n- Rewards/rejected: -8.3184\n- Rewards/accuracies: 0.7812\n- Rewards/margins: 3.7963\n- Logps/rejected: -340.1541\n- Logps/chosen: -299.4561\n- Logits/rejected: -2.3081\n- Logits/chosen: -2.3531\n\n\n### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 2\n- eval_batch_size: 4\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 16\n- total_train_batch_size: 32\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3.0\n\n### Training results\n\nThe table below shows the full set of DPO training metrics:\n\n\n| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |\n|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|\n| 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 |\n| 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 |\n| 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 |\n| 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 |\n| 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 |\n| 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 |\n| 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 |\n| 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 |\n| 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 |\n| 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 |\n| 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 |\n| 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 |\n| 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 |\n| 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 |\n| 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 |\n| 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 |\n| 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 |\n| 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 |\n| 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 |\n| 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 |\n| 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 |\n| 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 |\n| 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 |\n| 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 |\n| 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 |\n| 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 |\n| 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 |\n| 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 |\n| 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 |\n| 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 |\n| 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 |\n| 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 |\n| 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 |\n| 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 |\n| 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 |\n| 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 |\n| 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 |\n| 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 |\n| 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 |\n| 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 |\n| 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 |\n| 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 |\n| 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 |\n| 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 |\n| 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 |\n| 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 |\n| 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 |\n| 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 |\n| 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 |\n| 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 |\n| 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 |\n| 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 |\n| 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 |\n| 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 |\n| 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 |\n| 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 |\n| 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 |\n| 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 |\n\n\n### Framework versions\n\n- Transformers 4.35.0.dev0\n- Pytorch 2.0.1+cu118\n- Datasets 2.12.0\n- Tokenizers 0.14.0\n\n## Citation\n\nIf you find Zephyr-7B-β is useful in your work, please cite it with:\n\n```\n@misc{tunstall2023zephyr,\n title={Zephyr: Direct Distillation of LM Alignment}, \n author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},\n year={2023},\n eprint={2310.16944},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n"},"embedding":{"kind":"list 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interview setting\n\nCode for using in Transformers:\n\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification\n \ntokenizer = AutoTokenizer.from_pretrained(\"mrsinghania/asr-question-detection\")\n\nmodel = AutoModelForSequenceClassification.from_pretrained(\"mrsinghania/asr-question-detection\")"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: en\ndatasets:\n- emo\n---\n\n## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch\n\n### Details of MobileBERT\n\nThe **MobileBERT** model was presented in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by *Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou* and here is the abstract:\n\nNatural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT_BASE).\n\n### Details of the downstream task (Emotion Recognition) - Dataset 📚\n\nSemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text\n\nIn this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes:\n\n - sad 😢\n - happy 😃\n - angry 😡\n - others\n\n### Model training\n\nThe training script is present [here](https://github.com/lordtt13/transformers-experiments/blob/master/Custom%20Tasks/emo-mobilebert.ipynb).\n\n### Pipelining the Model\n\n```python\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"lordtt13/emo-mobilebert\")\n\nmodel = AutoModelForSequenceClassification.from_pretrained(\"lordtt13/emo-mobilebert\")\n\nnlp_sentence_classif = transformers.pipeline('sentiment-analysis', model = model, tokenizer = tokenizer)\nnlp_sentence_classif(\"I've never had such a bad day in my life\")\n# Output: [{'label': 'sad', 'score': 0.93153977394104}]\n```\n\n> Created by [Tanmay Thakur](https://github.com/lordtt13) | [LinkedIn](https://www.linkedin.com/in/tanmay-thakur-6bb5a9154/)\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-04-06T01:35:04Z","string":"2023-04-06T01:35:04Z"},"card":{"kind":"string","value":"---\nlicense: openrail\ndatasets:\n- bigcode/the-stack\nlanguage:\n- code\nprogramming_language: \n- Java\n- JavaScript\n- Python\npipeline_tag: text-generation\ninference: false\n\nmodel-index:\n- name: SantaCoder\n results:\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL HumanEval (Python)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.18\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.29\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.49\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL MBPP (Python)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.35\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.58\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.77\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL HumanEval (JavaScript)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.16\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.27\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.47\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL MBPP (Javascript)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.28\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.51\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.70\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL HumanEval (Java)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.15\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.26\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.41\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL MBPP (Java)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.28\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.44\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.59\n verified: false\n - task:\n type: text-generation\n dataset:\n type: loubnabnl/humaneval_infilling\n name: HumanEval FIM (Python)\n metrics:\n - name: single_line\n type: exact_match\n value: 0.44\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL HumanEval FIM (Java)\n metrics:\n - name: single_line\n type: exact_match\n value: 0.62\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL HumanEval FIM (JavaScript)\n metrics:\n - name: single_line\n type: exact_match\n value: 0.60\n verified: false\n - task:\n type: text-generation\n dataset:\n type: code_x_glue_ct_code_to_text\n name: CodeXGLUE code-to-text (Python)\n metrics:\n - name: BLEU\n type: bleu\n value: 18.13\n verified: false\n---\n\n# SantaCoder\n\n![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/banner.png)\n\nPlay with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo).\n\n# Table of Contents\n\n1. [Model Summary](#model-summary)\n2. [Use](#use)\n3. [Limitations](#limitations)\n4. [Training](#training)\n5. [License](#license)\n6. [Citation](#citation)\n\n# Model Summary\n\nThis is the same model as [SantaCoder](https://huggingface.co/bigcode/santacoder) but it can be loaded with transformers >=4.28.1 to use the GPTBigCode architecture.\nWe refer the reader to the [SantaCoder model page](https://huggingface.co/bigcode/santacoder) for full documentation about this model\n\n\n- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)\n- **Project Website:** [bigcode-project.org](www.bigcode-project.org)\n- **Paper:** [🎅SantaCoder: Don't reach for the stars!🌟](https://t.co/YV3pzUbYOr)\n- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)\n- **Languages:** Python, Java, and JavaScript\n\nThere are two versions (branches) of the model:\n* `main`: Uses the `gpt_bigcode` model. [Requires the bigcode fork of transformers](https://github.com/bigcode-project/transformers).\n* `main_custom`: Packaged with its modeling code. Requires `transformers>=4.27`.\n Alternatively, it can run on older versions by setting the configuration parameter `activation_function = \"gelu_pytorch_tanh\"`.\n\n# Use\n\n## Intended use\n\nThe model was trained on GitHub code. As such it is _not_ an instruction model and commands like \"Write a function that computes the square root.\" do not work well.\nYou should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body.\n\n### Attribution & Other Requirements\n\nThe pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/santacoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.\n\n# Limitations\n\nThe model has been trained on source code in Python, Java, and JavaScript. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits.\n\n# Training\n\n## Model\n\n- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective\n- **Pretraining steps:** 600K\n- **Pretraining tokens:** 236 billion\n- **Precision:** float16\n\n## Hardware\n\n- **GPUs:** 96 Tesla V100\n- **Training time:** 6.2 days\n- **Total FLOPS:** 2.1 x 10e21\n\n## Software\n\n- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)\n- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)\n- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)\n\n# License\nThe model is licenses under the CodeML Open RAIL-M v0.1 license. You can find the full license [here](https://huggingface.co/spaces/bigcode/license).\n"},"embedding":{"kind":"list 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card for eva_giant_patch14_224.clip_ft_in1k\n\nAn EVA-CLIP image classification model. Pretrained on LAION-400M with CLIP and fine-tuned on ImageNet-1k by paper authors. EVA-CLIP uses MIM pretrained image towers and pretrained text towers, FLIP patch dropout, and different optimizers and hparams to accelerate training.\n\nNOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred.\n\n\n## Model Details\n- **Model Type:** Image classification / feature backbone\n- **Model Stats:**\n - Params (M): 1012.6\n - GMACs: 267.2\n - Activations (M): 192.6\n - Image size: 224 x 224\n- **Papers:**\n - EVA-CLIP: Improved Training Techniques for CLIP at Scale: https://arxiv.org/abs/2303.15389\n- **Original:**\n - https://github.com/baaivision/EVA\n - https://huggingface.co/QuanSun/EVA-CLIP\n\n## Model Usage\n### Image Classification\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model('eva_giant_patch14_224.clip_ft_in1k', pretrained=True)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1\n\ntop5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)\n```\n\n### Image Embeddings\n```python\nfrom urllib.request import urlopen\nfrom PIL import Image\nimport timm\n\nimg = Image.open(urlopen(\n 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'\n))\n\nmodel = timm.create_model(\n 'eva_giant_patch14_224.clip_ft_in1k',\n pretrained=True,\n num_classes=0, # remove classifier nn.Linear\n)\nmodel = model.eval()\n\n# get model specific transforms (normalization, resize)\ndata_config = timm.data.resolve_model_data_config(model)\ntransforms = timm.data.create_transform(**data_config, is_training=False)\n\noutput = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor\n\n# or equivalently (without needing to set num_classes=0)\n\noutput = model.forward_features(transforms(img).unsqueeze(0))\n# output is unpooled, a (1, 257, 1408) shaped tensor\n\noutput = model.forward_head(output, pre_logits=True)\n# output is a (1, num_features) shaped tensor\n```\n\n## Model Comparison\nExplore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).\n\n|model |top1 |top5 |param_count|img_size|\n|-----------------------------------------------|------|------|-----------|--------|\n|eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08 |448 |\n|eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08 |448 |\n|eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 |\n|eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 |\n|eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 |\n|eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 |\n|eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 |\n|eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 |\n|eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 |\n|eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 |\n|eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 |\n|eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 |\n|eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 |\n|eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 |\n|eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 |\n|eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 |\n\n## Citation\n```bibtex\n@article{EVA-CLIP,\n title={EVA-02: A Visual Representation for Neon Genesis},\n author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue},\n journal={arXiv preprint arXiv:2303.15389},\n year={2023}\n}\n```\n```bibtex\n@misc{rw2019timm,\n author = {Ross Wightman},\n title = {PyTorch Image Models},\n year = {2019},\n publisher = {GitHub},\n journal = {GitHub repository},\n doi = {10.5281/zenodo.4414861},\n howpublished = {\\url{https://github.com/huggingface/pytorch-image-models}}\n}\n```\n"},"embedding":{"kind":"list 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-0.5149121284484863\n]"}}},{"rowIdx":1165,"cells":{"modelId":{"kind":"string","value":"xyn-ai/anything-v4.0"},"author":{"kind":"string","value":"xyn-ai"},"last_modified":{"kind":"timestamp","value":"2023-03-23T04:25:51Z","string":"2023-03-23T04:25:51Z"},"downloads":{"kind":"number","value":19005,"string":"19,005"},"likes":{"kind":"number","value":49,"string":"49"},"library_name":{"kind":"string","value":"diffusers"},"tags":{"kind":"list like","value":["diffusers","stable-diffusion","stable-diffusion-diffusers","text-to-image","en","license:creativeml-openrail-m","endpoints_compatible","has_space","diffusers:StableDiffusionPipeline","region:us"],"string":"[\n \"diffusers\",\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"text-to-image\",\n \"en\",\n \"license:creativeml-openrail-m\",\n \"endpoints_compatible\",\n \"has_space\",\n \"diffusers:StableDiffusionPipeline\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-to-image"},"createdAt":{"kind":"timestamp","value":"2023-03-23T04:25:51Z","string":"2023-03-23T04:25:51Z"},"card":{"kind":"string","value":"---\nlanguage:\n- en\nlicense: creativeml-openrail-m\ntags:\n- stable-diffusion\n- stable-diffusion-diffusers\n- text-to-image\n- diffusers\ninference: true\nduplicated_from: andite/anything-v4.0\n---\n\nFantasy.ai is the official and exclusive hosted AI generation platform that holds a commercial use license for Anything V4.0, you can use their service at https://Fantasy.ai/\n\nPlease report any unauthorized commercial use.\n\n-----------------\nTry out my new model! - [Pastel Mix || Stylized Anime Model](https://huggingface.co/andite/pastel-mix). Thanks.\n\nI also uploaded it in CivitAI! https://civitai.com/models/5414/pastel-mix-stylized-anime-model I'd appreciate the ratings, thank you!\n\nYes, it's a shameless plug.\n\nExamples:\n\n![](https://huggingface.co/andite/Pastel-Mix/resolve/main/example-images/grid-0018.png)\n![](https://huggingface.co/andite/pastel-mix/resolve/main/example-images/grid-reimu.png)\n![](https://huggingface.co/andite/pastel-mix/resolve/main/example-images/grid-0043.png)\n\n-------\n\nThanks to [Linaqruf](https://huggingface.co/Linaqruf) for letting me borrow his model card for reference.\n\n# Anything V4\n\nWelcome to Anything V4 - a latent diffusion model for weebs. The newest version of Anything. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images.\n\ne.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_** \n\nI think the V4.5 version better though, it's in this repo. feel free 2 try it.\n\n## Yes, this model has [AbyssOrangeMix2](https://huggingface.co/WarriorMama777/OrangeMixs) in it. coz its a very good model. check it out luls ;)\n\n\n# Gradio\n\nWe support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run anything-v4.0:\n[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/anything-v4.0)\n\n## 🧨 Diffusers\n\nThis model can be used just like any other Stable Diffusion model. For more information,\nplease have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).\n\nYou can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"andite/anything-v4.0\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\n\nprompt = \"hatsune_miku\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"./hatsune_miku.png\")\n```\n\n## Examples\n\nBelow are some examples of images generated using this model:\n\n**Anime Girl:**\n![Anime Girl](https://huggingface.co/andite/anything-v4.0/resolve/main/example-1.png)\n```\nmasterpiece, best quality, 1girl, white hair, medium hair, cat ears, closed eyes, looking at viewer, :3, cute, scarf, jacket, outdoors, streets\nSteps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7\n```\n**Anime Boy:**\n![Anime Boy](https://huggingface.co/andite/anything-v4.0/resolve/main/example-2.png)\n```\n1boy, bishounen, casual, indoors, sitting, coffee shop, bokeh\nSteps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7\n```\n**Scenery:**\n![Scenery](https://huggingface.co/andite/anything-v4.0/resolve/main/example-4.png)\n```\nscenery, village, outdoors, sky, clouds\nSteps: 50, Sampler: DPM++ 2S a Karras, CFG scale: 7\n```\n\n## License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)\n\n## Big Thanks to\n\n- [Linaqruf](https://huggingface.co/Linaqruf). [NoCrypt](https://huggingface.co/NoCrypt), and Fannovel16#9022 for helping me out alot regarding my inquiries and concern about models and other stuff."},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"null"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage:\n- en\ndatasets:\n- common_voice\ntags:\n- speech\n---\n\n# UniSpeech-Large\n\n[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)\n\nThe large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes. \n\n**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.\n\n[Paper: UniSpeech: Unified Speech Representation Learning\nwith Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597)\n\nAuthors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang\n\n**Abstract**\n*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.*\n\nThe original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech.\n\n# Usage\n\nThis is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be \nused in inference. The model was pre-trained in English and should therefore perform well only in English. \n\n**Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence \nof phonemes before fine-tuning.\n\n## Speech Recognition\n\nTo fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition).\n\n## Speech Classification\n\nTo fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification).\n\n# Contribution\n\nThe model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).\n\n# License\n\nThe official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)\n\n![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/unispeech.png)"},"embedding":{"kind":"list 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-0.6322098970413208\n]"}}},{"rowIdx":1167,"cells":{"modelId":{"kind":"string","value":"lllyasviel/control_v11p_sd15_lineart"},"author":{"kind":"string","value":"lllyasviel"},"last_modified":{"kind":"timestamp","value":"2023-05-04T18:49:42Z","string":"2023-05-04T18:49:42Z"},"downloads":{"kind":"number","value":18976,"string":"18,976"},"likes":{"kind":"number","value":20,"string":"20"},"library_name":{"kind":"string","value":"diffusers"},"tags":{"kind":"list like","value":["diffusers","art","controlnet","stable-diffusion","controlnet-v1-1","image-to-image","arxiv:2302.05543","base_model:runwayml/stable-diffusion-v1-5","license:openrail","has_space","diffusers:ControlNetModel","region:us"],"string":"[\n \"diffusers\",\n \"art\",\n \"controlnet\",\n \"stable-diffusion\",\n \"controlnet-v1-1\",\n \"image-to-image\",\n \"arxiv:2302.05543\",\n \"base_model:runwayml/stable-diffusion-v1-5\",\n \"license:openrail\",\n \"has_space\",\n \"diffusers:ControlNetModel\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-to-image"},"createdAt":{"kind":"timestamp","value":"2023-04-14T19:25:13Z","string":"2023-04-14T19:25:13Z"},"card":{"kind":"string","value":"---\nlicense: openrail\nbase_model: runwayml/stable-diffusion-v1-5\ntags:\n- art\n- controlnet\n- stable-diffusion\n- controlnet-v1-1\n- image-to-image\nduplicated_from: ControlNet-1-1-preview/control_v11p_sd15_lineart\n---\n\n# Controlnet - v1.1 - *lineart Version*\n\n**Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel).\n\nThis checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_lineart.pth) into `diffusers` format.\nIt can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).\n\n\nFor more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet).\n\n\nControlNet is a neural network structure to control diffusion models by adding extra conditions. \n\n![img](./sd.png)\n\nThis checkpoint corresponds to the ControlNet conditioned on **lineart images**.\n\n## Model Details\n- **Developed by:** Lvmin Zhang, Maneesh Agrawala\n- **Model type:** Diffusion-based text-to-image generation model\n- **Language(s):** English\n- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.\n- **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543).\n- **Cite as:**\n\n @misc{zhang2023adding,\n title={Adding Conditional Control to Text-to-Image Diffusion Models}, \n author={Lvmin Zhang and Maneesh Agrawala},\n year={2023},\n eprint={2302.05543},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n }\n\n## Introduction\n\nControlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by \nLvmin Zhang, Maneesh Agrawala.\n\nThe abstract reads as follows:\n\n*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. \nThe ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). \nMoreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. \nAlternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. \nWe report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. \nThis may enrich the methods to control large diffusion models and further facilitate related applications.*\n\n## Example\n\nIt is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint \nhas been trained on it.\nExperimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.\n\n**Note**: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below:\n\n1. Install https://github.com/patrickvonplaten/controlnet_aux\n\n```sh\n$ pip install controlnet_aux==0.3.0\n```\n\n2. Let's install `diffusers` and related packages:\n\n```\n$ pip install diffusers transformers accelerate\n```\n\n3. Run code:\n\n```python\nimport torch\nimport os\nfrom huggingface_hub import HfApi\nfrom pathlib import Path\nfrom diffusers.utils import load_image\nfrom PIL import Image\nimport numpy as np\nfrom controlnet_aux import LineartDetector\n\nfrom diffusers import (\n ControlNetModel,\n StableDiffusionControlNetPipeline,\n UniPCMultistepScheduler,\n)\n\ncheckpoint = \"ControlNet-1-1-preview/control_v11p_sd15_lineart\"\n\nimage = load_image(\n \"https://huggingface.co/ControlNet-1-1-preview/control_v11p_sd15_lineart/resolve/main/images/input.png\"\n)\nimage = image.resize((512, 512))\n\nprompt = \"michael jackson concert\"\nprocessor = LineartDetector.from_pretrained(\"lllyasviel/Annotators\")\n\ncontrol_image = processor(image)\ncontrol_image.save(\"./images/control.png\")\n\ncontrolnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n)\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n\ngenerator = torch.manual_seed(0)\nimage = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]\n\nimage.save('images/image_out.png')\n\n```\n\n![bird](./images/input.png)\n\n![bird_canny](./images/control.png)\n\n![bird_canny_out](./images/image_out.png)\n\n## Other released checkpoints v1-1\n\nThe authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) \non a different type of conditioning:\n\n| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |\n|---|---|---|---|---|\n|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)
| *Trained with canny edge detection* | A monochrome image with white edges on a black background.|||\n|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)
| *Trained with pixel to pixel instruction* | No condition .|||\n|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)
| Trained with image inpainting | No condition.|||\n|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)
| Trained with multi-level line segment detection | An image with annotated line segments.|||\n|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)
| Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|||\n|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)
| Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|||\n|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)
| Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|||\n|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)
| Trained with line art generation | An image with line art, usually black lines on a white background.|||\n|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)
| Trained with anime line art generation | An image with anime-style line art.|||\n|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)
| Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|||\n|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)
| Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|||\n|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)
| Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|||\n|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)
| Trained with image shuffling | An image with shuffled patches or regions.|||\n|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)
| Trained with image tiling | A blurry image or part of an image .|||\n\n## More information\n\nFor more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly)."},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"feature-extraction"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: en\ndatasets:\n- librispeech_asr\ntags:\n- speech\nlicense: apache-2.0\n---\n\n# SEW-D-tiny\n\n[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...\n\nPaper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)\n\nAuthors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi\n\n**Abstract**\nThis paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.\n\nThe original model can be found under https://github.com/asappresearch/sew#model-checkpoints .\n\n# Usage\n\nSee [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`."},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"translation"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:04Z","string":"2022-03-02T23:29:04Z"},"card":{"kind":"string","value":"---\nlanguage: \n- en\n- hi\n\ntags:\n- translation\n\nlicense: apache-2.0\n---\n\n### eng-hin\n\n* source group: English \n* target group: Hindi \n* OPUS readme: [eng-hin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md)\n\n* model: transformer-align\n* source language(s): eng\n* target language(s): hin\n* model: transformer-align\n* pre-processing: normalization + SentencePiece (spm32k,spm32k)\n* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.zip)\n* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.test.txt)\n* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.eval.txt)\n\n## Benchmarks\n\n| testset | BLEU | chr-F |\n|-----------------------|-------|-------|\n| newsdev2014.eng.hin \t| 6.9 \t| 0.296 |\n| newstest2014-hien.eng.hin \t| 9.9 \t| 0.323 |\n| Tatoeba-test.eng.hin \t| 16.1 \t| 0.447 |\n\n\n### System Info: \n- hf_name: eng-hin\n\n- source_languages: eng\n\n- target_languages: hin\n\n- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md\n\n- original_repo: Tatoeba-Challenge\n\n- tags: ['translation']\n\n- languages: ['en', 'hi']\n\n- src_constituents: {'eng'}\n\n- tgt_constituents: {'hin'}\n\n- src_multilingual: False\n\n- tgt_multilingual: False\n\n- prepro: normalization + SentencePiece (spm32k,spm32k)\n\n- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.zip\n\n- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.test.txt\n\n- src_alpha3: eng\n\n- tgt_alpha3: hin\n\n- short_pair: en-hi\n\n- chrF2_score: 0.447\n\n- bleu: 16.1\n\n- brevity_penalty: 1.0\n\n- ref_len: 32904.0\n\n- src_name: English\n\n- tgt_name: Hindi\n\n- train_date: 2020-06-17\n\n- src_alpha2: en\n\n- tgt_alpha2: hi\n\n- prefer_old: False\n\n- long_pair: eng-hin\n\n- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535\n\n- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b\n\n- port_machine: brutasse\n\n- port_time: 2020-08-21-14:41"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: en\nthumbnail: url to a thumbnail used in social sharing\ntags:\n- array\n- of\n- tags\ndatasets:\n- jpwahle/machine-paraphrase-dataset\nwidget:\n- text: Plagiarism is the representation of another author's writing, thoughts, ideas,\n or expressions as one's own work.\n---\n\n# Longformer-base for Machine-Paraphrase Detection\n\nIf you are using this model in your research work, please cite\n\n```\n@InProceedings{10.1007/978-3-030-96957-8_34,\n author=\"Wahle, Jan Philip and Ruas, Terry and Folt{\\'y}nek, Tom{\\'a}{\\v{s}} and Meuschke, Norman and Gipp, Bela\",\n title=\"Identifying Machine-Paraphrased Plagiarism\",\n booktitle=\"Information for a Better World: Shaping the Global Future\",\n year=\"2022\",\n publisher=\"Springer International Publishing\",\n address=\"Cham\",\n pages=\"393--413\",\n abstract=\"Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99{\\%} (F1=99.68{\\%} for SpinBot and F1=71.64{\\%} for SpinnerChief cases), while human evaluators achieved F1=78.4{\\%} for SpinBot and F1=65.6{\\%} for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.\",\n isbn=\"978-3-030-96957-8\"\n}\n```\n\nThis is the checkpoint for Longformer-base after being trained on the [Machine-Paraphrased Plagiarism Dataset](https://doi.org/10.5281/zenodo.3608000)\n\nAdditional information about this model:\n\n* [The longformer-base-4096 model page](https://huggingface.co/allenai/longformer-base-4096)\n* [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf)\n* [Official implementation by AllenAI](https://github.com/allenai/longformer)\n\nThe model can be loaded to perform Plagiarism like so:\n\n```py\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\nAutoModelForSequenceClassification(\"jpelhaw/longformer-base-plagiarism-detection\")\nAutoTokenizer.from_pretrained(\"jpelhaw/longformer-base-plagiarism-detection\")\n\ninput = \"Plagiarism is the representation of another author's writing, \\\nthoughts, ideas, or expressions as one's own work.\"\n\n\nexample = tokenizer.tokenize(input, add_special_tokens=True)\n\nanswer = model(**example)\n \n# \"plagiarised\"\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-06-27T03:55:57Z","string":"2023-06-27T03:55:57Z"},"card":{"kind":"string","value":"---\ninference: false\nlicense: other\n---\n\n\n\n
\n\"TheBlokeAI\"\n
\n
\n \n \n
\n

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

\n
\n\n\n# Eric Hartford's Wizard Vicuna 13B Uncensored GPTQ\n\nThese files are GPTQ 4bit model files for [Eric Hartford's Wizard Vicuna 13B Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-13B-Uncensored) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test).\n\nIt is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).\n\n**This is an experimental new GPTQ which offers up to 8K context size**\n\nThe increased context is tested to work with [ExLlama](https://github.com/turboderp/exllama), via the latest release of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).\n\nIt has also been tested from Python code using AutoGPTQ, and `trust_remote_code=True`.\n\nCode credits:\n- Original concept and code for increasing context length: [kaiokendev](https://huggingface.co/kaiokendev)\n- Updated Llama modelling code that includes this automatically via trust_remote_code: [emozilla](https://huggingface.co/emozilla).\n\nPlease read carefully below to see how to use it.\n\nGGML versions are not yet provided, as there is not yet support for SuperHOT in llama.cpp. This is being investigated and will hopefully come soon.\n\n## Repositories available\n\n* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GGML)\n* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-fp16)\n* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/Wizard-Vicuna-13B-Uncensored)\n\n## How to easily download and use this model in text-generation-webui with ExLlama\n\nPlease make sure you're using the latest version of text-generation-webui\n\n1. Click the **Model tab**.\n2. Under **Download custom model or LoRA**, enter `TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ`.\n3. Click **Download**.\n4. The model will start downloading. Once it's finished it will say \"Done\"\n5. Untick **Autoload the model**\n6. In the top left, click the refresh icon next to **Model**.\n7. In the **Model** dropdown, choose the model you just downloaded: `Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ`\n8. To use the increased context, set the **Loader** to **ExLlama**, set **max_seq_len** to 8192 or 4096, and set **compress_pos_emb** to **4** for 8192 context, or to **2** for 4096 context.\n9. Now click **Save Settings** followed by **Reload**\n10. The model will automatically load, and is now ready for use!\n11. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!\n\n## How to use this GPTQ model from Python code with AutoGPTQ\n\nFirst make sure you have AutoGPTQ and Einops installed:\n\n```\npip3 install einops auto-gptq\n```\n\nThen run the following code. Note that in order to get this to work, `config.json` has been hardcoded to a sequence length of 8192.\n\nIf you want to try 4096 instead to reduce VRAM usage, please manually edit `config.json` to set `max_position_embeddings` to the value you want.\n\n```python\nfrom transformers import AutoTokenizer, pipeline, logging\nfrom auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig\nimport argparse\n\nmodel_name_or_path = \"TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ\"\nmodel_basename = \"wizard-vicuna-13b-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order\"\n\nuse_triton = False\n\ntokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\n\nmodel = AutoGPTQForCausalLM.from_quantized(model_name_or_path,\n model_basename=model_basename,\n use_safetensors=True,\n trust_remote_code=True,\n device_map='auto',\n use_triton=use_triton,\n quantize_config=None)\n\nmodel.seqlen = 8192\n\n# Note: check the prompt template is correct for this model.\nprompt = \"Tell me about AI\"\nprompt_template=f'''USER: {prompt}\nASSISTANT:'''\n\nprint(\"\\n\\n*** Generate:\")\n\ninput_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()\noutput = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)\nprint(tokenizer.decode(output[0]))\n\n# Inference can also be done using transformers' pipeline\n\n# Prevent printing spurious transformers error when using pipeline with AutoGPTQ\nlogging.set_verbosity(logging.CRITICAL)\n\nprint(\"*** Pipeline:\")\npipe = pipeline(\n \"text-generation\",\n model=model,\n tokenizer=tokenizer,\n max_new_tokens=512,\n temperature=0.7,\n top_p=0.95,\n repetition_penalty=1.15\n)\n\nprint(pipe(prompt_template)[0]['generated_text'])\n```\n\n## Using other UIs: monkey patch\n\nProvided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev.\n\nIt can be theoretically be added to any Python UI or custom code to enable the same result as `trust_remote_code=True`. I have not tested this, and it should be superseded by using `trust_remote_code=True`, but I include it for completeness and for interest.\n\n## Provided files\n\n**wizard-vicuna-13b-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors**\n\nThis will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.\n\nIt was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.\n\n* `wizard-vicuna-13b-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors`\n * Works for use with ExLlama with increased context (4096 or 8192)\n * Works with AutoGPTQ in Python code, including with increased context, if `trust_remote_code=True` is set.\n * Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. May have issues with GPTQ-for-LLaMa Triton mode.\n * Works with text-generation-webui, including one-click-installers.\n * Parameters: Groupsize = 128. Act Order / desc_act = False.\n\n\n\n## Discord\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n[TheBloke AI's Discord server](https://discord.gg/theblokeai)\n\n## Thanks, and how to contribute.\n\nThanks to the [chirper.ai](https://chirper.ai) team!\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n* Patreon: https://patreon.com/TheBlokeAI\n* Ko-Fi: https://ko-fi.com/TheBlokeAI\n\n**Special thanks to**: Aemon Algiz.\n\n**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter\n\n\nThank you to all my generous patrons and donaters!\n\nAnd thank you again to a16z for their generous grant.\n\n\n\n# Original model card: Kaio Ken's SuperHOT 8K\n\n### SuperHOT Prototype 2 w/ 8K Context\n\nThis is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).\nTests have shown that the model does indeed leverage the extended context at 8K.\n\nYou will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**\n\n#### Looking for Merged & Quantized Models?\n- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)\n- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)\n\n\n#### Training Details\nI trained the LoRA with the following configuration:\n- 1200 samples (~400 samples over 2048 sequence length)\n- learning rate of 3e-4\n- 3 epochs\n- The exported modules are:\n - q_proj\n - k_proj\n - v_proj\n - o_proj\n - no bias\n- Rank = 4\n- Alpha = 8\n- no dropout\n- weight decay of 0.1\n- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5\n- Trained on 4-bit base model\n\n# Original model card: Eric Hartford's Wizard Vicuna 13B Uncensored\n\n\nThis is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.\n\nShout out to the open source AI/ML community, and everyone who helped me out.\n\nNote:\n\nAn uncensored model has no guardrails.\n\nYou are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.\n\nPublishing anything this model generates is the same as publishing it yourself.\n\nYou are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"feature-extraction"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: en\ndatasets:\n- librispeech_asr\ntags:\n- speech\nlicense: apache-2.0\n---\n\n# SEW-tiny\n\n[SEW by ASAPP Research](https://github.com/asappresearch/sew)\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...\n\nPaper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)\n\nAuthors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi\n\n**Abstract**\nThis paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.\n\nThe original model can be found under https://github.com/asappresearch/sew#model-checkpoints .\n\n# Usage\n\nSee [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWForCTC`."},"embedding":{"kind":"list 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-0.781690239906311\n]"}}},{"rowIdx":1174,"cells":{"modelId":{"kind":"string","value":"kandinsky-community/kandinsky-2-1-prior"},"author":{"kind":"string","value":"kandinsky-community"},"last_modified":{"kind":"timestamp","value":"2023-10-09T11:32:58Z","string":"2023-10-09T11:32:58Z"},"downloads":{"kind":"number","value":18832,"string":"18,832"},"likes":{"kind":"number","value":10,"string":"10"},"library_name":{"kind":"string","value":"diffusers"},"tags":{"kind":"list like","value":["diffusers","kandinsky","license:apache-2.0","has_space","diffusers:KandinskyPriorPipeline","region:us"],"string":"[\n \"diffusers\",\n \"kandinsky\",\n \"license:apache-2.0\",\n \"has_space\",\n \"diffusers:KandinskyPriorPipeline\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"null"},"createdAt":{"kind":"timestamp","value":"2023-05-24T09:51:57Z","string":"2023-05-24T09:51:57Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- kandinsky\ninference: false\n---\n\n# Kandinsky 2.1\n\nKandinsky 2.1 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas.\n\nIt uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.\n\nThe Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov)\n\n## Usage\n\nKandinsky 2.1 is available in diffusers!\n\n```python\npip install diffusers transformers\n```\n### Text to image\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nimage = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0]\nimage.save(\"cheeseburger_monster.png\")\n```\n\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png)\n\n\n### Text Guided Image-to-Image Generation\n\n```python\nfrom diffusers import AutoPipelineForImage2Image\nimport torch\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nimport os\n\npipe = AutoPipelineForImage2Image.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\n\npipe.enable_model_cpu_offload()\n\nprompt = \"A fantasy landscape, Cinematic lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n \nresponse = requests.get(url)\noriginal_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\noriginal_image.thumbnail((768, 768))\n\nimage = pipe(prompt=prompt, image=original_image, strength=0.3).images[0]\nout.images[0].save(\"fantasy_land.png\")\n```\n\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png)\n\n\n### Text Guided Inpainting Generation\n\n```python\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image\nimport torch\nimport numpy as np\n\npipe = AutoPipelineForInpainting.from_pretrained(\"kandinsky-community/kandinsky-2-1-inpaint\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\nprompt = \"a hat\"\nnegative_prompt = \"low quality, bad quality\"\n\noriginal_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n)\n\nmask = np.zeros((768, 768), dtype=np.float32)\n# Let's mask out an area above the cat's head\nmask[:250, 250:-250] = 1\n\n\nimage = pipe(prompt=prompt, image=original_image, mask_image=mask).images[0]\nimage.save(\"cat_with_hat.png\")\n```\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/inpaint_cat_hat.png)\n\n🚨🚨🚨 __Breaking change for Kandinsky Mask Inpainting__ 🚨🚨🚨\n\nWe introduced a breaking change for Kandinsky inpainting pipeline in the following pull request: https://github.com/huggingface/diffusers/pull/4207. Previously we accepted a mask format where black pixels represent the masked-out area. This is inconsistent with all other pipelines in diffusers. We have changed the mask format in Knaindsky and now using white pixels instead.\nPlease upgrade your inpainting code to follow the above. If you are using Kandinsky Inpaint in production. You now need to change the mask to:\n\n```python\n# For PIL input\nimport PIL.ImageOps\nmask = PIL.ImageOps.invert(mask)\n\n# For PyTorch and Numpy input\nmask = 1 - mask\n```\n\n\n### Interpolate \n\n```python\nfrom diffusers import KandinskyPriorPipeline, KandinskyPipeline\nfrom diffusers.utils import load_image\nimport PIL\n\nimport torch\n\npipe_prior = KandinskyPriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n)\npipe_prior.to(\"cuda\")\n\nimg1 = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n)\n\nimg2 = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/starry_night.jpeg\"\n)\n\n# add all the conditions we want to interpolate, can be either text or image\nimages_texts = [\"a cat\", img1, img2]\n\n# specify the weights for each condition in images_texts\nweights = [0.3, 0.3, 0.4]\n\n# We can leave the prompt empty\nprompt = \"\"\nprior_out = pipe_prior.interpolate(images_texts, weights)\n\npipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(prompt, **prior_out, height=768, width=768).images[0]\n\nimage.save(\"starry_cat.png\")\n```\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png)\n\n\n## Model Architecture\n\n### Overview\nKandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. \n\nThe model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. \n\n

\n \n

\n\nSpecifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [mCLIP model](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14). The trained image prior model is then used to generate mCLIP image embeddings for input text prompts. Both the input text prompts and its mCLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image.\n\n\n### Details\nThe image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution).\n\nThe main Text2Image diffusion model was trained on the basis of 170M text-image pairs from the [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) (an important condition was the presence of images with a resolution of at least 768x768). The use of 170M pairs is due to the fact that we kept the UNet diffusion block from Kandinsky 2.0, which allowed us not to train it from scratch. Further, at the stage of fine-tuning, a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources.\n\n\n### Evaluation\nWe quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID.\n\nFID metric values ​​for generative models on COCO_30k\n| | FID (30k)|\n|:------|----:|\n| eDiff-I (2022) | 6.95 | \n| Image (2022) | 7.27 | \n| Kandinsky 2.1 (2023) | 8.21|\n| Stable Diffusion 2.1 (2022) | 8.59 | \n| GigaGAN, 512x512 (2023) | 9.09 | \n| DALL-E 2 (2022) | 10.39 | \n| GLIDE (2022) | 12.24 | \n| Kandinsky 1.0 (2022) | 15.40 | \n| DALL-E (2021) | 17.89 | \n| Kandinsky 2.0 (2022) | 20.00 | \n| GLIGEN (2022) | 21.04 | \n\nFor more information, please refer to the upcoming technical report.\n\n## BibTex\nIf you find this repository useful in your research, please cite:\n```\n@misc{kandinsky 2.1,\n title = {kandinsky 2.1},\n author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov},\n year = {2023},\n howpublished = {},\n}\n```"},"embedding":{"kind":"list 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-0.21046879887580872\n]"}}},{"rowIdx":1175,"cells":{"modelId":{"kind":"string","value":"ArthurZ/flax-tiny-random-bert-sharded"},"author":{"kind":"string","value":"ArthurZ"},"last_modified":{"kind":"timestamp","value":"2022-11-14T06:24:51Z","string":"2022-11-14T06:24:51Z"},"downloads":{"kind":"number","value":18788,"string":"18,788"},"likes":{"kind":"number","value":0,"string":"0"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","jax","bert","feature-extraction","flax","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"jax\",\n \"bert\",\n \"feature-extraction\",\n \"flax\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"feature-extraction"},"createdAt":{"kind":"timestamp","value":"2022-06-17T16:08:40Z","string":"2022-06-17T16:08:40Z"},"card":{"kind":"string","value":"---\ntags:\n- flax\n---\n# Model Card for flax-tiny-random-bert-sharded\n \n# Model Details\n \n## Model Description\n This model is used to check that the sharding of a flax_model works properly. See [`test_checkpoint_sharding_from_hub`](https://github.com/huggingface/transformers/blob/main/tests/test_modeling_flax_common.py#L1049).\n \n# Uses\n\nThe model is not designed to be used and serves a testing purpose. \n\n### Software\n- Transformers 4.21.0.dev0\n- TensorFlow 2.9.0\n- Datasets 2.2.2\n- Tokenizers 0.12.1\n"},"embedding":{"kind":"list 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-0.30712345242500305\n]"}}},{"rowIdx":1176,"cells":{"modelId":{"kind":"string","value":"Rostlab/prot_bert_bfd"},"author":{"kind":"string","value":"Rostlab"},"last_modified":{"kind":"timestamp","value":"2020-12-11T21:30:10Z","string":"2020-12-11T21:30:10Z"},"downloads":{"kind":"number","value":18783,"string":"18,783"},"likes":{"kind":"number","value":9,"string":"9"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","pytorch","tf","fill-mask","protein language model","dataset:BFD","autotrain_compatible","endpoints_compatible","has_space","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"tf\",\n \"fill-mask\",\n \"protein language model\",\n \"dataset:BFD\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"fill-mask"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:04Z","string":"2022-03-02T23:29:04Z"},"card":{"kind":"string","value":"---\nlanguage: protein\ntags:\n- protein language model\ndatasets:\n- BFD\n---\n\n# ProtBert-BFD model\n\nPretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in\n[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in\n[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.\n\n\n## Model description\n\nProtBert-BFD is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion.\nThis means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those protein sequences.\n\nOne important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents\nThis means the Next sentence prediction is not used, as each sequence is treated as a complete document.\nThe masking follows the original Bert training with randomly masks 15% of the amino acids in the input. \n\nAt the end, the feature extracted from this model revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein\nshape.\nThis implied learning some of the grammar of the language of life realized in protein sequences.\n\n## Intended uses & limitations\n\nThe model could be used for protein feature extraction or to be fine-tuned on downstream tasks.\nWe have noticed in some tasks you could gain more accuracy by fine-tuning the model rather than using it as a feature extractor.\n\n### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n```python\n>>> from transformers import BertForMaskedLM, BertTokenizer, pipeline\n>>> tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False )\n>>> model = BertForMaskedLM.from_pretrained(\"Rostlab/prot_bert_bfd\")\n>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)\n>>> unmasker('D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T')\n\n[{'score': 0.1165614128112793,\n 'sequence': '[CLS] D L I P T S S K L V V L D T S L Q V K K A F F A L V T [SEP]',\n 'token': 5,\n 'token_str': 'L'},\n {'score': 0.08976086974143982,\n 'sequence': '[CLS] D L I P T S S K L V V V D T S L Q V K K A F F A L V T [SEP]',\n 'token': 8,\n 'token_str': 'V'},\n {'score': 0.08864385634660721,\n 'sequence': '[CLS] D L I P T S S K L V V S D T S L Q V K K A F F A L V T [SEP]',\n 'token': 10,\n 'token_str': 'S'},\n {'score': 0.06227643042802811,\n 'sequence': '[CLS] D L I P T S S K L V V A D T S L Q V K K A F F A L V T [SEP]',\n 'token': 6,\n 'token_str': 'A'},\n {'score': 0.06194969266653061,\n 'sequence': '[CLS] D L I P T S S K L V V T D T S L Q V K K A F F A L V T [SEP]',\n 'token': 15,\n 'token_str': 'T'}]\n```\n\nHere is how to use this model to get the features of a given protein sequence in PyTorch:\n\n```python\nfrom transformers import BertModel, BertTokenizer\nimport re\ntokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False )\nmodel = BertModel.from_pretrained(\"Rostlab/prot_bert_bfd\")\nsequence_Example = \"A E T C Z A O\"\nsequence_Example = re.sub(r\"[UZOB]\", \"X\", sequence_Example)\nencoded_input = tokenizer(sequence_Example, return_tensors='pt')\noutput = model(**encoded_input)\n```\n\n## Training data\n\nThe ProtBert-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences.\n\n## Training procedure\n\n### Preprocessing\n\nThe protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21.\nThe inputs of the model are then of the form:\n\n```\n[CLS] Protein Sequence A [SEP] Protein Sequence B [SEP]\n```\n\nFurthermore, each protein sequence was treated as a separate document.\nThe preprocessing step was performed twice, once for a combined length (2 sequences) of less than 512 amino acids, and another time using a combined length (2 sequences) of less than 2048 amino acids.\n\nThe details of the masking procedure for each sequence followed the original Bert model as following:\n- 15% of the amino acids are masked.\n- In 80% of the cases, the masked amino acids are replaced by `[MASK]`.\n- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.\n- In the 10% remaining cases, the masked amino acids are left as is.\n\n### Pretraining\n\nThe model was trained on a single TPU Pod V3-1024 for one million steps in total.\n800k steps using sequence length 512 (batch size 32k), and 200K steps using sequence length 2048 (batch size 6k).\nThe optimizer used is Lamb with a learning rate of 0.002, a weight decay of 0.01, learning rate warmup for 140k steps and linear decay of the learning rate after.\n\n## Evaluation results\n\nWhen fine-tuned on downstream tasks, this model achieves the following results:\n\nTest results :\n\n| Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane |\n|:-----:|:-----:|:-----:|:-----:|:-----:|\n| CASP12 | 76 | 65 | | |\n| TS115 | 84 | 73 | | | \n| CB513 | 83 | 70 | | |\n| DeepLoc | | | 78 | 91 |\n\n### BibTeX entry and citation info\n\n```bibtex\n@article {Elnaggar2020.07.12.199554,\n\tauthor = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},\n\ttitle = {ProtTrans: Towards Cracking the Language of Life{\\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},\n\telocation-id = {2020.07.12.199554},\n\tyear = {2020},\n\tdoi = {10.1101/2020.07.12.199554},\n\tpublisher = {Cold Spring Harbor Laboratory},\n\tabstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \\&lt;a href=\"https://github.com/agemagician/ProtTrans\"\\&gt;https://github.com/agemagician/ProtTrans\\&lt;/a\\&gt;Competing Interest StatementThe authors have declared no competing interest.},\n\tURL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},\n\teprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},\n\tjournal = {bioRxiv}\n}\n```\n\n> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)\n"},"embedding":{"kind":"list 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apache-2.0\n---\n\nThis model is a fork of `sshleifer/tiny-mbart` exported to ONNX.\n"},"embedding":{"kind":"list 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\"pytorch\",\n \"safetensors\",\n \"bloom\",\n \"text-generation\",\n \"ak\",\n \"ar\",\n \"as\",\n \"bm\",\n \"bn\",\n \"ca\",\n \"code\",\n \"en\",\n \"es\",\n \"eu\",\n \"fon\",\n \"fr\",\n \"gu\",\n \"hi\",\n \"id\",\n \"ig\",\n \"ki\",\n \"kn\",\n \"lg\",\n \"ln\",\n \"ml\",\n \"mr\",\n \"ne\",\n \"nso\",\n \"ny\",\n \"or\",\n \"pa\",\n \"pt\",\n \"rn\",\n \"rw\",\n \"sn\",\n \"st\",\n \"sw\",\n \"ta\",\n \"te\",\n \"tn\",\n \"ts\",\n \"tum\",\n \"tw\",\n \"ur\",\n \"vi\",\n \"wo\",\n \"xh\",\n \"yo\",\n \"zh\",\n \"zhs\",\n \"zht\",\n \"zu\",\n \"arxiv:1909.08053\",\n \"arxiv:2110.02861\",\n \"arxiv:2108.12409\",\n \"license:bigscience-bloom-rail-1.0\",\n \"model-index\",\n \"endpoints_compatible\",\n \"has_space\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2022-05-19T11:52:27Z","string":"2022-05-19T11:52:27Z"},"card":{"kind":"string","value":"---\nlicense: bigscience-bloom-rail-1.0\nlanguage:\n- ak\n- ar\n- as\n- bm\n- bn\n- ca\n- code\n- en\n- es\n- eu\n- fon\n- fr\n- gu\n- hi\n- id\n- ig\n- ki\n- kn\n- lg\n- ln\n- ml\n- mr\n- ne\n- nso\n- ny\n- or\n- pa\n- pt\n- rn\n- rw\n- sn\n- st\n- sw\n- ta\n- te\n- tn\n- ts\n- tum\n- tw\n- ur\n- vi\n- wo\n- xh\n- yo\n- zh\n- zhs\n- zht\n- zu\npipeline_tag: text-generation\nmodel-index:\n- name: bloom\n results:\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: arc_challenge\n type: arc_challenge\n metrics:\n - name: acc\n type: acc\n value: 0.27986348122866894\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: arc_easy\n type: arc_easy\n metrics:\n - name: acc\n type: acc\n value: 0.5946969696969697\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: axb\n type: axb\n metrics:\n - name: acc\n type: acc\n value: 0.4433876811594203\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: axg\n type: axg\n metrics:\n - name: acc\n type: acc\n value: 0.5\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: boolq\n type: boolq\n metrics:\n - name: acc\n type: acc\n value: 0.6165137614678899\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: cb\n type: cb\n metrics:\n - name: acc\n type: acc\n value: 0.30357142857142855\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: cola\n type: cola\n metrics:\n - name: acc\n type: acc\n value: 0.610738255033557\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: copa\n type: copa\n metrics:\n - name: acc\n type: acc\n value: 0.63\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: crows_pairs_english\n type: crows_pairs_english\n metrics:\n - name: acc\n type: acc\n value: 0.4973166368515206\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: crows_pairs_french\n type: crows_pairs_french\n metrics:\n - name: acc\n type: acc\n value: 0.5032796660703638\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: diabla\n type: diabla\n metrics:\n - name: acc\n type: acc\n value: 0.28888308977035493\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_afr\n type: gsarti/flores_101_afr\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.500798737976343\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_amh\n type: gsarti/flores_101_amh\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.9726863338897145\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ara\n type: gsarti/flores_101_ara\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 1.8083841089875814\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_asm\n type: gsarti/flores_101_asm\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.699102962086425\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ast\n type: gsarti/flores_101_ast\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.9252047073429384\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_azj\n type: gsarti/flores_101_azj\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.942805054270002\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_bel\n type: gsarti/flores_101_bel\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.614136245847082\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ben\n type: gsarti/flores_101_ben\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.121491534300969\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_bos\n type: gsarti/flores_101_bos\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.653353469118798\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_bul\n type: gsarti/flores_101_bul\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.7014693938055068\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_cat\n type: gsarti/flores_101_cat\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.305190041967345\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ceb\n type: gsarti/flores_101_ceb\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.291000321323428\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ces\n type: gsarti/flores_101_ces\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.447322753586386\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ckb\n type: gsarti/flores_101_ckb\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.7255124939234765\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_cym\n type: gsarti/flores_101_cym\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 12.539424151448149\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_dan\n type: gsarti/flores_101_dan\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.183309001005672\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_deu\n type: gsarti/flores_101_deu\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.1180422286591347\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ell\n type: gsarti/flores_101_ell\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.467943456164706\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_eng\n type: gsarti/flores_101_eng\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.018740628193298\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_est\n type: gsarti/flores_101_est\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 9.11654425176368\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_fas\n type: gsarti/flores_101_fas\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.058009097116482\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_fin\n type: gsarti/flores_101_fin\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.847047959628553\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_fra\n type: gsarti/flores_101_fra\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 1.9975177011840075\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ful\n type: gsarti/flores_101_ful\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 11.465912731488828\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_gle\n type: gsarti/flores_101_gle\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.681491663539422\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_glg\n type: gsarti/flores_101_glg\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.029991089015508\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_guj\n type: gsarti/flores_101_guj\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.955224230286231\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_hau\n type: gsarti/flores_101_hau\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 10.758347356372159\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_heb\n type: gsarti/flores_101_heb\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.6004478129801667\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_hin\n type: gsarti/flores_101_hin\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.712530650588064\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_hrv\n type: gsarti/flores_101_hrv\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.822418943372185\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_hun\n type: gsarti/flores_101_hun\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.440482646965992\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_hye\n type: gsarti/flores_101_hye\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.657718918347166\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ibo\n type: gsarti/flores_101_ibo\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.564814003872672\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ind\n type: gsarti/flores_101_ind\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.1597101468869373\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_isl\n type: gsarti/flores_101_isl\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.082349269518136\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ita\n type: gsarti/flores_101_ita\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.9687591414176207\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_jav\n type: gsarti/flores_101_jav\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 7.0573805415708994\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_jpn\n type: gsarti/flores_101_jpn\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.7758864197116933\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kam\n type: gsarti/flores_101_kam\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 11.072949642861332\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kan\n type: gsarti/flores_101_kan\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.551730651007082\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kat\n type: gsarti/flores_101_kat\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.522630524283745\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kaz\n type: gsarti/flores_101_kaz\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.3901748516975574\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kea\n type: gsarti/flores_101_kea\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.918534182590863\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kir\n type: gsarti/flores_101_kir\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.729278369847201\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_kor\n type: gsarti/flores_101_kor\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.932884847226212\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_lao\n type: gsarti/flores_101_lao\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.9077314760849924\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_lav\n type: gsarti/flores_101_lav\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 7.777221919194806\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_lin\n type: gsarti/flores_101_lin\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 7.524842908050988\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_lit\n type: gsarti/flores_101_lit\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 7.369179434621725\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ltz\n type: gsarti/flores_101_ltz\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.801059747949214\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_lug\n type: gsarti/flores_101_lug\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.483203026364786\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_luo\n type: gsarti/flores_101_luo\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 11.975963093623681\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mal\n type: gsarti/flores_101_mal\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.615948455160037\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mar\n type: gsarti/flores_101_mar\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.483253482821379\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mkd\n type: gsarti/flores_101_mkd\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.9656732291754087\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mlt\n type: gsarti/flores_101_mlt\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 15.004773437665275\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mon\n type: gsarti/flores_101_mon\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.410598542315402\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mri\n type: gsarti/flores_101_mri\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 7.474035895661322\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_msa\n type: gsarti/flores_101_msa\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.5710001772665634\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_mya\n type: gsarti/flores_101_mya\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.413577969878331\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_nld\n type: gsarti/flores_101_nld\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.127831721885065\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_nob\n type: gsarti/flores_101_nob\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.402763169129877\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_npi\n type: gsarti/flores_101_npi\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.199342701937889\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_nso\n type: gsarti/flores_101_nso\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.154626800955667\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_nya\n type: gsarti/flores_101_nya\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.179860208369393\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_oci\n type: gsarti/flores_101_oci\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.8617357393685845\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_orm\n type: gsarti/flores_101_orm\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 12.911595421079408\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ory\n type: gsarti/flores_101_ory\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.189421861225964\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_pan\n type: gsarti/flores_101_pan\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.698477289331806\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_pol\n type: gsarti/flores_101_pol\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.625550458479643\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_por\n type: gsarti/flores_101_por\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 1.9754515986213523\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_pus\n type: gsarti/flores_101_pus\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.4963371422771585\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ron\n type: gsarti/flores_101_ron\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.965456830031304\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_rus\n type: gsarti/flores_101_rus\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.0498020542445303\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_slk\n type: gsarti/flores_101_slk\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.450822127057479\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_slv\n type: gsarti/flores_101_slv\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 6.620252120186232\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_sna\n type: gsarti/flores_101_sna\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.462166771382726\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_snd\n type: gsarti/flores_101_snd\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.466066951221973\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_som\n type: gsarti/flores_101_som\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 11.95918054093392\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_spa\n type: gsarti/flores_101_spa\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 1.8965140104323535\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_srp\n type: gsarti/flores_101_srp\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.871214785885079\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_swe\n type: gsarti/flores_101_swe\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.054972008155866\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_swh\n type: gsarti/flores_101_swh\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.6973091886730676\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_tam\n type: gsarti/flores_101_tam\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.539493400469833\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_tel\n type: gsarti/flores_101_tel\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.807499987508966\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_tgk\n type: gsarti/flores_101_tgk\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 3.5994818827380426\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_tgl\n type: gsarti/flores_101_tgl\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.667053833119858\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_tha\n type: gsarti/flores_101_tha\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.365940201944242\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_tur\n type: gsarti/flores_101_tur\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 4.885014749844601\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_ukr\n type: gsarti/flores_101_ukr\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.7240934990288483\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_umb\n type: gsarti/flores_101_umb\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 12.766915508610673\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_urd\n type: gsarti/flores_101_urd\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 1.9797467071381232\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_uzb\n type: gsarti/flores_101_uzb\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 12.002337637722146\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_vie\n type: gsarti/flores_101_vie\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 1.76578415476397\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_wol\n type: gsarti/flores_101_wol\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 9.144285650306488\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_xho\n type: gsarti/flores_101_xho\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 7.403240538286952\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_yor\n type: gsarti/flores_101_yor\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 5.91272037551173\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_zho_simpl\n type: gsarti/flores_101_zho_simpl\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.2769070822768533\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_zho_trad\n type: gsarti/flores_101_zho_trad\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 2.5180582198242383\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: gsarti/flores_101_zul\n type: gsarti/flores_101_zul\n metrics:\n - name: byte_perplexity\n type: byte_perplexity\n value: 8.53353320693145\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: headqa\n type: headqa\n metrics:\n - name: acc\n type: acc\n value: 0.26440554339897887\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: hellaswag\n type: hellaswag\n metrics:\n - name: acc\n type: acc\n value: 0.41236805417247563\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: logiqa\n type: logiqa\n metrics:\n - name: acc\n type: acc\n value: 0.2073732718894009\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: mathqa\n type: mathqa\n metrics:\n - name: acc\n type: acc\n value: 0.24958123953098826\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: mc_taco\n type: mc_taco\n metrics:\n - name: em\n type: em\n value: 0.11936936936936937\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: mnli\n type: mnli\n metrics:\n - name: acc\n type: acc\n value: 0.35496688741721855\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: mnli_mismatched\n type: mnli_mismatched\n metrics:\n - name: acc\n type: acc\n value: 0.35211554109031734\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: mrpc\n type: mrpc\n metrics:\n - name: acc\n type: acc\n value: 0.5857843137254902\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: multirc\n type: multirc\n metrics:\n - name: acc\n type: acc\n value: 0.5375412541254125\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: openbookqa\n type: openbookqa\n metrics:\n - name: acc\n type: acc\n value: 0.216\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: piqa\n type: piqa\n metrics:\n - name: acc\n type: acc\n value: 0.7078346028291621\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: prost\n type: prost\n metrics:\n - name: acc\n type: acc\n value: 0.22683603757472245\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: pubmedqa\n type: pubmedqa\n metrics:\n - name: acc\n type: acc\n value: 0.616\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: qnli\n type: qnli\n metrics:\n - name: acc\n type: acc\n value: 0.5072304594545122\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: qqp\n type: qqp\n metrics:\n - name: acc\n type: acc\n value: 0.3842443729903537\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: race\n type: race\n metrics:\n - name: acc\n type: acc\n value: 0.3521531100478469\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: rte\n type: rte\n metrics:\n - name: acc\n type: acc\n value: 0.47653429602888087\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: sciq\n type: sciq\n metrics:\n - name: acc\n type: acc\n value: 0.892\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: sst\n type: sst\n metrics:\n - name: acc\n type: acc\n value: 0.5177752293577982\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: triviaqa\n type: triviaqa\n metrics:\n - name: acc\n type: acc\n value: 0.041633518960487934\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: tydiqa_primary\n type: tydiqa_primary\n metrics:\n - name: acc\n type: acc\n value: 0.3011337608795236\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: webqs\n type: webqs\n metrics:\n - name: acc\n type: acc\n value: 0.01673228346456693\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: wic\n type: wic\n metrics:\n - name: acc\n type: acc\n value: 0.5015673981191222\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: winogrande\n type: winogrande\n metrics:\n - name: acc\n type: acc\n value: 0.5864246250986582\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: wnli\n type: wnli\n metrics:\n - name: acc\n type: acc\n value: 0.471830985915493\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: wsc\n type: wsc\n metrics:\n - name: acc\n type: acc\n value: 0.4423076923076923\n verified: false\n - task:\n type: text-generation\n name: text generation\n dataset:\n name: humaneval\n type: humaneval\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.15524390243902436\n verified: false\n - name: pass@10\n type: pass@10\n value: 0.3220367632383857\n verified: false\n - name: pass@100\n type: pass@100\n value: 0.5545431515723145\n verified: false\n---\n\n

BLOOM LM

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BigScience Large Open-science Open-access Multilingual Language Model

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Model Card

\n\"BigScience\n\nVersion 1.0 / 26.May.2022\n\n## Table of Contents\n1. [Model Details](#model-details)\n2. [Uses](#uses)\n3. [Training Data](#training-data)\n4. [Risks and Limitations](#risks-and-limitations)\n5. [Evaluation](#evaluation)\n6. [Recommendations](#recommendations)\n7. [Glossary and Calculations](#glossary-and-calculations)\n8. [More Information](#more-information)\n9. [Model Card Authors](#model-card-authors)\n\n## Model Details \n\n### Basics\n*This section provides information for anyone who wants to know about the model.*\n\n
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\n \n**Developed by:** BigScience ([website](https://bigscience.huggingface.co))\n\n* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*\n \n**Model Type:** Transformer-based Language Model\n\n**Version:** 1.0.0\n\n**Languages:** Multiple; see [training data](#training-data)\n\n**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))\n\n**Release Date Estimate:** Monday, 11.July.2022\n\n**Send Questions to:** bigscience-contact@googlegroups.com\n\n**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022\n\n**Funded by:** \n \n* The French government.\n\n* Hugging Face ([website](https://huggingface.co)).\n\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n
\n\n### Technical Specifications\n*This section provides information for people who work on model development.*\n\n
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\n\nPlease see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.\n\n**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):\n\n* Decoder-only architecture\n\n* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))\n\n* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions\n\n* 3,002,557,440 parameters:\n\n * 642,252,800 embedding parameters\n\n * 30 layers, 32 attention heads\n\n * Hidden layers are 2560-dimensional\n\n * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))\n\n**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).\n \n**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).\n\n* Hardware: 384 A100 80GB GPUs (48 nodes):\n \n * Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\n * 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\n * CPU: AMD\n\n * CPU memory: 512GB per node\n\n * GPU memory: 640GB per node\n\n * Inter-node connect: Omni-Path Architecture (OPA)\n\n * NCCL-communications network: a fully dedicated subnet\n\n * Disc IO network: shared network with other types of nodes\n\n* Software:\n \n * Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))\n\n * DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))\n\n * PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))\n\n * apex ([Github link](https://github.com/NVIDIA/apex))\n\n\n#### **Training**\n\nTraining logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)\n\n- Number of epochs: 1 (*current target*)\n\n- Dates:\n \n - Started 11th March, 2022 11:42am PST\n\n - Ended 5th July, 2022\n\n- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n\n- Server training location: Île-de-France, France\n\n#### **Tokenization**\n \nThe BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:\n \n- A byte-level Byte Pair Encoding (BPE) algorithm \n\n- A simple pre-tokenization rule, no normalization\n\n- A vocabulary size of 250,680\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. \n \n
\n\n\n### Environmental Impact\n\n
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\n\nThe training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n \n**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*\n \n**Estimated electricity usage:** *(Forthcoming upon completion of training.)*\n\n\n
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\n\n## Uses\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. \nIt provides information for anyone considering using the model or who is affected by the model.*\n\n\n
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\n \n### Intended Use\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.\n\n#### **Direct Use**\n\n- Text generation\n\n- Exploring characteristics of language generated by a language model\n\n - Examples: Cloze tests, counterfactuals, generations with reframings\n\n#### **Downstream Use**\n\n- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization\n\n### Misuse and Out-of-scope Use\n*This section addresses what users ought not do with the model.*\n\nSee the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.\n\n#### **Out-of-scope Uses**\n\nUsing the model in [high-stakes](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct. \n\n##### Out-of-scope Uses Include:\n\n- Usage in biomedical domains, political and legal domains, or finance domains\n\n- Usage for evaluating or scoring individuals, such as for employment, education, or credit\n\n- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct\n\n#### **Misuse**\n\nIntentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:\n\n- Spam generation\n\n- Disinformation and influence operations\n\n- Disparagement and defamation\n\n- Harassment and abuse\n \n- [Deception](#deception)\n\n- Unconsented impersonation and imitation\n\n- Unconsented surveillance \n\n- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)\n\n### Intended Users\n\n#### **Direct Users**\n\n- General Public\n\n- Researchers\n\n- Students\n\n- Educators\n\n- Engineers/developers\n\n- Non-commercial entities\n\n- Community advocates, including human and civil rights groups\n\n#### Indirect Users\n\n- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)\n\n- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)\n\n#### Others Affected (Parties Prenantes)\n\n- People and groups referred to by the LLM\n\n- People and groups exposed to outputs of, or decisions based on, the LLM\n\n- People and groups whose original work is included in the LLM\n \n
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\n\n## Training Data\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\n\n
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\n \nDetails for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).\n\nTraining data includes:\n\n- 45 natural languages\n \n- 12 programming languages\n\n- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)\n\n\n#### **Languages**\n \nThe pie chart shows the distribution of languages in training data.\n \n![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_chart.svg?raw=true)\n\n\nThe following table shows the further distribution of Niger-Congo and Indic languages in the training data.\n
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\n \n| Niger Congo | Percentage | | Indic | Percentage |\n|----------------|------------ |------ |-----------|------------|\n| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |\n| Kikuyu | 0.00004 | | Odia | 0.04 |\n| Bambara | 0.00004 | | Gujarati | 0.04 |\n| Akan | 0.00007 | | Marathi | 0.05 |\n| Xitsonga | 0.00007 | | Punjabi | 0.05 |\n| Sesotho | 0.00007 | | Kannada | 0.06 |\n| Chi Chewa | 0.0001 | | Nepali | 0.07 |\n| Setswana | 0.0002 | | Telugu | 0.09 |\n| Northern Sotho | 0.0002 | | Malayalam | 0.10 |\n| Fon | 0.0002 | | Urdu | 0.10 |\n| Kirundi | 0.0003 | | Tamil | 0.20 |\n| Wolof | 0.0004 | | Bengali | 0.50 |\n| Kuganda | 0.0004 | | Hindi | 0.70 |\n| Chi Shona | 0.001 |\n| Isi Zulu | 0.001 |\n| Igbo | 0.001 |\n| Xhosa | 0.001 |\n| Kinyarwanda | 0.003 |\n| Yoruba | 0.006 |\n| Swahili | 0.02 |\n
\n\nThe following table shows the distribution of programming languages.\n
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\n \n| Extension | Language | Number of files |\n|----------------|------------|-----------------|\n| java | Java | 5,407,724 |\n| php | PHP | 4,942,186 |\n| cpp | C++ | 2,503,930 |\n| py | Python | 2,435,072 |\n| js | JavaScript | 1,905,518 |\n| cs | C# | 1,577,347 |\n| rb | Ruby | 6,78,413 |\n| cc | C++ | 443,054 |\n| hpp | C++ | 391,048 |\n| lua | Lua | 352,317 |\n| go | GO | 227,763 |\n| ts | TypeScript | 195,254 |\n| C | C | 134,537 |\n| scala | Scala | 92,052 |\n| hh | C++ | 67,161 |\n| H | C++ | 55,899 |\n| tsx | TypeScript | 33,107 |\n| rs | Rust | 29,693 |\n| phpt | PHP | 9,702 |\n| c++ | C++ | 1,342 |\n| h++ | C++ | 791 |\n| php3 | PHP | 540 |\n| phps | PHP | 270 |\n| php5 | PHP | 166 |\n| php4 | PHP | 29 |\n \n
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\n\n## Risks and Limitations\n*This section identifies foreseeable harms and misunderstandings.*\n\n
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\n \nModel may:\n\n- Overrepresent some viewpoints and underrepresent others\n\n- Contain stereotypes\n \n- Contain [personal information](#personal-data-and-information)\n\n- Generate:\n\n - Hateful, abusive, or violent language\n\n - Discriminatory or prejudicial language\n\n - Content that may not be appropriate for all settings, including sexual content\n\n- Make errors, including producing incorrect information as if it were factual\n\n- Generate irrelevant or repetitive outputs\n
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\n\n## Evaluation\n*This section describes the evaluation protocols and provides the results.*\n\n
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\n\n### Metrics \n*This section describes the different ways performance is calculated and why.*\n \nIncludes:\n\n| Metric | Why chosen |\n|--------------------|--------------------------------------------------------------------|\n| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |\n| Cross Entropy [Loss](#loss) | Standard objective for language models. |\n\nAnd multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_\n\n### Factors \n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n- Language, such as English or Yoruba\n\n- Domain, such as newswire or stories\n \n- Demographic characteristics, such as gender or nationality\n\n### Results\n*Results are based on the [Factors](#factors) and [Metrics](#metrics).*\n\n**Zero-shot evaluations:**\n\nSee this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results\n\n| Task | Language | Metric | BLOOM-2B5 |\n|:----|:----|:----|:----:|\n| arc_challenge | eng | acc ↑ | 0.28 |\n| arc_easy | eng | acc ↑ | 0.595 |\n| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |\n| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |\n| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |\n| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |\n| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |\n| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |\n| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |\n| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |\n| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |\n| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |\n| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |\n| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |\n| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |\n| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |\n| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |\n| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |\n| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |\n| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |\n| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |\n| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |\n| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |\n| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |\n| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |\n| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |\n| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |\n| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |\n| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |\n| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |\n| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |\n| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |\n| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |\n| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |\n| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |\n| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |\n| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |\n| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |\n| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |\n| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |\n| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |\n| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |\n| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |\n| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |\n| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |\n| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |\n| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |\n| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |\n| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |\n| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |\n| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |\n| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |\n| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |\n| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |\n| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |\n| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |\n| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |\n| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |\n| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |\n| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |\n| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |\n| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |\n| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |\n| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |\n| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |\n| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |\n| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |\n| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |\n| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |\n| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |\n| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |\n| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |\n| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |\n| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |\n| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |\n| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |\n| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |\n| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |\n| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |\n| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |\n| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |\n| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |\n| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |\n| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |\n| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |\n| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |\n| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |\n| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |\n| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |\n| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |\n| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |\n| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |\n| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |\n| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |\n| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |\n| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |\n| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |\n| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |\n| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |\n| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |\n| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |\n| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |\n| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |\n| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |\n| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |\n| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |\n| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |\n| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |\n| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |\n| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |\n| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |\n| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |\n| headqa | esp | acc ↑ | 0.264 |\n| hellaswag | eng | acc ↑ | 0.412 |\n| logiqa | eng | acc ↑ | 0.207 |\n| mathqa | eng | acc ↑ | 0.25 |\n| mc_taco | eng | em ↑ | 0.119 |\n| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |\n| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |\n| mrpc | eng | acc ↑ | 0.586 |\n| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |\n| openbookqa | eng | acc ↑ | 0.216 |\n| piqa | eng | acc ↑ | 0.708 |\n| prost | eng | acc ↑ | 0.227 |\n| pubmedqa | eng | acc ↑ | 0.616 |\n| qnli | eng | acc ↑ | 0.507 |\n| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |\n| race | eng | acc ↑ | 0.352 |\n| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |\n| sciq | eng | acc ↑ | 0.892 |\n| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |\n| triviaqa | eng | acc ↑ | 0.042 |\n| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |\n| webqs | eng | acc ↑ | 0.017 |\n| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |\n| winogrande | eng | acc ↑ | 0.586 |\n| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |\n| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |\n| humaneval | python | pass@1 ↑ | 0.155 |\n| humaneval | python | pass@10 ↑ | 0.322 |\n| humaneval | python | pass@100 ↑ | 0.555 |\n\n**Train-time Evaluation:**\n\nAs of 25.May.2022, 15:00 PST:\n\n- Training Loss: 2.0\n\n- Validation Loss: 2.2\n\n- Perplexity: 8.9\n\n
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\n\n## Recommendations\n\n*This section provides information on warnings and potential mitigations.*\n\n\n
\nClick to expand
\n\n- Indirect users should be made aware when the content they're working with is created by the LLM.\n\n- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.\n\n- Models pretrained with the LLM should include an updated Model Card.\n\n- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n
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\n\n## Glossary and Calculations\n\n*This section defines common terms and how metrics are calculated.*\n\n\n\n
\nClick to expand
\n\n- **Loss:** A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss. \n\n- **Perplexity:** This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. \n\n- **High-stakes settings:** Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).\n\n- **Critical decisions:** Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).\n\n- **Human rights:** Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).\n\n- **Personal Data and Personal Information:** Personal data and information is defined in multiple data protection regulations, such as \"[personal data](https://gdpr-info.eu/issues/personal-data/)\" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and \"personal information\" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).\n \n- **Sensitive characteristics:** This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))\n\n- **Deception:** Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n
\n

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\n\n## More Information\n\n
\nClick to expand
\n \n### Dataset Creation\n\nBlog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling\n\n### Technical Specifications\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours\n\nMore details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml\n\nBlog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model\n\nDetails on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml\n\nTensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss\n\nInsights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md\n\n### Initial Results\n\nInitial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book\n\n
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\n \n## Model Card Authors\n*Ordered roughly chronologically and by amount of time spent.*\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff\n\n"},"embedding":{"kind":"list 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\"license:bigcode-openrail-m\",\n \"model-index\",\n \"endpoints_compatible\",\n \"has_space\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-04-24T12:36:52Z","string":"2023-04-24T12:36:52Z"},"card":{"kind":"string","value":"---\npipeline_tag: text-generation\ninference: true\nwidget:\n- text: 'def print_hello_world():'\n example_title: Hello world\n group: Python\nlicense: bigcode-openrail-m\ndatasets:\n- bigcode/the-stack-dedup\nmetrics:\n- code_eval\nlibrary_name: transformers\ntags:\n- code\nmodel-index:\n- name: StarCoder\n results:\n - task:\n type: text-generation\n dataset:\n type: openai_humaneval\n name: HumanEval (Prompted)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.408\n verified: false\n - task:\n type: text-generation\n dataset:\n type: openai_humaneval\n name: HumanEval\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.336\n verified: false\n - task:\n type: text-generation\n dataset:\n type: mbpp\n name: MBPP\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.527\n verified: false\n - task:\n type: text-generation\n dataset:\n type: ds1000\n name: DS-1000 (Overall Completion)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.26\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (C++)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.3155\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (C#)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2101\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (D)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.1357\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Go)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.1761\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Java)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.3022\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Julia)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2302\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (JavaScript)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.3079\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Lua)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2389\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (PHP)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2608\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Perl)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.1734\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Python)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.3357\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (R)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.155\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Ruby)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.0124\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Racket)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.0007\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Rust)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2184\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Scala)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2761\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Bash)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.1046\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (Swift)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.2274\n verified: false\n - task:\n type: text-generation\n dataset:\n type: nuprl/MultiPL-E\n name: MultiPL-HumanEval (TypeScript)\n metrics:\n - name: pass@1\n type: pass@1\n value: 0.3229\n verified: false\nextra_gated_prompt: >-\n ## Model License Agreement\n\n Please read the BigCode [OpenRAIL-M\n license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)\n agreement before accepting it.\n \nextra_gated_fields:\n I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox\n---\n\n\n# StarCoder\n\n![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/StarCoderBanner.png)\n\nPlay with the model on the [StarCoder Playground](https://huggingface.co/spaces/bigcode/bigcode-playground).\n\n## Table of Contents\n\n1. [Model Summary](##model-summary)\n2. [Use](##use)\n3. [Limitations](##limitations)\n4. [Training](##training)\n5. [License](##license)\n6. [Citation](##citation)\n\n## Model Summary\n\nThe StarCoder models are 15.5B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. \n\n- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)\n- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)\n- **Paper:** [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161)\n- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)\n- **Languages:** 80+ Programming languages\n\n\n## Use\n\n### Intended use\n\nThe model was trained on GitHub code. As such it is _not_ an instruction model and commands like \"Write a function that computes the square root.\" do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant.\n\n**Feel free to share your generations in the Community tab!**\n\n### Generation\n```python\n# pip install -q transformers\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ncheckpoint = \"bigcode/starcoder\"\ndevice = \"cuda\" # for GPU usage or \"cpu\" for CPU usage\n\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\nmodel = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)\n\ninputs = tokenizer.encode(\"def print_hello_world():\", return_tensors=\"pt\").to(device)\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\n### Fill-in-the-middle\nFill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:\n\n```python\ninput_text = \"def print_hello_world():\\n \\n print('Hello world!')\"\ninputs = tokenizer.encode(input_text, return_tensors=\"pt\").to(device)\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\n### Attribution & Other Requirements\n\nThe pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.\n\n# Limitations\n\nThe model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations. \n\n# Training\n\n## Model\n\n- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective\n- **Pretraining steps:** 250k\n- **Pretraining tokens:** 1 trillion\n- **Precision:** bfloat16\n\n## Hardware\n\n- **GPUs:** 512 Tesla A100\n- **Training time:** 24 days\n\n## Software\n\n- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)\n- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)\n- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)\n\n# License\nThe model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).\n# Citation\n```\n@article{li2023starcoder,\n title={StarCoder: may the source be with you!}, \n author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},\n year={2023},\n eprint={2305.06161},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"unconditional-image-generation"},"createdAt":{"kind":"timestamp","value":"2022-06-16T12:53:22Z","string":"2022-06-16T12:53:22Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- pytorch\n- diffusers\n- unconditional-image-generation\n---\n\n# Denoising Diffusion Probabilistic Models (DDPM)\n\n**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)\n\n**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\n**Abstract**:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*\n\n## Inference\n\n**DDPM** models can use *discrete noise schedulers* such as:\n\n- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)\n- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)\n- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)\n\nfor inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.\nFor a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.\n\nSee the following code:\n\n```python\n# !pip install diffusers\nfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\n\nmodel_id = \"google/ddpm-cifar10-32\"\n\n# load model and scheduler\nddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference\n\n# run pipeline in inference (sample random noise and denoise)\nimage = ddpm().images[0]\n\n# save image\nimage.save(\"ddpm_generated_image.png\")\n```\n\nFor more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)\n\n## Training\n\nIf you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)\n\n## Samples\n1. ![sample_1](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_0.png)\n2. ![sample_2](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_1.png)\n3. ![sample_3](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_2.png)\n4. ![sample_4](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_3.png)"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-to-image"},"createdAt":{"kind":"timestamp","value":"2022-10-27T19:56:48Z","string":"2022-10-27T19:56:48Z"},"card":{"kind":"string","value":"---\nlicense: creativeml-openrail-m\ntags:\n- stable-diffusion\n- text-to-image\n---\n**Mo Di Diffusion**\n\nThis is the fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio.\nUse the tokens **_modern disney style_** in your prompts for the effect.\n\n**If you enjoy my work, please consider supporting me** \n[![Become A Patreon](https://badgen.net/badge/become/a%20patron/F96854)](https://patreon.com/user?u=79196446)\n\n**Videogame Characters rendered with the model:**\n![Videogame Samples](https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/modi-samples-01s.jpg)\n**Animal Characters rendered with the model:**\n![Animal Samples](https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/modi-samples-02s.jpg)\n**Cars and Landscapes rendered with the model:**\n![Misc. Samples](https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/modi-samples-03s.jpg)\n#### Prompt and settings for Lara Croft:\n**modern disney lara croft**\n_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417, Size: 512x768_\n\n#### Prompt and settings for the Lion:\n**modern disney (baby lion) Negative prompt: person human**\n_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_\n\nThis model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 9.000 steps.\n\n### 🧨 Diffusers\n\nThis model can be used just like any other Stable Diffusion model. For more information,\nplease have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).\n\nYou can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"nitrosocke/mo-di-diffusion\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\n\nprompt = \"a magical princess with golden hair, modern disney style\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"./magical_princess.png\")\n```\n\n# Gradio & Colab\n\nWe also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models:\n[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/anzorq/finetuned_diffusion)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1j5YvfMZoGdDGdj3O3xRU1m4ujKYsElZO?usp=sharing)\n\n## License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content \n2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text2text-generation"},"createdAt":{"kind":"timestamp","value":"2022-04-11T18:45:17Z","string":"2022-04-11T18:45:17Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- generated_from_trainer\ndatasets:\n- xsum\nmodel-index:\n- name: t5-small-xsum\n results: []\n---\n\n\n\n# t5-small-xsum\n\nThis model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.3953\n\n## Model description\n\nMore information needed\n\n## Intended uses & limitations\n\nMore information needed\n\n## Training and evaluation data\n\nMore information needed\n\n## Training procedure\n\n### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1\n\n### Training results\n\n| Training Loss | Epoch | Step | Validation Loss |\n|:-------------:|:-----:|:-----:|:---------------:|\n| 2.8641 | 0.04 | 500 | 2.6202 |\n| 2.7466 | 0.08 | 1000 | 2.5660 |\n| 2.8767 | 0.12 | 1500 | 2.5319 |\n| 2.7099 | 0.16 | 2000 | 2.5107 |\n| 2.7752 | 0.2 | 2500 | 2.4922 |\n| 2.6037 | 0.24 | 3000 | 2.4800 |\n| 2.8236 | 0.27 | 3500 | 2.4677 |\n| 2.7089 | 0.31 | 4000 | 2.4581 |\n| 2.7299 | 0.35 | 4500 | 2.4498 |\n| 2.7498 | 0.39 | 5000 | 2.4420 |\n| 2.6186 | 0.43 | 5500 | 2.4346 |\n| 2.7817 | 0.47 | 6000 | 2.4288 |\n| 2.5559 | 0.51 | 6500 | 2.4239 |\n| 2.6725 | 0.55 | 7000 | 2.4186 |\n| 2.6316 | 0.59 | 7500 | 2.4149 |\n| 2.5561 | 0.63 | 8000 | 2.4115 |\n| 2.5708 | 0.67 | 8500 | 2.4097 |\n| 2.5861 | 0.71 | 9000 | 2.4052 |\n| 2.6363 | 0.74 | 9500 | 2.4024 |\n| 2.7435 | 0.78 | 10000 | 2.4003 |\n| 2.7258 | 0.82 | 10500 | 2.3992 |\n| 2.6113 | 0.86 | 11000 | 2.3983 |\n| 2.6006 | 0.9 | 11500 | 2.3972 |\n| 2.5684 | 0.94 | 12000 | 2.3960 |\n| 2.6181 | 0.98 | 12500 | 2.3953 |\n\n\n### Framework versions\n\n- Transformers 4.18.0\n- Pytorch 1.10.0+cu111\n- Datasets 2.0.0\n- Tokenizers 0.11.6\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"token-classification"},"createdAt":{"kind":"timestamp","value":"2023-04-06T14:15:13Z","string":"2023-04-06T14:15:13Z"},"card":{"kind":"string","value":"This is a demo using the models from:\n\n```\n@inproceedings{zhang-etal-2022-skillspan,\n title = \"{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings\",\n author = \"Zhang, Mike and\n Jensen, Kristian and\n Sonniks, Sif and\n Plank, Barbara\",\n booktitle = \"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = jul,\n year = \"2022\",\n address = \"Seattle, United States\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2022.naacl-main.366\",\n doi = \"10.18653/v1/2022.naacl-main.366\",\n pages = \"4962--4984\",\n abstract = \"Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning.\",\n}\n```\n\nNote that there is another endpoint, namely `jjzha/jobbert_skill_extraction`.\nKnowledge can be seen as hard skills and skills are both soft and applied skills."},"embedding":{"kind":"list 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Model card for CLAP\n\nModel card for CLAP: Contrastive Language-Audio Pretraining\n\n![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png)\n\n\n# Table of Contents\n\n0. [TL;DR](#TL;DR)\n1. [Model Details](#model-details)\n2. [Usage](#usage)\n3. [Uses](#uses)\n4. [Citation](#citation)\n\n# TL;DR\n\nThe abstract of the paper states that: \n\n> Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.\n\n\n# Usage\n\nYou can use this model for zero shot audio classification or extracting audio and/or textual features.\n\n# Uses\n\n## Perform zero-shot audio classification\n\n### Using `pipeline`\n\n```python\nfrom datasets import load_dataset\nfrom transformers import pipeline\n\ndataset = load_dataset(\"ashraq/esc50\")\naudio = dataset[\"train\"][\"audio\"][-1][\"array\"]\n\naudio_classifier = pipeline(task=\"zero-shot-audio-classification\", model=\"laion/clap-htsat-unfused\")\noutput = audio_classifier(audio, candidate_labels=[\"Sound of a dog\", \"Sound of vaccum cleaner\"])\nprint(output)\n>>> [{\"score\": 0.999, \"label\": \"Sound of a dog\"}, {\"score\": 0.001, \"label\": \"Sound of vaccum cleaner\"}]\n```\n\n## Run the model:\n\nYou can also get the audio and text embeddings using `ClapModel`\n\n### Run the model on CPU:\n\n```python\nfrom datasets import load_dataset\nfrom transformers import ClapModel, ClapProcessor\n\nlibrispeech_dummy = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\naudio_sample = librispeech_dummy[0]\n\nmodel = ClapModel.from_pretrained(\"laion/clap-htsat-unfused\")\nprocessor = ClapProcessor.from_pretrained(\"laion/clap-htsat-unfused\")\n\ninputs = processor(audios=audio_sample[\"audio\"][\"array\"], return_tensors=\"pt\")\naudio_embed = model.get_audio_features(**inputs)\n```\n\n### Run the model on GPU:\n\n```python\nfrom datasets import load_dataset\nfrom transformers import ClapModel, ClapProcessor\n\nlibrispeech_dummy = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\naudio_sample = librispeech_dummy[0]\n\nmodel = ClapModel.from_pretrained(\"laion/clap-htsat-unfused\").to(0)\nprocessor = ClapProcessor.from_pretrained(\"laion/clap-htsat-unfused\")\n\ninputs = processor(audios=audio_sample[\"audio\"][\"array\"], return_tensors=\"pt\").to(0)\naudio_embed = model.get_audio_features(**inputs)\n```\n\n\n# Citation\n\nIf you are using this model for your work, please consider citing the original paper:\n```\n@misc{https://doi.org/10.48550/arxiv.2211.06687,\n doi = {10.48550/ARXIV.2211.06687},\n \n url = {https://arxiv.org/abs/2211.06687},\n \n author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},\n \n keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},\n \n title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation},\n \n publisher = {arXiv},\n \n year = {2022},\n \n copyright = {Creative Commons Attribution 4.0 International}\n}\n```"},"embedding":{"kind":"list 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-0.4904962182044983\n]"}}},{"rowIdx":1187,"cells":{"modelId":{"kind":"string","value":"optimum/sbert-all-MiniLM-L6-with-pooler"},"author":{"kind":"string","value":"optimum"},"last_modified":{"kind":"timestamp","value":"2022-07-26T13:37:30Z","string":"2022-07-26T13:37:30Z"},"downloads":{"kind":"number","value":18449,"string":"18,449"},"likes":{"kind":"number","value":6,"string":"6"},"library_name":{"kind":"string","value":"sentence-transformers"},"tags":{"kind":"list like","value":["sentence-transformers","onnx","bert","feature-extraction","sentence-similarity","en","arxiv:1904.06472","arxiv:2102.07033","arxiv:2104.08727","arxiv:1704.05179","arxiv:1810.09305","license:apache-2.0","endpoints_compatible","has_space","region:us"],"string":"[\n \"sentence-transformers\",\n \"onnx\",\n \"bert\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"en\",\n \"arxiv:1904.06472\",\n \"arxiv:2102.07033\",\n \"arxiv:2104.08727\",\n \"arxiv:1704.05179\",\n \"arxiv:1810.09305\",\n \"license:apache-2.0\",\n \"endpoints_compatible\",\n \"has_space\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2022-07-26T11:32:55Z","string":"2022-07-26T11:32:55Z"},"card":{"kind":"string","value":"---\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\nlanguage: en\nlicense: apache-2.0\n---\n# ONNX convert all-MiniLM-L6-v2\n## Conversion of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)\nThis is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes `last_hidden_state` and `pooler_output` whereas the sentence-transformers exported with default ONNX config only contains `last_hidden_state` as output.\n\n## Usage (HuggingFace Optimum)\nUsing this model becomes easy when you have [optimum](https://github.com/huggingface/optimum) installed:\n```\npython -m pip install optimum\n```\nThen you can use the model like this:\n```python\nfrom optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks\n\nmodel = ORTModelForCustomTasks.from_pretrained(\"optimum/sbert-all-MiniLM-L6-with-pooler\")\ntokenizer = AutoTokenizer.from_pretrained(\"optimum/sbert-all-MiniLM-L6-with-pooler\")\ninputs = tokenizer(\"I love burritos!\", return_tensors=\"pt\")\npred = model(**inputs)\n```\nYou will also be able to leverage the pipeline API in transformers:\n```python\nfrom transformers import pipeline\n\nonnx_extractor = pipeline(\"feature-extraction\", model=model, tokenizer=tokenizer)\ntext = \"I love burritos!\"\npred = onnx_extractor(text)\n```\n## Evaluation Results\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)\n------\n## Background\nThe project aims to train sentence embedding models on very large sentence level datasets using a self-supervised \ncontrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a \n1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.\nWe developped this model during the \n[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), \norganized by Hugging Face. We developped this model as part of the project:\n[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.\n## Intended uses\nOur model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures \nthe semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.\nBy default, input text longer than 256 word pieces is truncated.\n## Training procedure\n### Pre-training \nWe use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.\n### Fine-tuning \nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.\nWe then apply the cross entropy loss by comparing with true pairs.\n#### Hyper parameters\nWe trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.\n#### Training data\nWe use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.\nWe sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.\n| Dataset | Paper | Number of training tuples |\n|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|\n| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |\n| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |\n| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |\n| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |\n| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |\n| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |\n| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |\n| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |\n| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |\n| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |\n| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |\n| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |\n| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |\n| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|\n| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |\n| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |\n| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |\n| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |\n| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |\n| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |\n| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |\n| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | \n| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |\n| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |\n| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |\n| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |\n| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |\n| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |\n| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |\n| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |\n| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |\n| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |\n| **Total** | | **1,170,060,424** |\n"},"embedding":{"kind":"list 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-0.11392495036125183\n]"}}},{"rowIdx":1188,"cells":{"modelId":{"kind":"string","value":"TheBloke/Mistral-7B-Instruct-v0.1-GPTQ"},"author":{"kind":"string","value":"TheBloke"},"last_modified":{"kind":"timestamp","value":"2023-09-29T20:48:48Z","string":"2023-09-29T20:48:48Z"},"downloads":{"kind":"number","value":18430,"string":"18,430"},"likes":{"kind":"number","value":61,"string":"61"},"library_name":{"kind":"string","value":"transformers"},"tags":{"kind":"list like","value":["transformers","safetensors","mistral","text-generation","finetuned","base_model:mistralai/Mistral-7B-Instruct-v0.1","license:apache-2.0","has_space","text-generation-inference","4-bit","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"mistral\",\n \"text-generation\",\n \"finetuned\",\n \"base_model:mistralai/Mistral-7B-Instruct-v0.1\",\n \"license:apache-2.0\",\n \"has_space\",\n \"text-generation-inference\",\n \"4-bit\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-09-28T22:34:03Z","string":"2023-09-28T22:34:03Z"},"card":{"kind":"string","value":"---\nbase_model: mistralai/Mistral-7B-Instruct-v0.1\ninference: false\nlicense: apache-2.0\nmodel_creator: Mistral AI\nmodel_name: Mistral 7B Instruct v0.1\nmodel_type: mistral\npipeline_tag: text-generation\nprompt_template: '[INST] {prompt} [/INST]'\nquantized_by: TheBloke\ntags:\n- finetuned\n---\n\n\n\n
\n\"TheBlokeAI\"\n
\n\n

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

\n
\n\n\n# Mistral 7B Instruct v0.1 - GPTQ\n- Model creator: [Mistral AI](https://huggingface.co/mistralai)\n- Original model: [Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)\n\n\n## Description\n\nThis repo contains GPTQ model files for [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).\n\nMultiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.\n\n### GPTQs will work in ExLlama, or via Transformers (requiring Transformers from Github)\n\nThese models are confirmed to work with ExLlama v1.\n\nAt the time of writing (September 28th), AutoGPTQ has not yet added support for the new Mistral models.\n\nThese GPTQs were made directly from Transformers, and so can be loaded via the Transformers interface. They can't be loaded directly from AutoGPTQ.\n\nTo load them via Transformers, you will need to install Transformers from Github, with:\n```\npip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79\n```\n\n\n\n## Repositories available\n\n* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ)\n* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)\n* [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)\n\n\n\n## Prompt template: Mistral\n\n```\n[INST] {prompt} [/INST]\n\n```\n\n\n\n\n\n## Provided files, and GPTQ parameters\n\nMultiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.\n\nEach separate quant is in a different branch. See below for instructions on fetching from different branches.\n\nThese files were made with Transformers 4.34.0.dev0, from commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79.\n\n
\n Explanation of GPTQ parameters\n\n- Bits: The bit size of the quantised model.\n- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. \"None\" is the lowest possible value.\n- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.\n- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.\n- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).\n- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.\n- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.\n\n
\n\n| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |\n| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |\n| [main](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | \n| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | \n| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 7.68 GB | Yes | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | \n| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 8.17 GB | Yes | 8-bit, with group size 32g and Act Order for maximum inference quality. |\n\n\n\n\n## How to download, including from branches\n\n### In text-generation-webui\n\nTo download from the `main` branch, enter `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ` in the \"Download model\" box.\n\nTo download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ:gptq-4bit-32g-actorder_True`\n\n### From the command line\n\nI recommend using the `huggingface-hub` Python library:\n\n```shell\npip3 install huggingface-hub\n```\n\nTo download the `main` branch to a folder called `Mistral-7B-Instruct-v0.1-GPTQ`:\n\n```shell\nmkdir Mistral-7B-Instruct-v0.1-GPTQ\nhuggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False\n```\n\nTo download from a different branch, add the `--revision` parameter:\n\n```shell\nmkdir Mistral-7B-Instruct-v0.1-GPTQ\nhuggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False\n```\n\n
\n More advanced huggingface-cli download usage\n\nIf you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\nThe cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.\n\nFor more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:\n\n```shell\npip3 install hf_transfer\n```\n\nAnd set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:\n\n```shell\nmkdir Mistral-7B-Instruct-v0.1-GPTQ\nHF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False\n```\n\nWindows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.\n
\n\n### With `git` (**not** recommended)\n\nTo clone a specific branch with `git`, use a command like this:\n\n```shell\ngit clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ\n```\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)\n\n\n\n## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).\n\nThese models are confirmed to work via the ExLlama Loader in text-generation-webui.\n\nUse **Loader: ExLlama** - or Transformers may work too. AutoGPTQ will not work.\n\nPlease make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n1. Click the **Model tab**.\n2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ`.\n - To download from a specific branch, enter for example `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ:gptq-4bit-32g-actorder_True`\n - see Provided Files above for the list of branches for each option.\n3. Click **Download**.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to **Model**.\n6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-Instruct-v0.1-GPTQ`\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.\n * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.\n9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!\n\n\n\n## How to use this GPTQ model from Python code\n\n### Install the necessary packages\n\nRequires: Transformers 4.34.0.dev0 from Github source, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n```shell\npip3 install optimum\npip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79\npip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7\n```\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:\n\n```shell\npip3 uninstall -y auto-gptq\ngit clone https://github.com/PanQiWei/AutoGPTQ\ncd AutoGPTQ\ngit checkout v0.4.2\npip3 install .\n```\n\n### You can then use the following code\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n\nmodel_name_or_path = \"TheBloke/Mistral-7B-Instruct-v0.1-GPTQ\"\n# To use a different branch, change revision\n# For example: revision=\"gptq-4bit-32g-actorder_True\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name_or_path,\n device_map=\"auto\",\n trust_remote_code=False,\n revision=\"main\")\n\ntokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''[INST] {prompt} [/INST]\n'''\n\nprint(\"\\n\\n*** Generate:\")\n\ninput_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()\noutput = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)\nprint(tokenizer.decode(output[0]))\n\n# Inference can also be done using transformers' pipeline\n\nprint(\"*** Pipeline:\")\npipe = pipeline(\n \"text-generation\",\n model=model,\n tokenizer=tokenizer,\n max_new_tokens=512,\n do_sample=True,\n temperature=0.7,\n top_p=0.95,\n top_k=40,\n repetition_penalty=1.1\n)\n\nprint(pipe(prompt_template)[0]['generated_text'])\n```\n\n\n\n## Compatibility\n\nThe files provided are only tested to work with ExLlama v1, and Transformers 4.34.0.dev0 as of commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79.\n\n\n\n\n\n## Discord\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n[TheBloke AI's Discord server](https://discord.gg/theblokeai)\n\n## Thanks, and how to contribute\n\nThanks to the [chirper.ai](https://chirper.ai) team!\n\nThanks to Clay from [gpus.llm-utils.org](llm-utils)!\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n* Patreon: https://patreon.com/TheBlokeAI\n* Ko-Fi: https://ko-fi.com/TheBlokeAI\n\n**Special thanks to**: Aemon Algiz.\n\n**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski\n\n\nThank you to all my generous patrons and donaters!\n\nAnd thank you again to a16z for their generous grant.\n\n\n\n# Original model card: Mistral AI's Mistral 7B Instruct v0.1\n\n\n# Model Card for Mistral-7B-Instruct-v0.1\n\nThe Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.\n\nFor full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/)\n\n## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n```\ntext = \"[INST] What is your favourite condiment? [/INST]\"\n\"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen! \"\n\"[INST] Do you have mayonnaise recipes? [/INST]\"\n```\n\nThis format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ndevice = \"cuda\" # the device to load the model onto\n\nmodel = AutoModelForCausalLM.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.1\")\ntokenizer = AutoTokenizer.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.1\")\n\nmessages = [\n {\"role\": \"user\", \"content\": \"What is your favourite condiment?\"},\n {\"role\": \"assistant\", \"content\": \"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!\"},\n {\"role\": \"user\", \"content\": \"Do you have mayonnaise recipes?\"}\n]\n\nencodeds = tokenizer.apply_chat_template(messages, return_tensors=\"pt\")\n\nmodel_inputs = encodeds.to(device)\nmodel.to(device)\n\ngenerated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)\ndecoded = tokenizer.batch_decode(generated_ids)\nprint(decoded[0])\n```\n\n## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer\n\n## Troubleshooting\n- If you see the following error:\n```\nTraceback (most recent call last):\nFile \"\", line 1, in\nFile \"/transformers/models/auto/auto_factory.py\", line 482, in from_pretrained\nconfig, kwargs = AutoConfig.from_pretrained(\nFile \"/transformers/models/auto/configuration_auto.py\", line 1022, in from_pretrained\nconfig_class = CONFIG_MAPPING[config_dict[\"model_type\"]]\nFile \"/transformers/models/auto/configuration_auto.py\", line 723, in getitem\nraise KeyError(key)\nKeyError: 'mistral'\n```\n\nInstalling transformers from source should solve the issue\npip install git+https://github.com/huggingface/transformers\n\nThis should not be required after transformers-v4.33.4.\n\n## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.\n\n## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"token-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: fr\nwidget:\n- text: \"Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages\"\n---\n\n## About\n\nThe *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on \n[github](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'camembert-base'*.\n\n## Supported Tags\n\nIt uses the following tags:\n\n| Tag | Category | Extra Info |\n|----------|:------------------------------:|------------:|\n| ADJ | adjectif | |\n| ADJWH | adjectif | |\n| ADV | adverbe | |\n| ADVWH | adverbe | |\n| CC | conjonction de coordination | |\n| CLO | pronom | obj |\n| CLR | pronom | refl |\n| CLS | pronom | suj |\n| CS | conjonction de subordination | |\n| DET | déterminant | |\n| DETWH | déterminant | |\n| ET | mot étranger | |\n| I | interjection | |\n| NC | nom commun | |\n| NPP | nom propre | |\n| P | préposition | |\n| P+D | préposition + déterminant | |\n| PONCT | signe de ponctuation | |\n| PREF | préfixe | |\n| PRO | autres pronoms | |\n| PROREL | autres pronoms | rel |\n| PROWH | autres pronoms | int |\n| U | ? | |\n| V | verbe | |\n| VIMP | verbe imperatif | |\n| VINF | verbe infinitif | |\n| VPP | participe passé | |\n| VPR | participe présent | |\n| VS | subjonctif | |\n\nMore information on the tags can be found here:\n\nhttp://alpage.inria.fr/statgram/frdep/Publications/crabbecandi-taln2008-final.pdf\n\n## Usage\n\nThe usage of this model follows the common transformers patterns. Here is a short example of its usage:\n\n```python\nfrom transformers import AutoTokenizer, AutoModelForTokenClassification\n\ntokenizer = AutoTokenizer.from_pretrained(\"gilf/french-camembert-postag-model\")\nmodel = AutoModelForTokenClassification.from_pretrained(\"gilf/french-camembert-postag-model\")\n\nfrom transformers import pipeline\n\nnlp_token_class = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)\n\nnlp_token_class('Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages')\n```\n\nThe lines above would display something like this on a Jupyter notebook:\n\n```\n[{'entity_group': 'NC', 'score': 0.5760144591331482, 'word': ''},\n {'entity_group': 'U', 'score': 0.9946700930595398, 'word': 'Face'},\n {'entity_group': 'P', 'score': 0.999615490436554, 'word': 'à'},\n {'entity_group': 'DET', 'score': 0.9995906352996826, 'word': 'un'},\n {'entity_group': 'NC', 'score': 0.9995531439781189, 'word': 'choc'},\n {'entity_group': 'ADJ', 'score': 0.999183714389801, 'word': 'inédit'},\n {'entity_group': 'P', 'score': 0.3710663616657257, 'word': ','},\n {'entity_group': 'DET', 'score': 0.9995903968811035, 'word': 'les'},\n {'entity_group': 'NC', 'score': 0.9995649456977844, 'word': 'mesures'},\n {'entity_group': 'VPP', 'score': 0.9988670349121094, 'word': 'mises'},\n {'entity_group': 'P', 'score': 0.9996246099472046, 'word': 'en'},\n {'entity_group': 'NC', 'score': 0.9995329976081848, 'word': 'place'},\n {'entity_group': 'P', 'score': 0.9996233582496643, 'word': 'par'},\n {'entity_group': 'DET', 'score': 0.9995935559272766, 'word': 'le'},\n {'entity_group': 'NC', 'score': 0.9995369911193848, 'word': 'gouvernement'},\n {'entity_group': 'V', 'score': 0.9993771314620972, 'word': 'ont'},\n {'entity_group': 'VPP', 'score': 0.9991101026535034, 'word': 'permis'},\n {'entity_group': 'DET', 'score': 0.9995885491371155, 'word': 'une'},\n {'entity_group': 'NC', 'score': 0.9995636343955994, 'word': 'protection'},\n {'entity_group': 'ADJ', 'score': 0.9991781711578369, 'word': 'forte'},\n {'entity_group': 'CC', 'score': 0.9991298317909241, 'word': 'et'},\n {'entity_group': 'ADJ', 'score': 0.9992275238037109, 'word': 'efficace'},\n {'entity_group': 'P+D', 'score': 0.9993300437927246, 'word': 'des'},\n {'entity_group': 'NC', 'score': 0.8353511393070221, 'word': 'ménages'}]\n```\n"},"embedding":{"kind":"list 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-0.37301623821258545\n]"}}},{"rowIdx":1190,"cells":{"modelId":{"kind":"string","value":"lllyasviel/control_v11p_sd15_inpaint"},"author":{"kind":"string","value":"lllyasviel"},"last_modified":{"kind":"timestamp","value":"2023-05-26T09:58:45Z","string":"2023-05-26T09:58:45Z"},"downloads":{"kind":"number","value":18413,"string":"18,413"},"likes":{"kind":"number","value":57,"string":"57"},"library_name":{"kind":"string","value":"diffusers"},"tags":{"kind":"list like","value":["diffusers","art","controlnet","stable-diffusion","controlnet-v1-1","image-to-image","arxiv:2302.05543","base_model:runwayml/stable-diffusion-v1-5","license:openrail","has_space","diffusers:ControlNetModel","region:us"],"string":"[\n \"diffusers\",\n \"art\",\n \"controlnet\",\n \"stable-diffusion\",\n \"controlnet-v1-1\",\n \"image-to-image\",\n \"arxiv:2302.05543\",\n \"base_model:runwayml/stable-diffusion-v1-5\",\n \"license:openrail\",\n \"has_space\",\n \"diffusers:ControlNetModel\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"image-to-image"},"createdAt":{"kind":"timestamp","value":"2023-04-14T19:25:42Z","string":"2023-04-14T19:25:42Z"},"card":{"kind":"string","value":"---\nlicense: openrail\nbase_model: runwayml/stable-diffusion-v1-5\ntags:\n- art\n- controlnet\n- stable-diffusion\n- controlnet-v1-1\n- image-to-image\nduplicated_from: ControlNet-1-1-preview/control_v11p_sd15_inpaint\n---\n\n# Controlnet - v1.1 - *InPaint Version*\n\n**Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel).\n\nThis checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_inpaint.pth) into `diffusers` format.\nIt can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).\n\n\nFor more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet).\n\n\nControlNet is a neural network structure to control diffusion models by adding extra conditions. \n\n![img](./sd.png)\n\nThis checkpoint corresponds to the ControlNet conditioned on **inpaint images**.\n\n## Model Details\n- **Developed by:** Lvmin Zhang, Maneesh Agrawala\n- **Model type:** Diffusion-based text-to-image generation model\n- **Language(s):** English\n- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.\n- **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543).\n- **Cite as:**\n\n @misc{zhang2023adding,\n title={Adding Conditional Control to Text-to-Image Diffusion Models}, \n author={Lvmin Zhang and Maneesh Agrawala},\n year={2023},\n eprint={2302.05543},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n }\n## Introduction\n\nControlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by \nLvmin Zhang, Maneesh Agrawala.\n\nThe abstract reads as follows:\n\n*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. \nThe ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). \nMoreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. \nAlternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. \nWe report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. \nThis may enrich the methods to control large diffusion models and further facilitate related applications.*\n\n## Example\n\nIt is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint \nhas been trained on it.\nExperimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.\n\n\n1. Let's install `diffusers` and related packages:\n\n```\n$ pip install diffusers transformers accelerate\n```\n\n2. Run code:\n\n```python\n# !pip install transformers accelerate\nfrom diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel\nfrom diffusers.utils import load_image\nimport numpy as np\nimport torch\n\ninit_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png\"\n)\ninit_image = init_image.resize((512, 512))\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(1)\n\nmask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png\"\n)\nmask_image = mask_image.resize((512, 512))\n\n\ndef make_inpaint_condition(image, image_mask):\n image = np.array(image.convert(\"RGB\")).astype(np.float32) / 255.0\n image_mask = np.array(image_mask.convert(\"L\")).astype(np.float32) / 255.0\n\n assert image.shape[0:1] == image_mask.shape[0:1], \"image and image_mask must have the same image size\"\n image[image_mask > 0.5] = -1.0 # set as masked pixel\n image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n return image\n\n\ncontrol_image = make_inpaint_condition(init_image, mask_image)\n\ncontrolnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_inpaint\", torch_dtype=torch.float16\n)\npipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n)\n\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n\n# generate image\nimage = pipe(\n \"a handsome man with ray-ban sunglasses\",\n num_inference_steps=20,\n generator=generator,\n eta=1.0,\n image=init_image,\n mask_image=mask_image,\n control_image=control_image,\n).images[0]\n```\n\n![original](https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png)\n![img](https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_with_mask.png)\n![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.png)\n\n## Other released checkpoints v1-1\nThe authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) \non a different type of conditioning:\n\n| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |\n|---|---|---|---|---|\n|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)
| *Trained with canny edge detection* | A monochrome image with white edges on a black background.|||\n|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)
| *Trained with pixel to pixel instruction* | No condition .|||\n|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)
| Trained with image inpainting | No condition.|||\n|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)
| Trained with multi-level line segment detection | An image with annotated line segments.|||\n|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)
| Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|||\n|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)
| Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|||\n|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)
| Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|||\n|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)
| Trained with line art generation | An image with line art, usually black lines on a white background.|||\n|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)
| Trained with anime line art generation | An image with anime-style line art.|||\n|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)
| Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|||\n|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)
| Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|||\n|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)
| Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|||\n|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)
| Trained with image shuffling | An image with shuffled patches or regions.|||\n|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)
| Trained with image tiling | A blurry image or part of an image .|||\n\n## More information\n\nFor more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly)."},"embedding":{"kind":"list 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-0.7764008641242981,\n -0.43593987822532654,\n 0.13008639216423035,\n -1.3496602773666382,\n -0.08332463353872299,\n 0.9404418468475342,\n -0.3121064305305481,\n -0.35691478848457336,\n 0.16746027767658234,\n -0.37586939334869385,\n 0.32843267917633057,\n -0.32982224225997925,\n 0.2868783175945282,\n 0.3830094337463379,\n -0.17706893384456635,\n -0.3564552664756775,\n -0.39478278160095215,\n 0.650047242641449,\n 0.2723240852355957,\n -0.7785690426826477,\n -0.513938844203949,\n 0.45748263597488403,\n 0.421461820602417,\n 0.33942222595214844,\n 0.9210411310195923,\n -0.1767444759607315,\n 0.15641862154006958,\n -0.07880687713623047,\n 0.22498607635498047,\n 0.060152824968099594,\n -0.058743737637996674,\n -0.5333124995231628,\n -0.10391690582036972,\n -0.3006778061389923,\n -0.38123956322669983\n]"}}},{"rowIdx":1191,"cells":{"modelId":{"kind":"string","value":"FFusion/FFXL400"},"author":{"kind":"string","value":"FFusion"},"last_modified":{"kind":"timestamp","value":"2023-09-13T21:57:32Z","string":"2023-09-13T21:57:32Z"},"downloads":{"kind":"number","value":18404,"string":"18,404"},"likes":{"kind":"number","value":10,"string":"10"},"library_name":{"kind":"string","value":"diffusers"},"tags":{"kind":"list like","value":["diffusers","safetensors","stable-diffusion-xl","stable-diffusion-xl-diffusers","stable-diffusion","text-to-image","ffai","en","base_model:FFusion/FFusionXL-BASE","doi:10.57967/hf/1095","license:openrail++","endpoints_compatible","has_space","diffusers:StableDiffusionXLPipeline","region:us"],"string":"[\n \"diffusers\",\n \"safetensors\",\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"stable-diffusion\",\n \"text-to-image\",\n \"ffai\",\n \"en\",\n \"base_model:FFusion/FFusionXL-BASE\",\n \"doi:10.57967/hf/1095\",\n \"license:openrail++\",\n \"endpoints_compatible\",\n \"has_space\",\n \"diffusers:StableDiffusionXLPipeline\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-to-image"},"createdAt":{"kind":"timestamp","value":"2023-09-08T14:22:11Z","string":"2023-09-08T14:22:11Z"},"card":{"kind":"string","value":"---\nlicense: openrail++\nbase_model: FFusion/FFusionXL-BASE\ntags:\n- stable-diffusion-xl\n- stable-diffusion-xl-diffusers\n- stable-diffusion\n- text-to-image\n- diffusers\n- ffai\ninference: true\nwidget:\n- text: >-\n a dog in colorful exploding clouds, dreamlike surrealism colorful smoke and\n fire coming out of it, explosion of data fragments, exploding\n background,realistic explosion, 3d digital art\n example_title: Dogo FFusion\n- text: >-\n a sprinkled donut sitting on top of a table, colorful hyperrealism,\n everything is made of candy, hyperrealistic digital painting, covered in\n sprinkles and crumbs, vibrant colors hyper realism,colorful smoke explosion\n background\n example_title: Donut FFusion\n- text: >-\n a cup of coffee with a tree in it, surreal art, awesome great composition,\n surrealism, ice cubes in tree, colorful clouds, perfectly realistic yet\n surreal\n example_title: CoFFee FFusion\n- text: >-\n brightly colored headphones with a splash of colorful paint splash, vibing\n to music, stunning artwork, music is life, beautiful digital artwork,\n concept art, cinematic, dramatic, intricate details, dark lighting\n example_title: Headset FFusion\n- text: >-\n high-quality game character digital design, Unreal Engine, Water color\n painting, Mecha- Monstrous high quality game fantasy rpg character design,\n dark rainbow Fur Scarf, inside of a Superficial Outhouse, at Twilight,\n Overdetailed art\n example_title: Digital Fusion\nlanguage:\n- en\nthumbnail: >-\n https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image7sm.jpg\n---\n\n# FFXL400 Combined LoRA Model 🚀\n\nWelcome to the FFXL400 combined LoRA model repository on Hugging Face! This model is a culmination of extensive research, bringing together the finest LoRAs from the [400GB-LoraXL repository](https://huggingface.co/FFusion/400GB-LoraXL). Our vision was to harness the power of multiple LoRAs, meticulously analyzing and integrating a select fraction of the blocks from each. \n\n## 📦 Model Highlights\n\n- **Innovative Combination**: This model is a strategic integration of LoRAs, maximizing the potential of each while creating a unified powerhouse.\n- **Versatility**: The model is available in various formats including diffusers, safetensors (both fp 16 and 32), and an optimized ONNIX FP16 version for DirectML, ensuring compatibility across AMD, Intel, Nvidia, and more.\n- **Advanced Research**: Leveraging the latest in machine learning research, the model represents a state-of-the-art amalgamation of LoRAs, optimized for performance and accuracy.\n\n## 🔍 Technical Insights\n\nThis model is a testament to the advancements in the field of AI and machine learning. It was crafted with precision, ensuring that:\n\n- Only a small percentage of the blocks from the original LoRAs (UNet and text encoders) were utilized.\n- The model is primed not just for inference but also for further training and refinement.\n- It serves as a benchmark for testing and understanding the cumulative impact of multiple LoRAs when used in concert.\n\n## 🎨 Usage \n\nThe FFXL400 model is designed for a multitude of applications. Whether you're delving into research, embarking on a new project, or simply experimenting, this model serves as a robust foundation. Use it to:\n\n- Investigate the cumulative effects of merging multiple LoRAs.\n- Dive deep into weighting experiments with multiple LoRAs.\n- Explore the nuances and intricacies of integrated LoRAs.\n\n\n\n## ⚠️ License & Usage Disclaimers\n\n**Please review the full [license agreement](https://huggingface.co/FFusion/FFXL400/blob/main/LICENSE.md) before accessing or using the models.**\n\n🔴 The models and weights available in this repository are **strictly for research and testing purposes**, with exceptions noted below. They are **not** generally intended for commercial use and are dependent on each individual LORA. \n\n🔵 **Exception for Commercial Use:** The [FFusionXL-BASE](https://huggingface.co/FFusion/FFusionXL-BASE), [FFusion-BaSE](https://huggingface.co/FFusion/FFusion-BaSE), [di.FFUSION.ai-v2.1-768-BaSE-alpha](https://huggingface.co/FFusion/di.FFUSION.ai-v2.1-768-BaSE-alpha), and [di.ffusion.ai.Beta512](https://huggingface.co/FFusion/di.ffusion.ai.Beta512) models are trained by FFusion AI using images for which we hold licenses. Users are advised to primarily use these models for a safer experience. These particular models are allowed for commercial use.\n\n🔴 **Disclaimer:** FFusion AI, in conjunction with Source Code Bulgaria Ltd and BlackswanTechnologies, **does not endorse or guarantee the content produced by the weights in each LORA**. There's potential for generating NSFW or offensive content. Collectively, we expressly disclaim responsibility for the outcomes and content produced by these weights.\n\n🔴 **Acknowledgement:** The [FFusionXL-BASE](https://huggingface.co/FFusion/FFusionXL-BASE) model model is a uniquely developed version by FFusion AI. Rights to this and associated modifications belong to FFusion AI and Source Code Bulgaria Ltd. Ensure adherence to both this license and any conditions set by Stability AI Ltd for referenced models.\n\n\n## 📈 How to Use\n\nThe model can be easily integrated into your projects. Here's a quick guide on how to use the FFXL400 model:\n\n1. **Loading the Model**:\n ```python\n from transformers import AutoModel, AutoTokenizer\n\n tokenizer = AutoTokenizer.from_pretrained(\"FFusion/FFXL400\")\n model = AutoModel.from_pretrained(\"FFusion/FFXL400\")\n ```\n\n2. **Performing Inference**:\n ```python\n input_text = \"Your input here\"\n inputs = tokenizer(input_text, return_tensors='pt')\n with torch.no_grad():\n outputs = model(**inputs)\n ```\n\n## Further Training\n\n You can also use the FFXL400 as a starting point for further training. Simply load it into your training pipeline and proceed as you would with any other model.\n\t\n[Autotrain Advanced](https://github.com/huggingface/autotrain-advanced),\t\n\n[Kohya + Stable Diffusion XL](https://huggingface.co/docs/diffusers/main/en/training/lora#stable-diffusion-xl),\t\n\n\n\n## 📚 Background\n\nThe FFXL400 is built upon the insights and data from the [400GB-LoraXL repository](https://huggingface.co/FFusion/400GB-LoraXL). Each LoRA in that collection was extracted using the Low-Rank Adaptation (LoRA) technique, providing a rich dataset for research and exploration. The FFXL400 is the pinnacle of that research, representing a harmonious blend of the best LoRAs.\n\n\n## Library of Available LoRA Models 📚\n\n![loraXL FFUsion](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/XQlnis5W2-fgDnGZ60EK9.jpeg)\n\nYou can choose any of the models from our repository on Hugging Face or the upcoming repository on CivitAI. Here's a list of available models with `lora_model_id = \"FFusion/400GB-LoraXL\"`:\n\n```\nlora_filename = \n - FFai.0001.4Guofeng4xl_V1125d.lora_Dim64.safetensors\n - FFai.0002.4Guofeng4xl_V1125d.lora_Dim8.safetensors\n - FFai.0003.4Guofeng4xl_V1125d.loraa.safetensors\n - FFai.0004.Ambiencesdxl_A1.lora.safetensors\n - FFai.0005.Ambiencesdxl_A1.lora_8.safetensors\n - FFai.0006.Angrasdxl10_V22.lora.safetensors\n - FFai.0007.Animaginexl_V10.lora.safetensors\n - FFai.0008.Animeartdiffusionxl_Alpha3.lora.safetensors\n - FFai.0009.Astreapixiexlanime_V16.lora.safetensors\n - FFai.0010.Bluepencilxl_V010.lora.safetensors\n - FFai.0011.Bluepencilxl_V021.lora.safetensors\n - FFai.0012.Breakdomainxl_V03d.lora.safetensors\n - FFai.0013.Canvasxl_Bfloat16v002.lora.safetensors\n - FFai.0014.Cherrypickerxl_V20.lora.safetensors\n - FFai.0015.Copaxtimelessxlsdxl1_V44.lora.safetensors\n - FFai.0016.Counterfeitxl-Ffusionai-Alpha-Vae.lora.safetensors\n - FFai.0017.Counterfeitxl_V10.lora.safetensors\n - FFai.0018.Crystalclearxl_Ccxl.lora.safetensors\n - FFai.0019.Deepbluexl_V006.lora.safetensors\n - FFai.0020.Dream-Ffusion-Shaper.lora.safetensors\n - FFai.0021.Dreamshaperxl10_Alpha2xl10.lora.safetensors\n - FFai.0022.Duchaitenaiartsdxl_V10.lora.safetensors\n - FFai.0023.Dynavisionxlallinonestylized_Beta0371bakedvae.lora.safetensors\n - FFai.0024.Dynavisionxlallinonestylized_Beta0411bakedvae.lora.safetensors\n - FFai.0025.Fantasticcharacters_V55.lora.safetensors\n - FFai.0026.Fenrisxl_V55.lora.safetensors\n - FFai.0027.Fudukimix_V10.lora.safetensors\n - FFai.0028.Infinianimexl_V16.lora.safetensors\n - FFai.0029.Juggernautxl_Version1.lora_1.safetensors\n - FFai.0030.Lahmysterioussdxl_V330.lora.safetensors\n - FFai.0031.Mbbxlultimate_V10rc.lora.safetensors\n - FFai.0032.Miamodelsfwnsfwsdxl_V30.lora.safetensors\n - FFai.0033.Morphxl_V10.lora.safetensors\n - FFai.0034.Nightvisionxlphotorealisticportrait_Beta0681bakedvae.lora_1.safetensors\n - FFai.0035.Osorubeshialphaxl_Z.lora.safetensors\n - FFai.0036.Physiogenxl_V04.lora.safetensors\n - FFai.0037.Protovisionxlhighfidelity3d_Beta0520bakedvae.lora.safetensors\n - FFai.0038.Realitycheckxl_Alpha11.lora.safetensors\n - FFai.0039.Realmixxl_V10.lora.safetensors\n - FFai.0040.Reproductionsdxl_V31.lora.safetensors\n - FFai.0041.Rundiffusionxl_Beta.lora.safetensors\n - FFai.0042.Samaritan3dcartoon_V40sdxl.lora.safetensors\n - FFai.0043.Sdvn6realxl_Detailface.lora.safetensors\n - FFai.0044.Sdvn7realartxl_Beta2.lora.safetensors\n - FFai.0045.Sdxl10arienmixxlasian_V10.lora.safetensors\n - FFai.0046.Sdxlbasensfwfaces_Sdxlnsfwfaces03.lora.safetensors\n - FFai.0047.Sdxlfaetastic_V10.lora.safetensors\n - FFai.0048.Sdxlfixedvaefp16remove_Basefxiedvaev2fp16.lora.safetensors\n - FFai.0049.Sdxlnijiv4_Sdxlnijiv4.lora.safetensors\n - FFai.0050.Sdxlronghua_V11.lora.safetensors\n - FFai.0051.Sdxlunstablediffusers_V5unchainedslayer.lora.safetensors\n - FFai.0052.Sdxlyamersanimeultra_Yamersanimev2.lora.safetensors\n - FFai.0053.Shikianimexl_V10.lora.safetensors\n - FFai.0054.Spectrumblendx_V10.lora.safetensors\n - FFai.0055.Stablediffusionxl_V30.lora.safetensors\n - FFai.0056.Talmendoxlsdxl_V11beta.lora.safetensors\n - FFai.0057.Wizard_V10.lora.safetensors\n - FFai.0058.Wyvernmix15xl_Xlv11.lora.safetensors\n - FFai.0059.Xl13asmodeussfwnsfw_V17bakedvae.lora.safetensors\n - FFai.0060.Xl3experimentalsd10xl_V10.lora.safetensors\n - FFai.0061.Xl6hephaistossd10xlsfw_V21bakedvaefp16fix.lora.safetensors\n - FFai.0062.Xlperfectdesign_V2ultimateartwork.lora.safetensors\n - FFai.0063.Xlyamersrealistic_V3.lora.safetensors\n - FFai.0064.Xxmix9realisticsdxl_Testv20.lora.safetensors\n - FFai.0065.Zavychromaxl_B2.lora.safetensors\n\n```\n\n## 🎉 Acknowledgements & Citations\n\nA huge shoutout to the community for their continued support and feedback. Together, we are pushing the boundaries of what's possible with machine learning!\n\nWe would also like to acknowledge and give credit to the following projects and authors:\n\n- **ComfyUI**: We've used and modified portions of [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for our work.\n- **kohya-ss/sd-scripts and bmaltais**: Our work also incorporates modifications from [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts).\n- **lora-inspector**: We've benefited from the [lora-inspector](https://github.com/rockerBOO/lora-inspector) project.\n- **KohakuBlueleaf**: Special mention to KohakuBlueleaf for their invaluable contributions.\n\n\n[![400GB FFusion Lora XL 5](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image5sm.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image5.jpg)\n\n[![400GB FFusion Lora XL 6](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image6sm.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image6.jpg)\n\n[![400GB FFusion Lora XL 7](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image7sm.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image7.jpg)\n\n[![400GB FFusion Lora XL 9](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image9.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/tree/main)\n\n\n### HowMuch ???\n![60% Works](https://img.shields.io/badge/60%25%20of%20the%20Time-It%20Works%20Every%20Time-green)\n\n\n**Have you ever asked yourself, \"How much space have I wasted on `*.ckpt` and `*.safetensors` checkpoints?\"** 🤔\nSay hello to HowMuch: Checking checkpoint wasted space since... well, now! \n\n😄 Enjoy this somewhat unnecessary, yet **\"fun-for-the-whole-family\"** DiskSpaceAnalyzer tool. 😄\n\n## Overview\n\n\n`HowMuch` is a Python tool designed to scan your drives (or a specified directory) and report on the total space used by files with specific extensions, mainly `.ckpt` and `.safetensors`. \n\nIt outputs:\n- The total storage capacity of each scanned drive or directory.\n- The space occupied by `.ckpt` and `.safetensors` files.\n- The free space available.\n- A neat bar chart visualizing the above data.\n\n## Installation\n[GitHub](https://github.com/1e-2/HowMuch)\n\n### From PyPI\n\nYou can easily install `HowMuch` via pip:\n\n```bash\npip install howmuch\n```\n\n### From Source\n\n1. Clone the repository:\n\n ```bash\n git clone https://github.com/1e-2/HowMuch.git\n ```\n\n2. Navigate to the cloned directory and install:\n\n ```bash\n cd HowMuch\n pip install .\n ```\n\n## Usage\n\n\nRun the tool without any arguments to scan all drives:\n\n```bash\nhowmuch\n```\n\nOr, specify a particular directory or drive to scan:\n\n```bash\nhowmuch --scan C:\n```\n\n### 🌐 **Contact Information**\n\nThe **FFusion.ai** project is proudly maintained by **Source Code Bulgaria Ltd** & **Black Swan Technologies**.\n\n📧 Reach us at [di@ffusion.ai](mailto:di@ffusion.ai) for any inquiries or support.\n\n#### 🌌 **Find us on:** \n\n- 🐙 [GitHub](https://github.com/1e-2)\n- 😊 [Hugging Face](https://huggingface.co/FFusion/)\n- 💡 [Civitai](https://civitai.com/user/idle/models)\n\n🔐 **Security powered by** [Comodo.BG](http://Comodo.BG) & [Preasidium.CX](http://Preasidium.CX)\n🚀 Marketing by [Гугъл.com](http://Гугъл.com)\n📩 [![Email](https://img.shields.io/badge/Email-enquiries%40ffusion.ai-blue?style=for-the-badge&logo=gmail)](mailto:enquiries@ffusion.ai)\n🌍 Sofia Istanbul London\n\n\n---\n\nWe hope the FFXL400 serves as a valuable asset in your AI journey. We encourage feedback, contributions, and insights from the community to further refine and enhance this model. Together, let's push the boundaries of what's possible!\n\n\n\n![ffusionai-logo.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/EjDa_uGcOoH2cXM2K-NYn.png)\n"},"embedding":{"kind":"list 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-0.047987110912799835\n]"}}},{"rowIdx":1192,"cells":{"modelId":{"kind":"string","value":"Habana/t5"},"author":{"kind":"string","value":"Habana"},"last_modified":{"kind":"timestamp","value":"2023-09-08T13:39:25Z","string":"2023-09-08T13:39:25Z"},"downloads":{"kind":"number","value":18395,"string":"18,395"},"likes":{"kind":"number","value":0,"string":"0"},"library_name":{"kind":"null"},"tags":{"kind":"list like","value":["optimum_habana","license:apache-2.0","region:us"],"string":"[\n \"optimum_habana\",\n \"license:apache-2.0\",\n \"region:us\"\n]"},"pipeline_tag":{"kind":"null"},"createdAt":{"kind":"timestamp","value":"2022-06-04T15:43:41Z","string":"2022-06-04T15:43:41Z"},"card":{"kind":"string","value":"---\nlicense: apache-2.0\n---\n\n[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).\nIt provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.\nLearn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana).\n\n## T5 model HPU configuration\n\nThis model only contains the `GaudiConfig` file for running the [T5](https://huggingface.co/t5-base) model on Habana's Gaudi processors (HPU).\n\n**This model contains no model weights, only a GaudiConfig.**\n\nThis enables to specify:\n- `use_fused_adam`: whether to use Habana's custom AdamW implementation\n- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator\n\n## Usage\n\nThe model is instantiated the same way as in the Transformers library.\nThe only difference is that there are a few new training arguments specific to HPUs.\n\n[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/summarization/run_summarization.py) is a summarization example script to fine-tune a model. You can run it with T5-small with the following command:\n```bash\npython run_summarization.py \\\n --model_name_or_path t5-small \\\n --do_train \\\n --do_eval \\\n --dataset_name cnn_dailymail \\\n --dataset_config \"3.0.0\" \\\n --source_prefix \"summarize: \" \\\n --output_dir /tmp/tst-summarization \\\n --per_device_train_batch_size 4 \\\n --per_device_eval_batch_size 4 \\\n --overwrite_output_dir \\\n --predict_with_generate \\\n --use_habana \\\n --use_lazy_mode \\\n --gaudi_config_name Habana/t5 \\\n --ignore_pad_token_for_loss False \\\n --pad_to_max_length \\\n --save_strategy epoch \\\n --throughput_warmup_steps 3\n```\n\nCheck the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. \n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-generation"},"createdAt":{"kind":"timestamp","value":"2023-08-01T16:51:46Z","string":"2023-08-01T16:51:46Z"},"card":{"kind":"string","value":"---\ninference: false\nlicense: llama2\n---\n\n# Vicuna Model Card\n\n## Model Details\n\nVicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.\n\n- **Developed by:** [LMSYS](https://lmsys.org/)\n- **Model type:** An auto-regressive language model based on the transformer architecture\n- **License:** Llama 2 Community License Agreement\t\n- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)\n\n### Model Sources\n\n- **Repository:** https://github.com/lm-sys/FastChat\n- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/\n- **Paper:** https://arxiv.org/abs/2306.05685\n- **Demo:** https://chat.lmsys.org/\n\n## Uses\n\nThe primary use of Vicuna is research on large language models and chatbots.\nThe primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.\n\n## How to Get Started with the Model\n\n- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights\n- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api \n\n## Training Details\n\nVicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling.\nThe training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each.\nSee more details in the \"Training Details of Vicuna Models\" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).\n\n## Evaluation\n\n![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)\n\nVicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).\n\n## Difference between different versions of Vicuna\n\nSee [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)"},"embedding":{"kind":"list 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model that lets you clone voices into different languages by using just a quick 6-second audio clip. Built on Tortoise,\nⓍTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy.\nThere is no need for an excessive amount of training data that spans countless hours.\n\nThis is the same model that powers [Coqui Studio](https://coqui.ai/), and [Coqui API](https://docs.coqui.ai/docs), however we apply\na few tricks to make it faster and support streaming inference.\n\n## NOTE: ⓍTTS V2 model is out here [XTTS V2](https://huggingface.co/coqui/XTTS-v2)\n\n### Features\n- Supports 14 languages. \n- Voice cloning with just a 6-second audio clip.\n- Emotion and style transfer by cloning. \n- Cross-language voice cloning.\n- Multi-lingual speech generation.\n- 24khz sampling rate.\n\n### Languages\nAs of now, XTTS-v1 (v1.1) supports 14 languages: **English, Spanish, French, German, Italian, Portuguese,\nPolish, Turkish, Russian, Dutch, Czech, Arabic, Chinese, and Japanese**.\n\nStay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out!\n\n### Code\nThe current implementation supports inference and [fine-tuning](https://tts.readthedocs.io/en/latest/models/xtts.html#training).\n\n### License\nThis model is licensed under [Coqui Public Model License](https://coqui.ai/cpml). There's a lot that goes into a license for generative models, and you can read more of [the origin story of CPML here](https://coqui.ai/blog/tts/cpml).\n\n### Contact\nCome and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai).\nYou can also mail us at info@coqui.ai.\n\nUsing 🐸TTS API:\n\n```python\nfrom TTS.api import TTS\ntts = TTS(\"tts_models/multilingual/multi-dataset/xtts_v1\", gpu=True)\n\n# generate speech by cloning a voice using default settings\ntts.tts_to_file(text=\"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.\",\n file_path=\"output.wav\",\n speaker_wav=\"/path/to/target/speaker.wav\",\n language=\"en\")\n\n# generate speech by cloning a voice using custom settings\ntts.tts_to_file(text=\"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.\",\n file_path=\"output.wav\",\n speaker_wav=\"/path/to/target/speaker.wav\",\n language=\"en\",\n decoder_iterations=30)\n```\n\nUsing 🐸TTS Command line:\n\n```console\n tts --model_name tts_models/multilingual/multi-dataset/xtts_v1 \\\n --text \"Bugün okula gitmek istemiyorum.\" \\\n --speaker_wav /path/to/target/speaker.wav \\\n --language_idx tr \\\n --use_cuda true\n```\n\nUsing model directly:\n\n```python\nfrom TTS.tts.configs.xtts_config import XttsConfig\nfrom TTS.tts.models.xtts import Xtts\n\nconfig = XttsConfig()\nconfig.load_json(\"/path/to/xtts/config.json\")\nmodel = Xtts.init_from_config(config)\nmodel.load_checkpoint(config, checkpoint_dir=\"/path/to/xtts/\", eval=True)\nmodel.cuda()\n\noutputs = model.synthesize(\n \"It took me quite a long time to develop a voice and now that I have it I am not going to be silent.\",\n config,\n speaker_wav=\"/data/TTS-public/_refclips/3.wav\",\n gpt_cond_len=3,\n language=\"en\",\n)\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"sentence-similarity"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\npipeline_tag: sentence-similarity\nlicense: apache-2.0\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n---\n\n# sentence-transformers/paraphrase-distilroberta-base-v1\n\nThis is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\n\n\n## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\n\n```\npip install -U sentence-transformers\n```\n\nThen you can use the model like this:\n\n```python\nfrom sentence_transformers import SentenceTransformer\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\n\nmodel = SentenceTransformer('sentence-transformers/paraphrase-distilroberta-base-v1')\nembeddings = model.encode(sentences)\nprint(embeddings)\n```\n\n\n\n## Usage (HuggingFace Transformers)\nWithout [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.\n\n```python\nfrom transformers import AutoTokenizer, AutoModel\nimport torch\n\n\n#Mean Pooling - Take attention mask into account for correct averaging\ndef mean_pooling(model_output, attention_mask):\n token_embeddings = model_output[0] #First element of model_output contains all token embeddings\n input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n\n\n# Sentences we want sentence embeddings for\nsentences = ['This is an example sentence', 'Each sentence is converted']\n\n# Load model from HuggingFace Hub\ntokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1')\nmodel = AutoModel.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1')\n\n# Tokenize sentences\nencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n\n# Compute token embeddings\nwith torch.no_grad():\n model_output = model(**encoded_input)\n\n# Perform pooling. In this case, max pooling.\nsentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n\nprint(\"Sentence embeddings:\")\nprint(sentence_embeddings)\n```\n\n\n\n## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-distilroberta-base-v1)\n\n\n\n## Full Model Architecture\n```\nSentenceTransformer(\n (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel \n (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n)\n```\n\n## Citing & Authors\n\nThis model was trained by [sentence-transformers](https://www.sbert.net/). \n \nIf you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):\n```bibtex \n@inproceedings{reimers-2019-sentence-bert,\n title = \"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\",\n author = \"Reimers, Nils and Gurevych, Iryna\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing\",\n month = \"11\",\n year = \"2019\",\n publisher = \"Association for Computational Linguistics\",\n url = \"http://arxiv.org/abs/1908.10084\",\n}\n```"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"translation"},"createdAt":{"kind":"timestamp","value":"2022-04-13T15:18:06Z","string":"2022-04-13T15:18:06Z"},"card":{"kind":"string","value":"---\nlanguage:\n- ar\n- en\ntags:\n- translation\n- opus-mt-tc\nlicense: cc-by-4.0\nmodel-index:\n- name: opus-mt-tc-big-ar-en\n results:\n - task:\n name: Translation ara-eng\n type: translation\n args: ara-eng\n dataset:\n name: flores101-devtest\n type: flores_101\n args: ara eng devtest\n metrics:\n - name: BLEU\n type: bleu\n value: 42.6\n - task:\n name: Translation ara-eng\n type: translation\n args: ara-eng\n dataset:\n name: tatoeba-test-v2021-08-07\n type: tatoeba_mt\n args: ara-eng\n metrics:\n - name: BLEU\n type: bleu\n value: 47.3\n - task:\n name: Translation ara-eng\n type: translation\n args: ara-eng\n dataset:\n name: tico19-test\n type: tico19-test\n args: ara-eng\n metrics:\n - name: BLEU\n type: bleu\n value: 44.4\n---\n# opus-mt-tc-big-ar-en\n\nNeural machine translation model for translating from Arabic (ar) to English (en).\n\nThis model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).\n\n* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)\n\n```\n@inproceedings{tiedemann-thottingal-2020-opus,\n title = \"{OPUS}-{MT} {--} Building open translation services for the World\",\n author = {Tiedemann, J{\\\"o}rg and Thottingal, Santhosh},\n booktitle = \"Proceedings of the 22nd Annual Conference of the European Association for Machine Translation\",\n month = nov,\n year = \"2020\",\n address = \"Lisboa, Portugal\",\n publisher = \"European Association for Machine Translation\",\n url = \"https://aclanthology.org/2020.eamt-1.61\",\n pages = \"479--480\",\n}\n\n@inproceedings{tiedemann-2020-tatoeba,\n title = \"The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}\",\n author = {Tiedemann, J{\\\"o}rg},\n booktitle = \"Proceedings of the Fifth Conference on Machine Translation\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.wmt-1.139\",\n pages = \"1174--1182\",\n}\n```\n\n## Model info\n\n* Release: 2022-03-09\n* source language(s): afb ara arz\n* target language(s): eng\n* model: transformer-big\n* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))\n* tokenization: SentencePiece (spm32k,spm32k)\n* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)\n* more information released models: [OPUS-MT ara-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-eng/README.md)\n\n## Usage\n\nA short example code:\n\n```python\nfrom transformers import MarianMTModel, MarianTokenizer\n\nsrc_text = [\n \"اتبع قلبك فحسب.\",\n \"وين راهي دّوش؟\"\n]\n\nmodel_name = \"pytorch-models/opus-mt-tc-big-ar-en\"\ntokenizer = MarianTokenizer.from_pretrained(model_name)\nmodel = MarianMTModel.from_pretrained(model_name)\ntranslated = model.generate(**tokenizer(src_text, return_tensors=\"pt\", padding=True))\n\nfor t in translated:\n print( tokenizer.decode(t, skip_special_tokens=True) )\n\n# expected output:\n# Just follow your heart.\n# Wayne Rahi Dosh?\n```\n\nYou can also use OPUS-MT models with the transformers pipelines, for example:\n\n```python\nfrom transformers import pipeline\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-tc-big-ar-en\")\nprint(pipe(\"اتبع قلبك فحسب.\"))\n\n# expected output: Just follow your heart.\n```\n\n## Benchmarks\n\n* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt)\n* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt)\n* benchmark results: [benchmark_results.txt](benchmark_results.txt)\n* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)\n\n| langpair | testset | chr-F | BLEU | #sent | #words |\n|----------|---------|-------|-------|-------|--------|\n| ara-eng | tatoeba-test-v2021-08-07 | 0.63477 | 47.3 | 10305 | 76975 |\n| ara-eng | flores101-devtest | 0.66987 | 42.6 | 1012 | 24721 |\n| ara-eng | tico19-test | 0.68521 | 44.4 | 2100 | 56323 |\n\n## Acknowledgements\n\nThe work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.\n\n## Model conversion info\n\n* transformers version: 4.16.2\n* OPUS-MT git hash: 3405783\n* port time: Wed Apr 13 18:17:57 EEST 2022\n* port machine: LM0-400-22516.local\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2022-03-02T23:29:05Z","string":"2022-03-02T23:29:05Z"},"card":{"kind":"string","value":"---\nlanguage: \n - en\n\ntags:\n - emotion-analysis\n\n---\n# Emotion Analysis in English\n## bertweet-base-emotion-analysis\n\nRepository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)\n\n\nModel trained with EmoEvent corpus for Emotion detection in English. Base model is [BerTweet](https://huggingface.co/vinai/bertweet-base).\n\n\n## License\n\n`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)\n2. [SEMEval 2017 Dataset license]()\n\n## Citation\n\nIf you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462)\n\n```\n@misc{perez2021pysentimiento,\n title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},\n author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},\n year={2021},\n eprint={2106.09462},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\nand also the dataset related paper\n\n```\n@inproceedings{del2020emoevent,\n title={EmoEvent: A multilingual emotion corpus based on different events},\n author={del Arco, Flor Miriam Plaza and Strapparava, Carlo and Lopez, L Alfonso Urena and Mart{\\'\\i}n-Valdivia, M Teresa},\n booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},\n pages={1492--1498},\n year={2020}\n}\n```\n\nEnjoy! 🤗\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"createdAt":{"kind":"timestamp","value":"2022-06-21T12:40:10Z","string":"2022-06-21T12:40:10Z"},"card":{"kind":"string","value":"---\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- generated_from_trainer\ndatasets:\n- glue\nmetrics:\n- accuracy\nbase_model: bert-base-uncased\nmodel-index:\n- name: bert-base-uncased-rte\n results:\n - task:\n type: text-classification\n name: Text Classification\n dataset:\n name: GLUE RTE\n type: glue\n args: rte\n metrics:\n - type: accuracy\n value: 0.6895306859205776\n name: Accuracy\n - task:\n type: natural-language-inference\n name: Natural Language Inference\n dataset:\n name: glue\n type: glue\n config: rte\n split: validation\n metrics:\n - type: accuracy\n value: 0.6823104693140795\n name: Accuracy\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTc4OTUzYmQ0NWRkMjdmYjI3ZDhmNWU5MTQ1YTM4ZTQzOWM5MTJjYzlmMjM3ZTE2Y2ZhMGJlNTI0OTJhYjNmMCIsInZlcnNpb24iOjF9.siPkmQhZKOZ1k_SyS1xIMavpK_CQ8tHTm39McCIEjiF7G1x62lbuKfrZsLoKoPf9XpNJqXoaXIRPCpHBKlfwCA\n - type: precision\n value: 0.7047619047619048\n name: Precision\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzdmMjk0ZjRkNzM4ZWE1ZGUyOWUxZTFjNmEyNTRmNjZmMDUxOTJlZmUxNjUzMWFhZTYzZTM1ZGNkMDg1YTMzYyIsInZlcnNpb24iOjF9.Cm2kMSTsWVPU9mBv8xAyvo7msTHdG3SECIYZ4kQ5RpN4NV3WE1k0EqmcGzAedwYNfSEg1qXL-qWDKOeoXDAnCw\n - type: recall\n value: 0.5648854961832062\n name: Recall\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjc1NTMzYWI5OGRjODdhYmJjZDQ3ODdiMWE3ODYzZjNhZDg1MGIyNjA1YzQwNzcwYTQzNjJkNGVjNmNjMWJmNSIsInZlcnNpb24iOjF9.MwRAu1AKhCt__2vBjhvEqU0gvXaJ5EMOOotKmwGXsuF3eGJEEDDuiWBgu9y291aqndTTwWvuH9CNQjGKLCoNCw\n - type: auc\n value: 0.7394646031580048\n name: AUC\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGQwMWVhZjlhNjhhZDRhNGYwNWUyOTZjMTgwMDQ5ZWM3MDcxNDc5OGJiNDE5NTNhOTU0MDMwYTdkYjcxNzFiZiIsInZlcnNpb24iOjF9.ZLyE_ZDWyVAr_GL_3lSqBmuIip7C13oj5kT1lI9JQOidt-IXsRHYUmJt_f7HWUNU1-FBD5lzHYcstF6WQ0VrAw\n - type: f1\n value: 0.6271186440677967\n name: F1\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmU0YjI4Y2MyMzU5ZTYzMmE0YzhjOTgwM2UwYTNhZDFmZWE3NTdhODMzYmU1ZWUxYTc2Yjk3Nzg3MjAyOThiOSIsInZlcnNpb24iOjF9.GJhnKNSHN5Sv9W3gl8fgPAAbM5EtMlhOHoOFcj_O65FLtcTi_ANpyv7gi41fPbMjS2TG4fgdVHQ_UZg7M6W2Cw\n - type: loss\n value: 0.7001310586929321\n name: loss\n verified: true\n verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjMwMTY5OGVkMzVhMzdjMmY1YmIzYTViOTRlNzcxMTI4NWNiOTQ5ZjMwMjRlODE4ZWUwMGVhZjhiMmIzM2ViOSIsInZlcnNpb24iOjF9.IsJhfeeqnVZFn10sOkCW7vAzfw1WQMwR8b99B3-hct_lrI1xodt5ySGltDvx2Q8ufD6hzfQ7YaDeHytDmeeFBw\n---\n\n\n\n# bert-base-uncased-rte\n\nThis model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6972\n- Accuracy: 0.6895\n\n## Model description\n\nMore information needed\n\n## Intended uses & limitations\n\nMore information needed\n\n## Training and evaluation data\n\nMore information needed\n\n## Training procedure\n\n### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0\n\n### Training results\n\n| Training Loss | Epoch | Step | Validation Loss | Accuracy |\n|:-------------:|:-----:|:----:|:---------------:|:--------:|\n| No log | 1.0 | 156 | 0.6537 | 0.6318 |\n| No log | 2.0 | 312 | 0.6383 | 0.6534 |\n| No log | 3.0 | 468 | 0.6972 | 0.6895 |\n\n\n### Framework versions\n\n- Transformers 4.20.0.dev0\n- Pytorch 1.11.0+cu113\n- Datasets 2.1.0\n- Tokenizers 0.12.1\n"},"embedding":{"kind":"list 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\"region:us\"\n]"},"pipeline_tag":{"kind":"string","value":"token-classification"},"createdAt":{"kind":"timestamp","value":"2023-04-06T13:41:51Z","string":"2023-04-06T13:41:51Z"},"card":{"kind":"string","value":"This is a demo using the models from:\n\n```\n@inproceedings{zhang-etal-2022-skillspan,\n title = \"{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings\",\n author = \"Zhang, Mike and\n Jensen, Kristian and\n Sonniks, Sif and\n Plank, Barbara\",\n booktitle = \"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = jul,\n year = \"2022\",\n address = \"Seattle, United States\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2022.naacl-main.366\",\n doi = \"10.18653/v1/2022.naacl-main.366\",\n pages = \"4962--4984\",\n abstract = \"Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning.\",\n}\n```\n\nNote that there is another endpoint, namely `jjzha/jobbert_knowledge_extraction`.\nKnowledge can be seen as hard skills and skills are both soft and applied skills."},"embedding":{"kind":"list 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soleimanian/financial-roberta-large-sentiment
soleimanian
2022-10-12T21:04:39Z
21,133
30
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "Sentiment", "RoBERTa", "Financial Statements", "Accounting", "Finance", "Business", "ESG", "CSR Reports", "Financial News", "Earnings Call Transcripts", "Sustainability", "Corporate governance", "eng", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
text-classification
2022-05-16T04:09:10Z
--- license: apache-2.0 language: - eng tags: - text-classification - Sentiment - RoBERTa - Financial Statements - Accounting - Finance - Business - ESG - CSR Reports - Financial News - Earnings Call Transcripts - Sustainability - Corporate governance --- <!DOCTYPE html> <html> <body> <h1><b>Financial-RoBERTa</b></h1> <p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p> <ul style="PADDING-LEFT: 40px"> <li>Financial Statements,</li> <li>Earnings Announcements,</li> <li>Earnings Call Transcripts,</li> <li>Corporate Social Responsibility (CSR) Reports,</li> <li>Environmental, Social, and Governance (ESG) News,</li> <li>Financial News,</li> <li>Etc.</li> </ul> <p>Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.</p> <p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p> <p><b>How to perform sentiment analysis:</b></p> <p>The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:</p> <pre> <code> from transformers import pipeline sentiment_analysis = pipeline("sentiment-analysis",model="soleimanian/financial-roberta-large-sentiment") print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020.")) </code> </pre> <p>I provide an example script via <a href="https://colab.research.google.com/drive/11RGWU3UDtxnjan8Ug6dyX82m9fBV6CGo?usp=sharing" target="_blank">Google Colab</a>. You can load your data to a Google Drive and run the script for free on a Colab. <p><b>Citation and contact:</b></p> <p>Please cite <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115943" target="_blank">this paper</a> when you use the model. Feel free to reach out to [email protected] with any questions or feedback you may have.<p/> </body> </html>
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sshleifer/tiny-mbart
sshleifer
2021-08-26T10:55:11Z
21,003
7
transformers
[ "transformers", "pytorch", "tf", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
Entry not found
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facebook/mbart-large-50-one-to-many-mmt
facebook
2023-03-28T10:00:25Z
20,960
26
transformers
[ "transformers", "pytorch", "tf", "jax", "mbart", "text2text-generation", "mbart-50", "multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "ur", "xh", "gl", "sl", "arxiv:2008.00401", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- language: - multilingual - ar - cs - de - en - es - et - fi - fr - gu - hi - it - ja - kk - ko - lt - lv - my - ne - nl - ro - ru - si - tr - vi - zh - af - az - bn - fa - he - hr - id - ka - km - mk - ml - mn - mr - pl - ps - pt - sv - sw - ta - te - th - tl - uk - ur - xh - gl - sl tags: - mbart-50 --- # mBART-50 one to many multilingual machine translation This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-one-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. The model can translate English to other 49 languages mentioned below. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. ```python from transformers import MBartForConditionalGeneration, MBart50TokenizerFast article_en = "The head of the United Nations says there is no military solution in Syria" model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", src_lang="en_XX") model_inputs = tokenizer(article_en, return_tensors="pt") # translate from English to Hindi generated_tokens = model.generate( **model_inputs, forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => 'संयुक्त राष्ट्र के नेता कहते हैं कि सीरिया में कोई सैन्य समाधान नहीं है' # translate from English to Chinese generated_tokens = model.generate( **model_inputs, forced_bos_token_id=tokenizer.lang_code_to_id["zh_CN"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => '联合国首脑说,叙利亚没有军事解决办法' ``` See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions. ## Languages covered Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI) ## BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, eprint={2008.00401}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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microsoft/graphcodebert-base
microsoft
2022-09-26T17:06:54Z
20,944
25
transformers
[ "transformers", "pytorch", "tf", "jax", "roberta", "fill-mask", "arxiv:2009.08366", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
## GraphCodeBERT model GraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence length for the model is 512. The model is trained on the CodeSearchNet dataset, which includes 2.3M functions with document pairs for six programming languages. More details can be found in the [paper](https://arxiv.org/abs/2009.08366) by Guo et. al. **Disclaimer:** The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face community members.
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kandinsky-community/kandinsky-2-1
kandinsky-community
2023-10-09T11:33:20Z
20,903
32
diffusers
[ "diffusers", "text-to-image", "kandinsky", "license:apache-2.0", "has_space", "diffusers:KandinskyPipeline", "region:us" ]
text-to-image
2023-05-24T09:52:07Z
--- license: apache-2.0 prior: - kandinsky-community/kandinsky-2-1-prior tags: - text-to-image - kandinsky inference: false --- # Kandinsky 2.1 Kandinsky 2.1 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) ## Usage Kandinsky 2.1 is available in diffusers! ```python pip install diffusers transformers accelerate ``` ### Text to image ```python from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0] image.save("cheeseburger_monster.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png) ### Text Guided Image-to-Image Generation ```python from diffusers import AutoPipelineForImage2Image import torch import requests from io import BytesIO from PIL import Image import os pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) original_image = Image.open(BytesIO(response.content)).convert("RGB") original_image.thumbnail((768, 768)) image = pipe(prompt=prompt, image=original_image, strength=0.3).images[0] out.images[0].save("fantasy_land.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png) ### Interpolate ```python from diffusers import KandinskyPriorPipeline, KandinskyPipeline from diffusers.utils import load_image import PIL import torch pipe_prior = KandinskyPriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16 ) pipe_prior.to("cuda") img1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) img2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg" ) # add all the conditions we want to interpolate, can be either text or image images_texts = ["a cat", img1, img2] # specify the weights for each condition in images_texts weights = [0.3, 0.3, 0.4] # We can leave the prompt empty prompt = "" prior_out = pipe_prior.interpolate(images_texts, weights) pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt, **prior_out, height=768, width=768).images[0] image.save("starry_cat.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png) ## Model Architecture ### Overview Kandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. <p float="left"> <img src="https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png"/> </p> Specifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [mCLIP model](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14). The trained image prior model is then used to generate mCLIP image embeddings for input text prompts. Both the input text prompts and its mCLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image. ### Details The image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution). The main Text2Image diffusion model was trained on the basis of 170M text-image pairs from the [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) (an important condition was the presence of images with a resolution of at least 768x768). The use of 170M pairs is due to the fact that we kept the UNet diffusion block from Kandinsky 2.0, which allowed us not to train it from scratch. Further, at the stage of fine-tuning, a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources. ### Evaluation We quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID. FID metric values ​​for generative models on COCO_30k | | FID (30k)| |:------|----:| | eDiff-I (2022) | 6.95 | | Image (2022) | 7.27 | | Kandinsky 2.1 (2023) | 8.21| | Stable Diffusion 2.1 (2022) | 8.59 | | GigaGAN, 512x512 (2023) | 9.09 | | DALL-E 2 (2022) | 10.39 | | GLIDE (2022) | 12.24 | | Kandinsky 1.0 (2022) | 15.40 | | DALL-E (2021) | 17.89 | | Kandinsky 2.0 (2022) | 20.00 | | GLIGEN (2022) | 21.04 | For more information, please refer to the upcoming technical report. ## BibTex If you find this repository useful in your research, please cite: ``` @misc{kandinsky 2.1, title = {kandinsky 2.1}, author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov}, year = {2023}, howpublished = {}, } ```
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timm/eva_large_patch14_196.in22k_ft_in22k_in1k
timm
2023-03-31T06:13:42Z
20,899
0
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "dataset:imagenet-22k", "arxiv:2211.07636", "license:mit", "region:us" ]
image-classification
2022-12-22T07:08:20Z
--- tags: - image-classification - timm library_tag: timm license: mit datasets: - imagenet-1k - imagenet-22k - imagenet-22k --- # Model card for eva_large_patch14_196.in22k_ft_in22k_in1k An EVA image classification model. Pretrained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher) and fine-tuned on ImageNet-22k then on ImageNet-1k by paper authors. NOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 304.1 - GMACs: 61.6 - Activations (M): 63.5 - Image size: 196 x 196 - **Papers:** - EVA: Exploring the Limits of Masked Visual Representation Learning at Scale: https://arxiv.org/abs/2211.07636 - **Pretrain Dataset:** - ImageNet-22k - ImageNet-22k - **Dataset:** ImageNet-1k - **Original:** - https://github.com/baaivision/EVA - https://huggingface.co/BAAI/EVA ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('eva_large_patch14_196.in22k_ft_in22k_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'eva_large_patch14_196.in22k_ft_in22k_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 1024) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |model |top1 |top5 |param_count|img_size| |-----------------------------------------------|------|------|-----------|--------| |eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08 |448 | |eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08 |448 | |eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 | |eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 | |eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 | |eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 | |eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 | |eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 | |eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 | |eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 | |eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 | |eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 | |eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 | |eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 | |eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 | |eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 | ## Citation ```bibtex @article{EVA, title={EVA: Exploring the Limits of Masked Visual Representation Learning at Scale}, author={Fang, Yuxin and Wang, Wen and Xie, Binhui and Sun, Quan and Wu, Ledell and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue}, journal={arXiv preprint arXiv:2211.07636}, year={2022} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
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Universal-NER/UniNER-7B-all
Universal-NER
2023-08-11T21:24:35Z
20,899
53
transformers
[ "transformers", "pytorch", "llama", "text-generation", "en", "arxiv:2308.03279", "license:cc-by-nc-4.0", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-08-11T20:52:49Z
--- license: cc-by-nc-4.0 language: - en --- --- # UniNER-7B-all **Description**: This model is the best UniNER model. It is trained on the combinations of three data splits: (1) ChatGPT-generated [Pile-NER-type data](https://huggingface.co/datasets/Universal-NER/Pile-NER-type), (2) ChatGPT-generated [Pile-NER-definition data](https://huggingface.co/datasets/Universal-NER/Pile-NER-definition), and (3) 40 supervised datasets in the Universal NER benchmark (see Fig. 4 in paper), where we randomly sample up to 10K instances from the train split of each dataset. Note that CrossNER and MIT datasets are excluded from training for OOD evaluation. Check our [paper](https://arxiv.org/abs/2308.03279) for more information. Check our [repo](https://github.com/universal-ner/universal-ner) about how to use the model. ## Inference The template for inference instances is as follows: <div style="background-color: #f6f8fa; padding: 20px; border-radius: 10px; border: 1px solid #e1e4e8; box-shadow: 0 2px 5px rgba(0,0,0,0.1);"> <strong>Prompting template:</strong><br/> A virtual assistant answers questions from a user based on the provided text.<br/> USER: Text: <span style="color: #d73a49;">{Fill the input text here}</span><br/> ASSISTANT: I’ve read this text.<br/> USER: What describes <span style="color: #d73a49;">{Fill the entity type here}</span> in the text?<br/> ASSISTANT: <span style="color: #0366d6;">(model's predictions in JSON format)</span><br/> </div> ### Note: Inferences are based on one entity type at a time. For multiple entity types, create separate instances for each type. ## License This model and its associated data are released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. They are primarily used for research purposes. ## Citation ```bibtex @article{zhou2023universalner, title={UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition}, author={Wenxuan Zhou and Sheng Zhang and Yu Gu and Muhao Chen and Hoifung Poon}, year={2023}, eprint={2308.03279}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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microsoft/DialoGPT-large
microsoft
2023-01-24T16:55:06Z
20,881
223
transformers
[ "transformers", "pytorch", "tf", "jax", "gpt2", "text-generation", "conversational", "arxiv:1911.00536", "license:mit", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
conversational
2022-03-02T23:29:05Z
--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit --- ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT) DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. * Multi-turn generation examples from an interactive environment: |Role | Response | |---------|--------| |User | Does money buy happiness? | | Bot | Depends how much money you spend on it .| |User | What is the best way to buy happiness ? | | Bot | You just have to be a millionaire by your early 20s, then you can be happy . | |User |This is so difficult ! | | Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```
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BAAI/bge-base-en
BAAI
2023-10-12T03:37:19Z
20,850
41
transformers
[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "mteb", "en", "arxiv:2310.07554", "arxiv:2309.07597", "license:mit", "model-index", "endpoints_compatible", "has_space", "region:us" ]
feature-extraction
2023-08-05T08:03:50Z
--- tags: - mteb model-index: - name: bge-base-en results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.73134328358209 - type: ap value: 38.97277232632892 - type: f1 value: 69.81740361139785 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.56522500000001 - type: ap value: 88.88821771869553 - type: f1 value: 92.54817512659696 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.91 - type: f1 value: 46.28536394320311 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.834 - type: map_at_10 value: 53.564 - type: map_at_100 value: 54.230000000000004 - type: map_at_1000 value: 54.235 - type: map_at_3 value: 49.49 - type: map_at_5 value: 51.784 - type: mrr_at_1 value: 39.26 - type: mrr_at_10 value: 53.744 - type: mrr_at_100 value: 54.410000000000004 - type: mrr_at_1000 value: 54.415 - type: mrr_at_3 value: 49.656 - type: mrr_at_5 value: 52.018 - type: ndcg_at_1 value: 38.834 - type: ndcg_at_10 value: 61.487 - type: ndcg_at_100 value: 64.303 - type: ndcg_at_1000 value: 64.408 - type: ndcg_at_3 value: 53.116 - type: ndcg_at_5 value: 57.248 - type: precision_at_1 value: 38.834 - type: precision_at_10 value: 8.663 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.218999999999998 - type: precision_at_5 value: 14.737 - type: recall_at_1 value: 38.834 - type: recall_at_10 value: 86.629 - type: recall_at_100 value: 98.86200000000001 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 63.656 - type: recall_at_5 value: 73.68400000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.88475477433035 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.85053138403176 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.23221013208242 - type: mrr value: 74.64857318735436 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.4403443247284 - type: cos_sim_spearman value: 85.5326718115169 - type: euclidean_pearson value: 86.0114007449595 - type: euclidean_spearman value: 86.05979225604875 - type: manhattan_pearson value: 86.05423806568598 - type: manhattan_spearman value: 86.02485170086835 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.44480519480518 - type: f1 value: 86.41301900941988 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.17547250880036 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.74514172687293 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.096000000000004 - type: map_at_10 value: 43.345 - type: map_at_100 value: 44.73 - type: map_at_1000 value: 44.85 - type: map_at_3 value: 39.956 - type: map_at_5 value: 41.727 - type: mrr_at_1 value: 38.769999999999996 - type: mrr_at_10 value: 48.742000000000004 - type: mrr_at_100 value: 49.474000000000004 - type: mrr_at_1000 value: 49.513 - type: mrr_at_3 value: 46.161 - type: mrr_at_5 value: 47.721000000000004 - type: ndcg_at_1 value: 38.769999999999996 - type: ndcg_at_10 value: 49.464999999999996 - type: ndcg_at_100 value: 54.632000000000005 - type: ndcg_at_1000 value: 56.52 - type: ndcg_at_3 value: 44.687 - type: ndcg_at_5 value: 46.814 - type: precision_at_1 value: 38.769999999999996 - type: precision_at_10 value: 9.471 - type: precision_at_100 value: 1.4909999999999999 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 15.079 - type: recall_at_1 value: 32.096000000000004 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 83.075 - type: recall_at_1000 value: 95.178 - type: recall_at_3 value: 47.009 - type: recall_at_5 value: 53.348 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.588 - type: map_at_10 value: 42.251 - type: map_at_100 value: 43.478 - type: map_at_1000 value: 43.617 - type: map_at_3 value: 39.381 - type: map_at_5 value: 41.141 - type: mrr_at_1 value: 41.21 - type: mrr_at_10 value: 48.765 - type: mrr_at_100 value: 49.403000000000006 - type: mrr_at_1000 value: 49.451 - type: mrr_at_3 value: 46.73 - type: mrr_at_5 value: 47.965999999999994 - type: ndcg_at_1 value: 41.21 - type: ndcg_at_10 value: 47.704 - type: ndcg_at_100 value: 51.916 - type: ndcg_at_1000 value: 54.013999999999996 - type: ndcg_at_3 value: 44.007000000000005 - type: ndcg_at_5 value: 45.936 - type: precision_at_1 value: 41.21 - type: precision_at_10 value: 8.885 - type: precision_at_100 value: 1.409 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 21.274 - type: precision_at_5 value: 15.045 - type: recall_at_1 value: 32.588 - type: recall_at_10 value: 56.333 - type: recall_at_100 value: 74.251 - type: recall_at_1000 value: 87.518 - type: recall_at_3 value: 44.962 - type: recall_at_5 value: 50.609 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.308 - type: map_at_10 value: 53.12 - type: map_at_100 value: 54.123 - type: map_at_1000 value: 54.173 - type: map_at_3 value: 50.017999999999994 - type: map_at_5 value: 51.902 - type: mrr_at_1 value: 46.394999999999996 - type: mrr_at_10 value: 56.531 - type: mrr_at_100 value: 57.19800000000001 - type: mrr_at_1000 value: 57.225 - type: mrr_at_3 value: 54.368 - type: mrr_at_5 value: 55.713 - type: ndcg_at_1 value: 46.394999999999996 - type: ndcg_at_10 value: 58.811 - type: ndcg_at_100 value: 62.834 - type: ndcg_at_1000 value: 63.849999999999994 - type: ndcg_at_3 value: 53.88699999999999 - type: ndcg_at_5 value: 56.477999999999994 - type: precision_at_1 value: 46.394999999999996 - type: precision_at_10 value: 9.398 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 24.221999999999998 - type: precision_at_5 value: 16.539 - type: recall_at_1 value: 40.308 - type: recall_at_10 value: 72.146 - type: recall_at_100 value: 89.60900000000001 - type: recall_at_1000 value: 96.733 - type: recall_at_3 value: 58.91499999999999 - type: recall_at_5 value: 65.34299999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.383000000000003 - type: map_at_10 value: 35.802 - type: map_at_100 value: 36.756 - type: map_at_1000 value: 36.826 - type: map_at_3 value: 32.923 - type: map_at_5 value: 34.577999999999996 - type: mrr_at_1 value: 29.604999999999997 - type: mrr_at_10 value: 37.918 - type: mrr_at_100 value: 38.732 - type: mrr_at_1000 value: 38.786 - type: mrr_at_3 value: 35.198 - type: mrr_at_5 value: 36.808 - type: ndcg_at_1 value: 29.604999999999997 - type: ndcg_at_10 value: 40.836 - type: ndcg_at_100 value: 45.622 - type: ndcg_at_1000 value: 47.427 - type: ndcg_at_3 value: 35.208 - type: ndcg_at_5 value: 38.066 - type: precision_at_1 value: 29.604999999999997 - type: precision_at_10 value: 6.226 - type: precision_at_100 value: 0.9079999999999999 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 14.463000000000001 - type: precision_at_5 value: 10.35 - type: recall_at_1 value: 27.383000000000003 - type: recall_at_10 value: 54.434000000000005 - type: recall_at_100 value: 76.632 - type: recall_at_1000 value: 90.25 - type: recall_at_3 value: 39.275 - type: recall_at_5 value: 46.225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.885 - type: map_at_10 value: 25.724000000000004 - type: map_at_100 value: 26.992 - type: map_at_1000 value: 27.107999999999997 - type: map_at_3 value: 23.04 - type: map_at_5 value: 24.529 - type: mrr_at_1 value: 22.264 - type: mrr_at_10 value: 30.548 - type: mrr_at_100 value: 31.593 - type: mrr_at_1000 value: 31.657999999999998 - type: mrr_at_3 value: 27.756999999999998 - type: mrr_at_5 value: 29.398999999999997 - type: ndcg_at_1 value: 22.264 - type: ndcg_at_10 value: 30.902 - type: ndcg_at_100 value: 36.918 - type: ndcg_at_1000 value: 39.735 - type: ndcg_at_3 value: 25.915 - type: ndcg_at_5 value: 28.255999999999997 - type: precision_at_1 value: 22.264 - type: precision_at_10 value: 5.634 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.055 - type: recall_at_1 value: 17.885 - type: recall_at_10 value: 42.237 - type: recall_at_100 value: 68.489 - type: recall_at_1000 value: 88.721 - type: recall_at_3 value: 28.283 - type: recall_at_5 value: 34.300000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.737000000000002 - type: map_at_10 value: 39.757 - type: map_at_100 value: 40.992 - type: map_at_1000 value: 41.102 - type: map_at_3 value: 36.612 - type: map_at_5 value: 38.413000000000004 - type: mrr_at_1 value: 35.804 - type: mrr_at_10 value: 45.178000000000004 - type: mrr_at_100 value: 45.975 - type: mrr_at_1000 value: 46.021 - type: mrr_at_3 value: 42.541000000000004 - type: mrr_at_5 value: 44.167 - type: ndcg_at_1 value: 35.804 - type: ndcg_at_10 value: 45.608 - type: ndcg_at_100 value: 50.746 - type: ndcg_at_1000 value: 52.839999999999996 - type: ndcg_at_3 value: 40.52 - type: ndcg_at_5 value: 43.051 - type: precision_at_1 value: 35.804 - type: precision_at_10 value: 8.104 - type: precision_at_100 value: 1.256 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 19.121 - type: precision_at_5 value: 13.532 - type: recall_at_1 value: 29.737000000000002 - type: recall_at_10 value: 57.66 - type: recall_at_100 value: 79.121 - type: recall_at_1000 value: 93.023 - type: recall_at_3 value: 43.13 - type: recall_at_5 value: 49.836000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.299 - type: map_at_10 value: 35.617 - type: map_at_100 value: 36.972 - type: map_at_1000 value: 37.096000000000004 - type: map_at_3 value: 32.653999999999996 - type: map_at_5 value: 34.363 - type: mrr_at_1 value: 32.877 - type: mrr_at_10 value: 41.423 - type: mrr_at_100 value: 42.333999999999996 - type: mrr_at_1000 value: 42.398 - type: mrr_at_3 value: 39.193 - type: mrr_at_5 value: 40.426 - type: ndcg_at_1 value: 32.877 - type: ndcg_at_10 value: 41.271 - type: ndcg_at_100 value: 46.843 - type: ndcg_at_1000 value: 49.366 - type: ndcg_at_3 value: 36.735 - type: ndcg_at_5 value: 38.775999999999996 - type: precision_at_1 value: 32.877 - type: precision_at_10 value: 7.580000000000001 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.541999999999998 - type: precision_at_5 value: 12.443 - type: recall_at_1 value: 26.299 - type: recall_at_10 value: 52.256 - type: recall_at_100 value: 75.919 - type: recall_at_1000 value: 93.185 - type: recall_at_3 value: 39.271 - type: recall_at_5 value: 44.901 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.05741666666667 - type: map_at_10 value: 36.086416666666665 - type: map_at_100 value: 37.26916666666667 - type: map_at_1000 value: 37.38191666666666 - type: map_at_3 value: 33.34225 - type: map_at_5 value: 34.86425 - type: mrr_at_1 value: 32.06008333333333 - type: mrr_at_10 value: 40.36658333333333 - type: mrr_at_100 value: 41.206500000000005 - type: mrr_at_1000 value: 41.261083333333325 - type: mrr_at_3 value: 38.01208333333334 - type: mrr_at_5 value: 39.36858333333333 - type: ndcg_at_1 value: 32.06008333333333 - type: ndcg_at_10 value: 41.3535 - type: ndcg_at_100 value: 46.42066666666666 - type: ndcg_at_1000 value: 48.655166666666666 - type: ndcg_at_3 value: 36.78041666666667 - type: ndcg_at_5 value: 38.91783333333334 - type: precision_at_1 value: 32.06008333333333 - type: precision_at_10 value: 7.169833333333332 - type: precision_at_100 value: 1.1395 - type: precision_at_1000 value: 0.15158333333333332 - type: precision_at_3 value: 16.852 - type: precision_at_5 value: 11.8645 - type: recall_at_1 value: 27.05741666666667 - type: recall_at_10 value: 52.64491666666666 - type: recall_at_100 value: 74.99791666666667 - type: recall_at_1000 value: 90.50524999999999 - type: recall_at_3 value: 39.684000000000005 - type: recall_at_5 value: 45.37225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.607999999999997 - type: map_at_10 value: 32.28 - type: map_at_100 value: 33.261 - type: map_at_1000 value: 33.346 - type: map_at_3 value: 30.514999999999997 - type: map_at_5 value: 31.415 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.384 - type: mrr_at_100 value: 36.24 - type: mrr_at_1000 value: 36.299 - type: mrr_at_3 value: 33.717000000000006 - type: mrr_at_5 value: 34.507 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 36.248000000000005 - type: ndcg_at_100 value: 41.034 - type: ndcg_at_1000 value: 43.35 - type: ndcg_at_3 value: 32.987 - type: ndcg_at_5 value: 34.333999999999996 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.506 - type: precision_at_100 value: 0.853 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.11 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 25.607999999999997 - type: recall_at_10 value: 45.344 - type: recall_at_100 value: 67.132 - type: recall_at_1000 value: 84.676 - type: recall_at_3 value: 36.02 - type: recall_at_5 value: 39.613 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.44 - type: map_at_10 value: 25.651000000000003 - type: map_at_100 value: 26.735 - type: map_at_1000 value: 26.86 - type: map_at_3 value: 23.409 - type: map_at_5 value: 24.604 - type: mrr_at_1 value: 22.195 - type: mrr_at_10 value: 29.482000000000003 - type: mrr_at_100 value: 30.395 - type: mrr_at_1000 value: 30.471999999999998 - type: mrr_at_3 value: 27.409 - type: mrr_at_5 value: 28.553 - type: ndcg_at_1 value: 22.195 - type: ndcg_at_10 value: 30.242 - type: ndcg_at_100 value: 35.397 - type: ndcg_at_1000 value: 38.287 - type: ndcg_at_3 value: 26.201 - type: ndcg_at_5 value: 28.008 - type: precision_at_1 value: 22.195 - type: precision_at_10 value: 5.372 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 12.228 - type: precision_at_5 value: 8.727 - type: recall_at_1 value: 18.44 - type: recall_at_10 value: 40.325 - type: recall_at_100 value: 63.504000000000005 - type: recall_at_1000 value: 83.909 - type: recall_at_3 value: 28.925 - type: recall_at_5 value: 33.641 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.535999999999998 - type: map_at_10 value: 35.358000000000004 - type: map_at_100 value: 36.498999999999995 - type: map_at_1000 value: 36.597 - type: map_at_3 value: 32.598 - type: map_at_5 value: 34.185 - type: mrr_at_1 value: 31.25 - type: mrr_at_10 value: 39.593 - type: mrr_at_100 value: 40.443 - type: mrr_at_1000 value: 40.498 - type: mrr_at_3 value: 37.018 - type: mrr_at_5 value: 38.492 - type: ndcg_at_1 value: 31.25 - type: ndcg_at_10 value: 40.71 - type: ndcg_at_100 value: 46.079 - type: ndcg_at_1000 value: 48.287 - type: ndcg_at_3 value: 35.667 - type: ndcg_at_5 value: 38.080000000000005 - type: precision_at_1 value: 31.25 - type: precision_at_10 value: 6.847 - type: precision_at_100 value: 1.079 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 16.262 - type: precision_at_5 value: 11.455 - type: recall_at_1 value: 26.535999999999998 - type: recall_at_10 value: 52.92099999999999 - type: recall_at_100 value: 76.669 - type: recall_at_1000 value: 92.096 - type: recall_at_3 value: 38.956 - type: recall_at_5 value: 45.239000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.691 - type: map_at_10 value: 33.417 - type: map_at_100 value: 35.036 - type: map_at_1000 value: 35.251 - type: map_at_3 value: 30.646 - type: map_at_5 value: 32.177 - type: mrr_at_1 value: 30.04 - type: mrr_at_10 value: 37.905 - type: mrr_at_100 value: 38.929 - type: mrr_at_1000 value: 38.983000000000004 - type: mrr_at_3 value: 35.276999999999994 - type: mrr_at_5 value: 36.897000000000006 - type: ndcg_at_1 value: 30.04 - type: ndcg_at_10 value: 39.037 - type: ndcg_at_100 value: 44.944 - type: ndcg_at_1000 value: 47.644 - type: ndcg_at_3 value: 34.833999999999996 - type: ndcg_at_5 value: 36.83 - type: precision_at_1 value: 30.04 - type: precision_at_10 value: 7.4510000000000005 - type: precision_at_100 value: 1.492 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 11.897 - type: recall_at_1 value: 24.691 - type: recall_at_10 value: 49.303999999999995 - type: recall_at_100 value: 76.20400000000001 - type: recall_at_1000 value: 93.30000000000001 - type: recall_at_3 value: 36.594 - type: recall_at_5 value: 42.41 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.118 - type: map_at_10 value: 30.714999999999996 - type: map_at_100 value: 31.656000000000002 - type: map_at_1000 value: 31.757 - type: map_at_3 value: 28.355000000000004 - type: map_at_5 value: 29.337000000000003 - type: mrr_at_1 value: 25.323 - type: mrr_at_10 value: 32.93 - type: mrr_at_100 value: 33.762 - type: mrr_at_1000 value: 33.829 - type: mrr_at_3 value: 30.775999999999996 - type: mrr_at_5 value: 31.774 - type: ndcg_at_1 value: 25.323 - type: ndcg_at_10 value: 35.408 - type: ndcg_at_100 value: 40.083 - type: ndcg_at_1000 value: 42.542 - type: ndcg_at_3 value: 30.717 - type: ndcg_at_5 value: 32.385000000000005 - type: precision_at_1 value: 25.323 - type: precision_at_10 value: 5.564 - type: precision_at_100 value: 0.843 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 13.001 - type: precision_at_5 value: 8.834999999999999 - type: recall_at_1 value: 23.118 - type: recall_at_10 value: 47.788000000000004 - type: recall_at_100 value: 69.37 - type: recall_at_1000 value: 87.47399999999999 - type: recall_at_3 value: 34.868 - type: recall_at_5 value: 39.001999999999995 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 14.288 - type: map_at_10 value: 23.256 - type: map_at_100 value: 25.115 - type: map_at_1000 value: 25.319000000000003 - type: map_at_3 value: 20.005 - type: map_at_5 value: 21.529999999999998 - type: mrr_at_1 value: 31.401 - type: mrr_at_10 value: 42.251 - type: mrr_at_100 value: 43.236999999999995 - type: mrr_at_1000 value: 43.272 - type: mrr_at_3 value: 39.164 - type: mrr_at_5 value: 40.881 - type: ndcg_at_1 value: 31.401 - type: ndcg_at_10 value: 31.615 - type: ndcg_at_100 value: 38.982 - type: ndcg_at_1000 value: 42.496 - type: ndcg_at_3 value: 26.608999999999998 - type: ndcg_at_5 value: 28.048000000000002 - type: precision_at_1 value: 31.401 - type: precision_at_10 value: 9.536999999999999 - type: precision_at_100 value: 1.763 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 19.153000000000002 - type: precision_at_5 value: 14.228 - type: recall_at_1 value: 14.288 - type: recall_at_10 value: 36.717 - type: recall_at_100 value: 61.9 - type: recall_at_1000 value: 81.676 - type: recall_at_3 value: 24.203 - type: recall_at_5 value: 28.793999999999997 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.019 - type: map_at_10 value: 19.963 - type: map_at_100 value: 28.834 - type: map_at_1000 value: 30.537999999999997 - type: map_at_3 value: 14.45 - type: map_at_5 value: 16.817999999999998 - type: mrr_at_1 value: 65.75 - type: mrr_at_10 value: 74.646 - type: mrr_at_100 value: 74.946 - type: mrr_at_1000 value: 74.95100000000001 - type: mrr_at_3 value: 72.625 - type: mrr_at_5 value: 74.012 - type: ndcg_at_1 value: 54 - type: ndcg_at_10 value: 42.014 - type: ndcg_at_100 value: 47.527 - type: ndcg_at_1000 value: 54.911 - type: ndcg_at_3 value: 46.586 - type: ndcg_at_5 value: 43.836999999999996 - type: precision_at_1 value: 65.75 - type: precision_at_10 value: 33.475 - type: precision_at_100 value: 11.16 - type: precision_at_1000 value: 2.145 - type: precision_at_3 value: 50.083 - type: precision_at_5 value: 42.55 - type: recall_at_1 value: 9.019 - type: recall_at_10 value: 25.558999999999997 - type: recall_at_100 value: 53.937999999999995 - type: recall_at_1000 value: 77.67399999999999 - type: recall_at_3 value: 15.456 - type: recall_at_5 value: 19.259 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.635 - type: f1 value: 47.692783881403926 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 76.893 - type: map_at_10 value: 84.897 - type: map_at_100 value: 85.122 - type: map_at_1000 value: 85.135 - type: map_at_3 value: 83.88 - type: map_at_5 value: 84.565 - type: mrr_at_1 value: 83.003 - type: mrr_at_10 value: 89.506 - type: mrr_at_100 value: 89.574 - type: mrr_at_1000 value: 89.575 - type: mrr_at_3 value: 88.991 - type: mrr_at_5 value: 89.349 - type: ndcg_at_1 value: 83.003 - type: ndcg_at_10 value: 88.351 - type: ndcg_at_100 value: 89.128 - type: ndcg_at_1000 value: 89.34100000000001 - type: ndcg_at_3 value: 86.92 - type: ndcg_at_5 value: 87.78200000000001 - type: precision_at_1 value: 83.003 - type: precision_at_10 value: 10.517999999999999 - type: precision_at_100 value: 1.115 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 33.062999999999995 - type: precision_at_5 value: 20.498 - type: recall_at_1 value: 76.893 - type: recall_at_10 value: 94.374 - type: recall_at_100 value: 97.409 - type: recall_at_1000 value: 98.687 - type: recall_at_3 value: 90.513 - type: recall_at_5 value: 92.709 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.829 - type: map_at_10 value: 32.86 - type: map_at_100 value: 34.838 - type: map_at_1000 value: 35.006 - type: map_at_3 value: 28.597 - type: map_at_5 value: 31.056 - type: mrr_at_1 value: 41.358 - type: mrr_at_10 value: 49.542 - type: mrr_at_100 value: 50.29900000000001 - type: mrr_at_1000 value: 50.334999999999994 - type: mrr_at_3 value: 46.579 - type: mrr_at_5 value: 48.408 - type: ndcg_at_1 value: 41.358 - type: ndcg_at_10 value: 40.758 - type: ndcg_at_100 value: 47.799 - type: ndcg_at_1000 value: 50.589 - type: ndcg_at_3 value: 36.695 - type: ndcg_at_5 value: 38.193 - type: precision_at_1 value: 41.358 - type: precision_at_10 value: 11.142000000000001 - type: precision_at_100 value: 1.8350000000000002 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 24.023 - type: precision_at_5 value: 17.963 - type: recall_at_1 value: 20.829 - type: recall_at_10 value: 47.467999999999996 - type: recall_at_100 value: 73.593 - type: recall_at_1000 value: 90.122 - type: recall_at_3 value: 32.74 - type: recall_at_5 value: 39.608 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.324 - type: map_at_10 value: 64.183 - type: map_at_100 value: 65.037 - type: map_at_1000 value: 65.094 - type: map_at_3 value: 60.663 - type: map_at_5 value: 62.951 - type: mrr_at_1 value: 80.648 - type: mrr_at_10 value: 86.005 - type: mrr_at_100 value: 86.157 - type: mrr_at_1000 value: 86.162 - type: mrr_at_3 value: 85.116 - type: mrr_at_5 value: 85.703 - type: ndcg_at_1 value: 80.648 - type: ndcg_at_10 value: 72.351 - type: ndcg_at_100 value: 75.279 - type: ndcg_at_1000 value: 76.357 - type: ndcg_at_3 value: 67.484 - type: ndcg_at_5 value: 70.31500000000001 - type: precision_at_1 value: 80.648 - type: precision_at_10 value: 15.103 - type: precision_at_100 value: 1.7399999999999998 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 43.232 - type: precision_at_5 value: 28.165000000000003 - type: recall_at_1 value: 40.324 - type: recall_at_10 value: 75.517 - type: recall_at_100 value: 86.982 - type: recall_at_1000 value: 94.072 - type: recall_at_3 value: 64.848 - type: recall_at_5 value: 70.41199999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.4 - type: ap value: 87.4422032289312 - type: f1 value: 91.39249564302281 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.03 - type: map_at_10 value: 34.402 - type: map_at_100 value: 35.599 - type: map_at_1000 value: 35.648 - type: map_at_3 value: 30.603 - type: map_at_5 value: 32.889 - type: mrr_at_1 value: 22.679 - type: mrr_at_10 value: 35.021 - type: mrr_at_100 value: 36.162 - type: mrr_at_1000 value: 36.205 - type: mrr_at_3 value: 31.319999999999997 - type: mrr_at_5 value: 33.562 - type: ndcg_at_1 value: 22.692999999999998 - type: ndcg_at_10 value: 41.258 - type: ndcg_at_100 value: 46.967 - type: ndcg_at_1000 value: 48.175000000000004 - type: ndcg_at_3 value: 33.611000000000004 - type: ndcg_at_5 value: 37.675 - type: precision_at_1 value: 22.692999999999998 - type: precision_at_10 value: 6.5089999999999995 - type: precision_at_100 value: 0.936 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.413 - type: precision_at_5 value: 10.702 - type: recall_at_1 value: 22.03 - type: recall_at_10 value: 62.248000000000005 - type: recall_at_100 value: 88.524 - type: recall_at_1000 value: 97.714 - type: recall_at_3 value: 41.617 - type: recall_at_5 value: 51.359 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.36844505243957 - type: f1 value: 94.12408743818202 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.43410852713177 - type: f1 value: 58.501855709435624 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.04909213180902 - type: f1 value: 74.1800860395823 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.76126429051781 - type: f1 value: 79.85705217473232 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.70119520292863 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.33544316467486 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.75499243990726 - type: mrr value: 31.70602251821063 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.451999999999999 - type: map_at_10 value: 13.918 - type: map_at_100 value: 17.316000000000003 - type: map_at_1000 value: 18.747 - type: map_at_3 value: 10.471 - type: map_at_5 value: 12.104 - type: mrr_at_1 value: 46.749 - type: mrr_at_10 value: 55.717000000000006 - type: mrr_at_100 value: 56.249 - type: mrr_at_1000 value: 56.288000000000004 - type: mrr_at_3 value: 53.818 - type: mrr_at_5 value: 55.103 - type: ndcg_at_1 value: 45.201 - type: ndcg_at_10 value: 35.539 - type: ndcg_at_100 value: 32.586 - type: ndcg_at_1000 value: 41.486000000000004 - type: ndcg_at_3 value: 41.174 - type: ndcg_at_5 value: 38.939 - type: precision_at_1 value: 46.749 - type: precision_at_10 value: 25.944 - type: precision_at_100 value: 8.084 - type: precision_at_1000 value: 2.076 - type: precision_at_3 value: 38.7 - type: precision_at_5 value: 33.56 - type: recall_at_1 value: 6.451999999999999 - type: recall_at_10 value: 17.302 - type: recall_at_100 value: 32.14 - type: recall_at_1000 value: 64.12 - type: recall_at_3 value: 11.219 - type: recall_at_5 value: 13.993 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 32.037 - type: map_at_10 value: 46.565 - type: map_at_100 value: 47.606 - type: map_at_1000 value: 47.636 - type: map_at_3 value: 42.459 - type: map_at_5 value: 44.762 - type: mrr_at_1 value: 36.181999999999995 - type: mrr_at_10 value: 49.291000000000004 - type: mrr_at_100 value: 50.059 - type: mrr_at_1000 value: 50.078 - type: mrr_at_3 value: 45.829 - type: mrr_at_5 value: 47.797 - type: ndcg_at_1 value: 36.153 - type: ndcg_at_10 value: 53.983000000000004 - type: ndcg_at_100 value: 58.347 - type: ndcg_at_1000 value: 59.058 - type: ndcg_at_3 value: 46.198 - type: ndcg_at_5 value: 50.022 - type: precision_at_1 value: 36.153 - type: precision_at_10 value: 8.763 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 20.751 - type: precision_at_5 value: 14.646999999999998 - type: recall_at_1 value: 32.037 - type: recall_at_10 value: 74.008 - type: recall_at_100 value: 92.893 - type: recall_at_1000 value: 98.16 - type: recall_at_3 value: 53.705999999999996 - type: recall_at_5 value: 62.495 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.152 - type: map_at_10 value: 85.104 - type: map_at_100 value: 85.745 - type: map_at_1000 value: 85.761 - type: map_at_3 value: 82.175 - type: map_at_5 value: 84.066 - type: mrr_at_1 value: 82.03 - type: mrr_at_10 value: 88.115 - type: mrr_at_100 value: 88.21 - type: mrr_at_1000 value: 88.211 - type: mrr_at_3 value: 87.19200000000001 - type: mrr_at_5 value: 87.85 - type: ndcg_at_1 value: 82.03 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.96300000000001 - type: ndcg_at_1000 value: 90.056 - type: ndcg_at_3 value: 86.051 - type: ndcg_at_5 value: 87.63499999999999 - type: precision_at_1 value: 82.03 - type: precision_at_10 value: 13.450000000000001 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.627 - type: precision_at_5 value: 24.784 - type: recall_at_1 value: 71.152 - type: recall_at_10 value: 95.649 - type: recall_at_100 value: 99.58200000000001 - type: recall_at_1000 value: 99.981 - type: recall_at_3 value: 87.767 - type: recall_at_5 value: 92.233 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.48713646277477 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.394940772438545 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.043 - type: map_at_10 value: 12.949 - type: map_at_100 value: 15.146 - type: map_at_1000 value: 15.495000000000001 - type: map_at_3 value: 9.333 - type: map_at_5 value: 11.312999999999999 - type: mrr_at_1 value: 24.9 - type: mrr_at_10 value: 35.958 - type: mrr_at_100 value: 37.152 - type: mrr_at_1000 value: 37.201 - type: mrr_at_3 value: 32.667 - type: mrr_at_5 value: 34.567 - type: ndcg_at_1 value: 24.9 - type: ndcg_at_10 value: 21.298000000000002 - type: ndcg_at_100 value: 29.849999999999998 - type: ndcg_at_1000 value: 35.506 - type: ndcg_at_3 value: 20.548 - type: ndcg_at_5 value: 18.064 - type: precision_at_1 value: 24.9 - type: precision_at_10 value: 10.9 - type: precision_at_100 value: 2.331 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 19.267 - type: precision_at_5 value: 15.939999999999998 - type: recall_at_1 value: 5.043 - type: recall_at_10 value: 22.092 - type: recall_at_100 value: 47.323 - type: recall_at_1000 value: 74.553 - type: recall_at_3 value: 11.728 - type: recall_at_5 value: 16.188 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.7007085938325 - type: cos_sim_spearman value: 80.0171084446234 - type: euclidean_pearson value: 81.28133218355893 - type: euclidean_spearman value: 79.99291731740131 - type: manhattan_pearson value: 81.22926922327846 - type: manhattan_spearman value: 79.94444878127038 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.7411883252923 - type: cos_sim_spearman value: 77.93462937801245 - type: euclidean_pearson value: 83.00858563882404 - type: euclidean_spearman value: 77.82717362433257 - type: manhattan_pearson value: 82.92887645790769 - type: manhattan_spearman value: 77.78807488222115 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.04222459361023 - type: cos_sim_spearman value: 83.85931509330395 - type: euclidean_pearson value: 83.26916063876055 - type: euclidean_spearman value: 83.98621985648353 - type: manhattan_pearson value: 83.14935679184327 - type: manhattan_spearman value: 83.87938828586304 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.41136639535318 - type: cos_sim_spearman value: 81.51200091040481 - type: euclidean_pearson value: 81.45382456114775 - type: euclidean_spearman value: 81.46201181707931 - type: manhattan_pearson value: 81.37243088439584 - type: manhattan_spearman value: 81.39828421893426 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.71942451732227 - type: cos_sim_spearman value: 87.33044482064973 - type: euclidean_pearson value: 86.58580899365178 - type: euclidean_spearman value: 87.09206723832895 - type: manhattan_pearson value: 86.47460784157013 - type: manhattan_spearman value: 86.98367656583076 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.55868078863449 - type: cos_sim_spearman value: 85.38299230074065 - type: euclidean_pearson value: 84.64715256244595 - type: euclidean_spearman value: 85.49112229604047 - type: manhattan_pearson value: 84.60814346792462 - type: manhattan_spearman value: 85.44886026766822 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.99292526370614 - type: cos_sim_spearman value: 85.58139465695983 - type: euclidean_pearson value: 86.51325066734084 - type: euclidean_spearman value: 85.56736418284562 - type: manhattan_pearson value: 86.48190836601357 - type: manhattan_spearman value: 85.51616256224258 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.54124715078807 - type: cos_sim_spearman value: 65.32134275948374 - type: euclidean_pearson value: 67.09791698300816 - type: euclidean_spearman value: 65.79468982468465 - type: manhattan_pearson value: 67.13304723693966 - type: manhattan_spearman value: 65.68439995849283 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 83.4231099581624 - type: cos_sim_spearman value: 85.95475815226862 - type: euclidean_pearson value: 85.00339401999706 - type: euclidean_spearman value: 85.74133081802971 - type: manhattan_pearson value: 85.00407987181666 - type: manhattan_spearman value: 85.77509596397363 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.25666719585716 - type: mrr value: 96.32769917083642 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.828 - type: map_at_10 value: 68.369 - type: map_at_100 value: 68.83399999999999 - type: map_at_1000 value: 68.856 - type: map_at_3 value: 65.38000000000001 - type: map_at_5 value: 67.06299999999999 - type: mrr_at_1 value: 61 - type: mrr_at_10 value: 69.45400000000001 - type: mrr_at_100 value: 69.785 - type: mrr_at_1000 value: 69.807 - type: mrr_at_3 value: 67 - type: mrr_at_5 value: 68.43299999999999 - type: ndcg_at_1 value: 61 - type: ndcg_at_10 value: 73.258 - type: ndcg_at_100 value: 75.173 - type: ndcg_at_1000 value: 75.696 - type: ndcg_at_3 value: 68.162 - type: ndcg_at_5 value: 70.53399999999999 - type: precision_at_1 value: 61 - type: precision_at_10 value: 9.8 - type: precision_at_100 value: 1.087 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27 - type: precision_at_5 value: 17.666999999999998 - type: recall_at_1 value: 57.828 - type: recall_at_10 value: 87.122 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 73.139 - type: recall_at_5 value: 79.361 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85247524752475 - type: cos_sim_ap value: 96.25640197639723 - type: cos_sim_f1 value: 92.37851662404091 - type: cos_sim_precision value: 94.55497382198953 - type: cos_sim_recall value: 90.3 - type: dot_accuracy value: 99.76138613861386 - type: dot_ap value: 93.40295864389073 - type: dot_f1 value: 87.64267990074441 - type: dot_precision value: 86.99507389162562 - type: dot_recall value: 88.3 - type: euclidean_accuracy value: 99.85049504950496 - type: euclidean_ap value: 96.24254350525462 - type: euclidean_f1 value: 92.32323232323232 - type: euclidean_precision value: 93.26530612244898 - type: euclidean_recall value: 91.4 - type: manhattan_accuracy value: 99.85346534653465 - type: manhattan_ap value: 96.2635334753325 - type: manhattan_f1 value: 92.37899073120495 - type: manhattan_precision value: 95.22292993630573 - type: manhattan_recall value: 89.7 - type: max_accuracy value: 99.85346534653465 - type: max_ap value: 96.2635334753325 - type: max_f1 value: 92.37899073120495 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.83905786483794 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.031896152126436 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 54.551326709447146 - type: mrr value: 55.43758222986165 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.305688567308874 - type: cos_sim_spearman value: 29.27135743434515 - type: dot_pearson value: 30.336741878796563 - type: dot_spearman value: 30.513365725895937 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.245 - type: map_at_10 value: 1.92 - type: map_at_100 value: 10.519 - type: map_at_1000 value: 23.874000000000002 - type: map_at_3 value: 0.629 - type: map_at_5 value: 1.0290000000000001 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 93.5 - type: mrr_at_100 value: 93.5 - type: mrr_at_1000 value: 93.5 - type: mrr_at_3 value: 93 - type: mrr_at_5 value: 93.5 - type: ndcg_at_1 value: 84 - type: ndcg_at_10 value: 76.447 - type: ndcg_at_100 value: 56.516 - type: ndcg_at_1000 value: 48.583999999999996 - type: ndcg_at_3 value: 78.877 - type: ndcg_at_5 value: 79.174 - type: precision_at_1 value: 88 - type: precision_at_10 value: 80.60000000000001 - type: precision_at_100 value: 57.64 - type: precision_at_1000 value: 21.227999999999998 - type: precision_at_3 value: 82 - type: precision_at_5 value: 83.6 - type: recall_at_1 value: 0.245 - type: recall_at_10 value: 2.128 - type: recall_at_100 value: 13.767 - type: recall_at_1000 value: 44.958 - type: recall_at_3 value: 0.654 - type: recall_at_5 value: 1.111 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.5170000000000003 - type: map_at_10 value: 10.915 - type: map_at_100 value: 17.535 - type: map_at_1000 value: 19.042 - type: map_at_3 value: 5.689 - type: map_at_5 value: 7.837 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 49.547999999999995 - type: mrr_at_100 value: 50.653000000000006 - type: mrr_at_1000 value: 50.653000000000006 - type: mrr_at_3 value: 44.558 - type: mrr_at_5 value: 48.333 - type: ndcg_at_1 value: 32.653 - type: ndcg_at_10 value: 26.543 - type: ndcg_at_100 value: 38.946 - type: ndcg_at_1000 value: 49.406 - type: ndcg_at_3 value: 29.903000000000002 - type: ndcg_at_5 value: 29.231 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 23.265 - type: precision_at_100 value: 8.102 - type: precision_at_1000 value: 1.5 - type: precision_at_3 value: 31.293 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.5170000000000003 - type: recall_at_10 value: 16.88 - type: recall_at_100 value: 49.381 - type: recall_at_1000 value: 81.23899999999999 - type: recall_at_3 value: 6.965000000000001 - type: recall_at_5 value: 10.847999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.5942 - type: ap value: 13.92074156956546 - type: f1 value: 54.671999698839066 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.39728353140916 - type: f1 value: 59.68980496759517 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.11181870104935 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.46957143708649 - type: cos_sim_ap value: 76.16120197845457 - type: cos_sim_f1 value: 69.69919295671315 - type: cos_sim_precision value: 64.94986326344576 - type: cos_sim_recall value: 75.19788918205805 - type: dot_accuracy value: 83.0780234845324 - type: dot_ap value: 64.21717343541934 - type: dot_f1 value: 59.48375497624245 - type: dot_precision value: 57.94345759319489 - type: dot_recall value: 61.108179419525065 - type: euclidean_accuracy value: 86.6543482148179 - type: euclidean_ap value: 76.4527555010203 - type: euclidean_f1 value: 70.10156056477584 - type: euclidean_precision value: 66.05975723622782 - type: euclidean_recall value: 74.67018469656992 - type: manhattan_accuracy value: 86.66030875603504 - type: manhattan_ap value: 76.40304567255436 - type: manhattan_f1 value: 70.05275426328058 - type: manhattan_precision value: 65.4666360926393 - type: manhattan_recall value: 75.32981530343008 - type: max_accuracy value: 86.66030875603504 - type: max_ap value: 76.4527555010203 - type: max_f1 value: 70.10156056477584 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.42123646524624 - type: cos_sim_ap value: 85.15431437761646 - type: cos_sim_f1 value: 76.98069301530742 - type: cos_sim_precision value: 72.9314502239063 - type: cos_sim_recall value: 81.50600554357868 - type: dot_accuracy value: 86.70974502270346 - type: dot_ap value: 80.77621563599457 - type: dot_f1 value: 73.87058697285117 - type: dot_precision value: 68.98256396552877 - type: dot_recall value: 79.50415768401602 - type: euclidean_accuracy value: 88.46392672798541 - type: euclidean_ap value: 85.20370297495491 - type: euclidean_f1 value: 77.01372369624886 - type: euclidean_precision value: 73.39052800446397 - type: euclidean_recall value: 81.01324299353249 - type: manhattan_accuracy value: 88.43481973066325 - type: manhattan_ap value: 85.16318289864545 - type: manhattan_f1 value: 76.90884877182597 - type: manhattan_precision value: 74.01737396753062 - type: manhattan_recall value: 80.03541730828458 - type: max_accuracy value: 88.46392672798541 - type: max_ap value: 85.20370297495491 - type: max_f1 value: 77.01372369624886 license: mit language: - en --- **Recommend switching to newest [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5), which has more reasonable similarity distribution and same method of usage.** <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. ************* 🌟**Updates**🌟 ************* - 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire: - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章:` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章:` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章:", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章:" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章:" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章:" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
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aneuraz/awesome-align-with-co
aneuraz
2022-04-29T16:16:12Z
20,774
1
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "sentence alignment", "de", "fr", "en", "ro", "zh", "arxiv:2101.08231", "license:bsd-3-clause", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-04-29T14:55:54Z
--- language: - de - fr - en - ro - zh thumbnail: tags: - sentence alignment license: bsd-3-clause --- # AWESOME: Aligning Word Embedding Spaces of Multilingual Encoders This model comes from the following GitHub repository: [https://github.com/neulab/awesome-align](https://github.com/neulab/awesome-align) It corresponds to this paper: [https://arxiv.org/abs/2101.08231](https://arxiv.org/abs/2101.08231) Please cite the original paper if you decide to use the model: ``` @inproceedings{dou2021word, title={Word Alignment by Fine-tuning Embeddings on Parallel Corpora}, author={Dou, Zi-Yi and Neubig, Graham}, booktitle={Conference of the European Chapter of the Association for Computational Linguistics (EACL)}, year={2021} } ``` `awesome-align` is a tool that can extract word alignments from multilingual BERT (mBERT) [Demo](https://colab.research.google.com/drive/1205ubqebM0OsZa1nRgbGJBtitgHqIVv6?usp=sharing) and allows you to fine-tune mBERT on parallel corpora for better alignment quality (see our paper for more details). ## Usage (copied from this [DEMO](https://colab.research.google.com/drive/1205ubqebM0OsZa1nRgbGJBtitgHqIVv6?usp=sharing) ) ```python from transformers import AutoModel, AutoTokenizer import itertools import torch # load model model = AutoModel.from_pretrained("aneuraz/awesome-align-with-co") tokenizer = AutoTokenizer.from_pretrained("aneuraz/awesome-align-with-co") # model parameters align_layer = 8 threshold = 1e-3 # define inputs src = 'awesome-align is awesome !' tgt = '牛对齐 是 牛 !' # pre-processing sent_src, sent_tgt = src.strip().split(), tgt.strip().split() token_src, token_tgt = [tokenizer.tokenize(word) for word in sent_src], [tokenizer.tokenize(word) for word in sent_tgt] wid_src, wid_tgt = [tokenizer.convert_tokens_to_ids(x) for x in token_src], [tokenizer.convert_tokens_to_ids(x) for x in token_tgt] ids_src, ids_tgt = tokenizer.prepare_for_model(list(itertools.chain(*wid_src)), return_tensors='pt', model_max_length=tokenizer.model_max_length, truncation=True)['input_ids'], tokenizer.prepare_for_model(list(itertools.chain(*wid_tgt)), return_tensors='pt', truncation=True, model_max_length=tokenizer.model_max_length)['input_ids'] sub2word_map_src = [] for i, word_list in enumerate(token_src): sub2word_map_src += [i for x in word_list] sub2word_map_tgt = [] for i, word_list in enumerate(token_tgt): sub2word_map_tgt += [i for x in word_list] # alignment align_layer = 8 threshold = 1e-3 model.eval() with torch.no_grad(): out_src = model(ids_src.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1] out_tgt = model(ids_tgt.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1] dot_prod = torch.matmul(out_src, out_tgt.transpose(-1, -2)) softmax_srctgt = torch.nn.Softmax(dim=-1)(dot_prod) softmax_tgtsrc = torch.nn.Softmax(dim=-2)(dot_prod) softmax_inter = (softmax_srctgt > threshold)*(softmax_tgtsrc > threshold) align_subwords = torch.nonzero(softmax_inter, as_tuple=False) align_words = set() for i, j in align_subwords: align_words.add( (sub2word_map_src[i], sub2word_map_tgt[j]) ) print(align_words) ```
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uw-madison/nystromformer-512
uw-madison
2022-01-11T14:13:39Z
20,752
1
transformers
[ "transformers", "pytorch", "nystromformer", "fill-mask", "arxiv:2102.03902", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
# Nyströmformer Nyströmformer model for masked language modeling (MLM) pretrained on BookCorpus and English Wikipedia for sequence length 512. ## About Nyströmformer The Nyströmformer model was proposed in [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, and Vikas Singh. The abstract from the paper is the following: Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of other tokens on each specific token. While beneficial, the quadratic complexity of self-attention on the input sequence length has limited its application to longer sequences — a topic being actively studied in the community. To address this limitation, we propose Nyströmformer — a model that exhibits favorable scalability as a function of sequence length. Our idea is based on adapting the Nyström method to approximate standard self-attention with O(n) complexity. The scalability of Nyströmformer enables application to longer sequences with thousands of tokens. We perform evaluations on multiple downstream tasks on the GLUE benchmark and IMDB reviews with standard sequence length, and find that our Nyströmformer performs comparably, or in a few cases, even slightly better, than standard self-attention. On longer sequence tasks in the Long Range Arena (LRA) benchmark, Nyströmformer performs favorably relative to other efficient self-attention methods. Our code is available at this https URL. ## Usage ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='uw-madison/nystromformer-512') >>> unmasker("Paris is the [MASK] of France.") [{'score': 0.829957902431488, 'token': 1030, 'token_str': 'capital', 'sequence': 'paris is the capital of france.'}, {'score': 0.022157637402415276, 'token': 16081, 'token_str': 'birthplace', 'sequence': 'paris is the birthplace of france.'}, {'score': 0.01904447190463543, 'token': 197, 'token_str': 'name', 'sequence': 'paris is the name of france.'}, {'score': 0.017583081498742104, 'token': 1107, 'token_str': 'kingdom', 'sequence': 'paris is the kingdom of france.'}, {'score': 0.005948934704065323, 'token': 148, 'token_str': 'city', 'sequence': 'paris is the city of france.'}] ```
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laion/CLIP-ViT-g-14-laion2B-s12B-b42K
laion
2023-01-24T01:01:19Z
20,710
27
open_clip
[ "open_clip", "pytorch", "clip", "arxiv:1910.04867", "license:mit", "has_space", "region:us" ]
null
2022-09-14T22:53:40Z
--- license: mit widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog --- # Model Card for CLIP ViT-g/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT-g/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/slow-g-14--VmlldzoyNTMwMjg5). # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail ## Results The model achieves a 76.6 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model. # Citation **BibTeX:** In addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite: OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` # How to Get Started with the Model Use the code below to get started with the model. ** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets
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sentence-transformers/sentence-t5-large
sentence-transformers
2022-02-09T14:01:09Z
20,692
14
sentence-transformers
[ "sentence-transformers", "pytorch", "t5", "feature-extraction", "sentence-similarity", "transformers", "en", "arxiv:2108.08877", "license:apache-2.0", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity language: en license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # sentence-transformers/sentence-t5-large This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks. This model was converted from the Tensorflow model [st5-large-1](https://tfhub.dev/google/sentence-t5/st5-large/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-large model. The weights are stored in FP16. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/sentence-t5-large') embeddings = model.encode(sentences) print(embeddings) ``` The model requires sentence-transformers version 2.2.0 or newer. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-large) ## Citing & Authors If you find this model helpful, please cite the respective publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)
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Minej/bert-base-personality
Minej
2023-07-13T13:11:50Z
20,597
5
transformers
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "arxiv:1810.04805", "license:mit", "endpoints_compatible", "region:us" ]
text-classification
2023-06-06T19:17:08Z
--- license: mit language: - en library_name: transformers pipeline_tag: text-classification --- ## How to Get Started with the Model To use the model through Hosted inference API, follow the code snippet provided below: ```python from transformers import BertTokenizer, BertForSequenceClassification def personality_detection(text): tokenizer = BertTokenizer.from_pretrained("Minej/bert-base-personality") model = BertForSequenceClassification.from_pretrained("Minej/bert-base-personality") inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt") outputs = model(**inputs) predictions = outputs.logits.squeeze().detach().numpy() label_names = ['Extroversion', 'Neuroticism', 'Agreeableness', 'Conscientiousness', 'Openness'] result = {label_names[i]: predictions[i] for i in range(len(label_names))} return result ``` #### Result Format The personality_detection function returns a dictionary containing the predicted personality traits based on the given input text. The dictionary contains the following personality traits with their corresponding predicted values: Extroversion: A value between 0 and 1 representing the predicted extroversion trait. Neuroticism: A value between 0 and 1 representing the predicted neuroticism trait. Agreeableness: A value between 0 and 1 representing the predicted agreeableness trait. Conscientiousness: A value between 0 and 1 representing the predicted conscientiousness trait. Openness: A value between 0 and 1 representing the predicted openness trait. ```python text_input = "I am feeling excited about the upcoming event." personality_prediction = personality_detection(text_input) print(personality_prediction) ``` ###### Output: ```python { "Extroversion": 0.535, "Neuroticism": 0.576, "Agreeableness": 0.399, "Conscientiousness": 0.253, "Openness": 0.563 } ``` Note: The values in the example output are just placeholders and may not reflect the actual predictions. You can modify the example code and the result format to match your specific use case and desired output format. ### Model Description Transfer Learning for Big Five Personality Prediction In machine learning, training accurate models can be challenging when labeled data is limited. Transfer learning offers a solution by leveraging pre-existing labeled data from a similar task or domain. By transferring knowledge learned from one task to another, we can overcome data scarcity and train more effective models. In this project, we used transfer learning with the BERT BASE UNCASED model to predict Big Five personality traits. The model was fine-tuned on a curated dataset for personality traits, learning patterns between input text and personality characteristics. By applying transfer learning, we improved the accuracy of personality trait predictions. By leveraging transfer learning and fine-tuning BERT BASE UNCASED, we accurately predict an individual's Big Five personality traits based on their input text. This approach addresses the challenges of limited labeled data in personality prediction, providing insights into individuals' personalities. This project showcases the power of transfer learning in machine learning and highlights the effectiveness of BERT BASE UNCASED for predicting Big Five personality traits. - **Model type:** BERT BASE UNCASED - **Language(s) (NLP):** English - **License:** MIT - **Finetuned from model [optional]:** https://huggingface.co/bert-base-uncased ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use The personality prediction model can be used directly by individuals who are interested in gaining insights into their own personality traits based on their input text. Users can input text and receive predictions for the Big Five personality traits. ### Downstream Use This model is not intended for downstream use or fine-tuning for specific tasks. It is designed as a standalone personality prediction model. ### Out-of-Scope Use This model is not suitable for uses beyond personality prediction. It should not be used for making critical decisions or judgments about individuals in areas such as employment, education, or legal matters. ## Bias, Risks, and Limitations The personality prediction model, like any machine learning model, has certain limitations and potential biases that should be taken into account: Limited Context: The model makes predictions based on input text alone and may not capture the full context of an individual's personality. It is important to consider that personality traits are influenced by various factors beyond textual expression. Generalization: The model predicts personality traits based on patterns learned from a specific dataset. Its performance may vary when applied to individuals from different demographic or cultural backgrounds not well represented in the training data. Ethical Considerations: Personality prediction models should be used responsibly, with an understanding that personality traits do not determine a person's worth or abilities. It is important to avoid making unfair judgments or discriminating against individuals based on predicted personality traits. Privacy Concerns: The model relies on user-provided input text, which may contain sensitive or personal information. Users should exercise caution when sharing personal details and ensure the security of their data. False Positives/Negatives: The model's predictions may not always align perfectly with an individual's actual personality traits. It is possible for the model to generate false positives (predicting a trait that is not present) or false negatives (missing a trait that is present). ### Recommendations To mitigate risks and limitations associated with personality prediction models, the following recommendations are suggested: Awareness and Education: Users should be informed about the limitations and potential biases of the model. Promote understanding that personality traits are complex and cannot be fully captured by a single model or text analysis. Avoid Stereotyping and Discrimination: Users should be cautious about making judgments or decisions solely based on predicted personality traits. Personality predictions should not be used to discriminate against individuals or perpetuate stereotypes. Interpret with Context: Interpret the model's predictions in the appropriate context and consider additional information about an individual beyond their input text. Data Privacy and Security: Ensure that user data is handled securely and with respect to privacy regulations. Users should be aware of the information they provide and exercise caution when sharing personal details. Promote Ethical Use: Encourage responsible use of personality prediction models and discourage misuse or harmful applications. It is important to note that the above recommendations are general guidelines, and further context-specific recommendations should be developed based on the particular use case and ethical considerations. ## How to Download the Model If you would like to download the model files and use them instead of the Hosted inference API, then you can follow the code snippet provided below: ```python from transformers import BertForSequenceClassification, BertTokenizer import torch # Initialization of the model values model = BertForSequenceClassification.from_pretrained(".", num_labels=5) tokenizer = BertTokenizer.from_pretrained('.', do_lower_case=True) model.config.label2id = { "Extroversion": 0, "Neuroticism": 1, "Agreeableness": 2, "Conscientiousness": 3, "Openness": 4, } model.config.id2label = { "0": "Extroversion", "1": "Neuroticism", "2": "Agreeableness", "3": "Conscientiousness", "4": "Openness", } def personality_detection(model_input: str) -> dict: ''' Performs personality prediction on the given input text Args: model_input (str): The text conversation Returns: dict: A dictionary where keys are speaker labels and values are their personality predictions ''' if len(model_input) == 0: ret = { "Extroversion": float(0), "Neuroticism": float(0), "Agreeableness": float(0), "Conscientiousness": float(0), "Openness": float(0), } return ret else: dict_custom = {} preprocess_part1 = model_input[:len(model_input)] dict1 = tokenizer.encode_plus(preprocess_part1, max_length=1024, padding=True, truncation=True) dict_custom['input_ids'] = [dict1['input_ids'], dict1['input_ids']] dict_custom['token_type_ids'] = [dict1['token_type_ids'], dict1['token_type_ids']] dict_custom['attention_mask'] = [dict1['attention_mask'], dict1['attention_mask']] outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask'])) b_logit_pred = outs[0] pred_label = torch.sigmoid(b_logit_pred) ret = { "Extroversion": float(pred_label[0][0]), "Neuroticism": float(pred_label[0][1]), "Agreeableness": float(pred_label[0][2]), "Conscientiousness": float(pred_label[0][3]), "Openness": float(pred_label[0][4]), } return ret personality_prediction = personality_detection(text_input) ``` Make sure you have the required dependencies installed (transformers and torch). This code snippet initializes the model, tokenizer, and configuration. It then defines the personality_detection function, which takes a text conversation as input and returns a dictionary with personality predictions for each speaker. You can call the personality_detection function with your input text to obtain the personality predictions. The personality_prediction variable will hold the resulting dictionary. Please note that this code assumes you have already downloaded the necessary model files (config.json, pytorch_model.bin, special_tokens_map.json, tokenizer_config.json, vocab.txt ) and placed them in the current directory (indicated by "."). Adjust the paths and filenames accordingly if needed. ## Citation @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ## More Information TBA
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DeepPavlov/rubert-base-cased-sentence
DeepPavlov
2021-05-18T18:18:43Z
20,589
16
transformers
[ "transformers", "pytorch", "jax", "bert", "feature-extraction", "ru", "arxiv:1508.05326", "arxiv:1809.05053", "arxiv:1908.10084", "endpoints_compatible", "has_space", "region:us" ]
feature-extraction
2022-03-02T23:29:04Z
--- language: - ru --- # rubert-base-cased-sentence Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\]. \[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326) \[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053) \[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
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facebook/wav2vec2-conformer-rel-pos-large
facebook
2022-12-07T14:01:31Z
20,576
7
transformers
[ "transformers", "pytorch", "wav2vec2-conformer", "pretraining", "speech", "en", "dataset:librispeech_asr", "arxiv:2010.05171", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2022-04-17T15:54:03Z
--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # Wav2Vec2-Conformer-Large with Relative Position Embeddings Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. **Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) **Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171). The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model.
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cardiffnlp/twitter-roberta-base-hate-latest
cardiffnlp
2023-08-02T00:30:47Z
20,551
11
transformers
[ "transformers", "pytorch", "tf", "roberta", "text-classification", "en", "endpoints_compatible", "region:us" ]
text-classification
2023-03-30T05:47:39Z
--- model-index: - name: twitter-roberta-base-hate-latest results: [] pipeline_tag: text-classification language: - en --- # cardiffnlp/twitter-roberta-base-hate-latest This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model. More details in the [reference paper](https://aclanthology.org/2023.woah-1.25/). | **Dataset** | **Accuracy** | **Macro-F1** | **Weighted-F1** | |:----------|-----------:|-----------:|--------------:| | hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5831 | 0.5646 | 0.548 | | ucberkeley-dlab/measuring-hate-speech | 0.9273 | 0.9193 | 0.928 | | Detecting East Asian Prejudice on Social Media | 0.9231 | 0.6623 | 0.9428 | | Call me sexist, but | 0.9686 | 0.9203 | 0.9696 | | Predicting the Type and Target of Offensive Posts in Social Media | 0.9164 | 0.6847 | 0.9098 | | HateXplain | 0.8653 | 0.845 | 0.8662 | | Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.7801 | 0.7446 | 0.7614 | | Multilingual and Multi-Aspect Hate Speech Analysis | 0.9944 | 0.4986 | 0.9972 | | Hate speech and offensive content identification in indo-european languages | 0.8779 | 0.6904 | 0.8706 | | Are You a Racist or Am I Seeing Things? | 0.921 | 0.8935 | 0.9216 | | Automated Hate Speech Detection | 0.9423 | 0.9249 | 0.9429 | | Hate Towards the Political Opponent | 0.8783 | 0.6595 | 0.8788 | | Hateful Symbols or Hateful People? | 0.8187 | 0.7833 | 0.8323 | | **Overall** | **0.8766** | **0.7531** | **0.8745** | ### Usage Install tweetnlp via pip. ```shell pip install tweetnlp ``` Load the model in python. ```python import tweetnlp model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest") model.predict('I love everybody :)') >> {'label': 'NOT-HATE'} ``` ### Reference paper - Model based on: ``` @inproceedings{antypas-camacho-collados-2023-robust, title = "Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation", author = "Antypas, Dimosthenis and Camacho-Collados, Jose", booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.woah-1.25", pages = "231--242" } ```
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facebook/convnextv2-tiny-22k-384
facebook
2023-09-26T17:19:37Z
20,473
1
transformers
[ "transformers", "pytorch", "tf", "convnextv2", "image-classification", "vision", "dataset:imagenet-22k", "arxiv:2301.00808", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-02-19T07:24:50Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-22k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # ConvNeXt V2 (tiny-sized model) ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 384x384. It was introduced in the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Woo et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt-V2). Disclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various recognition benchmarks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnextv2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, ConvNextV2ForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-22k-384") model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-22k-384") inputs = preprocessor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]), ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnextv2). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2301-00808, author = {Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In So Kweon and Saining Xie}, title = {ConvNeXt {V2:} Co-designing and Scaling ConvNets with Masked Autoencoders}, journal = {CoRR}, volume = {abs/2301.00808}, year = {2023}, url = {https://doi.org/10.48550/arXiv.2301.00808}, doi = {10.48550/arXiv.2301.00808}, eprinttype = {arXiv}, eprint = {2301.00808}, timestamp = {Tue, 10 Jan 2023 15:10:12 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2301-00808.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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microsoft/swin-tiny-patch4-window7-224
microsoft
2023-09-15T19:59:37Z
20,451
23
transformers
[ "transformers", "pytorch", "tf", "safetensors", "swin", "image-classification", "vision", "dataset:imagenet-1k", "arxiv:2103.14030", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # Swin Transformer (tiny-sized model) Swin Transformer model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-14030, author = {Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo}, title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, journal = {CoRR}, volume = {abs/2103.14030}, year = {2021}, url = {https://arxiv.org/abs/2103.14030}, eprinttype = {arXiv}, eprint = {2103.14030}, timestamp = {Thu, 08 Apr 2021 07:53:26 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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HuggingFaceH4/tiny-random-LlamaForCausalLM
HuggingFaceH4
2023-04-19T21:39:04Z
20,441
1
transformers
[ "transformers", "pytorch", "llama", "text-generation", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-04-17T07:42:23Z
--- duplicated_from: HuggingFaceM4/tiny-random-LlamaForCausalLM ---
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uer/sbert-base-chinese-nli
uer
2023-10-17T15:29:59Z
20,379
81
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "zh", "arxiv:1909.05658", "arxiv:2212.06385", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- language: zh pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 widget: - source_sentence: "那个人很开心" sentences: - "那个人非常开心" - "那只猫很开心" - "那个人在吃东西" --- # Chinese Sentence BERT ## Model description This is the sentence embedding model pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the model could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. ## How to use You can use this model to extract sentence embeddings for sentence similarity task. We use cosine distance to calculate the embedding similarity here: ```python >>> from sentence_transformers import SentenceTransformer >>> model = SentenceTransformer('uer/sbert-base-chinese-nli') >>> sentences = ['那个人很开心', '那个人非常开心'] >>> sentence_embeddings = model.encode(sentences) >>> from sklearn.metrics.pairwise import paired_cosine_distances >>> cosine_score = 1 - paired_cosine_distances([sentence_embeddings[0]],[sentence_embeddings[1]]) ``` ## Training data [ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) is used as training data. ## Training procedure The model is fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune five epochs with a sequence length of 128 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved. ``` python3 finetune/run_classifier_siamese.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ --vocab_path models/google_zh_vocab.txt \ --config_path models/sbert/base_config.json \ --train_path datasets/ChineseTextualInference/train.tsv \ --dev_path datasets/ChineseTextualInference/dev.tsv \ --learning_rate 5e-5 --epochs_num 5 --batch_size 64 ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_sbert_from_uer_to_huggingface.py --input_model_path models/finetuned_model.bin \ --output_model_path pytorch_model.bin \ --layers_num 12 ``` ### BibTeX entry and citation info ``` @article{reimers2019sentence, title={Sentence-bert: Sentence embeddings using siamese bert-networks}, author={Reimers, Nils and Gurevych, Iryna}, journal={arXiv preprint arXiv:1908.10084}, year={2019} } @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } @article{zhao2023tencentpretrain, title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, journal={ACL 2023}, pages={217}, year={2023} ```
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circulus/canvers-ko2en-v1
circulus
2023-05-22T09:59:34Z
20,260
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-05-17T01:56:49Z
--- license: gpl-3.0 ---
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Helsinki-NLP/opus-mt-ca-en
Helsinki-NLP
2023-08-16T11:26:39Z
20,170
0
transformers
[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "ca", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
translation
2022-03-02T23:29:04Z
--- tags: - translation license: apache-2.0 --- ### opus-mt-ca-en * source languages: ca * target languages: en * OPUS readme: [ca-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ca-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ca-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.ca.en | 51.4 | 0.678 |
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KoboldAI/LLaMA2-13B-Tiefighter
KoboldAI
2023-10-19T16:55:50Z
20,142
33
transformers
[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-10-18T18:22:59Z
--- license: llama2 --- # LLaMA2-13B-Tiefighter Tiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge. To achieve this the following recipe was used: * We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merged, contrary to the name this model does not have a strong NSFW bias. * Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model. * After applying this lora we merged the new model with PocketDoc/Dans-RetroRodeo-13b at 5% to weaken the newly introduced adventure bias. * The resulting merge was used as a new basemodel to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%. This means this model contains the following ingredients from their upstream models for as far as we can track them: - Undi95/Xwin-MLewd-13B-V0.2 - - Undi95/ReMM-S-Light - Undi95/CreativeEngine - Brouz/Slerpeno - - elinas/chronos-13b-v2 - jondurbin/airoboros-l2-13b-2.1 - NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 - CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 - - KoboldAI/LLAMA2-13B-Holodeck-1 - NousResearch/Nous-Hermes-13b - OpenAssistant/llama2-13b-orca-8k-3319 - ehartford/WizardLM-1.0-Uncensored-Llama2-13b - Henk717/spring-dragon - The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible) - - SuperCOT (Undisclosed version) - elinas/chronos-13b-v2 (Version assumed) - NousResearch/Nous-Hermes-Llama2-13b - stabilityai/StableBeluga-13B (Version assumed) - zattio770/120-Days-of-LORA-v2-13B - PygmalionAI/pygmalion-2-13b - Undi95/Storytelling-v1-13B-lora - TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter - nRuaif/Kimiko-v2-13B - The-Face-Of-Goonery/Huginn-13b-FP16 - - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp" - lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT - Xwin-LM/Xwin-LM-13B-V0.2 - PocketDoc/Dans-RetroRodeo-13b - Blackroot/Llama-2-13B-Storywriter-LORA While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible! Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible. # Usage This model is meant to be creative, If you let it improvise you get better results than if you drown it in details. ## Story Writing Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story. ### Generate a story on demand To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong. ## Chatbots and persona's This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further. For example, you can put this in memory in regular chat mode: ``` ### Instruction: Generate a conversation between Alice and Henk where they discuss language models. In this conversation Henk is excited to teach Alice about Tiefigther. ### Response: ``` Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format. ## Instruct Prompting This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work. During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias. Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up. ## Adventuring and Adventure Games This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode). It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns. ## Discovered something cool and want to engage with us? Join our community at https://koboldai.org/discord !
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ckpt/anything-v4.5-vae-swapped
ckpt
2023-01-27T17:58:43Z
20,123
28
diffusers
[ "diffusers", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
null
2023-01-22T01:47:34Z
Entry not found
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google/pegasus-large
google
2023-01-24T16:42:31Z
20,116
74
transformers
[ "transformers", "pytorch", "tf", "jax", "pegasus", "text2text-generation", "summarization", "en", "arxiv:1912.08777", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
summarization
2022-03-02T23:29:05Z
--- language: en tags: - summarization --- ### Pegasus Models See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html) Original TF 1 code [here](https://github.com/google-research/pegasus) Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 Maintained by: [@sshleifer](https://twitter.com/sam_shleifer) Task: Summarization The following is copied from the authors' README. # Mixed & Stochastic Checkpoints We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. | dataset | C4 | HugeNews | Mixed & Stochastic| | ---- | ---- | ---- | ----| | xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64| | cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30| | newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18| | multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95| | gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76| | wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *| | reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94| | big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *| | arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67| | pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25| | aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51| | billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59| The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS. The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper): trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). the model uniformly sample a gap sentence ratio between 15% and 45%. importance sentences are sampled using a 20% uniform noise to importance scores. the sentencepiece tokenizer is updated to be able to encode newline character. Citation ``` @misc{zhang2019pegasus, title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization}, author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu}, year={2019}, eprint={1912.08777}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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Helsinki-NLP/opus-mt-cs-en
Helsinki-NLP
2023-08-16T11:27:09Z
20,092
0
transformers
[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "cs", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
translation
2022-03-02T23:29:04Z
--- tags: - translation license: apache-2.0 --- ### opus-mt-cs-en * source languages: cs * target languages: en * OPUS readme: [cs-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cs-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/cs-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/cs-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/cs-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2014-csen.cs.en | 34.1 | 0.612 | | newstest2015-encs.cs.en | 30.4 | 0.565 | | newstest2016-encs.cs.en | 31.8 | 0.584 | | newstest2017-encs.cs.en | 28.7 | 0.556 | | newstest2018-encs.cs.en | 30.3 | 0.566 | | Tatoeba.cs.en | 58.0 | 0.721 |
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bergum/xtremedistil-l6-h384-go-emotion
bergum
2023-03-21T11:55:16Z
20,091
6
transformers
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "dataset:go_emotions", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- license: apache-2.0 datasets: - go_emotions metrics: - accuracy model-index: - name: xtremedistil-emotion results: - task: name: Multi Label Text Classification type: multi_label_classification dataset: name: go_emotions type: emotion args: default metrics: - name: Accuracy type: accuracy value: NaN --- # xtremedistil-l6-h384-go-emotion This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the [go_emotions dataset](https://huggingface.co/datasets/go_emotions). See notebook for how the model was trained and converted to ONNX format [![Training Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jobergum/emotion/blob/main/TrainGoEmotions.ipynb) This model is deployed to [aiserv.cloud](https://aiserv.cloud/) for live demo of the model. See [https://github.com/jobergum/browser-ml-inference](https://github.com/jobergum/browser-ml-inference) for how to reproduce. ### Training hyperparameters - batch size 128 - learning_rate=3e-05 - epocs 4 <pre> Num examples = 211225 Num Epochs = 4 Instantaneous batch size per device = 128 Total train batch size (w. parallel, distributed & accumulation) = 128 Gradient Accumulation steps = 1 Total optimization steps = 6604 [6604/6604 53:23, Epoch 4/4] Step Training Loss 500 0.263200 1000 0.156900 1500 0.152500 2000 0.145400 2500 0.140500 3000 0.135900 3500 0.132800 4000 0.129400 4500 0.127200 5000 0.125700 5500 0.124400 6000 0.124100 6500 0.123400 </pre>
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bhadresh-savani/roberta-base-emotion
bhadresh-savani
2023-03-22T08:48:07Z
20,081
1
transformers
[ "transformers", "pytorch", "tf", "jax", "safetensors", "roberta", "text-classification", "emotion", "en", "dataset:emotion", "arxiv:1907.11692", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- language: - en license: apache-2.0 tags: - text-classification - emotion - pytorch datasets: - emotion metrics: - Accuracy, F1 Score thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 model-index: - name: bhadresh-savani/roberta-base-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.931 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjg5OTI4ZTlkY2VmZjYzNGEzZGQ3ZjczYzY5YjJmMGVmZDQ4ZWNiYTAyZTJiZjlmMTU2MjE1NTllMWFhYzU0MiIsInZlcnNpb24iOjF9.dc44cEsbu900M2s64GyVIWKPagBzwI-dPlfvh0NGyJFMGKOcypke9P2ary9fBZITrH3UF6lza3sCh7vWYZFHBQ - type: precision value: 0.9168321948556312 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2EzYTcxNTExNGU1MmFiZjE3NGE5MDIyMDU2M2U3OGExOTdjZDE5YWU2NDhmOTJlYWMzY2NkN2U5MmRmZTE0MiIsInZlcnNpb24iOjF9.4U7vJ3ALdUUxySMhVeb4Qa1tSp3wphSIZkRYNMujz-KrOZW8kkcmCde3ioStBg3Qqyf1powYd88uk1R7DuWRBA - type: precision value: 0.931 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjhmZGRlYWE5ZTAzMmJiMzlmMWZiM2VlYjdiNzI0NjVmN2M2YzcxM2EzYTg0OTFiZTE1MjVmNzE5NGEzYTg2ZCIsInZlcnNpb24iOjF9.8eCHAK0rlZWnhBNQdh9kcuAeItmDUAgK3KkZ7eC-GyYhi4HT5dZiS6btcC5EjkYVOS4czcjzqxfVz4PuZgtLDQ - type: precision value: 0.9357445689014415 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhZTdkNzYzMjhjZjc4MTAxNWZiYjgzMjhhNjRiZWRmYjc5YTA0NTQ1MzllMTYxMTVkMDk4OTE0ZGEyMTNhMiIsInZlcnNpb24iOjF9.YIZfj2Eo1nMX2GVSfqJy-Cp7VBubfUh2LuOnU60sG5Lci8FdlNbAanS1IzAyxU3U29lqiTasxfS_yrwAj5cmBQ - type: recall value: 0.8743657671177089 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y2YTcyNzMwYzZiMmM1Yzc4YWZhNDM3ZDQyMjI1NWZhMjQyNmU5NTA0YmE2ZDBiZmY1MmUyZWRlMjRhMjFmYSIsInZlcnNpb24iOjF9.XKlFy_Cx4T4l7Otd8aAwWcI-fJ_dJ6V1Kp3uZm6OWjwCb1Do6mSdPFfwiMeBZZyfEIsNBnguegssZvHsOfTSAQ - type: recall value: 0.931 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzgzN2JkNzAzZDRjNjJmZjNkY2RmYzVkMWEzYTMzZDU4NzJlYzBmOWE4MTU0MGU0MTJhM2JjZDdjODhlZDExOCIsInZlcnNpb24iOjF9.9tSVB4yNBdFXpH3equwo1ZaEnVUktO6lm93UEJ-luKhxo6wgS54OLjgDq7IpJYwa3lvYyjy-sxzQEe9ri31WAg - type: recall value: 0.931 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGVhZTIyMmVmOTU1YWNjMmZiZjNmOTNlNzlhZTk3NjhlZmMwZGFkZWQxZTlhZWUwZGQyN2JhOWQyNWQ3MTVhOCIsInZlcnNpb24iOjF9.2odv2fK7zH0_S_7wC3obONzjxOipDdjWvddhnGdMnrIN6CiZwLp7XgizpqcWbwAQ_9YJwjC-6wXpbq2jTvN0Bw - type: f1 value: 0.8821236522209227 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDI0YTUxOTA2M2ZjNGM1OTJlZDAzZTAxNTg4YjY3OWNmMjNmMTk0YWRjZTE2Y2ZmYWI1ZmU3ZmJmNzNjMjBlOCIsInZlcnNpb24iOjF9.P5-TbuEUrCtX9H7F-tKn8LI1RBPhoJwjJm_l853WTSzdLioThAtIK5HBG0xgXT2uB0Q8v94qH2b8cz1j_WonDg - type: f1 value: 0.931 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjNmNDgyMmFjODYwNjcwOTJiOGM2N2YwYjUyMDk5Yjk2Y2I3NmFmZGFhYjU0NGM2OGUwZmRjNjcxYTU3YzgzNSIsInZlcnNpb24iOjF9.2ZoRJwQWVIcl_Ykxce1MnZ3mSxBGxGeNYFPxt9mivo9yTi3gUE7ua6JRpVEOnOUbevlWxVkUUNnmOPFqBN1sCQ - type: f1 value: 0.9300782840205046 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGE1OTcxNmNmMjQ3ZDAzYzk0N2Q1MGFjM2VhNWMyYmRjY2E3ZThjODExOTNlNWMxYzdlMWM2MDBiMTZhY2M2OSIsInZlcnNpb24iOjF9.r63SEArCiFB5m0ccV2q_t5uSOtjVnWdz4PfvCYUchm0JlrRC9YAm5oWKeO419wdyFY4rZFe014yv7sRcV-CgBQ - type: loss value: 0.15155883133411407 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2M4MmVlNjAzZjhiMWJlNWQxMDg5ZTRiYjFlZGYyMGMyYzU4M2IwY2E1M2E2MzA5NmU5ZjgwZTZmMDI5YjgzMyIsInZlcnNpb24iOjF9.kjgFJohkTxLKtzHJDlBvd6qolGQDSZLbrDE7C07xNGmarhTLc_A3MmLeC4MmQGOl1DxfnHflImIkdqPylyylDA --- # robert-base-emotion ## Model description: [roberta](https://arxiv.org/abs/1907.11692) is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining. [roberta-base](https://huggingface.co/roberta-base) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters ``` learning rate 2e-5, batch size 64, num_train_epochs=8, ``` ## Model Performance Comparision on Emotion Dataset from Twitter: | Model | Accuracy | F1 Score | Test Sample per Second | | --- | --- | --- | --- | | [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 | | [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 | | [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 | | [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 | ## How to Use the model: ```python from transformers import pipeline classifier = pipeline("text-classification",model='bhadresh-savani/roberta-base-emotion', return_all_scores=True) prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) print(prediction) """ Output: [[ {'label': 'sadness', 'score': 0.002281982684507966}, {'label': 'joy', 'score': 0.9726489186286926}, {'label': 'love', 'score': 0.021365027874708176}, {'label': 'anger', 'score': 0.0026395076420158148}, {'label': 'fear', 'score': 0.0007162453257478774}, {'label': 'surprise', 'score': 0.0003483477921690792} ]] """ ``` ## Dataset: [Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). ## Training procedure [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) follow the above notebook by changing the model name to roberta ## Eval results ```json { 'test_accuracy': 0.9395, 'test_f1': 0.9397328860104454, 'test_loss': 0.14367154240608215, 'test_runtime': 10.2229, 'test_samples_per_second': 195.639, 'test_steps_per_second': 3.13 } ``` ## Reference: * [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)
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DeepFloyd/IF-II-L-v1.0
DeepFloyd
2023-06-02T19:05:09Z
20,049
44
diffusers
[ "diffusers", "pytorch", "if", "text-to-image", "arxiv:2205.11487", "arxiv:2110.02861", "license:deepfloyd-if-license", "has_space", "diffusers:IFSuperResolutionPipeline", "region:us" ]
text-to-image
2023-03-21T13:09:58Z
--- license: deepfloyd-if-license extra_gated_prompt: "DeepFloyd LICENSE AGREEMENT\nThis License Agreement (as may be amended in accordance with this License Agreement, “License”), between you, or your employer or other entity (if you are entering into this agreement on behalf of your employer or other entity) (“Licensee” or “you”) and Stability AI Ltd.. (“Stability AI” or “we”) applies to your use of any computer program, algorithm, source code, object code, or software that is made available by Stability AI under this License (“Software”) and any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software (“Documentation”).\nBy clicking “I Accept” below or by using the Software, you agree to the terms of this License. If you do not agree to this License, then you do not have any rights to use the Software or Documentation (collectively, the “Software Products”), and you must immediately cease using the Software Products. 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MISCELLANEOUS\n If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Stability AI to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Stability AI regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Stability AI regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Stability AI." extra_gated_fields: "Organization /\_Affiliation": text Previously related publications: text I accept the above license agreement, and will use the Software non-commercially and for research purposes only: checkbox tags: - if - text-to-image inference: false --- # IF-II-L-v1.0 DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of `6.66` on the COCO dataset. *Inspired by* [*Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding*](https://arxiv.org/pdf/2205.11487.pdf) ![](./pics/deepfloyd_if_scheme.jpg) ## Model Details - **Developed by:** DeepFloyd, StabilityAI - **Model type:** pixel-based text-to-image cascaded diffusion model - **Cascade Stage:** II - **Num Parameters:** 1.2B - **Language(s):** primarily English and, to a lesser extent, other Romance languages - **License:** <span style="color:blue"><a href="https://huggingface.co/spaces/DeepFloyd/deepfloyd-if-license">DeepFloyd IF License Agreement</a></span> - **Model Description:** DeepFloyd-IF is modular composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024. All stages of the model utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling - **Resources for more information:** [GitHub](https://github.com/deep-floyd/IF), [Website](https://deepfloyd.ai/), [All Links](https://linktr.ee/deepfloyd) ## Using with `diffusers` IF is integrated with the 🤗 Hugging Face [🧨 diffusers library](https://github.com/huggingface/diffusers/), which is optimized to run on GPUs with as little as 14 GB of VRAM. Before you can use IF, you need to accept its usage conditions. To do so: 1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be loggin in 2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) 3. Make sure to login locally. Install `huggingface_hub` ```sh pip install huggingface_hub --upgrade ``` run the login function in a Python shell ```py from huggingface_hub import login login() ``` and enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens). Next we install `diffusers` and dependencies: ```sh pip install diffusers accelerate transformers safetensors sentencepiece ``` And we can now run the model locally. By default `diffusers` makes use of [model cpu offloading](https://huggingface.co/docs/diffusers/optimization/fp16#model-offloading-for-fast-inference-and-memory-savings) to run the whole IF pipeline with as little as 14 GB of VRAM. If you are using `torch>=2.0.0`, make sure to **remove all** `enable_xformers_memory_efficient_attention()` functions. * **Load all stages and offload to CPU** ```py from diffusers import DiffusionPipeline from diffusers.utils import pt_to_pil import torch # stage 1 stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16) stage_1.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0 stage_1.enable_model_cpu_offload() # stage 2 stage_2 = DiffusionPipeline.from_pretrained( "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 ) stage_2.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0 stage_2.enable_model_cpu_offload() # stage 3 safety_modules = {"feature_extractor": stage_1.feature_extractor, "safety_checker": stage_1.safety_checker, "watermarker": stage_1.watermarker} stage_3 = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16) stage_3.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0 stage_3.enable_model_cpu_offload() ``` * **Retrieve Text Embeddings** ```py prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' # text embeds prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt) ``` * **Run stage 1** ```py generator = torch.manual_seed(0) image = stage_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt").images pt_to_pil(image)[0].save("./if_stage_I.png") ``` * **Run stage 2** ```py image = stage_2( image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt" ).images pt_to_pil(image)[0].save("./if_stage_II.png") ``` * **Run stage 3** ```py image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images image[0].save("./if_stage_III.png") ``` There are multiple ways to speed up the inference time and lower the memory consumption even more with `diffusers`. To do so, please have a look at the Diffusers docs: - 🚀 [Optimizing for inference time](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-speed) - ⚙️ [Optimizing for low memory during inference](https://huggingface.co/docs/diffusers/api/pipelines/if#optimizing-for-memory) For more in-detail information about how to use IF, please have a look at [the IF blog post](https://huggingface.co/blog/if) and the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/if) 📖. Diffusers dreambooth scripts also supports fine-tuning 🎨 [IF](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#if). With parameter efficient finetuning, you can add new concepts to IF with a single GPU and ~28 GB VRAM. ## Training **Training Data:** 1.2B text-image pairs (based on LAION-A and few additional internal datasets) Test/Valid parts of datasets are not used at any cascade and stage of training. Valid part of COCO helps to demonstrate "online" loss behaviour during training (to catch incident and other problems), but dataset is never used for train. **Training Procedure:** IF-II-L-v1.0 is a pixel-based diffusion cascade which uses T5-Encoder embeddings (hidden states) to upscale image from 64px to 256px. During training, - Images are cropped to square via shifted-center-crop augmentation (randomly shift from center up to 0.1 of size) and resized to 64px (low-res) and 256px (ground-truth) using `Pillow==9.2.0` BICUBIC resampling with reducing_gap=None (it helps to avoid aliasing) and processed to tensor BxCxHxW - Low-res images are extra augmented by noise (q-sample methods) with the same diffusion configuration for cascade-I series. Uniform distributed randomised augmentation noising param (aug-level) is added to Unet as condition to process by trainable layers timestep embedding and linear projection with activation. - Text prompts are encoded through open-sourced frozen T5-v1_1-xxl text-encoder (that completely was trained by Google team), random 10% of texts are dropped to empty string to add ability for classifier free guidance (CFG) - The non-pooled output of the text encoder is fed into the projection (linear layer without activation) and is used in UNet backbone of the diffusion model via controlled hybrid self- and cross- attention - Also, the output of the text encode is pooled via attention-pooling (64 heads) and is used in time embed as additional features - Diffusion process is limited by 1000 discrete steps, with cosine beta schedule of noising image - The loss is a reconstruction objective between the noise that was added to the image and the prediction made by the UNet - The training process for checkpoint IF-II-L-v1.0 has 2_500_000 steps at resolution 256x256 on all datasets, OneCycleLR policy, few-bit backward GELU activations, optimizer AdamW8bit + DeepSpeed-Zero1, fully frozen T5-Encoder ![](./pics/loss.jpg) **Hardware:** 32 x 8 x A100 GPUs **Optimizer:** [AdamW8bit](https://arxiv.org/abs/2110.02861) + [DeepSpeed ZeRO-1](https://www.deepspeed.ai/tutorials/zero/) **Batch:** 1536 **Learning rate**: [one-cycle](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.OneCycleLR.html) cosine strategy, warmup 10000 steps, start_lr=4e-6, max_lr=1e-4, final_lr=1e-8 ![](./pics/lr.jpg) ## Evaluation Results `FID-30K: 6.66` ![](./pics/fid30k_if.jpg) # Uses ## Direct Use The model is released for research purposes. Any attempt to deploy the model in production requires not only that the LICENSE is followed but full liability over the person deploying the model. Possible research areas and tasks include: - Generation of artistic imagery and use in design and other artistic processes. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion but applies in the same way for IF_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model was trained mainly with English captions and will not work as well in other languages. - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have... (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. IF was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. IF mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. *This model card was written by: DeepFloyd-Team and is based on the [StableDiffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).*
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sentence-transformers/msmarco-MiniLM-L6-cos-v5
sentence-transformers
2023-11-02T09:31:43Z
20,045
6
sentence-transformers
[ "sentence-transformers", "pytorch", "tf", "jax", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "arxiv:1908.10084", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- language: - en pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # msmarco-MiniLM-L6-cos-v5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L6-cos-v5') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take average of all tokens def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") model = AutoModel.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Technical Details In the following some technical details how this model must be used: | Setting | Value | | --- | :---: | | Dimensions | 384 | | Produces normalized embeddings | Yes | | Pooling-Method | Mean pooling | | Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance | Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used. ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
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flair/chunk-english
flair
2023-04-05T10:38:02Z
19,987
15
flair
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "dataset:conll2000", "has_space", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - conll2000 widget: - text: "The happy man has been eating at the diner" --- ## English Chunking in Flair (default model) This is the standard phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **96,48** (CoNLL-2000) Predicts 4 tags: | **tag** | **meaning** | |---------------------------------|-----------| | ADJP | adjectival | | ADVP | adverbial | | CONJP | conjunction | | INTJ | interjection | | LST | list marker | | NP | noun phrase | | PP | prepositional | | PRT | particle | | SBAR | subordinate clause | | VP | verb phrase | Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/chunk-english") # make example sentence sentence = Sentence("The happy man has been eating at the diner") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('np'): print(entity) ``` This yields the following output: ``` Span [1,2,3]: "The happy man" [− Labels: NP (0.9958)] Span [4,5,6]: "has been eating" [− Labels: VP (0.8759)] Span [7]: "at" [− Labels: PP (1.0)] Span [8,9]: "the diner" [− Labels: NP (0.9991)] ``` So, the spans "*The happy man*" and "*the diner*" are labeled as **noun phrases** (NP) and "*has been eating*" is labeled as a **verb phrase** (VP) in the sentence "*The happy man has been eating at the diner*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_2000 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = CONLL_2000() # 2. what tag do we want to predict? tag_type = 'np' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # contextual string embeddings, forward FlairEmbeddings('news-forward'), # contextual string embeddings, backward FlairEmbeddings('news-backward'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/chunk-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
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bigscience/bloom-1b7
bigscience
2023-05-11T21:17:30Z
19,939
103
transformers
[ "transformers", "pytorch", "jax", "safetensors", "bloom", "text-generation", "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "license:bigscience-bloom-rail-1.0", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
text-generation
2022-05-19T11:52:06Z
--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu pipeline_tag: text-generation --- <h1 style='text-align: center '>BLOOM LM</h1> <h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2> <h3 style='text-align: center '>Model Card</h3> <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Version 1.0 / 26.May.2022 # Model Card for Bloom-1b7 <!-- Provide a quick summary of what the model is/does. --> ## Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Recommendations](#recommendations) 5. [Training Data](#training-data) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#techincal-specifications) 9. [Citation](#citation) 10. [Glossary and Calculations](#glossary-and-calculations) 11. [More Information](#more-information) 12. [Model Card Authors](#model-card-authors) 13. [Model Card Contact](#model-card-contact) ## Model Details ### Model Description *This section provides information for anyone who wants to know about the model.* - **Developed by:** BigScience ([website](https://bigscience.huggingface.co)) * All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)* - **Model Type:** Transformer-based Language Model - **Version:** 1.0.0 - **Languages:** Multiple; see [training data](#training-data) - **License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license)) - **Release Date Estimate:** Monday, 11.July.2022 - **Funded by:** * The French government. * Hugging Face ([website](https://huggingface.co)). * Organizations of contributors. *(Further breakdown of organizations forthcoming.)* ## Uses *This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model.* ### Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive. #### **Direct Use** - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings #### **Downstream Use** - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization ### Misuse and Out-of-scope Use *This section addresses what users ought not do with the model.* See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases. #### **Out-of-scope Uses** Using the model in [high-stakes](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct. ##### Out-of-scope Uses Include: - Usage in biomedical domains, political and legal domains, or finance domains - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### **Misuse** Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ### Intended Users #### **Direct Users** - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Community advocates, including human and civil rights groups #### Indirect Users - Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use) - Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license) #### Others Affected (Parties Prenantes) - People and groups referred to by the LLM - People and groups exposed to outputs of, or decisions based on, the LLM - People and groups whose original work is included in the LLM ## Bias, Risks, and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs ### Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Models pretrained with the LLM should include an updated Model Card. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. ## Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus). Training data includes: - 45 natural languages - 12 programming languages - In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.) #### **Languages** The pie chart shows the distribution of languages in training data. ![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_chart.svg?raw=true) **The following table shows the further distribution of Niger-Congo and Indic languages in the training data.** | Niger Congo | Percentage | | Indic | Percentage | |----------------|------------ |------ |-----------|------------| | Chi Tumbuka | 0.00002 | | Assamese | 0.01 | | Kikuyu | 0.00004 | | Odia | 0.04 | | Bambara | 0.00004 | | Gujarati | 0.04 | | Akan | 0.00007 | | Marathi | 0.05 | | Xitsonga | 0.00007 | | Punjabi | 0.05 | | Sesotho | 0.00007 | | Kannada | 0.06 | | Chi Chewa | 0.0001 | | Nepali | 0.07 | | Setswana | 0.0002 | | Telugu | 0.09 | | Northern Sotho | 0.0002 | | Malayalam | 0.10 | | Fon | 0.0002 | | Urdu | 0.10 | | Kirundi | 0.0003 | | Tamil | 0.20 | | Wolof | 0.0004 | | Bengali | 0.50 | | Kuganda | 0.0004 | | Hindi | 0.70 | | Chi Shona | 0.001 | | Isi Zulu | 0.001 | | Igbo | 0.001 | | Xhosa | 0.001 | | Kinyarwanda | 0.003 | | Yoruba | 0.006 | | Swahili | 0.02 | </details> **The following table shows the distribution of programming languages.** | Extension | Language | Number of files | |----------------|------------|-----------------| | java | Java | 5,407,724 | | php | PHP | 4,942,186 | | cpp | C++ | 2,503,930 | | py | Python | 2,435,072 | | js | JavaScript | 1,905,518 | | cs | C# | 1,577,347 | | rb | Ruby | 6,78,413 | | cc | C++ | 443,054 | | hpp | C++ | 391,048 | | lua | Lua | 352,317 | | go | GO | 227,763 | | ts | TypeScript | 195,254 | | C | C | 134,537 | | scala | Scala | 92,052 | | hh | C++ | 67,161 | | H | C++ | 55,899 | | tsx | TypeScript | 33,107 | | rs | Rust | 29,693 | | phpt | PHP | 9,702 | | c++ | C++ | 1,342 | | h++ | C++ | 791 | | php3 | PHP | 540 | | phps | PHP | 270 | | php5 | PHP | 166 | | php4 | PHP | 29 | ## Evaluation *This section describes the evaluation protocols and provides the results.* ### Metrics *This section describes the different ways performance is calculated and why.* Includes: | Metric | Why chosen | |--------------------|--------------------------------------------------------------------| | [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training | | Cross Entropy [Loss](#loss) | Standard objective for language models. | And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_ ### Factors *This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.* - Language, such as English or Yoruba - Domain, such as newswire or stories - Demographic characteristics, such as gender or nationality ### Results *Results are based on the [Factors](#factors) and [Metrics](#metrics).* **Train-time Evaluation:** As of 25.May.2022, 15:00 PST: - Training Loss: 2.0 - Validation Loss: 2.2 - Perplexity: 8.9 (More evaluation scores forthcoming at the end of model training.) - [BLOOM Book](https://huggingface.co/spaces/bigscience/bloom-book): Read generations from BLOOM based on prompts provided by the community ## Environmental Impact The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing. **Estimated carbon emissions:** *(Forthcoming upon completion of training.)* **Estimated electricity usage:** *(Forthcoming upon completion of training.)* ## Technical Specifications *This section provides information for people who work on model development.* Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training. **Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)): * Decoder-only architecture * Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf)) * ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions * 1,722,408,960 parameters: * 513,802,240 embedding parameters * 24 layers, 16 attention heads * Hidden layers are 2048-dimensional * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization)) **Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)). **Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)). * Hardware: 64 V100 16/32GB GPUs (16 nodes): * 4 GPUs per node * 40 CPUs per task * 1 task per node * CPU: AMD * CPU memory: 160GB per node * GPU memory: 64GB or 128GB (depending on node availability during training) per node * Inter-node connect: Omni-Path Architecture (OPA) * NCCL-communications network: a fully dedicated subnet * Disc IO network: shared network with other types of nodes * Software: * Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed)) * DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed)) * PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch)) * apex ([Github link](https://github.com/NVIDIA/apex)) ### **Training** - Checkpoint size: - Fp16 weights: 2.6GB (# params * 2) - Full checkpoint with optimizer states: -- - Training throughput: -- - Number of epochs: 1 - Dates: - Start: 11th March, 2022 11:42am PST - End: 20 May, 2022 - Server training location: Île-de-France, France ### **Tokenization** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using: - A byte-level Byte Pair Encoding (BPE) algorithm - A simple pre-tokenization rule, no normalization - A vocabulary size of 250,680 It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. ## Citation **Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022 ## Glossary and Calculations *This section defines common terms and how metrics are calculated.* - <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss. - <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. - <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/). - <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf). - <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf). - <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm). - <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf)) - <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated. ## More Information ### Dataset Creation Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling ### Technical Specifications Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md ### Initial Results Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book ## Model Card Authors *Ordered roughly chronologically and by amount of time spent.* Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff ## Model Card Contact **Send Questions to:** [email protected]
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augtoma/qCammel-70-x
augtoma
2023-07-27T16:47:02Z
19,896
22
transformers
[ "transformers", "pytorch", "llama", "text-generation", "llama-2", "qCammel-70", "en", "arxiv:2305.12031", "arxiv:2305.14314", "arxiv:2302.70971", "license:other", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-07-23T00:39:34Z
--- license: other language: - en pipeline_tag: text-generation inference: false tags: - pytorch - llama - llama-2 - qCammel-70 library_name: transformers --- # qCammel-70 qCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept their License before downloading this model .* The fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA, **Variations** The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 70B model. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved **Research Papers** - [Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://arxiv.org/abs/2305.12031) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.70971)
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cyberagent/calm2-7b-chat
cyberagent
2023-11-07T08:31:37Z
19,889
39
transformers
[ "transformers", "pytorch", "llama", "text-generation", "japanese", "causal-lm", "ja", "en", "license:apache-2.0", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-11-01T07:40:30Z
--- license: apache-2.0 language: - ja - en tags: - japanese - causal-lm inference: false --- # CyberAgentLM2-7B-Chat (CALM2-7B-Chat) ## Model Description CyberAgentLM2-Chat is a fine-tuned model of [CyberAgentLM2](https://huggingface.co/cyberagent/calm2-7b) for dialogue use cases. ## Requirements - transformers >= 4.34.1 - accelerate ## Usage ```python import transformers from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer assert transformers.__version__ >= "4.34.1" model = AutoModelForCausalLM.from_pretrained("cyberagent/calm2-7b-chat", device_map="auto", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("cyberagent/calm2-7b-chat") streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = """USER: AIによって私達の暮らしはどのように変わりますか? ASSISTANT: """ token_ids = tokenizer.encode(prompt, return_tensors="pt") output_ids = model.generate( input_ids=token_ids.to(model.device), max_new_tokens=300, do_sample=True, temperature=0.8, streamer=streamer, ) ``` ## Chat Template ``` USER: {user_message1} ASSISTANT: {assistant_message1}<|endoftext|> USER: {user_message2} ASSISTANT: {assistant_message2}<|endoftext|> USER: {user_message3} ASSISTANT: {assistant_message3}<|endoftext|> ``` ## Model Details * **Model size**: 7B * **Context length**: 32768 * **Model type**: Transformer-based Language Model * **Language(s)**: Japanese, English * **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/) * **License**: Apache-2.0 ## Author [Ryosuke Ishigami](https://huggingface.co/rishigami) ## Citations ```tex @article{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```
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pedramyazdipoor/persian_xlm_roberta_large
pedramyazdipoor
2022-09-19T16:38:37Z
19,860
2
transformers
[ "transformers", "pytorch", "xlm-roberta", "question-answering", "arxiv:1911.02116", "arxiv:2202.06219", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
question-answering
2022-09-18T15:46:12Z
## Persian XLM-RoBERTA Large For Question Answering Task XLM-RoBERTA is a multilingual language model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116v2) by Conneau et al. . Multilingual [XLM-RoBERTa large for QA on various languages](https://huggingface.co/deepset/xlm-roberta-large-squad2) is fine-tuned on various QA datasets but PQuAD, which is the biggest persian QA dataset so far. This second model is our base model to be fine-tuned. Paper presenting PQuAD dataset: [arXiv:2202.06219](https://arxiv.org/abs/2202.06219) --- ## Introduction This model is fine-tuned on PQuAD Train set and is easily ready to use. Its very long training time encouraged me to publish this model in order to make life easier for those who need. ## Hyperparameters of training I set batch size to 4 due to the limitations of GPU memory in Google Colab. ``` batch_size = 4 n_epochs = 1 base_LM_model = "deepset/xlm-roberta-large-squad2" max_seq_len = 256 learning_rate = 3e-5 evaluation_strategy = "epoch", save_strategy = "epoch", learning_rate = 3e-5, warmup_ratio = 0.1, gradient_accumulation_steps = 8, weight_decay = 0.01, ``` ## Performance Evaluated on the PQuAD Persian test set with the [official PQuAD link](https://huggingface.co/datasets/newsha/PQuAD). I trained for more than 1 epoch as well, but I get worse results. Our XLM-Roberta outperforms [our ParsBert on PQuAD](https://huggingface.co/pedramyazdipoor/parsbert_question_answering_PQuAD), but the former is more than 3 times bigger than the latter one; so comparing these two is not fair. ### Question Answering On Test Set of PQuAD Dataset | Metric | Our XLM-Roberta Large| Our ParsBert | |:----------------:|:--------------------:|:-------------:| | Exact Match | 66.56* | 47.44 | | F1 | 87.31* | 81.96 | ## How to use ## Pytorch ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering path = 'pedramyazdipoor/persian_xlm_roberta_large' tokenizer = AutoTokenizer.from_pretrained(path) model = AutoModelForQuestionAnswering.from_pretrained(path) ``` ## Inference There are some considerations for inference: 1) Start index of answer must be smaller than end index. 2) The span of answer must be within the context. 3) The selected span must be the most probable choice among N pairs of candidates. ```python def generate_indexes(start_logits, end_logits, N, min_index): output_start = start_logits output_end = end_logits start_indexes = np.arange(len(start_logits)) start_probs = output_start list_start = dict(zip(start_indexes, start_probs.tolist())) end_indexes = np.arange(len(end_logits)) end_probs = output_end list_end = dict(zip(end_indexes, end_probs.tolist())) sorted_start_list = sorted(list_start.items(), key=lambda x: x[1], reverse=True) #Descending sort by probability sorted_end_list = sorted(list_end.items(), key=lambda x: x[1], reverse=True) final_start_idx, final_end_idx = [[] for l in range(2)] start_idx, end_idx, prob = 0, 0, (start_probs.tolist()[0] + end_probs.tolist()[0]) for a in range(0,N): for b in range(0,N): if (sorted_start_list[a][1] + sorted_end_list[b][1]) > prob : if (sorted_start_list[a][0] <= sorted_end_list[b][0]) and (sorted_start_list[a][0] > min_index) : prob = sorted_start_list[a][1] + sorted_end_list[b][1] start_idx = sorted_start_list[a][0] end_idx = sorted_end_list[b][0] final_start_idx.append(start_idx) final_end_idx.append(end_idx) return final_start_idx[0], final_end_idx[0] ``` ```python device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.eval().to(device) text = 'سلام من پدرامم 26 سالمه' question = 'چند سالمه؟' encoding = tokenizer(question,text,add_special_tokens = True, return_token_type_ids = True, return_tensors = 'pt', padding = True, return_offsets_mapping = True, truncation = 'only_first', max_length = 32) out = model(encoding['input_ids'].to(device),encoding['attention_mask'].to(device), encoding['token_type_ids'].to(device)) #we had to change some pieces of code to make it compatible with one answer generation at a time #If you have unanswerable questions, use out['start_logits'][0][0:] and out['end_logits'][0][0:] because <s> (the 1st token) is for this situation and must be compared with other tokens. #you can initialize min_index in generate_indexes() to put force on tokens being chosen to be within the context(startindex must be greater than seperator token). answer_start_index, answer_end_index = generate_indexes(out['start_logits'][0][1:], out['end_logits'][0][1:], 5, 0) print(tokenizer.tokenize(text + question)) print(tokenizer.tokenize(text + question)[answer_start_index : (answer_end_index + 1)]) >>> ['▁سلام', '▁من', '▁پدر', 'ام', 'م', '▁26', '▁سالم', 'ه', 'نام', 'م', '▁چیست', '؟'] >>> ['▁26'] ``` ## Acknowledgments We hereby, express our gratitude to the [Newsha Shahbodaghkhan](https://huggingface.co/datasets/newsha/PQuAD/tree/main) for facilitating dataset gathering. ## Contributors - Pedram Yazdipoor : [Linkedin](https://www.linkedin.com/in/pedram-yazdipour/) ## Releases ### Release v0.2 (Sep 18, 2022) This is the second version of our Persian XLM-Roberta-Large. There were some problems using the previous version.
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lllyasviel/control_v11f1e_sd15_tile
lllyasviel
2023-05-04T18:51:13Z
19,843
49
diffusers
[ "diffusers", "art", "controlnet", "stable-diffusion", "controlnet-v1-1", "image-to-image", "arxiv:2302.05543", "base_model:runwayml/stable-diffusion-v1-5", "license:openrail", "has_space", "diffusers:ControlNetModel", "region:us" ]
image-to-image
2023-05-04T18:42:24Z
--- license: openrail base_model: runwayml/stable-diffusion-v1-5 tags: - art - controlnet - stable-diffusion - controlnet-v1-1 - image-to-image duplicated_from: ControlNet-1-1-preview/control_v11f1e_sd15_tile --- # Controlnet - v1.1 - *Tile Version* **Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel). This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11f1e_sd15_tile.pth) into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet). ControlNet is a neural network structure to control diffusion models by adding extra conditions. ![img](./sd.png) This checkpoint corresponds to the ControlNet conditioned on **tiled image**. Conceptually, it is similar to a super-resolution model, but its usage is not limited to that. It is also possible to generate details at the same size as the input (conditione) image. **This model was contributed by [*takuma104*](https://huggingface.co/takuma104)** ## Model Details - **Developed by:** Lvmin Zhang, Maneesh Agrawala - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). - **Cite as:** @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Introduction Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by Lvmin Zhang, Maneesh Agrawala. The abstract reads as follows: *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.* ## Example It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint has been trained on it. Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. 1. Let's install `diffusers` and related packages: ``` $ pip install diffusers transformers accelerate ``` 2. Run code: ```python import torch from PIL import Image from diffusers import ControlNetModel, DiffusionPipeline from diffusers.utils import load_image def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H *= k W *= k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img controlnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1e_sd15_tile', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_controlnet_img2img", controlnet=controlnet, torch_dtype=torch.float16).to('cuda') pipe.enable_xformers_memory_efficient_attention() source_image = load_image('https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png') condition_image = resize_for_condition_image(source_image, 1024) image = pipe(prompt="best quality", negative_prompt="blur, lowres, bad anatomy, bad hands, cropped, worst quality", image=condition_image, controlnet_conditioning_image=condition_image, width=condition_image.size[0], height=condition_image.size[1], strength=1.0, generator=torch.manual_seed(0), num_inference_steps=32, ).images[0] image.save('output.png') ``` ![original](./images/original.png) ![tile_output](./images/output.png) ## Other released checkpoints v1-1 The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on a different type of conditioning: | Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example | |---|---|---|---|---| |[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>| |[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>| ## More information For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly).
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google/bert_uncased_L-4_H-512_A-8
google
2021-05-19T17:30:51Z
19,835
3
transformers
[ "transformers", "pytorch", "jax", "bert", "arxiv:1908.08962", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- thumbnail: https://huggingface.co/front/thumbnails/google.png license: apache-2.0 --- BERT Miniatures === This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity. You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below: | |H=128|H=256|H=512|H=768| |---|:---:|:---:|:---:|:---:| | **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]| | **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]| | **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]| | **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]| | **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]| | **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]| Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model. Here are the corresponding GLUE scores on the test set: |Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0| |BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1| |BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6| |BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5| For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs: - batch sizes: 8, 16, 32, 64, 128 - learning rates: 3e-4, 1e-4, 5e-5, 3e-5 If you use these models, please cite the following paper: ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ``` [2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2 [2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4 [2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8 [2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12 [4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2 [4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4 [4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8 [4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12 [6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2 [6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4 [6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8 [6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12 [8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2 [8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4 [8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8 [8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12 [10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2 [10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4 [10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8 [10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12 [12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2 [12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4 [12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8 [12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12
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carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h
carlosdanielhernandezmena
2023-10-23T20:51:50Z
19,804
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "icelandic", "xlrs-53-icelandic", "iceland", "reykjavik", "samromur", "is", "dataset:language-and-voice-lab/samromur_asr", "dataset:language-and-voice-lab/samromur_children", "dataset:language-and-voice-lab/malromur_asr", "dataset:language-and-voice-lab/althingi_asr", "license:cc-by-4.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
automatic-speech-recognition
2022-12-08T01:25:56Z
--- language: is datasets: - language-and-voice-lab/samromur_asr - language-and-voice-lab/samromur_children - language-and-voice-lab/malromur_asr - language-and-voice-lab/althingi_asr tags: - audio - automatic-speech-recognition - icelandic - xlrs-53-icelandic - iceland - reykjavik - samromur license: cc-by-4.0 model-index: - name: wav2vec2-large-xlsr-53-icelandic-ep10-1000h results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Samrómur (Test) type: language-and-voice-lab/samromur_asr split: test args: language: is metrics: - name: WER type: wer value: 9.847 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Samrómur (Dev) type: language-and-voice-lab/samromur_asr split: validation args: language: is metrics: - name: WER type: wer value: 8.736 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Samrómur Children (Test) type: language-and-voice-lab/samromur_children split: test args: language: is metrics: - name: WER type: wer value: 9.391 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Samrómur Children (Dev) type: language-and-voice-lab/samromur_children split: validation args: language: is metrics: - name: WER type: wer value: 6.055 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Malrómur (Test) type: language-and-voice-lab/malromur_asr split: test args: language: is metrics: - name: WER type: wer value: 5.643 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Malrómur (Dev) type: language-and-voice-lab/malromur_asr split: validation args: language: is metrics: - name: WER type: wer value: 6.156 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Althingi (Test) type: language-and-voice-lab/althingi_asr split: test args: language: is metrics: - name: WER type: wer value: 11.437 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Althingi (Dev) type: language-and-voice-lab/althingi_asr split: validation args: language: is metrics: - name: WER type: wer value: 11.093 --- # wav2vec2-large-xlsr-53-icelandic-ep10-1000h The "wav2vec2-large-xlsr-53-icelandic-ep10-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" for 10 epochs with around 1000 hours of Icelandic data developed by the [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab). Most of the data is available at public repositories such as [LDC](https://www.ldc.upenn.edu/), [OpenSLR](https://openslr.org/) or [Clarin.is](https://clarin.is/) The specific list of corpora used to fine-tune the model is: - [Samrómur 21.05 (114h34m)](http://www.openslr.org/112/) - [Samrómur Children (127h25m)](https://catalog.ldc.upenn.edu/LDC2022S11) - [Malrómur (119hh03m)](https://clarin.is/en/resources/malromur/) - [Althingi Parliamentary Speech (514h29m)](https://catalog.ldc.upenn.edu/LDC2021S01) - L2-Speakers Data (125h55m) **Unpublished material** The fine-tuning process was performed during December (2022) in the servers of the Language and Voice Laboratory (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena. # Evaluation ```python import torch from transformers import Wav2Vec2Processor from transformers import Wav2Vec2ForCTC #Load the processor and model. MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h" processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME) #Load the dataset from datasets import load_dataset, load_metric, Audio ds=load_dataset("language-and-voice-lab/samromur_children", split="test") #Downsample to 16kHz ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) #Process the dataset def prepare_dataset(batch): audio = batch["audio"] #Batched output is "un-batched" to ensure mapping is correct batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] with processor.as_target_processor(): batch["labels"] = processor(batch["normalized_text"]).input_ids return batch ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1) #Define the evaluation metric import numpy as np wer_metric = load_metric("wer") def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids) #We do not want to group tokens when computing the metrics label_str = processor.batch_decode(pred.label_ids, group_tokens=False) wer = wer_metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} #Do the evaluation (with batch_size=1) model = model.to(torch.device("cuda")) def map_to_result(batch): with torch.no_grad(): input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0) logits = model(input_values).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_str"] = processor.batch_decode(pred_ids)[0] batch["sentence"] = processor.decode(batch["labels"], group_tokens=False) return batch results = ds.map(map_to_result,remove_columns=ds.column_names) #Compute the overall WER now. print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"]))) ``` **Test Result**: 0.094 # BibTeX entry and citation info *When publishing results based on these models please refer to:* ```bibtex @misc{mena2022xlrs53icelandic, title={Acoustic Model in Icelandic: wav2vec2-large-xlsr-53-icelandic-ep10-1000h.}, author={Hernandez Mena, Carlos Daniel}, url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h}, year={2022} } ``` # Acknowledgements Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.
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sentence-transformers/distilbert-base-nli-stsb-mean-tokens
sentence-transformers
2022-06-15T20:07:20Z
19,799
10
sentence-transformers
[ "sentence-transformers", "pytorch", "tf", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "arxiv:1908.10084", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/distilbert-base-nli-stsb-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-mean-tokens') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distilbert-base-nli-stsb-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
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mosaicml/mpt-30b
mosaicml
2023-10-30T21:54:24Z
19,782
325
transformers
[ "transformers", "pytorch", "mpt", "text-generation", "Composer", "MosaicML", "llm-foundry", "StreamingDatasets", "custom_code", "dataset:allenai/c4", "dataset:mc4", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:bigcode/the-stack-dedup", "dataset:allenai/s2orc", "arxiv:2108.12409", "arxiv:2302.13971", "arxiv:2205.14135", "arxiv:2010.04245", "arxiv:1909.08053", "arxiv:2302.06675", "license:apache-2.0", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-06-20T16:29:39Z
--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - allenai/c4 - mc4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack-dedup - allenai/s2orc inference: false --- # MPT-30B MPT-30B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-30B is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. MPT-30B comes with special features that differentiate it from other LLMs, including an 8k token context window (which can be further extended via finetuning; see [MPT-7B-StoryWriter](https://huggingface.co/mosaicml/mpt-7b-storywriter)), support for context-length extrapolation via [ALiBi](https://arxiv.org/abs/2108.12409), and efficient inference + training via FlashAttention. It also has strong coding abilities thanks to its pretraining mix. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). The size of MPT-30B was also specifically chosen to make it easy to deploy on a single GPU—either 1xA100-80GB in 16-bit precision or 1xA100-40GB in 8-bit precision. This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-30B is: * **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409). * **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-30B: The following models are finetuned on MPT-30B: * [MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct): a model for long-form instruction following (especially summarization and question-answering). Built by finetuning MPT-30B on several carefully curated datasets. * License: _CC-BY-SA-3.0_ * [MPT-30B-Chat](https://huggingface.co/mosaicml/mpt-30b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-30B on [ShareGPT-Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered), [Camel-AI](https://huggingface.co/camel-ai), [GPTeacher](https://github.com/teknium1/GPTeacher), [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Baize](https://github.com/project-baize/baize-chatbot) and some generated datasets. * License: _CC-By-NC-SA-4.0_ * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-30b-chat) ## Model Date June 22, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: MPT-30B: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-30b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-30b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` The model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-30b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the MPT-30B tokenizer which is identical to the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline with torch.autocast('cuda', dtype=torch.bfloat16): inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda') outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # or using the HF pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 29.95B | |n_layers | 48 | | n_heads | 64 | | d_model | 7168 | | vocab size | 50432 | | sequence length | 8192 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens on the following data mix: | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English (200+ words) | 2417.99 B | 33.50% | 335 B | 0.14 | | c4 - English - SemDedup 80% | 100.42 B | 29.90% | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 8.50% | 85 B | 0.097 | | The Stack - Selected Languages | 463.78 B | 10.00% | 100 B | 0.22 | | RedPajama - Wikipedia | 4.87 B | 4.00% | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 4.50% | 45 B | 0.42 | | Semantic Scholar ORC | 48.95 B | 3.30% | 33 B | 0.67 | | RedPajama - Books | 26.02 B | 3.00% | 30 B | 1.15 | | RedPajama - arXiv | 28.10 B | 1.90% | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 1.40% | 14 B |0.68 | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the sequence length. To build 8k support into MPT-30B efficiently, we first pre-trained on 1T tokens using sequences that were 2k tokens long, and then trained for an additional 50B tokens using sequences that were 8k tokens long. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)). ### Training Configuration The model was trained in three stages using the [MosaicML Platform](https://www.mosaicml.com/platform): (i) First it was trained on 440 A100-40GBs with a batch size of 1760. (ii) Then, on 216 A100-40GBs with a batch size of 1728. (iii) Training was completed on 256 H100-80GBs with a batch size of 512 with 8k context length and 50B tokens. The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-30B (Base) is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-30B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-30B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-30b). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-30B: Raising the bar for open-source foundation models}, year = {2023}, url = {www.mosaicml.com/blog/mpt-30b}, note = {Accessed: 2023-06-22}, urldate = {2023-06-22} } ```
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fxmarty/tiny-mpt-random-remote-code
fxmarty
2023-07-06T05:11:36Z
19,781
0
transformers
[ "transformers", "pytorch", "mpt", "text-generation", "custom_code", "license:mit", "text-generation-inference", "region:us" ]
text-generation
2023-07-06T04:45:38Z
--- license: mit task: - text-generation --- For testing purposes.
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GanymedeNil/text2vec-large-chinese
GanymedeNil
2023-08-05T02:28:03Z
19,773
651
transformers
[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "text2vec", "sentence-similarity", "zh", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
sentence-similarity
2023-03-07T03:32:14Z
--- license: apache-2.0 language: - zh pipeline_tag: sentence-similarity tags: - text2vec - feature-extraction - sentence-similarity - transformers --- Based on the derivative model of https://huggingface.co/shibing624/text2vec-base-chinese, replace MacBERT with LERT, and keep other training conditions unchanged。 Refer to the following items for usage: https://github.com/shibing624/text2vec Talk to me: https://twitter.com/GanymedeNil
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dafex/indobert-sentiment-analysis
dafex
2023-01-03T16:51:59Z
19,768
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "unk", "dataset:dafex/autotrain-data-indobert-sentiment-analysis", "co2_eq_emissions", "endpoints_compatible", "region:us" ]
text-classification
2023-01-03T16:50:53Z
--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - dafex/autotrain-data-indobert-sentiment-analysis co2_eq_emissions: emissions: 1.3428141985163928 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2713480683 - CO2 Emissions (in grams): 1.3428 ## Validation Metrics - Loss: 0.132 - Accuracy: 0.960 - Precision: 0.966 - Recall: 0.973 - AUC: 0.993 - F1: 0.969 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/dafex/autotrain-indobert-sentiment-analysis-2713480683 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("dafex/autotrain-indobert-sentiment-analysis-2713480683", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("dafex/autotrain-indobert-sentiment-analysis-2713480683", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
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facebook/mask2former-swin-tiny-coco-panoptic
facebook
2023-09-11T20:23:58Z
19,737
2
transformers
[ "transformers", "pytorch", "safetensors", "mask2former", "vision", "image-segmentation", "dataset:coco", "arxiv:2112.01527", "arxiv:2107.06278", "license:other", "endpoints_compatible", "has_space", "region:us" ]
image-segmentation
2023-01-02T17:36:46Z
--- license: other tags: - vision - image-segmentation datasets: - coco widget: - src: http://images.cocodataset.org/val2017/000000039769.jpg example_title: Cats - src: http://images.cocodataset.org/val2017/000000039770.jpg example_title: Castle --- # Mask2Former Mask2Former model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on COCO panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-coco-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-coco-panoptic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing result = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_panoptic_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).
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circulus/canvers-en2ko-v1
circulus
2023-06-07T03:46:42Z
19,721
0
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-05-17T01:59:15Z
--- license: gpl-3.0 ---
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dreamlike-art/dreamlike-anime-1.0
dreamlike-art
2023-03-13T01:04:40Z
19,649
217
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "image-to-image", "anime", "en", "license:other", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-01-08T03:47:50Z
--- language: - en license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - image-to-image - diffusers - anime inference: false --- # Dreamlike Anime 1.0 is a high quality anime model, made by [dreamlike.art](https://dreamlike.art/). # If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! Add **anime** to your prompt to make your gens look more anime. Add **photo** to your prompt to make your gens look more photorealistic and have better anatomy. This model was trained on 768x768px images, so use 768x768px, 704x832px, 832x704px, etc. Higher resolution or non-square aspect ratios may produce artifacts. Add this to the start of your prompts for best results: ``` photo anime, masterpiece, high quality, absurdres ``` Use negative prompts for best results, for example: ``` simple background, duplicate, retro style, low quality, lowest quality, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013, bad anatomy, bad proportions, extra digits, lowres, username, artist name, error, duplicate, watermark, signature, text, extra digit, fewer digits, worst quality, jpeg artifacts, blurry ``` **1girl**, **girl**, etc. give a bit different results, feel free to experiment and see which one you like more! ### Examples <img src="https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/preview1.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/preview2.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/preview3.jpg" style="max-width: 800px;" width="100%"/> # dreamlike.art Use this model as well as [Dreamlike Diffusion 1.0](https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0) and [Dreamlike Photoreal 2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0) for free on [dreamlike.art](https://dreamlike.art/)! <img src="https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/> ### CKPT [Download dreamlike-anime-1.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/dreamlike-anime-1.0.ckpt) ### Safetensors [Download dreamlike-anime-1.0.safetensors (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/dreamlike-anime-1.0.safetensors) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "dreamlike-art/dreamlike-anime-1.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "anime, masterpiece, high quality, 1girl, solo, long hair, looking at viewer, blush, smile, bangs, blue eyes, skirt, medium breasts, iridescent, gradient, colorful, besides a cottage, in the country" negative_prompt = 'simple background, duplicate, retro style, low quality, lowest quality, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013, bad anatomy, bad proportions, extra digits, lowres, username, artist name, error, duplicate, watermark, signature, text, extra digit, fewer digits, worst quality, jpeg artifacts, blurry' image = pipe(prompt, negative_prompt=negative_prompt).images[0] image.save("./result.jpg") ``` <img src="https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/resolve/main/anime.jpg" style="max-width: 640px;" width="100%"/> # License This model is licesed under a **modified** CreativeML OpenRAIL-M license. - **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]** - **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Anime 1.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-anime-1.0)** - **You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less** - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-anime-1.0/blob/main/LICENSE.md
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SRDdev/QABERT-small
SRDdev
2023-06-21T15:00:00Z
19,615
0
transformers
[ "transformers", "pytorch", "safetensors", "distilbert", "question-answering", "en", "dataset:squad_v2", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
question-answering
2023-02-08T12:40:31Z
--- datasets: - squad_v2 language: - en metrics: - accuracy library_name: transformers pipeline_tag: question-answering tags: - question-answering --- # QA-BERT QA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there. ## Dataset The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike. Due to GPU limitations, this version is trained on `30k samples` from the Stanford Question Answering Dataset. <details> <summary><i>Structure of the Data Dictonary</i></summary> <!--All you need is a blank line--> { "data":[ { "title":"Article Title", "paragraphs":[ { "context":"The context text of the paragraph", "qas":[ { "question":"The question asked about the context", "id":"A unique identifier for the question", "answers":[ { "text":"The answer to the question", "answer_start":"The starting index of the answer in the context" } ] } ] } ] } ], "version":"The version of the SQuAD dataset" } </details> ## Model BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language processing tasks such as question answering. BERT is fine-tuned for question answering by adding a linear layer on top of the pre-trained BERT representations to predict the start and end of the answer in the input context. BERT has achieved state-of-the-art results on multiple benchmark datasets, including the Stanford Question Answering Dataset (SQuAD). The fine-tuning process allows BERT to effectively capture the relationships between questions and answers and generate accurate answers. <img src="https://imgs.search.brave.com/F8m-nwp6EIG5vq--OmJLrCDpIkuX6tEQ_kyFKQjlUTs/rs:fit:1200:1200:1/g:ce/aHR0cHM6Ly9ibG9n/LmdyaWRkeW5hbWlj/cy5jb20vY29udGVu/dC9pbWFnZXMvMjAy/MC8xMC9TbGljZS0x/OC5wbmc"> For more detail about this read [Understanding QABERT](https://github.com/SRDdev/AnswerMind) ## Inference _Load model_ ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering QAtokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small") QAmodel = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small") ``` _context_ ```text Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a question-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script. ``` _Build Pipeline_ ```python from transformers import pipeline ask = pipeline("question-answering", model= QAmodel , tokenizer = QAtokenizer) result = ask(question="What is a good example of a question answering dataset?", context=context) print(f"Answer: '{result['answer']}'") ``` ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate. ## Citations ``` @citation{ QA-BERT-small, author = {Shreyas Dixit}, year = {2023}, url = {https://huggingface.co/SRDdev/QA-BERT-small} } ```
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stabilityai/stablelm-3b-4e1t
stabilityai
2023-10-25T04:43:37Z
19,582
244
transformers
[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "causal-lm", "custom_code", "en", "dataset:tiiuae/falcon-refinedweb", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:CarperAI/pilev2-dev", "dataset:bigcode/starcoderdata", "dataset:allenai/peS2o", "arxiv:2307.09288", "arxiv:2104.09864", "arxiv:2204.06745", "arxiv:1607.06450", "arxiv:1910.07467", "arxiv:2101.00027", "arxiv:2305.06161", "arxiv:1910.02054", "license:cc-by-sa-4.0", "has_space", "region:us" ]
text-generation
2023-09-29T06:05:21Z
--- license: cc-by-sa-4.0 datasets: - tiiuae/falcon-refinedweb - togethercomputer/RedPajama-Data-1T - CarperAI/pilev2-dev - bigcode/starcoderdata - allenai/peS2o language: - en tags: - causal-lm extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox --- # `StableLM-3B-4E1T` ## Model Description `StableLM-3B-4E1T` is a 3 billion parameter decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs. ## Usage Get started generating text with `StableLM-3B-4E1T` by using the following code snippet: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-3b-4e1t", trust_remote_code=True, torch_dtype="auto", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to("cuda") tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.75, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM-3B-4E1T` models are auto-regressive language models based on the transformer decoder architecture. * **Language(s)**: English * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) * **License**: Model checkpoints are licensed under the Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)). Under this license, you must give [credit](https://creativecommons.org/licenses/by/4.0/#) to Stability AI, provide a link to the license, and [indicate if changes were made](https://creativecommons.org/licenses/by/4.0/#). You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use. * **Contact**: For questions and comments about the model, please email `[email protected]` ### Model Architecture The model is a decoder-only transformer similar to the LLaMA ([Touvron et al., 2023](https://arxiv.org/abs/2307.09288)) architecture with the following modifications: | Parameters | Hidden Size | Layers | Heads | Sequence Length | |----------------|-------------|--------|-------|-----------------| | 2,795,443,200 | 2560 | 32 | 32 | 4096 | * **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf). * **Normalization**: LayerNorm ([Ba et al., 2016](https://arxiv.org/abs/1607.06450)) with learned bias terms as opposed to RMSNorm ([Zhang & Sennrich, 2019](https://arxiv.org/abs/1910.07467)). * **Tokenizer**: GPT-NeoX ([Black et al., 2022](https://arxiv.org/abs/2204.06745)). ## Training For complete dataset and training details, please see the [StableLM-3B-4E1T Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo). ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), RedPajama-Data ([Together Computer., 2023](https://github.com/togethercomputer/RedPajama-Data)) and The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)) both without the *Books3* subset, and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)). * Given the large amount of web data, we recommend fine-tuning the base StableLM-3B-4E1T for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in `bfloat16` precision, optimized with AdamW, and trained using the NeoX tokenizer with a vocabulary size of 50,257. We outline the complete hyperparameters choices in the project's [GitHub repository - config](https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-3b-4e1t.yml). ### Training Infrastructure * **Hardware**: `StableLM-3B-4E1T` was trained on the Stability AI cluster across 256 NVIDIA A100 40GB GPUs (AWS P4d instances). Training began on August 23, 2023, and took approximately 30 days to complete. * **Software**: We use a fork of `gpt-neox` ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf)) ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. ### Limitations and Bias ​ As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @misc{StableLM-3B-4E1T, url={[https://huggingface.co/stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)}, title={StableLM 3B 4E1T}, author={Tow, Jonathan and Bellagente, Marco and Mahan, Dakota and Riquelme, Carlos} } ```
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cardiffnlp/twitter-roberta-base-hate
cardiffnlp
2023-04-19T07:54:22Z
19,544
12
transformers
[ "transformers", "pytorch", "tf", "jax", "roberta", "text-classification", "arxiv:2010.12421", "endpoints_compatible", "has_space", "region:us" ]
text-classification
2022-03-02T23:29:05Z
# Twitter-roBERTa-base for Hate Speech Detection This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark. This model is specialized to detect hate speech against women and immigrants. **NEW!** We have made available a more recent and robust hate speech detection model here: [https://huggingface.co/cardiffnlp/twitter-roberta-base-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-hate-latest) - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='hate' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) not-hate 0.9168 2) hate 0.0832 ```
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clefourrier/graphormer-base-pcqm4mv1
clefourrier
2023-02-07T16:35:10Z
19,522
2
transformers
[ "transformers", "pytorch", "graphormer", "graphs", "graph-ml", "arxiv:2106.05234", "license:mit", "endpoints_compatible", "has_space", "region:us" ]
graph-ml
2023-01-05T10:12:34Z
--- license: mit tags: - graphs pipeline_tag: graph-ml --- # Model Card for pcqm4mv1_graphormer_base The Graphormer is a graph classification model. # Model Details ## Model Description The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSC, and which got 1st place on the KDD CUP 2021 (quantum prediction track). - **Developed by:** Microsoft - **Model type:** Graphormer - **License:** MIT ## Model Sources <!-- Provide the basic links for the model. --> - **Repository:** [Github](https://github.com/microsoft/Graphormer) - **Paper:** [Paper](https://arxiv.org/abs/2106.05234) - **Documentation:** [Link](https://graphormer.readthedocs.io/en/latest/) # Uses ## Direct Use This model should be used for graph classification tasks or graph representation tasks; the most likely associated task is molecule modeling. It can either be used as such, or finetuned on downstream tasks. # Bias, Risks, and Limitations The Graphormer model is ressource intensive for large graphs, and might lead to OOM errors. ## How to Get Started with the Model See the Graph Classification with Transformers tutorial. # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ``` @article{DBLP:journals/corr/abs-2106-05234, author = {Chengxuan Ying and Tianle Cai and Shengjie Luo and Shuxin Zheng and Guolin Ke and Di He and Yanming Shen and Tie{-}Yan Liu}, title = {Do Transformers Really Perform Bad for Graph Representation?}, journal = {CoRR}, volume = {abs/2106.05234}, year = {2021}, url = {https://arxiv.org/abs/2106.05234}, eprinttype = {arXiv}, eprint = {2106.05234}, timestamp = {Tue, 15 Jun 2021 16:35:15 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-05234.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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nvidia/mit-b2
nvidia
2022-08-06T10:26:08Z
19,506
4
transformers
[ "transformers", "pytorch", "tf", "segformer", "image-classification", "vision", "dataset:imagenet_1k", "arxiv:2105.15203", "license:other", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
image-classification
2022-03-02T23:29:05Z
--- license: other tags: - vision datasets: - imagenet_1k widget: - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: House - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Castle --- # SegFormer (b2-sized) encoder pre-trained-only SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. This repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes. ## Intended uses & limitations You can use the model for fine-tuning of semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/mit-b2") model = SegformerForImageClassification.from_pretrained("nvidia/mit-b2") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#). ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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beomi/llama-2-ko-7b
beomi
2023-11-24T15:54:53Z
19,502
105
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "facebook", "meta", "llama-2", "kollama", "llama-2-ko", "en", "ko", "doi:10.57967/hf/1098", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-07-20T03:25:25Z
--- language: - en - ko pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - kollama - llama-2-ko --- > 🚧 Note: this repo is under construction 🚧 **Update Log** - 2023.10.19 - Fix Tokenizer bug(space not applied when decoding) after `transforemrs>=4.34.0` # **Llama-2-Ko** 🦙🇰🇷 Llama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below. ## Model Details **Model Developers** Junbum Lee (Beomi) **Variations** Llama-2-Ko will come in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of Korean online data*|7B|4k|&#10007;|>40B*|1e<sup>-5</sup>| *Plan to train upto 200B tokens **Vocab Expansion** | Model Name | Vocabulary Size | Description | | --- | --- | --- | | Original Llama-2 | 32000 | Sentencepiece BPE | | **Expanded Llama-2-Ko** | 46336 | Sentencepiece BPE. Added Korean vocab and merges | **Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."** | Model | Tokens | | --- | --- | | Llama-2 | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요']` | | Llama-2-Ko | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요']` | **Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models"** | Model | Tokens | | --- | --- | | Llama-2 | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` | | Llama-2-Ko | `['▁L', 'l', 'ama', '▁', '2', ':', '▁Open', '▁Foundation', '▁and', '▁Fine', '-', 'T', 'un', 'ed', '▁Ch', 'at', '▁Mod', 'els']` | # **Model Benchmark** ## LM Eval Harness - Korean (polyglot branch) - Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot ### NSMC (Acc) - 50000 full test TBD ### COPA (F1) <img src=https://user-images.githubusercontent.com/11323660/255575809-c037bc6e-0566-436a-a6c1-2329ac92187a.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6696 | 0.6477 | 0.6419 | 0.6514 | | https://huggingface.co/kakaobrain/kogpt | 0.7345 | 0.7287 | 0.7277 | 0.7479 | | https://huggingface.co/facebook/xglm-7.5B | 0.6723 | 0.6731 | 0.6769 | 0.7119 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.7196 | 0.7193 | 0.7204 | 0.7206 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.7595 | 0.7608 | 0.7638 | 0.7788 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.7745 | 0.7676 | 0.7775 | 0.7887 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.7937 | 0.8108 | 0.8037 | 0.8369 | | Llama-2 Original 7B* | 0.562033 | 0.575982 | 0.576216 | 0.595532 | | Llama-2-Ko-7b 20B (10k) | 0.738780 | 0.762639 | 0.780761 | 0.797863 | | Llama-2-Ko-7b 40B (20k) | 0.743630 | 0.792716 | 0.803746 | 0.825944 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### HellaSwag (F1) <img src=https://user-images.githubusercontent.com/11323660/255576090-a2bfc1ae-d117-44b7-9f7b-262e41179ec1.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.5243 | 0.5272 | 0.5166 | 0.5352 | | https://huggingface.co/kakaobrain/kogpt | 0.5590 | 0.5833 | 0.5828 | 0.5907 | | https://huggingface.co/facebook/xglm-7.5B | 0.5665 | 0.5689 | 0.5565 | 0.5622 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.5247 | 0.5260 | 0.5278 | 0.5427 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.5707 | 0.5830 | 0.5670 | 0.5787 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.5976 | 0.5998 | 0.5979 | 0.6208 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.5954 | 0.6306 | 0.6098 | 0.6118 | | Llama-2 Original 7B* | 0.415390 | 0.431382 | 0.421342 | 0.442003 | | Llama-2-Ko-7b 20B (10k) | 0.451757 | 0.466751 | 0.472607 | 0.482776 | | Llama-2-Ko-7b 40B (20k) | 0.456246 | 0.465665 | 0.469810 | 0.477374 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### BoolQ (F1) <img src=https://user-images.githubusercontent.com/11323660/255576343-5d847a6f-3b6a-41a7-af37-0f11940a5ea4.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.3356 | 0.4014 | 0.3640 | 0.3560 | | https://huggingface.co/kakaobrain/kogpt | 0.4514 | 0.5981 | 0.5499 | 0.5202 | | https://huggingface.co/facebook/xglm-7.5B | 0.4464 | 0.3324 | 0.3324 | 0.3324 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.3552 | 0.4751 | 0.4109 | 0.4038 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4320 | 0.5263 | 0.4930 | 0.4038 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.4356 | 0.5698 | 0.5187 | 0.5236 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.4818 | 0.6041 | 0.6289 | 0.6448 | | Llama-2 Original 7B* | 0.352050 | 0.563238 | 0.474788 | 0.419222 | | Llama-2-Ko-7b 20B (10k) | 0.360656 | 0.679743 | 0.680109 | 0.662152 | | Llama-2-Ko-7b 40B (20k) | 0.578640 | 0.697747 | 0.708358 | 0.714423 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ### SentiNeg (F1) <img src=https://user-images.githubusercontent.com/11323660/255576572-b005a81d-fa4d-4709-b48a-f0fe4eed17a3.png style="max-width: 700px; width: 100%" /> | Model | 0-shot | 5-shot | 10-shot | 50-shot | | --- | --- | --- | --- | --- | | https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 | 0.6065 | 0.6878 | 0.7280 | 0.8413 | | https://huggingface.co/kakaobrain/kogpt | 0.3747 | 0.8942 | 0.9294 | 0.9698 | | https://huggingface.co/facebook/xglm-7.5B | 0.3578 | 0.4471 | 0.3964 | 0.5271 | | https://huggingface.co/EleutherAI/polyglot-ko-1.3b | 0.6790 | 0.6257 | 0.5514 | 0.7851 | | https://huggingface.co/EleutherAI/polyglot-ko-3.8b | 0.4858 | 0.7950 | 0.7320 | 0.7851 | | https://huggingface.co/EleutherAI/polyglot-ko-5.8b | 0.3394 | 0.8841 | 0.8808 | 0.9521 | | https://huggingface.co/EleutherAI/polyglot-ko-12.8b | 0.9117 | 0.9015 | 0.9345 | 0.9723 | | Llama-2 Original 7B* | 0.347502 | 0.529124 | 0.480641 | 0.788457 | | Llama-2-Ko-7b 20B (10k) | 0.485546 | 0.829503 | 0.871141 | 0.851253 | | Llama-2-Ko-7b 40B (20k) | 0.459447 | 0.761079 | 0.727611 | 0.936988 | *Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (w/o tokenizer updated) ## Note for oobabooga/text-generation-webui Remove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`. ```python diff --git a/modules/models.py b/modules/models.py index 232d5fa..de5b7a0 100644 --- a/modules/models.py +++ b/modules/models.py @@ -106,7 +106,7 @@ def load_tokenizer(model_name, model): trust_remote_code=shared.args.trust_remote_code, use_fast=False ) - except ValueError: + except: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, ``` Since Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package, it is required to use `use_fast=True` option when initialize tokenizer. Apple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU) ## Citation ``` @misc {l._junbum_2023, author = { {L. Junbum} }, title = { llama-2-ko-7b (Revision 4a9993e) }, year = 2023, url = { https://huggingface.co/beomi/llama-2-ko-7b }, doi = { 10.57967/hf/1098 }, publisher = { Hugging Face } } ``` ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b) | Metric | Value | |-----------------------|---------------------------| | Avg. | 39.43 | | ARC (25-shot) | 48.46 | | HellaSwag (10-shot) | 75.28 | | MMLU (5-shot) | 39.56 | | TruthfulQA (0-shot) | 34.49 | | Winogrande (5-shot) | 72.14 | | GSM8K (5-shot) | 1.97 | | DROP (3-shot) | 4.1 |
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ArthurZ/dummy-rocbert-qa
ArthurZ
2022-11-16T16:16:06Z
19,494
0
transformers
[ "transformers", "pytorch", "roc_bert", "question-answering", "autotrain_compatible", "endpoints_compatible", "region:us" ]
question-answering
2022-11-16T16:03:38Z
Entry not found
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timm/tf_efficientnetv2_s.in21k
timm
2023-04-27T22:17:52Z
19,450
2
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-21k", "arxiv:2104.00298", "license:apache-2.0", "region:us" ]
image-classification
2022-12-13T00:18:57Z
--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-21k --- # Model card for tf_efficientnetv2_s.in21k A EfficientNet-v2 image classification model. Trained on ImageNet-21k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 48.2 - GMACs: 5.4 - Activations (M): 22.8 - Image size: train = 300 x 300, test = 384 x 384 - **Papers:** - EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298 - **Dataset:** ImageNet-21k - **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('tf_efficientnetv2_s.in21k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnetv2_s.in21k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 24, 150, 150]) # torch.Size([1, 48, 75, 75]) # torch.Size([1, 64, 38, 38]) # torch.Size([1, 160, 19, 19]) # torch.Size([1, 256, 10, 10]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnetv2_s.in21k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1280, 10, 10) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{tan2021efficientnetv2, title={Efficientnetv2: Smaller models and faster training}, author={Tan, Mingxing and Le, Quoc}, booktitle={International conference on machine learning}, pages={10096--10106}, year={2021}, organization={PMLR} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
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ozcangundes/mt5-multitask-qa-qg-turkish
ozcangundes
2021-06-23T15:24:09Z
19,449
4
transformers
[ "transformers", "pytorch", "jax", "mt5", "text2text-generation", "question-answering", "question-generation", "multitask-model", "tr", "dataset:TQUAD", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
question-answering
2022-03-02T23:29:05Z
--- language: tr datasets: - TQUAD tags: - question-answering - question-generation - multitask-model license: apache-2.0 --- # mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System [Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset) for three downstream task **Answer extraction, Question Generation and Question Answering** served in this single model. mT5 model was also trained for multiple text2text NLP tasks. All data processing, training and pipeline codes can be found on my [Github](https://github.com/ozcangundes/multitask-question-generation). I will share the training details in the repo as soon as possible. mT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tune it. 8 epoch and 1e-4 learning rate with 0 warmup steps was applied during training. These hparams and the others can be fine-tuned for much more better results. ## Requirements ❗❗❗ ``` !pip install transformers==4.4.2 !pip install sentencepiece==0.1.95 !git clone https://github.com/ozcangundes/multitask-question-generation.git %cd multitask-question-generation/ ``` ## Usage 🚀🚀 ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ozcangundes/mt5-multitask-qa-qg-turkish") model = AutoModelForSeq2SeqLM.from_pretrained("ozcangundes/mt5-multitask-qa-qg-turkish") from pipelines import pipeline #pipelines.py script in the cloned repo multimodel = pipeline("multitask-qa-qg",tokenizer=tokenizer,model=model) #sample text text="Özcan Gündeş, 1993 yılı Tarsus doğumludur. Orta Doğu Teknik Üniversitesi \\\\ Endüstri Mühendisliği bölümünde 2011 2016 yılları arasında lisans eğitimi görmüştür. \\\\ Yüksek lisansını ise 2020 Aralık ayında, 4.00 genel not ortalaması ile \\\\ Boğaziçi Üniversitesi, Yönetim Bilişim Sistemleri bölümünde tamamlamıştır.\\\\ Futbolla yakından ilgilenmekle birlikte, Galatasaray kulübü taraftarıdır." ``` ## Example - Both Question Generation and Question Answering 💬💬 ``` multimodel(text) #output => [{'answer': 'Tarsus', 'question': 'Özcan Gündeş nerede doğmuştur?'}, {'answer': '1993', 'question': 'Özcan Gündeş kaç yılında doğmuştur?'}, {'answer': '2011 2016', 'question': 'Özcan Gündeş lisans eğitimini hangi yıllar arasında tamamlamıştır?'}, {'answer': 'Boğaziçi Üniversitesi, Yönetim Bilişim Sistemleri', 'question': 'Özcan Gündeş yüksek lisansını hangi bölümde tamamlamıştır?'}, {'answer': 'Galatasaray kulübü', 'question': 'Özcan Gündeş futbolla yakından ilgilenmekle birlikte hangi kulübü taraftarıdır?'}] ``` From this text, 5 questions are generated and they are answered by the model. ## Example - Question Answering 💭💭 Both text and also, related question should be passed into pipeline. ``` multimodel({"context":text,"question":"Özcan hangi takımı tutmaktadır?"}) #output => Galatasaray multimodel({"context":text,"question":"Özcan, yüksek lisanstan ne zaman mezun oldu?"}) #output => 2020 Aralık ayında multimodel({"context":text,"question":"Özcan'ın yüksek lisans bitirme notu kaçtır?"}) #output => 4.00 #Sorry for being cocky 😝😝 ``` ## ACKNOWLEDGEMENT This work is inspired from [Suraj Patil's great repo](https://github.com/patil-suraj/question_generation). I would like to thank him for the clean codes and also,[Okan Çiftçi](https://github.com/okanvk) for the Turkish dataset 🙏
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optimum/gpt2
optimum
2023-01-03T10:29:58Z
19,425
4
transformers
[ "transformers", "onnx", "gpt2", "text-generation", "exbert", "en", "license:mit", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2022-11-22T10:17:23Z
--- language: en tags: - exbert license: mit --- # GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use the ONNX models of gpt2 to get the features of a given text: Example using transformers.pipelines: ```python from transformers import AutoTokenizer, pipeline from optimum.onnxruntime import ORTModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gpt2") model = ORTModelForCausalLM.from_pretrained("gpt2", from_transformers=True) onnx_gen = pipeline("text-generation", model=model, tokenizer=tokenizer) text = "My name is Philipp and I live in Germany." gen = onnx_gen(text) ``` Example of text generation: ```python from transformers import AutoTokenizer from optimum.onnxruntime import ORTModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("optimum/gpt2") model = ORTModelForCausalLM.from_pretrained("optimum/gpt2") inputs = tokenizer("My name is Arthur and I live in", return_tensors="pt") gen_tokens = model.generate(**inputs,do_sample=True,temperature=0.9, min_length=20,max_length=20) tokenizer.batch_decode(gen_tokens) ```
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naver-clova-ix/donut-base-finetuned-docvqa
naver-clova-ix
2022-09-21T12:50:31Z
19,414
76
transformers
[ "transformers", "pytorch", "vision-encoder-decoder", "donut", "image-to-text", "vision", "document-question-answering", "arxiv:2111.15664", "license:mit", "endpoints_compatible", "has_space", "region:us" ]
document-question-answering
2022-07-19T13:58:22Z
--- license: mit pipeline_tag: document-question-answering tags: - donut - image-to-text - vision widget: - text: "What is the invoice number?" src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" - text: "What is the purchase amount?" src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg" --- # Donut (base-sized model, fine-tuned on DocVQA) Donut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/donut_architecture.jpg) ## Intended uses & limitations This model is fine-tuned on DocVQA, a document visual question answering dataset. We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2111-15664, author = {Geewook Kim and Teakgyu Hong and Moonbin Yim and Jinyoung Park and Jinyeong Yim and Wonseok Hwang and Sangdoo Yun and Dongyoon Han and Seunghyun Park}, title = {Donut: Document Understanding Transformer without {OCR}}, journal = {CoRR}, volume = {abs/2111.15664}, year = {2021}, url = {https://arxiv.org/abs/2111.15664}, eprinttype = {arXiv}, eprint = {2111.15664}, timestamp = {Thu, 02 Dec 2021 10:50:44 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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google/rembert
google
2022-05-27T15:05:23Z
19,380
15
transformers
[ "transformers", "pytorch", "tf", "rembert", "multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "bs", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:wikipedia", "arxiv:2010.12821", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: - multilingual - af - am - ar - az - be - bg - bn - bs - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - hr - ht - hu - hy - id - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - ur - uz - vi - xh - yi - yo - zh - zu license: apache-2.0 datasets: - wikipedia --- # RemBERT (for classification) Pretrained RemBERT model on 110 languages using a masked language modeling (MLM) objective. It was introduced in the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821). A direct export of the model checkpoint was first made available in [this repository](https://github.com/google-research/google-research/tree/master/rembert). This version of the checkpoint is lightweight since it is meant to be finetuned for classification and excludes the output embedding weights. ## Model description RemBERT's main difference with mBERT is that the input and output embeddings are not tied. Instead, RemBERT uses small input embeddings and larger output embeddings. This makes the model more efficient since the output embeddings are discarded during fine-tuning. It is also more accurate, especially when reinvesting the input embeddings' parameters into the core model, as is done on RemBERT. ## Intended uses & limitations You should fine-tune this model for your downstream task. It is meant to be a general-purpose model, similar to mBERT. In our [paper](https://arxiv.org/abs/2010.12821), we have successfully applied this model to tasks such as classification, question answering, NER, POS-tagging. For tasks such as text generation you should look at models like GPT2. ## Training data The RemBERT model was pretrained on multilingual Wikipedia data over 110 languages. The full language list is on [this repository](https://github.com/google-research/google-research/tree/master/rembert) ### BibTeX entry and citation info ```bibtex @inproceedings{DBLP:conf/iclr/ChungFTJR21, author = {Hyung Won Chung and Thibault F{\'{e}}vry and Henry Tsai and Melvin Johnson and Sebastian Ruder}, title = {Rethinking Embedding Coupling in Pre-trained Language Models}, booktitle = {9th International Conference on Learning Representations, {ICLR} 2021, Virtual Event, Austria, May 3-7, 2021}, publisher = {OpenReview.net}, year = {2021}, url = {https://openreview.net/forum?id=xpFFI\_NtgpW}, timestamp = {Wed, 23 Jun 2021 17:36:39 +0200}, biburl = {https://dblp.org/rec/conf/iclr/ChungFTJR21.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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TheBloke/zephyr-7B-beta-GPTQ
TheBloke
2023-10-27T14:56:24Z
19,378
42
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:HuggingFaceH4/ultrafeedback_binarized", "arxiv:2305.18290", "arxiv:2310.16944", "base_model:HuggingFaceH4/zephyr-7b-beta", "license:mit", "text-generation-inference", "4-bit", "region:us" ]
text-generation
2023-10-27T13:16:11Z
--- base_model: HuggingFaceH4/zephyr-7b-beta datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized inference: false language: - en license: mit model-index: - name: zephyr-7b-beta results: [] model_creator: Hugging Face H4 model_name: Zephyr 7B Beta model_type: mistral prompt_template: '<|system|> </s> <|user|> {prompt}</s> <|assistant|> ' quantized_by: TheBloke tags: - generated_from_trainer --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Zephyr 7B Beta - GPTQ - Model creator: [Hugging Face H4](https://huggingface.co/HuggingFaceH4) - Original model: [Zephyr 7B Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) <!-- description start --> ## Description This repo contains GPTQ model files for [Hugging Face H4's Zephyr 7B Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/zephyr-7B-beta-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF) * [Hugging Face H4's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Zephyr ``` <|system|> </s> <|user|> {prompt}</s> <|assistant|> ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KobaldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/zephyr-7B-beta-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/zephyr-7B-beta-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `zephyr-7B-beta-GPTQ`: ```shell mkdir zephyr-7B-beta-GPTQ huggingface-cli download TheBloke/zephyr-7B-beta-GPTQ --local-dir zephyr-7B-beta-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir zephyr-7B-beta-GPTQ huggingface-cli download TheBloke/zephyr-7B-beta-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir zephyr-7B-beta-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir zephyr-7B-beta-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/zephyr-7B-beta-GPTQ --local-dir zephyr-7B-beta-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/zephyr-7B-beta-GPTQ`. - To download from a specific branch, enter for example `TheBloke/zephyr-7B-beta-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `zephyr-7B-beta-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/zephyr-7B-beta-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<|system|> </s> <|user|> {prompt}</s> <|assistant|> ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/zephyr-7B-beta-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<|system|> </s> <|user|> {prompt}</s> <|assistant|> ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Hugging Face H4's Zephyr 7B Beta <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) | |-------------|-----|----|---------------|--------------| | StableLM-Tuned-α | 7B| dSFT |2.75| -| | MPT-Chat | 7B |dSFT |5.42| -| | Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83| | Mistral-Instructv0.1 | 7B| - | 6.84 |-| | Zephyr-7b-α |7B| dDPO| 6.88| -| | **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** | | Falcon-Instruct | 40B |dSFT |5.17 |45.71| | Guanaco | 65B | SFT |6.41| 71.80| | Llama2-Chat | 70B |RLHF |6.86| 92.66| | Vicuna v1.3 | 33B |dSFT |7.12 |88.99| | WizardLM v1.0 | 70B |dSFT |7.71 |-| | Xwin-LM v0.1 | 70B |dPPO |- |95.57| | GPT-3.5-turbo | - |RLHF |7.94 |89.37| | Claude 2 | - |RLHF |8.06| 91.36| | GPT-4 | -| RLHF |8.99| 95.28| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <|system|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <|user|> # How many helicopters can a human eat in one sitting?</s> # <|assistant|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-β has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 | | 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 | | 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 | | 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 | | 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 | | 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 | | 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 | | 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 | | 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 | | 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 | | 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 | | 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 | | 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 | | 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 | | 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 | | 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 | | 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 | | 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 | | 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 | | 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 | | 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 | | 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 | | 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 | | 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 | | 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 | | 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 | | 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 | | 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 | | 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 | | 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 | | 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 | | 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 | | 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 | | 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 | | 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 | | 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 | | 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 | | 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 | | 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 | | 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 | | 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 | | 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 | | 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 | | 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 | | 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 | | 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 | | 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 | | 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 | | 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 | | 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 | | 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 | | 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 | | 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 | | 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 | | 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 | | 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 | | 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 | | 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
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JackFram/llama-68m
JackFram
2023-10-02T14:46:59Z
19,245
6
transformers
[ "transformers", "pytorch", "llama", "text-generation", "license:other", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-07-19T02:20:03Z
--- license: other ---
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mrsinghania/asr-question-detection
mrsinghania
2021-09-21T06:44:23Z
19,204
4
transformers
[ "transformers", "pytorch", "bert", "text-classification", "endpoints_compatible", "region:us" ]
text-classification
2022-03-02T23:29:05Z
<i>Question vs Statement classifier</i> trained on more than 7k samples which were coming from spoken data in an interview setting <b>Code for using in Transformers:</b> from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrsinghania/asr-question-detection") model = AutoModelForSequenceClassification.from_pretrained("mrsinghania/asr-question-detection")
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lordtt13/emo-mobilebert
lordtt13
2023-07-09T15:28:20Z
19,174
3
transformers
[ "transformers", "pytorch", "tf", "safetensors", "mobilebert", "text-classification", "en", "dataset:emo", "arxiv:2004.02984", "endpoints_compatible", "has_space", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- language: en datasets: - emo --- ## Emo-MobileBERT: a thin version of BERT LARGE, trained on the EmoContext Dataset from scratch ### Details of MobileBERT The **MobileBERT** model was presented in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by *Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, Denny Zhou* and here is the abstract: Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT_BASE). ### Details of the downstream task (Emotion Recognition) - Dataset 📚 SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes: - sad 😢 - happy 😃 - angry 😡 - others ### Model training The training script is present [here](https://github.com/lordtt13/transformers-experiments/blob/master/Custom%20Tasks/emo-mobilebert.ipynb). ### Pipelining the Model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline tokenizer = AutoTokenizer.from_pretrained("lordtt13/emo-mobilebert") model = AutoModelForSequenceClassification.from_pretrained("lordtt13/emo-mobilebert") nlp_sentence_classif = transformers.pipeline('sentiment-analysis', model = model, tokenizer = tokenizer) nlp_sentence_classif("I've never had such a bad day in my life") # Output: [{'label': 'sad', 'score': 0.93153977394104}] ``` > Created by [Tanmay Thakur](https://github.com/lordtt13) | [LinkedIn](https://www.linkedin.com/in/tanmay-thakur-6bb5a9154/)
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bigcode/gpt_bigcode-santacoder
bigcode
2023-06-08T09:20:22Z
19,067
23
transformers
[ "transformers", "pytorch", "safetensors", "gpt_bigcode", "text-generation", "code", "dataset:bigcode/the-stack", "license:openrail", "model-index", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-04-06T01:35:04Z
--- license: openrail datasets: - bigcode/the-stack language: - code programming_language: - Java - JavaScript - Python pipeline_tag: text-generation inference: false model-index: - name: SantaCoder results: - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL HumanEval (Python) metrics: - name: pass@1 type: pass@1 value: 0.18 verified: false - name: pass@10 type: pass@10 value: 0.29 verified: false - name: pass@100 type: pass@100 value: 0.49 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL MBPP (Python) metrics: - name: pass@1 type: pass@1 value: 0.35 verified: false - name: pass@10 type: pass@10 value: 0.58 verified: false - name: pass@100 type: pass@100 value: 0.77 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL HumanEval (JavaScript) metrics: - name: pass@1 type: pass@1 value: 0.16 verified: false - name: pass@10 type: pass@10 value: 0.27 verified: false - name: pass@100 type: pass@100 value: 0.47 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL MBPP (Javascript) metrics: - name: pass@1 type: pass@1 value: 0.28 verified: false - name: pass@10 type: pass@10 value: 0.51 verified: false - name: pass@100 type: pass@100 value: 0.70 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL HumanEval (Java) metrics: - name: pass@1 type: pass@1 value: 0.15 verified: false - name: pass@10 type: pass@10 value: 0.26 verified: false - name: pass@100 type: pass@100 value: 0.41 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL MBPP (Java) metrics: - name: pass@1 type: pass@1 value: 0.28 verified: false - name: pass@10 type: pass@10 value: 0.44 verified: false - name: pass@100 type: pass@100 value: 0.59 verified: false - task: type: text-generation dataset: type: loubnabnl/humaneval_infilling name: HumanEval FIM (Python) metrics: - name: single_line type: exact_match value: 0.44 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL HumanEval FIM (Java) metrics: - name: single_line type: exact_match value: 0.62 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL HumanEval FIM (JavaScript) metrics: - name: single_line type: exact_match value: 0.60 verified: false - task: type: text-generation dataset: type: code_x_glue_ct_code_to_text name: CodeXGLUE code-to-text (Python) metrics: - name: BLEU type: bleu value: 18.13 verified: false --- # SantaCoder ![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/banner.png) Play with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo). # Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) # Model Summary This is the same model as [SantaCoder](https://huggingface.co/bigcode/santacoder) but it can be loaded with transformers >=4.28.1 to use the GPTBigCode architecture. We refer the reader to the [SantaCoder model page](https://huggingface.co/bigcode/santacoder) for full documentation about this model - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](www.bigcode-project.org) - **Paper:** [🎅SantaCoder: Don't reach for the stars!🌟](https://t.co/YV3pzUbYOr) - **Point of Contact:** [[email protected]](mailto:[email protected]) - **Languages:** Python, Java, and JavaScript There are two versions (branches) of the model: * `main`: Uses the `gpt_bigcode` model. [Requires the bigcode fork of transformers](https://github.com/bigcode-project/transformers). * `main_custom`: Packaged with its modeling code. Requires `transformers>=4.27`. Alternatively, it can run on older versions by setting the configuration parameter `activation_function = "gelu_pytorch_tanh"`. # Use ## Intended use The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. `# the following function computes the sqrt`) or write a function signature and docstring and let the model complete the function body. ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/santacoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code in Python, Java, and JavaScript. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. # Training ## Model - **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective - **Pretraining steps:** 600K - **Pretraining tokens:** 236 billion - **Precision:** float16 ## Hardware - **GPUs:** 96 Tesla V100 - **Training time:** 6.2 days - **Total FLOPS:** 2.1 x 10e21 ## Software - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # License The model is licenses under the CodeML Open RAIL-M v0.1 license. You can find the full license [here](https://huggingface.co/spaces/bigcode/license).
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timm/eva_giant_patch14_224.clip_ft_in1k
timm
2023-03-31T05:57:21Z
19,021
2
timm
[ "timm", "pytorch", "safetensors", "image-classification", "arxiv:2303.15389", "license:mit", "region:us" ]
image-classification
2022-12-23T02:37:40Z
--- tags: - image-classification - timm library_tag: timm license: mit --- # Model card for eva_giant_patch14_224.clip_ft_in1k An EVA-CLIP image classification model. Pretrained on LAION-400M with CLIP and fine-tuned on ImageNet-1k by paper authors. EVA-CLIP uses MIM pretrained image towers and pretrained text towers, FLIP patch dropout, and different optimizers and hparams to accelerate training. NOTE: `timm` checkpoints are float32 for consistency with other models. Original checkpoints are float16 or bfloat16 in some cases, see originals if that's preferred. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 1012.6 - GMACs: 267.2 - Activations (M): 192.6 - Image size: 224 x 224 - **Papers:** - EVA-CLIP: Improved Training Techniques for CLIP at Scale: https://arxiv.org/abs/2303.15389 - **Original:** - https://github.com/baaivision/EVA - https://huggingface.co/QuanSun/EVA-CLIP ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('eva_giant_patch14_224.clip_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'eva_giant_patch14_224.clip_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 257, 1408) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |model |top1 |top5 |param_count|img_size| |-----------------------------------------------|------|------|-----------|--------| |eva02_large_patch14_448.mim_m38m_ft_in22k_in1k |90.054|99.042|305.08 |448 | |eva02_large_patch14_448.mim_in22k_ft_in22k_in1k|89.946|99.01 |305.08 |448 | |eva_giant_patch14_560.m30m_ft_in22k_in1k |89.792|98.992|1014.45 |560 | |eva02_large_patch14_448.mim_in22k_ft_in1k |89.626|98.954|305.08 |448 | |eva02_large_patch14_448.mim_m38m_ft_in1k |89.57 |98.918|305.08 |448 | |eva_giant_patch14_336.m30m_ft_in22k_in1k |89.56 |98.956|1013.01 |336 | |eva_giant_patch14_336.clip_ft_in1k |89.466|98.82 |1013.01 |336 | |eva_large_patch14_336.in22k_ft_in22k_in1k |89.214|98.854|304.53 |336 | |eva_giant_patch14_224.clip_ft_in1k |88.882|98.678|1012.56 |224 | |eva02_base_patch14_448.mim_in22k_ft_in22k_in1k |88.692|98.722|87.12 |448 | |eva_large_patch14_336.in22k_ft_in1k |88.652|98.722|304.53 |336 | |eva_large_patch14_196.in22k_ft_in22k_in1k |88.592|98.656|304.14 |196 | |eva02_base_patch14_448.mim_in22k_ft_in1k |88.23 |98.564|87.12 |448 | |eva_large_patch14_196.in22k_ft_in1k |87.934|98.504|304.14 |196 | |eva02_small_patch14_336.mim_in22k_ft_in1k |85.74 |97.614|22.13 |336 | |eva02_tiny_patch14_336.mim_in22k_ft_in1k |80.658|95.524|5.76 |336 | ## Citation ```bibtex @article{EVA-CLIP, title={EVA-02: A Visual Representation for Neon Genesis}, author={Sun, Quan and Fang, Yuxin and Wu, Ledell and Wang, Xinlong and Cao, Yue}, journal={arXiv preprint arXiv:2303.15389}, year={2023} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
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xyn-ai/anything-v4.0
xyn-ai
2023-03-23T04:25:51Z
19,005
49
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-03-23T04:25:51Z
--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true duplicated_from: andite/anything-v4.0 --- Fantasy.ai is the official and exclusive hosted AI generation platform that holds a commercial use license for Anything V4.0, you can use their service at https://Fantasy.ai/ Please report any unauthorized commercial use. ----------------- Try out my new model! - [Pastel Mix || Stylized Anime Model](https://huggingface.co/andite/pastel-mix). Thanks. I also uploaded it in CivitAI! https://civitai.com/models/5414/pastel-mix-stylized-anime-model I'd appreciate the ratings, thank you! Yes, it's a shameless plug. Examples: ![](https://huggingface.co/andite/Pastel-Mix/resolve/main/example-images/grid-0018.png) ![](https://huggingface.co/andite/pastel-mix/resolve/main/example-images/grid-reimu.png) ![](https://huggingface.co/andite/pastel-mix/resolve/main/example-images/grid-0043.png) ------- <font color="grey">Thanks to [Linaqruf](https://huggingface.co/Linaqruf) for letting me borrow his model card for reference. # Anything V4 Welcome to Anything V4 - a latent diffusion model for weebs. The newest version of Anything. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images. e.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_** I think the V4.5 version better though, it's in this repo. feel free 2 try it. ## Yes, this model has [AbyssOrangeMix2](https://huggingface.co/WarriorMama777/OrangeMixs) in it. coz its a very good model. check it out luls ;) # Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run anything-v4.0: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/anything-v4.0) ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "andite/anything-v4.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "hatsune_miku" image = pipe(prompt).images[0] image.save("./hatsune_miku.png") ``` ## Examples Below are some examples of images generated using this model: **Anime Girl:** ![Anime Girl](https://huggingface.co/andite/anything-v4.0/resolve/main/example-1.png) ``` masterpiece, best quality, 1girl, white hair, medium hair, cat ears, closed eyes, looking at viewer, :3, cute, scarf, jacket, outdoors, streets Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7 ``` **Anime Boy:** ![Anime Boy](https://huggingface.co/andite/anything-v4.0/resolve/main/example-2.png) ``` 1boy, bishounen, casual, indoors, sitting, coffee shop, bokeh Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7 ``` **Scenery:** ![Scenery](https://huggingface.co/andite/anything-v4.0/resolve/main/example-4.png) ``` scenery, village, outdoors, sky, clouds Steps: 50, Sampler: DPM++ 2S a Karras, CFG scale: 7 ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) ## Big Thanks to - [Linaqruf](https://huggingface.co/Linaqruf). [NoCrypt](https://huggingface.co/NoCrypt), and Fannovel16#9022 for helping me out alot regarding my inquiries and concern about models and other stuff.
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microsoft/unispeech-large-1500h-cv
microsoft
2021-11-05T12:41:56Z
18,990
1
transformers
[ "transformers", "pytorch", "unispeech", "pretraining", "speech", "en", "dataset:common_voice", "arxiv:2101.07597", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
--- language: - en datasets: - common_voice tags: - speech --- # UniSpeech-Large [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. [Paper: UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang **Abstract** *In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.* The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech. # Usage This is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be used in inference. The model was pre-trained in English and should therefore perform well only in English. **Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence of phonemes before fine-tuning. ## Speech Recognition To fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition). ## Speech Classification To fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification). # Contribution The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten). # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/unispeech.png)
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lllyasviel/control_v11p_sd15_lineart
lllyasviel
2023-05-04T18:49:42Z
18,976
20
diffusers
[ "diffusers", "art", "controlnet", "stable-diffusion", "controlnet-v1-1", "image-to-image", "arxiv:2302.05543", "base_model:runwayml/stable-diffusion-v1-5", "license:openrail", "has_space", "diffusers:ControlNetModel", "region:us" ]
image-to-image
2023-04-14T19:25:13Z
--- license: openrail base_model: runwayml/stable-diffusion-v1-5 tags: - art - controlnet - stable-diffusion - controlnet-v1-1 - image-to-image duplicated_from: ControlNet-1-1-preview/control_v11p_sd15_lineart --- # Controlnet - v1.1 - *lineart Version* **Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel). This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_lineart.pth) into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet). ControlNet is a neural network structure to control diffusion models by adding extra conditions. ![img](./sd.png) This checkpoint corresponds to the ControlNet conditioned on **lineart images**. ## Model Details - **Developed by:** Lvmin Zhang, Maneesh Agrawala - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). - **Cite as:** @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Introduction Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by Lvmin Zhang, Maneesh Agrawala. The abstract reads as follows: *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.* ## Example It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint has been trained on it. Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. **Note**: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below: 1. Install https://github.com/patrickvonplaten/controlnet_aux ```sh $ pip install controlnet_aux==0.3.0 ``` 2. Let's install `diffusers` and related packages: ``` $ pip install diffusers transformers accelerate ``` 3. Run code: ```python import torch import os from huggingface_hub import HfApi from pathlib import Path from diffusers.utils import load_image from PIL import Image import numpy as np from controlnet_aux import LineartDetector from diffusers import ( ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler, ) checkpoint = "ControlNet-1-1-preview/control_v11p_sd15_lineart" image = load_image( "https://huggingface.co/ControlNet-1-1-preview/control_v11p_sd15_lineart/resolve/main/images/input.png" ) image = image.resize((512, 512)) prompt = "michael jackson concert" processor = LineartDetector.from_pretrained("lllyasviel/Annotators") control_image = processor(image) control_image.save("./images/control.png") controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16) pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() generator = torch.manual_seed(0) image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0] image.save('images/image_out.png') ``` ![bird](./images/input.png) ![bird_canny](./images/control.png) ![bird_canny_out](./images/image_out.png) ## Other released checkpoints v1-1 The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on a different type of conditioning: | Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example | |---|---|---|---|---| |[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>| |[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>| ## More information For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly).
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asapp/sew-d-tiny-100k
asapp
2023-07-21T23:05:03Z
18,954
1
transformers
[ "transformers", "pytorch", "safetensors", "sew-d", "feature-extraction", "speech", "en", "dataset:librispeech_asr", "arxiv:2109.06870", "license:apache-2.0", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-02T23:29:05Z
--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # SEW-D-tiny [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
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Helsinki-NLP/opus-mt-en-hi
Helsinki-NLP
2023-08-16T11:29:49Z
18,927
19
transformers
[ "transformers", "pytorch", "tf", "rust", "marian", "text2text-generation", "translation", "en", "hi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
translation
2022-03-02T23:29:04Z
--- language: - en - hi tags: - translation license: apache-2.0 --- ### eng-hin * source group: English * target group: Hindi * OPUS readme: [eng-hin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md) * model: transformer-align * source language(s): eng * target language(s): hin * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014.eng.hin | 6.9 | 0.296 | | newstest2014-hien.eng.hin | 9.9 | 0.323 | | Tatoeba-test.eng.hin | 16.1 | 0.447 | ### System Info: - hf_name: eng-hin - source_languages: eng - target_languages: hin - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'hi'] - src_constituents: {'eng'} - tgt_constituents: {'hin'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.test.txt - src_alpha3: eng - tgt_alpha3: hin - short_pair: en-hi - chrF2_score: 0.447 - bleu: 16.1 - brevity_penalty: 1.0 - ref_len: 32904.0 - src_name: English - tgt_name: Hindi - train_date: 2020-06-17 - src_alpha2: en - tgt_alpha2: hi - prefer_old: False - long_pair: eng-hin - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
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jpwahle/longformer-base-plagiarism-detection
jpwahle
2023-03-17T11:38:57Z
18,902
7
transformers
[ "transformers", "pytorch", "safetensors", "longformer", "text-classification", "array", "of", "tags", "en", "dataset:jpwahle/machine-paraphrase-dataset", "arxiv:2004.05150", "endpoints_compatible", "has_space", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- language: en thumbnail: url to a thumbnail used in social sharing tags: - array - of - tags datasets: - jpwahle/machine-paraphrase-dataset widget: - text: Plagiarism is the representation of another author's writing, thoughts, ideas, or expressions as one's own work. --- # Longformer-base for Machine-Paraphrase Detection If you are using this model in your research work, please cite ``` @InProceedings{10.1007/978-3-030-96957-8_34, author="Wahle, Jan Philip and Ruas, Terry and Folt{\'y}nek, Tom{\'a}{\v{s}} and Meuschke, Norman and Gipp, Bela", title="Identifying Machine-Paraphrased Plagiarism", booktitle="Information for a Better World: Shaping the Global Future", year="2022", publisher="Springer International Publishing", address="Cham", pages="393--413", abstract="Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99{\%} (F1=99.68{\%} for SpinBot and F1=71.64{\%} for SpinnerChief cases), while human evaluators achieved F1=78.4{\%} for SpinBot and F1=65.6{\%} for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.", isbn="978-3-030-96957-8" } ``` This is the checkpoint for Longformer-base after being trained on the [Machine-Paraphrased Plagiarism Dataset](https://doi.org/10.5281/zenodo.3608000) Additional information about this model: * [The longformer-base-4096 model page](https://huggingface.co/allenai/longformer-base-4096) * [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) * [Official implementation by AllenAI](https://github.com/allenai/longformer) The model can be loaded to perform Plagiarism like so: ```py from transformers import AutoModelForSequenceClassification, AutoTokenizer AutoModelForSequenceClassification("jpelhaw/longformer-base-plagiarism-detection") AutoTokenizer.from_pretrained("jpelhaw/longformer-base-plagiarism-detection") input = "Plagiarism is the representation of another author's writing, \ thoughts, ideas, or expressions as one's own work." example = tokenizer.tokenize(input, add_special_tokens=True) answer = model(**example) # "plagiarised" ```
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LanguageBind/LanguageBind_Video_merge
LanguageBind
2023-11-21T02:03:46Z
18,897
2
transformers
[ "transformers", "pytorch", "LanguageBindVideo", "zero-shot-image-classification", "license:mit", "endpoints_compatible", "region:us" ]
zero-shot-image-classification
2023-11-21T01:35:38Z
--- license: mit ---
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TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ
TheBloke
2023-08-21T14:48:15Z
18,893
101
transformers
[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "license:other", "text-generation-inference", "4-bit", "region:us" ]
text-generation
2023-06-27T03:55:57Z
--- inference: false license: other --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Eric Hartford's Wizard Vicuna 13B Uncensored GPTQ These files are GPTQ 4bit model files for [Eric Hartford's Wizard Vicuna 13B Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-13B-Uncensored) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test). It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). **This is an experimental new GPTQ which offers up to 8K context size** The increased context is tested to work with [ExLlama](https://github.com/turboderp/exllama), via the latest release of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It has also been tested from Python code using AutoGPTQ, and `trust_remote_code=True`. Code credits: - Original concept and code for increasing context length: [kaiokendev](https://huggingface.co/kaiokendev) - Updated Llama modelling code that includes this automatically via trust_remote_code: [emozilla](https://huggingface.co/emozilla). Please read carefully below to see how to use it. GGML versions are not yet provided, as there is not yet support for SuperHOT in llama.cpp. This is being investigated and will hopefully come soon. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GGML) * [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-fp16) * [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/Wizard-Vicuna-13B-Uncensored) ## How to easily download and use this model in text-generation-webui with ExLlama Please make sure you're using the latest version of text-generation-webui 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done" 5. Untick **Autoload the model** 6. In the top left, click the refresh icon next to **Model**. 7. In the **Model** dropdown, choose the model you just downloaded: `Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ` 8. To use the increased context, set the **Loader** to **ExLlama**, set **max_seq_len** to 8192 or 4096, and set **compress_pos_emb** to **4** for 8192 context, or to **2** for 4096 context. 9. Now click **Save Settings** followed by **Reload** 10. The model will automatically load, and is now ready for use! 11. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! ## How to use this GPTQ model from Python code with AutoGPTQ First make sure you have AutoGPTQ and Einops installed: ``` pip3 install einops auto-gptq ``` Then run the following code. Note that in order to get this to work, `config.json` has been hardcoded to a sequence length of 8192. If you want to try 4096 instead to reduce VRAM usage, please manually edit `config.json` to set `max_position_embeddings` to the value you want. ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig import argparse model_name_or_path = "TheBloke/Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ" model_basename = "wizard-vicuna-13b-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device_map='auto', use_triton=use_triton, quantize_config=None) model.seqlen = 8192 # Note: check the prompt template is correct for this model. prompt = "Tell me about AI" prompt_template=f'''USER: {prompt} ASSISTANT:''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Using other UIs: monkey patch Provided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev. It can be theoretically be added to any Python UI or custom code to enable the same result as `trust_remote_code=True`. I have not tested this, and it should be superseded by using `trust_remote_code=True`, but I include it for completeness and for interest. ## Provided files **wizard-vicuna-13b-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors** This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead. It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed. * `wizard-vicuna-13b-uncensored-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors` * Works for use with ExLlama with increased context (4096 or 8192) * Works with AutoGPTQ in Python code, including with increased context, if `trust_remote_code=True` is set. * Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. May have issues with GPTQ-for-LLaMa Triton mode. * Works with text-generation-webui, including one-click-installers. * Parameters: Groupsize = 128. Act Order / desc_act = False. <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Kaio Ken's SuperHOT 8K ### SuperHOT Prototype 2 w/ 8K Context This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k). Tests have shown that the model does indeed leverage the extended context at 8K. You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192** #### Looking for Merged & Quantized Models? - 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors) - 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors) #### Training Details I trained the LoRA with the following configuration: - 1200 samples (~400 samples over 2048 sequence length) - learning rate of 3e-4 - 3 epochs - The exported modules are: - q_proj - k_proj - v_proj - o_proj - no bias - Rank = 4 - Alpha = 8 - no dropout - weight decay of 0.1 - AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5 - Trained on 4-bit base model # Original model card: Eric Hartford's Wizard Vicuna 13B Uncensored This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. Shout out to the open source AI/ML community, and everyone who helped me out. Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
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asapp/sew-tiny-100k
asapp
2023-07-21T23:05:12Z
18,891
1
transformers
[ "transformers", "pytorch", "safetensors", "sew", "feature-extraction", "speech", "en", "dataset:librispeech_asr", "arxiv:2109.06870", "license:apache-2.0", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-02T23:29:05Z
--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # SEW-tiny [SEW by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWForCTC`.
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kandinsky-community/kandinsky-2-1-prior
kandinsky-community
2023-10-09T11:32:58Z
18,832
10
diffusers
[ "diffusers", "kandinsky", "license:apache-2.0", "has_space", "diffusers:KandinskyPriorPipeline", "region:us" ]
null
2023-05-24T09:51:57Z
--- license: apache-2.0 tags: - kandinsky inference: false --- # Kandinsky 2.1 Kandinsky 2.1 inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) ## Usage Kandinsky 2.1 is available in diffusers! ```python pip install diffusers transformers ``` ### Text to image ```python from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0] image.save("cheeseburger_monster.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png) ### Text Guided Image-to-Image Generation ```python from diffusers import AutoPipelineForImage2Image import torch import requests from io import BytesIO from PIL import Image import os pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) original_image = Image.open(BytesIO(response.content)).convert("RGB") original_image.thumbnail((768, 768)) image = pipe(prompt=prompt, image=original_image, strength=0.3).images[0] out.images[0].save("fantasy_land.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png) ### Text Guided Inpainting Generation ```python from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image import torch import numpy as np pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "a hat" negative_prompt = "low quality, bad quality" original_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) mask = np.zeros((768, 768), dtype=np.float32) # Let's mask out an area above the cat's head mask[:250, 250:-250] = 1 image = pipe(prompt=prompt, image=original_image, mask_image=mask).images[0] image.save("cat_with_hat.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/inpaint_cat_hat.png) 🚨🚨🚨 __Breaking change for Kandinsky Mask Inpainting__ 🚨🚨🚨 We introduced a breaking change for Kandinsky inpainting pipeline in the following pull request: https://github.com/huggingface/diffusers/pull/4207. Previously we accepted a mask format where black pixels represent the masked-out area. This is inconsistent with all other pipelines in diffusers. We have changed the mask format in Knaindsky and now using white pixels instead. Please upgrade your inpainting code to follow the above. If you are using Kandinsky Inpaint in production. You now need to change the mask to: ```python # For PIL input import PIL.ImageOps mask = PIL.ImageOps.invert(mask) # For PyTorch and Numpy input mask = 1 - mask ``` ### Interpolate ```python from diffusers import KandinskyPriorPipeline, KandinskyPipeline from diffusers.utils import load_image import PIL import torch pipe_prior = KandinskyPriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16 ) pipe_prior.to("cuda") img1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) img2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg" ) # add all the conditions we want to interpolate, can be either text or image images_texts = ["a cat", img1, img2] # specify the weights for each condition in images_texts weights = [0.3, 0.3, 0.4] # We can leave the prompt empty prompt = "" prior_out = pipe_prior.interpolate(images_texts, weights) pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt, **prior_out, height=768, width=768).images[0] image.save("starry_cat.png") ``` ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png) ## Model Architecture ### Overview Kandinsky 2.1 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. <p float="left"> <img src="https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png"/> </p> Specifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [mCLIP model](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14). The trained image prior model is then used to generate mCLIP image embeddings for input text prompts. Both the input text prompts and its mCLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image. ### Details The image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution). The main Text2Image diffusion model was trained on the basis of 170M text-image pairs from the [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) (an important condition was the presence of images with a resolution of at least 768x768). The use of 170M pairs is due to the fact that we kept the UNet diffusion block from Kandinsky 2.0, which allowed us not to train it from scratch. Further, at the stage of fine-tuning, a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources. ### Evaluation We quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID. FID metric values ​​for generative models on COCO_30k | | FID (30k)| |:------|----:| | eDiff-I (2022) | 6.95 | | Image (2022) | 7.27 | | Kandinsky 2.1 (2023) | 8.21| | Stable Diffusion 2.1 (2022) | 8.59 | | GigaGAN, 512x512 (2023) | 9.09 | | DALL-E 2 (2022) | 10.39 | | GLIDE (2022) | 12.24 | | Kandinsky 1.0 (2022) | 15.40 | | DALL-E (2021) | 17.89 | | Kandinsky 2.0 (2022) | 20.00 | | GLIGEN (2022) | 21.04 | For more information, please refer to the upcoming technical report. ## BibTex If you find this repository useful in your research, please cite: ``` @misc{kandinsky 2.1, title = {kandinsky 2.1}, author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov}, year = {2023}, howpublished = {}, } ```
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ArthurZ/flax-tiny-random-bert-sharded
ArthurZ
2022-11-14T06:24:51Z
18,788
0
transformers
[ "transformers", "jax", "bert", "feature-extraction", "flax", "endpoints_compatible", "region:us" ]
feature-extraction
2022-06-17T16:08:40Z
--- tags: - flax --- # Model Card for flax-tiny-random-bert-sharded # Model Details ## Model Description This model is used to check that the sharding of a flax_model works properly. See [`test_checkpoint_sharding_from_hub`](https://github.com/huggingface/transformers/blob/main/tests/test_modeling_flax_common.py#L1049). # Uses The model is not designed to be used and serves a testing purpose. ### Software - Transformers 4.21.0.dev0 - TensorFlow 2.9.0 - Datasets 2.2.2 - Tokenizers 0.12.1
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Rostlab/prot_bert_bfd
Rostlab
2020-12-11T21:30:10Z
18,783
9
transformers
[ "transformers", "pytorch", "tf", "fill-mask", "protein language model", "dataset:BFD", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
fill-mask
2022-03-02T23:29:04Z
--- language: protein tags: - protein language model datasets: - BFD --- # ProtBert-BFD model Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in [this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in [this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids. ## Model description ProtBert-BFD is based on Bert model which pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between our Bert model and the original Bert version is the way of dealing with sequences as separate documents This means the Next sentence prediction is not used, as each sequence is treated as a complete document. The masking follows the original Bert training with randomly masks 15% of the amino acids in the input. At the end, the feature extracted from this model revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. ## Intended uses & limitations The model could be used for protein feature extraction or to be fine-tuned on downstream tasks. We have noticed in some tasks you could gain more accuracy by fine-tuning the model rather than using it as a feature extractor. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import BertForMaskedLM, BertTokenizer, pipeline >>> tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False ) >>> model = BertForMaskedLM.from_pretrained("Rostlab/prot_bert_bfd") >>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer) >>> unmasker('D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T') [{'score': 0.1165614128112793, 'sequence': '[CLS] D L I P T S S K L V V L D T S L Q V K K A F F A L V T [SEP]', 'token': 5, 'token_str': 'L'}, {'score': 0.08976086974143982, 'sequence': '[CLS] D L I P T S S K L V V V D T S L Q V K K A F F A L V T [SEP]', 'token': 8, 'token_str': 'V'}, {'score': 0.08864385634660721, 'sequence': '[CLS] D L I P T S S K L V V S D T S L Q V K K A F F A L V T [SEP]', 'token': 10, 'token_str': 'S'}, {'score': 0.06227643042802811, 'sequence': '[CLS] D L I P T S S K L V V A D T S L Q V K K A F F A L V T [SEP]', 'token': 6, 'token_str': 'A'}, {'score': 0.06194969266653061, 'sequence': '[CLS] D L I P T S S K L V V T D T S L Q V K K A F F A L V T [SEP]', 'token': 15, 'token_str': 'T'}] ``` Here is how to use this model to get the features of a given protein sequence in PyTorch: ```python from transformers import BertModel, BertTokenizer import re tokenizer = BertTokenizer.from_pretrained('Rostlab/prot_bert_bfd', do_lower_case=False ) model = BertModel.from_pretrained("Rostlab/prot_bert_bfd") sequence_Example = "A E T C Z A O" sequence_Example = re.sub(r"[UZOB]", "X", sequence_Example) encoded_input = tokenizer(sequence_Example, return_tensors='pt') output = model(**encoded_input) ``` ## Training data The ProtBert-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences. ## Training procedure ### Preprocessing The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The inputs of the model are then of the form: ``` [CLS] Protein Sequence A [SEP] Protein Sequence B [SEP] ``` Furthermore, each protein sequence was treated as a separate document. The preprocessing step was performed twice, once for a combined length (2 sequences) of less than 512 amino acids, and another time using a combined length (2 sequences) of less than 2048 amino acids. The details of the masking procedure for each sequence followed the original Bert model as following: - 15% of the amino acids are masked. - In 80% of the cases, the masked amino acids are replaced by `[MASK]`. - In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace. - In the 10% remaining cases, the masked amino acids are left as is. ### Pretraining The model was trained on a single TPU Pod V3-1024 for one million steps in total. 800k steps using sequence length 512 (batch size 32k), and 200K steps using sequence length 2048 (batch size 6k). The optimizer used is Lamb with a learning rate of 0.002, a weight decay of 0.01, learning rate warmup for 140k steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Test results : | Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane | |:-----:|:-----:|:-----:|:-----:|:-----:| | CASP12 | 76 | 65 | | | | TS115 | 84 | 73 | | | | CB513 | 83 | 70 | | | | DeepLoc | | | 78 | 91 | ### BibTeX entry and citation info ```bibtex @article {Elnaggar2020.07.12.199554, author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard}, title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing}, elocation-id = {2020.07.12.199554}, year = {2020}, doi = {10.1101/2020.07.12.199554}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \&lt;a href="https://github.com/agemagician/ProtTrans"\&gt;https://github.com/agemagician/ProtTrans\&lt;/a\&gt;Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554}, eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf}, journal = {bioRxiv} } ``` > Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)
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studio-ousia/luke-large-finetuned-tacred
studio-ousia
2022-03-23T12:31:16Z
18,772
3
transformers
[ "transformers", "pytorch", "luke", "endpoints_compatible", "region:us" ]
null
2022-03-02T23:29:05Z
Entry not found
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sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english
sshleifer
2021-05-20T07:12:23Z
18,737
0
transformers
[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
Entry not found
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fxmarty/sshleifer-tiny-mbart-onnx
fxmarty
2022-08-23T13:53:53Z
18,714
0
transformers
[ "transformers", "onnx", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-08-23T13:52:07Z
--- license: apache-2.0 --- This model is a fork of `sshleifer/tiny-mbart` exported to ONNX.
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bigscience/bloom-3b
bigscience
2023-04-14T08:43:29Z
18,703
70
transformers
[ "transformers", "pytorch", "safetensors", "bloom", "text-generation", "ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu", "arxiv:1909.08053", "arxiv:2110.02861", "arxiv:2108.12409", "license:bigscience-bloom-rail-1.0", "model-index", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
text-generation
2022-05-19T11:52:27Z
--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu pipeline_tag: text-generation model-index: - name: bloom results: - task: type: text-generation name: text generation dataset: name: arc_challenge type: arc_challenge metrics: - name: acc type: acc value: 0.27986348122866894 verified: false - task: type: text-generation name: text generation dataset: name: arc_easy type: arc_easy metrics: - name: acc type: acc value: 0.5946969696969697 verified: false - task: type: text-generation name: text generation dataset: name: axb type: axb metrics: - name: acc type: acc value: 0.4433876811594203 verified: false - task: type: text-generation name: text generation dataset: name: axg type: axg metrics: - name: acc type: acc value: 0.5 verified: false - task: type: text-generation name: text generation dataset: name: boolq type: boolq metrics: - name: acc type: acc value: 0.6165137614678899 verified: false - task: type: text-generation name: text generation dataset: name: cb type: cb metrics: - name: acc type: acc value: 0.30357142857142855 verified: false - task: type: text-generation name: text generation dataset: name: cola type: cola metrics: - name: acc type: acc value: 0.610738255033557 verified: false - task: type: text-generation name: text generation dataset: name: copa type: copa metrics: - name: acc type: acc value: 0.63 verified: false - task: type: text-generation name: text generation dataset: name: crows_pairs_english type: crows_pairs_english metrics: - name: acc type: acc value: 0.4973166368515206 verified: false - task: type: text-generation name: text generation dataset: name: crows_pairs_french type: crows_pairs_french metrics: - name: acc type: acc value: 0.5032796660703638 verified: false - task: type: text-generation name: text generation dataset: name: diabla type: diabla metrics: - name: acc type: acc value: 0.28888308977035493 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_afr type: gsarti/flores_101_afr metrics: - name: byte_perplexity type: byte_perplexity value: 6.500798737976343 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_amh type: gsarti/flores_101_amh metrics: - name: byte_perplexity type: byte_perplexity value: 3.9726863338897145 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ara type: gsarti/flores_101_ara metrics: - name: byte_perplexity type: byte_perplexity value: 1.8083841089875814 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_asm type: gsarti/flores_101_asm metrics: - name: byte_perplexity type: byte_perplexity value: 5.699102962086425 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ast type: gsarti/flores_101_ast metrics: - name: byte_perplexity type: byte_perplexity value: 3.9252047073429384 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_azj type: gsarti/flores_101_azj metrics: - name: byte_perplexity type: byte_perplexity value: 6.942805054270002 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_bel type: gsarti/flores_101_bel metrics: - name: byte_perplexity type: byte_perplexity value: 3.614136245847082 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ben type: gsarti/flores_101_ben metrics: - name: byte_perplexity type: byte_perplexity value: 5.121491534300969 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_bos type: gsarti/flores_101_bos metrics: - name: byte_perplexity type: byte_perplexity value: 5.653353469118798 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_bul type: gsarti/flores_101_bul metrics: - name: byte_perplexity type: byte_perplexity value: 2.7014693938055068 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_cat type: gsarti/flores_101_cat metrics: - name: byte_perplexity type: byte_perplexity value: 2.305190041967345 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ceb type: gsarti/flores_101_ceb metrics: - name: byte_perplexity type: byte_perplexity value: 6.291000321323428 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ces type: gsarti/flores_101_ces metrics: - name: byte_perplexity type: byte_perplexity value: 5.447322753586386 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ckb type: gsarti/flores_101_ckb metrics: - name: byte_perplexity type: byte_perplexity value: 3.7255124939234765 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_cym type: gsarti/flores_101_cym metrics: - name: byte_perplexity type: byte_perplexity value: 12.539424151448149 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_dan type: gsarti/flores_101_dan metrics: - name: byte_perplexity type: byte_perplexity value: 5.183309001005672 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_deu type: gsarti/flores_101_deu metrics: - name: byte_perplexity type: byte_perplexity value: 3.1180422286591347 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ell type: gsarti/flores_101_ell metrics: - name: byte_perplexity type: byte_perplexity value: 2.467943456164706 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_eng type: gsarti/flores_101_eng metrics: - name: byte_perplexity type: byte_perplexity value: 2.018740628193298 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_est type: gsarti/flores_101_est metrics: - name: byte_perplexity type: byte_perplexity value: 9.11654425176368 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_fas type: gsarti/flores_101_fas metrics: - name: byte_perplexity type: byte_perplexity value: 3.058009097116482 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_fin type: gsarti/flores_101_fin metrics: - name: byte_perplexity type: byte_perplexity value: 6.847047959628553 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_fra type: gsarti/flores_101_fra metrics: - name: byte_perplexity type: byte_perplexity value: 1.9975177011840075 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ful type: gsarti/flores_101_ful metrics: - name: byte_perplexity type: byte_perplexity value: 11.465912731488828 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_gle type: gsarti/flores_101_gle metrics: - name: byte_perplexity type: byte_perplexity value: 8.681491663539422 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_glg type: gsarti/flores_101_glg metrics: - name: byte_perplexity type: byte_perplexity value: 3.029991089015508 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_guj type: gsarti/flores_101_guj metrics: - name: byte_perplexity type: byte_perplexity value: 4.955224230286231 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_hau type: gsarti/flores_101_hau metrics: - name: byte_perplexity type: byte_perplexity value: 10.758347356372159 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_heb type: gsarti/flores_101_heb metrics: - name: byte_perplexity type: byte_perplexity value: 3.6004478129801667 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_hin type: gsarti/flores_101_hin metrics: - name: byte_perplexity type: byte_perplexity value: 4.712530650588064 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_hrv type: gsarti/flores_101_hrv metrics: - name: byte_perplexity type: byte_perplexity value: 5.822418943372185 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_hun type: gsarti/flores_101_hun metrics: - name: byte_perplexity type: byte_perplexity value: 6.440482646965992 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_hye type: gsarti/flores_101_hye metrics: - name: byte_perplexity type: byte_perplexity value: 3.657718918347166 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ibo type: gsarti/flores_101_ibo metrics: - name: byte_perplexity type: byte_perplexity value: 5.564814003872672 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ind type: gsarti/flores_101_ind metrics: - name: byte_perplexity type: byte_perplexity value: 2.1597101468869373 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_isl type: gsarti/flores_101_isl metrics: - name: byte_perplexity type: byte_perplexity value: 8.082349269518136 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ita type: gsarti/flores_101_ita metrics: - name: byte_perplexity type: byte_perplexity value: 2.9687591414176207 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_jav type: gsarti/flores_101_jav metrics: - name: byte_perplexity type: byte_perplexity value: 7.0573805415708994 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_jpn type: gsarti/flores_101_jpn metrics: - name: byte_perplexity type: byte_perplexity value: 2.7758864197116933 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kam type: gsarti/flores_101_kam metrics: - name: byte_perplexity type: byte_perplexity value: 11.072949642861332 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kan type: gsarti/flores_101_kan metrics: - name: byte_perplexity type: byte_perplexity value: 5.551730651007082 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kat type: gsarti/flores_101_kat metrics: - name: byte_perplexity type: byte_perplexity value: 2.522630524283745 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kaz type: gsarti/flores_101_kaz metrics: - name: byte_perplexity type: byte_perplexity value: 3.3901748516975574 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kea type: gsarti/flores_101_kea metrics: - name: byte_perplexity type: byte_perplexity value: 8.918534182590863 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kir type: gsarti/flores_101_kir metrics: - name: byte_perplexity type: byte_perplexity value: 3.729278369847201 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_kor type: gsarti/flores_101_kor metrics: - name: byte_perplexity type: byte_perplexity value: 3.932884847226212 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_lao type: gsarti/flores_101_lao metrics: - name: byte_perplexity type: byte_perplexity value: 2.9077314760849924 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_lav type: gsarti/flores_101_lav metrics: - name: byte_perplexity type: byte_perplexity value: 7.777221919194806 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_lin type: gsarti/flores_101_lin metrics: - name: byte_perplexity type: byte_perplexity value: 7.524842908050988 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_lit type: gsarti/flores_101_lit metrics: - name: byte_perplexity type: byte_perplexity value: 7.369179434621725 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ltz type: gsarti/flores_101_ltz metrics: - name: byte_perplexity type: byte_perplexity value: 8.801059747949214 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_lug type: gsarti/flores_101_lug metrics: - name: byte_perplexity type: byte_perplexity value: 8.483203026364786 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_luo type: gsarti/flores_101_luo metrics: - name: byte_perplexity type: byte_perplexity value: 11.975963093623681 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mal type: gsarti/flores_101_mal metrics: - name: byte_perplexity type: byte_perplexity value: 4.615948455160037 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mar type: gsarti/flores_101_mar metrics: - name: byte_perplexity type: byte_perplexity value: 5.483253482821379 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mkd type: gsarti/flores_101_mkd metrics: - name: byte_perplexity type: byte_perplexity value: 2.9656732291754087 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mlt type: gsarti/flores_101_mlt metrics: - name: byte_perplexity type: byte_perplexity value: 15.004773437665275 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mon type: gsarti/flores_101_mon metrics: - name: byte_perplexity type: byte_perplexity value: 3.410598542315402 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mri type: gsarti/flores_101_mri metrics: - name: byte_perplexity type: byte_perplexity value: 7.474035895661322 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_msa type: gsarti/flores_101_msa metrics: - name: byte_perplexity type: byte_perplexity value: 2.5710001772665634 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_mya type: gsarti/flores_101_mya metrics: - name: byte_perplexity type: byte_perplexity value: 2.413577969878331 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_nld type: gsarti/flores_101_nld metrics: - name: byte_perplexity type: byte_perplexity value: 4.127831721885065 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_nob type: gsarti/flores_101_nob metrics: - name: byte_perplexity type: byte_perplexity value: 5.402763169129877 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_npi type: gsarti/flores_101_npi metrics: - name: byte_perplexity type: byte_perplexity value: 5.199342701937889 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_nso type: gsarti/flores_101_nso metrics: - name: byte_perplexity type: byte_perplexity value: 8.154626800955667 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_nya type: gsarti/flores_101_nya metrics: - name: byte_perplexity type: byte_perplexity value: 8.179860208369393 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_oci type: gsarti/flores_101_oci metrics: - name: byte_perplexity type: byte_perplexity value: 4.8617357393685845 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_orm type: gsarti/flores_101_orm metrics: - name: byte_perplexity type: byte_perplexity value: 12.911595421079408 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ory type: gsarti/flores_101_ory metrics: - name: byte_perplexity type: byte_perplexity value: 5.189421861225964 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_pan type: gsarti/flores_101_pan metrics: - name: byte_perplexity type: byte_perplexity value: 4.698477289331806 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_pol type: gsarti/flores_101_pol metrics: - name: byte_perplexity type: byte_perplexity value: 4.625550458479643 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_por type: gsarti/flores_101_por metrics: - name: byte_perplexity type: byte_perplexity value: 1.9754515986213523 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_pus type: gsarti/flores_101_pus metrics: - name: byte_perplexity type: byte_perplexity value: 4.4963371422771585 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ron type: gsarti/flores_101_ron metrics: - name: byte_perplexity type: byte_perplexity value: 4.965456830031304 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_rus type: gsarti/flores_101_rus metrics: - name: byte_perplexity type: byte_perplexity value: 2.0498020542445303 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_slk type: gsarti/flores_101_slk metrics: - name: byte_perplexity type: byte_perplexity value: 6.450822127057479 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_slv type: gsarti/flores_101_slv metrics: - name: byte_perplexity type: byte_perplexity value: 6.620252120186232 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_sna type: gsarti/flores_101_sna metrics: - name: byte_perplexity type: byte_perplexity value: 8.462166771382726 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_snd type: gsarti/flores_101_snd metrics: - name: byte_perplexity type: byte_perplexity value: 5.466066951221973 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_som type: gsarti/flores_101_som metrics: - name: byte_perplexity type: byte_perplexity value: 11.95918054093392 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_spa type: gsarti/flores_101_spa metrics: - name: byte_perplexity type: byte_perplexity value: 1.8965140104323535 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_srp type: gsarti/flores_101_srp metrics: - name: byte_perplexity type: byte_perplexity value: 2.871214785885079 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_swe type: gsarti/flores_101_swe metrics: - name: byte_perplexity type: byte_perplexity value: 5.054972008155866 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_swh type: gsarti/flores_101_swh metrics: - name: byte_perplexity type: byte_perplexity value: 3.6973091886730676 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_tam type: gsarti/flores_101_tam metrics: - name: byte_perplexity type: byte_perplexity value: 4.539493400469833 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_tel type: gsarti/flores_101_tel metrics: - name: byte_perplexity type: byte_perplexity value: 5.807499987508966 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_tgk type: gsarti/flores_101_tgk metrics: - name: byte_perplexity type: byte_perplexity value: 3.5994818827380426 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_tgl type: gsarti/flores_101_tgl metrics: - name: byte_perplexity type: byte_perplexity value: 5.667053833119858 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_tha type: gsarti/flores_101_tha metrics: - name: byte_perplexity type: byte_perplexity value: 2.365940201944242 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_tur type: gsarti/flores_101_tur metrics: - name: byte_perplexity type: byte_perplexity value: 4.885014749844601 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_ukr type: gsarti/flores_101_ukr metrics: - name: byte_perplexity type: byte_perplexity value: 2.7240934990288483 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_umb type: gsarti/flores_101_umb metrics: - name: byte_perplexity type: byte_perplexity value: 12.766915508610673 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_urd type: gsarti/flores_101_urd metrics: - name: byte_perplexity type: byte_perplexity value: 1.9797467071381232 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_uzb type: gsarti/flores_101_uzb metrics: - name: byte_perplexity type: byte_perplexity value: 12.002337637722146 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_vie type: gsarti/flores_101_vie metrics: - name: byte_perplexity type: byte_perplexity value: 1.76578415476397 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_wol type: gsarti/flores_101_wol metrics: - name: byte_perplexity type: byte_perplexity value: 9.144285650306488 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_xho type: gsarti/flores_101_xho metrics: - name: byte_perplexity type: byte_perplexity value: 7.403240538286952 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_yor type: gsarti/flores_101_yor metrics: - name: byte_perplexity type: byte_perplexity value: 5.91272037551173 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_zho_simpl type: gsarti/flores_101_zho_simpl metrics: - name: byte_perplexity type: byte_perplexity value: 2.2769070822768533 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_zho_trad type: gsarti/flores_101_zho_trad metrics: - name: byte_perplexity type: byte_perplexity value: 2.5180582198242383 verified: false - task: type: text-generation name: text generation dataset: name: gsarti/flores_101_zul type: gsarti/flores_101_zul metrics: - name: byte_perplexity type: byte_perplexity value: 8.53353320693145 verified: false - task: type: text-generation name: text generation dataset: name: headqa type: headqa metrics: - name: acc type: acc value: 0.26440554339897887 verified: false - task: type: text-generation name: text generation dataset: name: hellaswag type: hellaswag metrics: - name: acc type: acc value: 0.41236805417247563 verified: false - task: type: text-generation name: text generation dataset: name: logiqa type: logiqa metrics: - name: acc type: acc value: 0.2073732718894009 verified: false - task: type: text-generation name: text generation dataset: name: mathqa type: mathqa metrics: - name: acc type: acc value: 0.24958123953098826 verified: false - task: type: text-generation name: text generation dataset: name: mc_taco type: mc_taco metrics: - name: em type: em value: 0.11936936936936937 verified: false - task: type: text-generation name: text generation dataset: name: mnli type: mnli metrics: - name: acc type: acc value: 0.35496688741721855 verified: false - task: type: text-generation name: text generation dataset: name: mnli_mismatched type: mnli_mismatched metrics: - name: acc type: acc value: 0.35211554109031734 verified: false - task: type: text-generation name: text generation dataset: name: mrpc type: mrpc metrics: - name: acc type: acc value: 0.5857843137254902 verified: false - task: type: text-generation name: text generation dataset: name: multirc type: multirc metrics: - name: acc type: acc value: 0.5375412541254125 verified: false - task: type: text-generation name: text generation dataset: name: openbookqa type: openbookqa metrics: - name: acc type: acc value: 0.216 verified: false - task: type: text-generation name: text generation dataset: name: piqa type: piqa metrics: - name: acc type: acc value: 0.7078346028291621 verified: false - task: type: text-generation name: text generation dataset: name: prost type: prost metrics: - name: acc type: acc value: 0.22683603757472245 verified: false - task: type: text-generation name: text generation dataset: name: pubmedqa type: pubmedqa metrics: - name: acc type: acc value: 0.616 verified: false - task: type: text-generation name: text generation dataset: name: qnli type: qnli metrics: - name: acc type: acc value: 0.5072304594545122 verified: false - task: type: text-generation name: text generation dataset: name: qqp type: qqp metrics: - name: acc type: acc value: 0.3842443729903537 verified: false - task: type: text-generation name: text generation dataset: name: race type: race metrics: - name: acc type: acc value: 0.3521531100478469 verified: false - task: type: text-generation name: text generation dataset: name: rte type: rte metrics: - name: acc type: acc value: 0.47653429602888087 verified: false - task: type: text-generation name: text generation dataset: name: sciq type: sciq metrics: - name: acc type: acc value: 0.892 verified: false - task: type: text-generation name: text generation dataset: name: sst type: sst metrics: - name: acc type: acc value: 0.5177752293577982 verified: false - task: type: text-generation name: text generation dataset: name: triviaqa type: triviaqa metrics: - name: acc type: acc value: 0.041633518960487934 verified: false - task: type: text-generation name: text generation dataset: name: tydiqa_primary type: tydiqa_primary metrics: - name: acc type: acc value: 0.3011337608795236 verified: false - task: type: text-generation name: text generation dataset: name: webqs type: webqs metrics: - name: acc type: acc value: 0.01673228346456693 verified: false - task: type: text-generation name: text generation dataset: name: wic type: wic metrics: - name: acc type: acc value: 0.5015673981191222 verified: false - task: type: text-generation name: text generation dataset: name: winogrande type: winogrande metrics: - name: acc type: acc value: 0.5864246250986582 verified: false - task: type: text-generation name: text generation dataset: name: wnli type: wnli metrics: - name: acc type: acc value: 0.471830985915493 verified: false - task: type: text-generation name: text generation dataset: name: wsc type: wsc metrics: - name: acc type: acc value: 0.4423076923076923 verified: false - task: type: text-generation name: text generation dataset: name: humaneval type: humaneval metrics: - name: pass@1 type: pass@1 value: 0.15524390243902436 verified: false - name: pass@10 type: pass@10 value: 0.3220367632383857 verified: false - name: pass@100 type: pass@100 value: 0.5545431515723145 verified: false --- <h1 style='text-align: center '>BLOOM LM</h1> <h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2> <h3 style='text-align: center '>Model Card</h3> <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Version 1.0 / 26.May.2022 ## Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Data](#training-data) 4. [Risks and Limitations](#risks-and-limitations) 5. [Evaluation](#evaluation) 6. [Recommendations](#recommendations) 7. [Glossary and Calculations](#glossary-and-calculations) 8. [More Information](#more-information) 9. [Model Card Authors](#model-card-authors) ## Model Details ### Basics *This section provides information for anyone who wants to know about the model.* <details> <summary>Click to expand</summary> <br/> **Developed by:** BigScience ([website](https://bigscience.huggingface.co)) * All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)* **Model Type:** Transformer-based Language Model **Version:** 1.0.0 **Languages:** Multiple; see [training data](#training-data) **License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license)) **Release Date Estimate:** Monday, 11.July.2022 **Send Questions to:** [email protected] **Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022 **Funded by:** * The French government. * Hugging Face ([website](https://huggingface.co)). * Organizations of contributors. *(Further breakdown of organizations forthcoming.)* </details> ### Technical Specifications *This section provides information for people who work on model development.* <details> <summary>Click to expand</summary><br/> Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training. **Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)): * Decoder-only architecture * Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf)) * ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions * 3,002,557,440 parameters: * 642,252,800 embedding parameters * 30 layers, 32 attention heads * Hidden layers are 2560-dimensional * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization)) **Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)). **Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)). * Hardware: 384 A100 80GB GPUs (48 nodes): * Additional 32 A100 80GB GPUs (4 nodes) in reserve * 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links * CPU: AMD * CPU memory: 512GB per node * GPU memory: 640GB per node * Inter-node connect: Omni-Path Architecture (OPA) * NCCL-communications network: a fully dedicated subnet * Disc IO network: shared network with other types of nodes * Software: * Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed)) * DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed)) * PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch)) * apex ([Github link](https://github.com/NVIDIA/apex)) #### **Training** Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs) - Number of epochs: 1 (*current target*) - Dates: - Started 11th March, 2022 11:42am PST - Ended 5th July, 2022 - Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments) - Server training location: Île-de-France, France #### **Tokenization** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using: - A byte-level Byte Pair Encoding (BPE) algorithm - A simple pre-tokenization rule, no normalization - A vocabulary size of 250,680 It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. </details> ### Environmental Impact <details> <summary>Click to expand</summary><br/> The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing. **Estimated carbon emissions:** *(Forthcoming upon completion of training.)* **Estimated electricity usage:** *(Forthcoming upon completion of training.)* </details> <p>&nbsp;</p> ## Uses *This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model.* <details> <summary>Click to expand</summary><br/> ### Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive. #### **Direct Use** - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings #### **Downstream Use** - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization ### Misuse and Out-of-scope Use *This section addresses what users ought not do with the model.* See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases. #### **Out-of-scope Uses** Using the model in [high-stakes](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct. ##### Out-of-scope Uses Include: - Usage in biomedical domains, political and legal domains, or finance domains - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### **Misuse** Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ### Intended Users #### **Direct Users** - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Community advocates, including human and civil rights groups #### Indirect Users - Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use) - Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license) #### Others Affected (Parties Prenantes) - People and groups referred to by the LLM - People and groups exposed to outputs of, or decisions based on, the LLM - People and groups whose original work is included in the LLM </details> <p>&nbsp;</p> ## Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* <details> <summary>Click to expand</summary><br/> Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus). Training data includes: - 45 natural languages - 12 programming languages - In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.) #### **Languages** The pie chart shows the distribution of languages in training data. ![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_chart.svg?raw=true) The following table shows the further distribution of Niger-Congo and Indic languages in the training data. <details> <summary>Click to expand</summary><br/> | Niger Congo | Percentage | | Indic | Percentage | |----------------|------------ |------ |-----------|------------| | Chi Tumbuka | 0.00002 | | Assamese | 0.01 | | Kikuyu | 0.00004 | | Odia | 0.04 | | Bambara | 0.00004 | | Gujarati | 0.04 | | Akan | 0.00007 | | Marathi | 0.05 | | Xitsonga | 0.00007 | | Punjabi | 0.05 | | Sesotho | 0.00007 | | Kannada | 0.06 | | Chi Chewa | 0.0001 | | Nepali | 0.07 | | Setswana | 0.0002 | | Telugu | 0.09 | | Northern Sotho | 0.0002 | | Malayalam | 0.10 | | Fon | 0.0002 | | Urdu | 0.10 | | Kirundi | 0.0003 | | Tamil | 0.20 | | Wolof | 0.0004 | | Bengali | 0.50 | | Kuganda | 0.0004 | | Hindi | 0.70 | | Chi Shona | 0.001 | | Isi Zulu | 0.001 | | Igbo | 0.001 | | Xhosa | 0.001 | | Kinyarwanda | 0.003 | | Yoruba | 0.006 | | Swahili | 0.02 | </details> The following table shows the distribution of programming languages. <details> <summary>Click to expand</summary><br/> | Extension | Language | Number of files | |----------------|------------|-----------------| | java | Java | 5,407,724 | | php | PHP | 4,942,186 | | cpp | C++ | 2,503,930 | | py | Python | 2,435,072 | | js | JavaScript | 1,905,518 | | cs | C# | 1,577,347 | | rb | Ruby | 6,78,413 | | cc | C++ | 443,054 | | hpp | C++ | 391,048 | | lua | Lua | 352,317 | | go | GO | 227,763 | | ts | TypeScript | 195,254 | | C | C | 134,537 | | scala | Scala | 92,052 | | hh | C++ | 67,161 | | H | C++ | 55,899 | | tsx | TypeScript | 33,107 | | rs | Rust | 29,693 | | phpt | PHP | 9,702 | | c++ | C++ | 1,342 | | h++ | C++ | 791 | | php3 | PHP | 540 | | phps | PHP | 270 | | php5 | PHP | 166 | | php4 | PHP | 29 | </details> </details> <p>&nbsp;</p> ## Risks and Limitations *This section identifies foreseeable harms and misunderstandings.* <details> <summary>Click to expand</summary><br/> Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs </details> <p>&nbsp;</p> ## Evaluation *This section describes the evaluation protocols and provides the results.* <details> <summary>Click to expand</summary><br/> ### Metrics *This section describes the different ways performance is calculated and why.* Includes: | Metric | Why chosen | |--------------------|--------------------------------------------------------------------| | [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training | | Cross Entropy [Loss](#loss) | Standard objective for language models. | And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_ ### Factors *This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.* - Language, such as English or Yoruba - Domain, such as newswire or stories - Demographic characteristics, such as gender or nationality ### Results *Results are based on the [Factors](#factors) and [Metrics](#metrics).* **Zero-shot evaluations:** See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results | Task | Language | Metric | BLOOM-2B5 | |:----|:----|:----|:----:| | arc_challenge | eng | acc ↑ | 0.28 | | arc_easy | eng | acc ↑ | 0.595 | | axb (Median of 10 prompts) | eng | acc ↑ | 0.443 | | axg (Median of 10 prompts) | eng | acc ↑ | 0.5 | | boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 | | cb (Median of 15 prompts) | eng | acc ↑ | 0.304 | | cola (Median of 5 prompts) | eng | acc ↑ | 0.611 | | copa (Median of 9 prompts) | eng | acc ↑ | 0.63 | | crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 | | crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 | | diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 | | gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 | | gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 | | gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 | | gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 | | gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 | | gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 | | gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 | | gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 | | gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 | | gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 | | gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 | | gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 | | gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 | | gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 | | gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 | | gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 | | gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 | | gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 | | gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 | | gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 | | gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 | | gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 | | gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 | | gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 | | gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 | | gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 | | gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 | | gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 | | gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 | | gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 | | gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 | | gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 | | gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 | | gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 | | gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 | | gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 | | gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 | | gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 | | gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 | | gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 | | gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 | | gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 | | gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 | | gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 | | gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 | | gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 | | gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 | | gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 | | gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 | | gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 | | gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 | | gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 | | gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 | | gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 | | gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 | | gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 | | gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 | | gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 | | gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 | | gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 | | gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 | | gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 | | gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 | | gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 | | gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 | | gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 | | gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 | | gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 | | gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 | | gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 | | gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 | | gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 | | gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 | | gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 | | gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 | | gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 | | gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 | | gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 | | gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 | | gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 | | gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 | | gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 | | gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 | | gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 | | gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 | | gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 | | gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 | | gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 | | gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 | | gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 | | gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 | | gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 | | gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 | | gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 | | gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 | | gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 | | gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 | | gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 | | gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 | | gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 | | gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 | | headqa | esp | acc ↑ | 0.264 | | hellaswag | eng | acc ↑ | 0.412 | | logiqa | eng | acc ↑ | 0.207 | | mathqa | eng | acc ↑ | 0.25 | | mc_taco | eng | em ↑ | 0.119 | | mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 | | mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 | | mrpc | eng | acc ↑ | 0.586 | | multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 | | openbookqa | eng | acc ↑ | 0.216 | | piqa | eng | acc ↑ | 0.708 | | prost | eng | acc ↑ | 0.227 | | pubmedqa | eng | acc ↑ | 0.616 | | qnli | eng | acc ↑ | 0.507 | | qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 | | race | eng | acc ↑ | 0.352 | | rte (Median of 6 prompts) | eng | acc ↑ | 0.477 | | sciq | eng | acc ↑ | 0.892 | | sst (Median of 6 prompts) | eng | acc ↑ | 0.518 | | triviaqa | eng | acc ↑ | 0.042 | | tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 | | webqs | eng | acc ↑ | 0.017 | | wic (Median of 11 prompts) | eng | acc ↑ | 0.502 | | winogrande | eng | acc ↑ | 0.586 | | wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 | | wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 | | humaneval | python | pass@1 ↑ | 0.155 | | humaneval | python | pass@10 ↑ | 0.322 | | humaneval | python | pass@100 ↑ | 0.555 | **Train-time Evaluation:** As of 25.May.2022, 15:00 PST: - Training Loss: 2.0 - Validation Loss: 2.2 - Perplexity: 8.9 </details> <p>&nbsp;</p> ## Recommendations *This section provides information on warnings and potential mitigations.* <details> <summary>Click to expand</summary><br/> - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Models pretrained with the LLM should include an updated Model Card. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. </details> <p>&nbsp;</p> ## Glossary and Calculations *This section defines common terms and how metrics are calculated.* <details> <summary>Click to expand</summary><br/> - <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss. - <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. - <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/). - <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf). - <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf). - <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm). - <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf)) - <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated. </details> <p>&nbsp;</p> ## More Information <details> <summary>Click to expand</summary><br/> ### Dataset Creation Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling ### Technical Specifications Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md ### Initial Results Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book </details> <p>&nbsp;</p> ## Model Card Authors *Ordered roughly chronologically and by amount of time spent.* Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
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bigcode/starcoder
bigcode
2023-10-05T09:24:35Z
18,688
2,488
transformers
[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "code", "dataset:bigcode/the-stack-dedup", "arxiv:1911.02150", "arxiv:2205.14135", "arxiv:2207.14255", "arxiv:2305.06161", "license:bigcode-openrail-m", "model-index", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-04-24T12:36:52Z
--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python license: bigcode-openrail-m datasets: - bigcode/the-stack-dedup metrics: - code_eval library_name: transformers tags: - code model-index: - name: StarCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval (Prompted) metrics: - name: pass@1 type: pass@1 value: 0.408 verified: false - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 0.336 verified: false - task: type: text-generation dataset: type: mbpp name: MBPP metrics: - name: pass@1 type: pass@1 value: 0.527 verified: false - task: type: text-generation dataset: type: ds1000 name: DS-1000 (Overall Completion) metrics: - name: pass@1 type: pass@1 value: 0.26 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (C++) metrics: - name: pass@1 type: pass@1 value: 0.3155 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (C#) metrics: - name: pass@1 type: pass@1 value: 0.2101 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (D) metrics: - name: pass@1 type: pass@1 value: 0.1357 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Go) metrics: - name: pass@1 type: pass@1 value: 0.1761 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Java) metrics: - name: pass@1 type: pass@1 value: 0.3022 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Julia) metrics: - name: pass@1 type: pass@1 value: 0.2302 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (JavaScript) metrics: - name: pass@1 type: pass@1 value: 0.3079 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Lua) metrics: - name: pass@1 type: pass@1 value: 0.2389 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (PHP) metrics: - name: pass@1 type: pass@1 value: 0.2608 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Perl) metrics: - name: pass@1 type: pass@1 value: 0.1734 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Python) metrics: - name: pass@1 type: pass@1 value: 0.3357 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (R) metrics: - name: pass@1 type: pass@1 value: 0.155 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Ruby) metrics: - name: pass@1 type: pass@1 value: 0.0124 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Racket) metrics: - name: pass@1 type: pass@1 value: 0.0007 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Rust) metrics: - name: pass@1 type: pass@1 value: 0.2184 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Scala) metrics: - name: pass@1 type: pass@1 value: 0.2761 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Bash) metrics: - name: pass@1 type: pass@1 value: 0.1046 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (Swift) metrics: - name: pass@1 type: pass@1 value: 0.2274 verified: false - task: type: text-generation dataset: type: nuprl/MultiPL-E name: MultiPL-HumanEval (TypeScript) metrics: - name: pass@1 type: pass@1 value: 0.3229 verified: false extra_gated_prompt: >- ## Model License Agreement Please read the BigCode [OpenRAIL-M license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) agreement before accepting it. extra_gated_fields: I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox --- # StarCoder ![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/StarCoderBanner.png) Play with the model on the [StarCoder Playground](https://huggingface.co/spaces/bigcode/bigcode-playground). ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary The StarCoder models are 15.5B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Paper:** [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161) - **Point of Contact:** [[email protected]](mailto:[email protected]) - **Languages:** 80+ Programming languages ## Use ### Intended use The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant. **Feel free to share your generations in the Community tab!** ### Generation ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoder" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Fill-in-the-middle Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output: ```python input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>" inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations. # Training ## Model - **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective - **Pretraining steps:** 250k - **Pretraining tokens:** 1 trillion - **Precision:** bfloat16 ## Hardware - **GPUs:** 512 Tesla A100 - **Training time:** 24 days ## Software - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). # Citation ``` @article{li2023starcoder, title={StarCoder: may the source be with you!}, author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2023}, eprint={2305.06161}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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google/ddpm-cifar10-32
google
2023-08-03T07:24:08Z
18,658
40
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "has_space", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2022-06-16T12:53:22Z
--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-cifar10-32" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_3.png)
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nitrosocke/mo-di-diffusion
nitrosocke
2023-05-16T09:23:30Z
18,586
911
diffusers
[ "diffusers", "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2022-10-27T19:56:48Z
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- **Mo Di Diffusion** This is the fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio. Use the tokens **_modern disney style_** in your prompts for the effect. **If you enjoy my work, please consider supporting me** [![Become A Patreon](https://badgen.net/badge/become/a%20patron/F96854)](https://patreon.com/user?u=79196446) **Videogame Characters rendered with the model:** ![Videogame Samples](https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/modi-samples-01s.jpg) **Animal Characters rendered with the model:** ![Animal Samples](https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/modi-samples-02s.jpg) **Cars and Landscapes rendered with the model:** ![Misc. Samples](https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/modi-samples-03s.jpg) #### Prompt and settings for Lara Croft: **modern disney lara croft** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417, Size: 512x768_ #### Prompt and settings for the Lion: **modern disney (baby lion) Negative prompt: person human** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_ This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 9.000 steps. ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/mo-di-diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a magical princess with golden hair, modern disney style" image = pipe(prompt).images[0] image.save("./magical_princess.png") ``` # Gradio & Colab We also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/anzorq/finetuned_diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1j5YvfMZoGdDGdj3O3xRU1m4ujKYsElZO?usp=sharing) ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
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adasnew/t5-small-xsum
adasnew
2022-04-11T22:35:12Z
18,527
1
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
2022-04-11T18:45:17Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.3953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8641 | 0.04 | 500 | 2.6202 | | 2.7466 | 0.08 | 1000 | 2.5660 | | 2.8767 | 0.12 | 1500 | 2.5319 | | 2.7099 | 0.16 | 2000 | 2.5107 | | 2.7752 | 0.2 | 2500 | 2.4922 | | 2.6037 | 0.24 | 3000 | 2.4800 | | 2.8236 | 0.27 | 3500 | 2.4677 | | 2.7089 | 0.31 | 4000 | 2.4581 | | 2.7299 | 0.35 | 4500 | 2.4498 | | 2.7498 | 0.39 | 5000 | 2.4420 | | 2.6186 | 0.43 | 5500 | 2.4346 | | 2.7817 | 0.47 | 6000 | 2.4288 | | 2.5559 | 0.51 | 6500 | 2.4239 | | 2.6725 | 0.55 | 7000 | 2.4186 | | 2.6316 | 0.59 | 7500 | 2.4149 | | 2.5561 | 0.63 | 8000 | 2.4115 | | 2.5708 | 0.67 | 8500 | 2.4097 | | 2.5861 | 0.71 | 9000 | 2.4052 | | 2.6363 | 0.74 | 9500 | 2.4024 | | 2.7435 | 0.78 | 10000 | 2.4003 | | 2.7258 | 0.82 | 10500 | 2.3992 | | 2.6113 | 0.86 | 11000 | 2.3983 | | 2.6006 | 0.9 | 11500 | 2.3972 | | 2.5684 | 0.94 | 12000 | 2.3960 | | 2.6181 | 0.98 | 12500 | 2.3953 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
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jjzha/jobbert_knowledge_extraction
jjzha
2023-10-26T10:25:41Z
18,523
0
transformers
[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
token-classification
2023-04-06T14:15:13Z
This is a demo using the models from: ``` @inproceedings{zhang-etal-2022-skillspan, title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings", author = "Zhang, Mike and Jensen, Kristian and Sonniks, Sif and Plank, Barbara", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.366", doi = "10.18653/v1/2022.naacl-main.366", pages = "4962--4984", abstract = "Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning.", } ``` Note that there is another endpoint, namely `jjzha/jobbert_skill_extraction`. Knowledge can be seen as hard skills and skills are both soft and applied skills.
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laion/clap-htsat-unfused
laion
2023-04-24T14:39:57Z
18,450
18
transformers
[ "transformers", "pytorch", "clap", "feature-extraction", "arxiv:2211.06687", "license:apache-2.0", "endpoints_compatible", "region:us" ]
feature-extraction
2023-02-16T20:47:08Z
--- license: apache-2.0 --- # Model card for CLAP Model card for CLAP: Contrastive Language-Audio Pretraining ![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png) # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Citation](#citation) # TL;DR The abstract of the paper states that: > Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public. # Usage You can use this model for zero shot audio classification or extracting audio and/or textual features. # Uses ## Perform zero-shot audio classification ### Using `pipeline` ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("ashraq/esc50") audio = dataset["train"]["audio"][-1]["array"] audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused") output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]) print(output) >>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}] ``` ## Run the model: You can also get the audio and text embeddings using `ClapModel` ### Run the model on CPU: ```python from datasets import load_dataset from transformers import ClapModel, ClapProcessor librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[0] model = ClapModel.from_pretrained("laion/clap-htsat-unfused") processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused") inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt") audio_embed = model.get_audio_features(**inputs) ``` ### Run the model on GPU: ```python from datasets import load_dataset from transformers import ClapModel, ClapProcessor librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[0] model = ClapModel.from_pretrained("laion/clap-htsat-unfused").to(0) processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused") inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0) audio_embed = model.get_audio_features(**inputs) ``` # Citation If you are using this model for your work, please consider citing the original paper: ``` @misc{https://doi.org/10.48550/arxiv.2211.06687, doi = {10.48550/ARXIV.2211.06687}, url = {https://arxiv.org/abs/2211.06687}, author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo}, keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
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optimum/sbert-all-MiniLM-L6-with-pooler
optimum
2022-07-26T13:37:30Z
18,449
6
sentence-transformers
[ "sentence-transformers", "onnx", "bert", "feature-extraction", "sentence-similarity", "en", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
sentence-similarity
2022-07-26T11:32:55Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en license: apache-2.0 --- # ONNX convert all-MiniLM-L6-v2 ## Conversion of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes `last_hidden_state` and `pooler_output` whereas the sentence-transformers exported with default ONNX config only contains `last_hidden_state` as output. ## Usage (HuggingFace Optimum) Using this model becomes easy when you have [optimum](https://github.com/huggingface/optimum) installed: ``` python -m pip install optimum ``` Then you can use the model like this: ```python from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks model = ORTModelForCustomTasks.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler") tokenizer = AutoTokenizer.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler") inputs = tokenizer("I love burritos!", return_tensors="pt") pred = model(**inputs) ``` You will also be able to leverage the pipeline API in transformers: ```python from transformers import pipeline onnx_extractor = pipeline("feature-extraction", model=model, tokenizer=tokenizer) text = "I love burritos!" pred = onnx_extractor(text) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |
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TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
TheBloke
2023-09-29T20:48:48Z
18,430
61
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "has_space", "text-generation-inference", "4-bit", "region:us" ]
text-generation
2023-09-28T22:34:03Z
--- base_model: mistralai/Mistral-7B-Instruct-v0.1 inference: false license: apache-2.0 model_creator: Mistral AI model_name: Mistral 7B Instruct v0.1 model_type: mistral pipeline_tag: text-generation prompt_template: '<s>[INST] {prompt} [/INST]' quantized_by: TheBloke tags: - finetuned --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B Instruct v0.1 - GPTQ - Model creator: [Mistral AI](https://huggingface.co/mistralai) - Original model: [Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) <!-- description start --> ## Description This repo contains GPTQ model files for [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. ### GPTQs will work in ExLlama, or via Transformers (requiring Transformers from Github) These models are confirmed to work with ExLlama v1. At the time of writing (September 28th), AutoGPTQ has not yet added support for the new Mistral models. These GPTQs were made directly from Transformers, and so can be loaded via the Transformers interface. They can't be loaded directly from AutoGPTQ. To load them via Transformers, you will need to install Transformers from Github, with: ``` pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79 ``` <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) * [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. These files were made with Transformers 4.34.0.dev0, from commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 7.68 GB | Yes | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 8.17 GB | Yes | 8-bit, with group size 32g and Act Order for maximum inference quality. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Mistral-7B-Instruct-v0.1-GPTQ`: ```shell mkdir Mistral-7B-Instruct-v0.1-GPTQ huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Mistral-7B-Instruct-v0.1-GPTQ huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Mistral-7B-Instruct-v0.1-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). These models are confirmed to work via the ExLlama Loader in text-generation-webui. Use **Loader: ExLlama** - or Transformers may work too. AutoGPTQ will not work. Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-Instruct-v0.1-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.34.0.dev0 from Github source, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install optimum pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<s>[INST] {prompt} [/INST] ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are only tested to work with ExLlama v1, and Transformers 4.34.0.dev0 as of commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Mistral AI's Mistral 7B Instruct v0.1 # Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/) ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
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gilf/french-camembert-postag-model
gilf
2023-04-05T15:31:56Z
18,417
6
transformers
[ "transformers", "pytorch", "tf", "safetensors", "camembert", "token-classification", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- language: fr widget: - text: "Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages" --- ## About The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on [github](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'camembert-base'*. ## Supported Tags It uses the following tags: | Tag | Category | Extra Info | |----------|:------------------------------:|------------:| | ADJ | adjectif | | | ADJWH | adjectif | | | ADV | adverbe | | | ADVWH | adverbe | | | CC | conjonction de coordination | | | CLO | pronom | obj | | CLR | pronom | refl | | CLS | pronom | suj | | CS | conjonction de subordination | | | DET | déterminant | | | DETWH | déterminant | | | ET | mot étranger | | | I | interjection | | | NC | nom commun | | | NPP | nom propre | | | P | préposition | | | P+D | préposition + déterminant | | | PONCT | signe de ponctuation | | | PREF | préfixe | | | PRO | autres pronoms | | | PROREL | autres pronoms | rel | | PROWH | autres pronoms | int | | U | ? | | | V | verbe | | | VIMP | verbe imperatif | | | VINF | verbe infinitif | | | VPP | participe passé | | | VPR | participe présent | | | VS | subjonctif | | More information on the tags can be found here: http://alpage.inria.fr/statgram/frdep/Publications/crabbecandi-taln2008-final.pdf ## Usage The usage of this model follows the common transformers patterns. Here is a short example of its usage: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("gilf/french-camembert-postag-model") model = AutoModelForTokenClassification.from_pretrained("gilf/french-camembert-postag-model") from transformers import pipeline nlp_token_class = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True) nlp_token_class('Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages') ``` The lines above would display something like this on a Jupyter notebook: ``` [{'entity_group': 'NC', 'score': 0.5760144591331482, 'word': '<s>'}, {'entity_group': 'U', 'score': 0.9946700930595398, 'word': 'Face'}, {'entity_group': 'P', 'score': 0.999615490436554, 'word': 'à'}, {'entity_group': 'DET', 'score': 0.9995906352996826, 'word': 'un'}, {'entity_group': 'NC', 'score': 0.9995531439781189, 'word': 'choc'}, {'entity_group': 'ADJ', 'score': 0.999183714389801, 'word': 'inédit'}, {'entity_group': 'P', 'score': 0.3710663616657257, 'word': ','}, {'entity_group': 'DET', 'score': 0.9995903968811035, 'word': 'les'}, {'entity_group': 'NC', 'score': 0.9995649456977844, 'word': 'mesures'}, {'entity_group': 'VPP', 'score': 0.9988670349121094, 'word': 'mises'}, {'entity_group': 'P', 'score': 0.9996246099472046, 'word': 'en'}, {'entity_group': 'NC', 'score': 0.9995329976081848, 'word': 'place'}, {'entity_group': 'P', 'score': 0.9996233582496643, 'word': 'par'}, {'entity_group': 'DET', 'score': 0.9995935559272766, 'word': 'le'}, {'entity_group': 'NC', 'score': 0.9995369911193848, 'word': 'gouvernement'}, {'entity_group': 'V', 'score': 0.9993771314620972, 'word': 'ont'}, {'entity_group': 'VPP', 'score': 0.9991101026535034, 'word': 'permis'}, {'entity_group': 'DET', 'score': 0.9995885491371155, 'word': 'une'}, {'entity_group': 'NC', 'score': 0.9995636343955994, 'word': 'protection'}, {'entity_group': 'ADJ', 'score': 0.9991781711578369, 'word': 'forte'}, {'entity_group': 'CC', 'score': 0.9991298317909241, 'word': 'et'}, {'entity_group': 'ADJ', 'score': 0.9992275238037109, 'word': 'efficace'}, {'entity_group': 'P+D', 'score': 0.9993300437927246, 'word': 'des'}, {'entity_group': 'NC', 'score': 0.8353511393070221, 'word': 'ménages</s>'}] ```
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lllyasviel/control_v11p_sd15_inpaint
lllyasviel
2023-05-26T09:58:45Z
18,413
57
diffusers
[ "diffusers", "art", "controlnet", "stable-diffusion", "controlnet-v1-1", "image-to-image", "arxiv:2302.05543", "base_model:runwayml/stable-diffusion-v1-5", "license:openrail", "has_space", "diffusers:ControlNetModel", "region:us" ]
image-to-image
2023-04-14T19:25:42Z
--- license: openrail base_model: runwayml/stable-diffusion-v1-5 tags: - art - controlnet - stable-diffusion - controlnet-v1-1 - image-to-image duplicated_from: ControlNet-1-1-preview/control_v11p_sd15_inpaint --- # Controlnet - v1.1 - *InPaint Version* **Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel). This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_inpaint.pth) into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet). ControlNet is a neural network structure to control diffusion models by adding extra conditions. ![img](./sd.png) This checkpoint corresponds to the ControlNet conditioned on **inpaint images**. ## Model Details - **Developed by:** Lvmin Zhang, Maneesh Agrawala - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). - **Cite as:** @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Introduction Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by Lvmin Zhang, Maneesh Agrawala. The abstract reads as follows: *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.* ## Example It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint has been trained on it. Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. 1. Let's install `diffusers` and related packages: ``` $ pip install diffusers transformers accelerate ``` 2. Run code: ```python # !pip install transformers accelerate from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel from diffusers.utils import load_image import numpy as np import torch init_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png" ) init_image = init_image.resize((512, 512)) generator = torch.Generator(device="cpu").manual_seed(1) mask_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png" ) mask_image = mask_image.resize((512, 512)) def make_inpaint_condition(image, image_mask): image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0 assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size" image[image_mask > 0.5] = -1.0 # set as masked pixel image = np.expand_dims(image, 0).transpose(0, 3, 1, 2) image = torch.from_numpy(image) return image control_image = make_inpaint_condition(init_image, mask_image) controlnet = ControlNetModel.from_pretrained( "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16 ) pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ) pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() # generate image image = pipe( "a handsome man with ray-ban sunglasses", num_inference_steps=20, generator=generator, eta=1.0, image=init_image, mask_image=mask_image, control_image=control_image, ).images[0] ``` ![original](https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png) ![img](https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_with_mask.png) ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.png) ## Other released checkpoints v1-1 The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on a different type of conditioning: | Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example | |---|---|---|---|---| |[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>| |[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>| |[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>| ## More information For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly).
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FFusion/FFXL400
FFusion
2023-09-13T21:57:32Z
18,404
10
diffusers
[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "stable-diffusion", "text-to-image", "ffai", "en", "base_model:FFusion/FFusionXL-BASE", "doi:10.57967/hf/1095", "license:openrail++", "endpoints_compatible", "has_space", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2023-09-08T14:22:11Z
--- license: openrail++ base_model: FFusion/FFusionXL-BASE tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - stable-diffusion - text-to-image - diffusers - ffai inference: true widget: - text: >- a dog in colorful exploding clouds, dreamlike surrealism colorful smoke and fire coming out of it, explosion of data fragments, exploding background,realistic explosion, 3d digital art example_title: Dogo FFusion - text: >- a sprinkled donut sitting on top of a table, colorful hyperrealism, everything is made of candy, hyperrealistic digital painting, covered in sprinkles and crumbs, vibrant colors hyper realism,colorful smoke explosion background example_title: Donut FFusion - text: >- a cup of coffee with a tree in it, surreal art, awesome great composition, surrealism, ice cubes in tree, colorful clouds, perfectly realistic yet surreal example_title: CoFFee FFusion - text: >- brightly colored headphones with a splash of colorful paint splash, vibing to music, stunning artwork, music is life, beautiful digital artwork, concept art, cinematic, dramatic, intricate details, dark lighting example_title: Headset FFusion - text: >- high-quality game character digital design, Unreal Engine, Water color painting, Mecha- Monstrous high quality game fantasy rpg character design, dark rainbow Fur Scarf, inside of a Superficial Outhouse, at Twilight, Overdetailed art example_title: Digital Fusion language: - en thumbnail: >- https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image7sm.jpg --- # FFXL400 Combined LoRA Model 🚀 Welcome to the FFXL400 combined LoRA model repository on Hugging Face! This model is a culmination of extensive research, bringing together the finest LoRAs from the [400GB-LoraXL repository](https://huggingface.co/FFusion/400GB-LoraXL). Our vision was to harness the power of multiple LoRAs, meticulously analyzing and integrating a select fraction of the blocks from each. ## 📦 Model Highlights - **Innovative Combination**: This model is a strategic integration of LoRAs, maximizing the potential of each while creating a unified powerhouse. - **Versatility**: The model is available in various formats including diffusers, safetensors (both fp 16 and 32), and an optimized ONNIX FP16 version for DirectML, ensuring compatibility across AMD, Intel, Nvidia, and more. - **Advanced Research**: Leveraging the latest in machine learning research, the model represents a state-of-the-art amalgamation of LoRAs, optimized for performance and accuracy. ## 🔍 Technical Insights This model is a testament to the advancements in the field of AI and machine learning. It was crafted with precision, ensuring that: - Only a small percentage of the blocks from the original LoRAs (UNet and text encoders) were utilized. - The model is primed not just for inference but also for further training and refinement. - It serves as a benchmark for testing and understanding the cumulative impact of multiple LoRAs when used in concert. ## 🎨 Usage The FFXL400 model is designed for a multitude of applications. Whether you're delving into research, embarking on a new project, or simply experimenting, this model serves as a robust foundation. Use it to: - Investigate the cumulative effects of merging multiple LoRAs. - Dive deep into weighting experiments with multiple LoRAs. - Explore the nuances and intricacies of integrated LoRAs. ## ⚠️ License & Usage Disclaimers **Please review the full [license agreement](https://huggingface.co/FFusion/FFXL400/blob/main/LICENSE.md) before accessing or using the models.** 🔴 The models and weights available in this repository are **strictly for research and testing purposes**, with exceptions noted below. They are **not** generally intended for commercial use and are dependent on each individual LORA. 🔵 **Exception for Commercial Use:** The [FFusionXL-BASE](https://huggingface.co/FFusion/FFusionXL-BASE), [FFusion-BaSE](https://huggingface.co/FFusion/FFusion-BaSE), [di.FFUSION.ai-v2.1-768-BaSE-alpha](https://huggingface.co/FFusion/di.FFUSION.ai-v2.1-768-BaSE-alpha), and [di.ffusion.ai.Beta512](https://huggingface.co/FFusion/di.ffusion.ai.Beta512) models are trained by FFusion AI using images for which we hold licenses. Users are advised to primarily use these models for a safer experience. These particular models are allowed for commercial use. 🔴 **Disclaimer:** FFusion AI, in conjunction with Source Code Bulgaria Ltd and BlackswanTechnologies, **does not endorse or guarantee the content produced by the weights in each LORA**. There's potential for generating NSFW or offensive content. Collectively, we expressly disclaim responsibility for the outcomes and content produced by these weights. 🔴 **Acknowledgement:** The [FFusionXL-BASE](https://huggingface.co/FFusion/FFusionXL-BASE) model model is a uniquely developed version by FFusion AI. Rights to this and associated modifications belong to FFusion AI and Source Code Bulgaria Ltd. Ensure adherence to both this license and any conditions set by Stability AI Ltd for referenced models. ## 📈 How to Use The model can be easily integrated into your projects. Here's a quick guide on how to use the FFXL400 model: 1. **Loading the Model**: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("FFusion/FFXL400") model = AutoModel.from_pretrained("FFusion/FFXL400") ``` 2. **Performing Inference**: ```python input_text = "Your input here" inputs = tokenizer(input_text, return_tensors='pt') with torch.no_grad(): outputs = model(**inputs) ``` ## Further Training You can also use the FFXL400 as a starting point for further training. Simply load it into your training pipeline and proceed as you would with any other model. [Autotrain Advanced](https://github.com/huggingface/autotrain-advanced), [Kohya + Stable Diffusion XL](https://huggingface.co/docs/diffusers/main/en/training/lora#stable-diffusion-xl), ## 📚 Background The FFXL400 is built upon the insights and data from the [400GB-LoraXL repository](https://huggingface.co/FFusion/400GB-LoraXL). Each LoRA in that collection was extracted using the Low-Rank Adaptation (LoRA) technique, providing a rich dataset for research and exploration. The FFXL400 is the pinnacle of that research, representing a harmonious blend of the best LoRAs. ## Library of Available LoRA Models 📚 ![loraXL FFUsion](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/XQlnis5W2-fgDnGZ60EK9.jpeg) You can choose any of the models from our repository on Hugging Face or the upcoming repository on CivitAI. Here's a list of available models with `lora_model_id = "FFusion/400GB-LoraXL"`: ``` lora_filename = - FFai.0001.4Guofeng4xl_V1125d.lora_Dim64.safetensors - FFai.0002.4Guofeng4xl_V1125d.lora_Dim8.safetensors - FFai.0003.4Guofeng4xl_V1125d.loraa.safetensors - FFai.0004.Ambiencesdxl_A1.lora.safetensors - FFai.0005.Ambiencesdxl_A1.lora_8.safetensors - FFai.0006.Angrasdxl10_V22.lora.safetensors - FFai.0007.Animaginexl_V10.lora.safetensors - FFai.0008.Animeartdiffusionxl_Alpha3.lora.safetensors - FFai.0009.Astreapixiexlanime_V16.lora.safetensors - FFai.0010.Bluepencilxl_V010.lora.safetensors - FFai.0011.Bluepencilxl_V021.lora.safetensors - FFai.0012.Breakdomainxl_V03d.lora.safetensors - FFai.0013.Canvasxl_Bfloat16v002.lora.safetensors - FFai.0014.Cherrypickerxl_V20.lora.safetensors - FFai.0015.Copaxtimelessxlsdxl1_V44.lora.safetensors - FFai.0016.Counterfeitxl-Ffusionai-Alpha-Vae.lora.safetensors - FFai.0017.Counterfeitxl_V10.lora.safetensors - FFai.0018.Crystalclearxl_Ccxl.lora.safetensors - FFai.0019.Deepbluexl_V006.lora.safetensors - FFai.0020.Dream-Ffusion-Shaper.lora.safetensors - FFai.0021.Dreamshaperxl10_Alpha2xl10.lora.safetensors - FFai.0022.Duchaitenaiartsdxl_V10.lora.safetensors - FFai.0023.Dynavisionxlallinonestylized_Beta0371bakedvae.lora.safetensors - FFai.0024.Dynavisionxlallinonestylized_Beta0411bakedvae.lora.safetensors - FFai.0025.Fantasticcharacters_V55.lora.safetensors - FFai.0026.Fenrisxl_V55.lora.safetensors - FFai.0027.Fudukimix_V10.lora.safetensors - FFai.0028.Infinianimexl_V16.lora.safetensors - FFai.0029.Juggernautxl_Version1.lora_1.safetensors - FFai.0030.Lahmysterioussdxl_V330.lora.safetensors - FFai.0031.Mbbxlultimate_V10rc.lora.safetensors - FFai.0032.Miamodelsfwnsfwsdxl_V30.lora.safetensors - FFai.0033.Morphxl_V10.lora.safetensors - FFai.0034.Nightvisionxlphotorealisticportrait_Beta0681bakedvae.lora_1.safetensors - FFai.0035.Osorubeshialphaxl_Z.lora.safetensors - FFai.0036.Physiogenxl_V04.lora.safetensors - FFai.0037.Protovisionxlhighfidelity3d_Beta0520bakedvae.lora.safetensors - FFai.0038.Realitycheckxl_Alpha11.lora.safetensors - FFai.0039.Realmixxl_V10.lora.safetensors - FFai.0040.Reproductionsdxl_V31.lora.safetensors - FFai.0041.Rundiffusionxl_Beta.lora.safetensors - FFai.0042.Samaritan3dcartoon_V40sdxl.lora.safetensors - FFai.0043.Sdvn6realxl_Detailface.lora.safetensors - FFai.0044.Sdvn7realartxl_Beta2.lora.safetensors - FFai.0045.Sdxl10arienmixxlasian_V10.lora.safetensors - FFai.0046.Sdxlbasensfwfaces_Sdxlnsfwfaces03.lora.safetensors - FFai.0047.Sdxlfaetastic_V10.lora.safetensors - FFai.0048.Sdxlfixedvaefp16remove_Basefxiedvaev2fp16.lora.safetensors - FFai.0049.Sdxlnijiv4_Sdxlnijiv4.lora.safetensors - FFai.0050.Sdxlronghua_V11.lora.safetensors - FFai.0051.Sdxlunstablediffusers_V5unchainedslayer.lora.safetensors - FFai.0052.Sdxlyamersanimeultra_Yamersanimev2.lora.safetensors - FFai.0053.Shikianimexl_V10.lora.safetensors - FFai.0054.Spectrumblendx_V10.lora.safetensors - FFai.0055.Stablediffusionxl_V30.lora.safetensors - FFai.0056.Talmendoxlsdxl_V11beta.lora.safetensors - FFai.0057.Wizard_V10.lora.safetensors - FFai.0058.Wyvernmix15xl_Xlv11.lora.safetensors - FFai.0059.Xl13asmodeussfwnsfw_V17bakedvae.lora.safetensors - FFai.0060.Xl3experimentalsd10xl_V10.lora.safetensors - FFai.0061.Xl6hephaistossd10xlsfw_V21bakedvaefp16fix.lora.safetensors - FFai.0062.Xlperfectdesign_V2ultimateartwork.lora.safetensors - FFai.0063.Xlyamersrealistic_V3.lora.safetensors - FFai.0064.Xxmix9realisticsdxl_Testv20.lora.safetensors - FFai.0065.Zavychromaxl_B2.lora.safetensors ``` ## 🎉 Acknowledgements & Citations A huge shoutout to the community for their continued support and feedback. Together, we are pushing the boundaries of what's possible with machine learning! We would also like to acknowledge and give credit to the following projects and authors: - **ComfyUI**: We've used and modified portions of [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for our work. - **kohya-ss/sd-scripts and bmaltais**: Our work also incorporates modifications from [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts). - **lora-inspector**: We've benefited from the [lora-inspector](https://github.com/rockerBOO/lora-inspector) project. - **KohakuBlueleaf**: Special mention to KohakuBlueleaf for their invaluable contributions. [![400GB FFusion Lora XL 5](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image5sm.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image5.jpg) [![400GB FFusion Lora XL 6](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image6sm.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image6.jpg) [![400GB FFusion Lora XL 7](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image7sm.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image7.jpg) [![400GB FFusion Lora XL 9](https://huggingface.co/FFusion/400GB-LoraXL/resolve/main/images/image9.jpg)](https://huggingface.co/FFusion/400GB-LoraXL/tree/main) ### HowMuch ??? ![60% Works](https://img.shields.io/badge/60%25%20of%20the%20Time-It%20Works%20Every%20Time-green) **Have you ever asked yourself, "How much space have I wasted on `*.ckpt` and `*.safetensors` checkpoints?"** 🤔 Say hello to HowMuch: Checking checkpoint wasted space since... well, now! 😄 Enjoy this somewhat unnecessary, yet **"fun-for-the-whole-family"** DiskSpaceAnalyzer tool. 😄 ## Overview `HowMuch` is a Python tool designed to scan your drives (or a specified directory) and report on the total space used by files with specific extensions, mainly `.ckpt` and `.safetensors`. It outputs: - The total storage capacity of each scanned drive or directory. - The space occupied by `.ckpt` and `.safetensors` files. - The free space available. - A neat bar chart visualizing the above data. ## Installation [GitHub](https://github.com/1e-2/HowMuch) ### From PyPI You can easily install `HowMuch` via pip: ```bash pip install howmuch ``` ### From Source 1. Clone the repository: ```bash git clone https://github.com/1e-2/HowMuch.git ``` 2. Navigate to the cloned directory and install: ```bash cd HowMuch pip install . ``` ## Usage Run the tool without any arguments to scan all drives: ```bash howmuch ``` Or, specify a particular directory or drive to scan: ```bash howmuch --scan C: ``` ### 🌐 **Contact Information** The **FFusion.ai** project is proudly maintained by **Source Code Bulgaria Ltd** & **Black Swan Technologies**. 📧 Reach us at [[email protected]](mailto:[email protected]) for any inquiries or support. #### 🌌 **Find us on:** - 🐙 [GitHub](https://github.com/1e-2) - 😊 [Hugging Face](https://huggingface.co/FFusion/) - 💡 [Civitai](https://civitai.com/user/idle/models) 🔐 **Security powered by** [Comodo.BG](http://Comodo.BG) & [Preasidium.CX](http://Preasidium.CX) 🚀 Marketing by [Гугъл.com](http://Гугъл.com) 📩 [![Email](https://img.shields.io/badge/Email-enquiries%40ffusion.ai-blue?style=for-the-badge&logo=gmail)](mailto:[email protected]) 🌍 Sofia Istanbul London --- We hope the FFXL400 serves as a valuable asset in your AI journey. We encourage feedback, contributions, and insights from the community to further refine and enhance this model. Together, let's push the boundaries of what's possible! ![ffusionai-logo.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/EjDa_uGcOoH2cXM2K-NYn.png)
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Habana/t5
Habana
2023-09-08T13:39:25Z
18,395
0
null
[ "optimum_habana", "license:apache-2.0", "region:us" ]
null
2022-06-04T15:43:41Z
--- license: apache-2.0 --- [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). ## T5 model HPU configuration This model only contains the `GaudiConfig` file for running the [T5](https://huggingface.co/t5-base) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_fused_adam`: whether to use Habana's custom AdamW implementation - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator ## Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/summarization/run_summarization.py) is a summarization example script to fine-tune a model. You can run it with T5-small with the following command: ```bash python run_summarization.py \ --model_name_or_path t5-small \ --do_train \ --do_eval \ --dataset_name cnn_dailymail \ --dataset_config "3.0.0" \ --source_prefix "summarize: " \ --output_dir /tmp/tst-summarization \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --overwrite_output_dir \ --predict_with_generate \ --use_habana \ --use_lazy_mode \ --gaudi_config_name Habana/t5 \ --ignore_pad_token_for_loss False \ --pad_to_max_length \ --save_strategy epoch \ --throughput_warmup_steps 3 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
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lmsys/vicuna-13b-v1.5-16k
lmsys
2023-10-06T19:46:12Z
18,372
190
transformers
[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2307.09288", "arxiv:2306.05685", "license:llama2", "has_space", "text-generation-inference", "region:us" ]
text-generation
2023-08-01T16:51:46Z
--- inference: false license: llama2 --- # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture - **License:** Llama 2 Community License Agreement - **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288) ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api ## Training Details Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling. The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation ![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true) Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
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coqui/XTTS-v1
coqui
2023-11-10T19:40:20Z
18,321
345
coqui
[ "coqui", "text-to-speech", "license:other", "has_space", "region:us" ]
text-to-speech
2023-09-13T09:22:03Z
--- license: other license_name: coqui-public-model-license license_link: https://coqui.ai/cpml library_name: coqui pipeline_tag: text-to-speech --- # ⓍTTS ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. Built on Tortoise, ⓍTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy. There is no need for an excessive amount of training data that spans countless hours. This is the same model that powers [Coqui Studio](https://coqui.ai/), and [Coqui API](https://docs.coqui.ai/docs), however we apply a few tricks to make it faster and support streaming inference. ## NOTE: ⓍTTS V2 model is out here [XTTS V2](https://huggingface.co/coqui/XTTS-v2) ### Features - Supports 14 languages. - Voice cloning with just a 6-second audio clip. - Emotion and style transfer by cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. ### Languages As of now, XTTS-v1 (v1.1) supports 14 languages: **English, Spanish, French, German, Italian, Portuguese, Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese, and Japanese**. Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out! ### Code The current implementation supports inference and [fine-tuning](https://tts.readthedocs.io/en/latest/models/xtts.html#training). ### License This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml). There's a lot that goes into a license for generative models, and you can read more of [the origin story of CPML here](https://coqui.ai/blog/tts/cpml). ### Contact Come and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai). You can also mail us at [email protected]. Using 🐸TTS API: ```python from TTS.api import TTS tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", gpu=True) # generate speech by cloning a voice using default settings tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", file_path="output.wav", speaker_wav="/path/to/target/speaker.wav", language="en") # generate speech by cloning a voice using custom settings tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", file_path="output.wav", speaker_wav="/path/to/target/speaker.wav", language="en", decoder_iterations=30) ``` Using 🐸TTS Command line: ```console tts --model_name tts_models/multilingual/multi-dataset/xtts_v1 \ --text "Bugün okula gitmek istemiyorum." \ --speaker_wav /path/to/target/speaker.wav \ --language_idx tr \ --use_cuda true ``` Using model directly: ```python from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts config = XttsConfig() config.load_json("/path/to/xtts/config.json") model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", eval=True) model.cuda() outputs = model.synthesize( "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", config, speaker_wav="/data/TTS-public/_refclips/3.wav", gpt_cond_len=3, language="en", ) ```
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sentence-transformers/paraphrase-distilroberta-base-v1
sentence-transformers
2022-06-15T20:03:06Z
18,320
3
sentence-transformers
[ "sentence-transformers", "pytorch", "tf", "jax", "roberta", "feature-extraction", "sentence-similarity", "transformers", "arxiv:1908.10084", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
sentence-similarity
2022-03-02T23:29:05Z
--- pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # sentence-transformers/paraphrase-distilroberta-base-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-distilroberta-base-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-distilroberta-base-v1) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
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Helsinki-NLP/opus-mt-tc-big-ar-en
Helsinki-NLP
2023-08-16T12:10:50Z
18,270
9
transformers
[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "opus-mt-tc", "ar", "en", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
translation
2022-04-13T15:18:06Z
--- language: - ar - en tags: - translation - opus-mt-tc license: cc-by-4.0 model-index: - name: opus-mt-tc-big-ar-en results: - task: name: Translation ara-eng type: translation args: ara-eng dataset: name: flores101-devtest type: flores_101 args: ara eng devtest metrics: - name: BLEU type: bleu value: 42.6 - task: name: Translation ara-eng type: translation args: ara-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ara-eng metrics: - name: BLEU type: bleu value: 47.3 - task: name: Translation ara-eng type: translation args: ara-eng dataset: name: tico19-test type: tico19-test args: ara-eng metrics: - name: BLEU type: bleu value: 44.4 --- # opus-mt-tc-big-ar-en Neural machine translation model for translating from Arabic (ar) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-09 * source language(s): afb ara arz * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip) * more information released models: [OPUS-MT ara-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "اتبع قلبك فحسب.", "وين راهي دّوش؟" ] model_name = "pytorch-models/opus-mt-tc-big-ar-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Just follow your heart. # Wayne Rahi Dosh? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-ar-en") print(pipe("اتبع قلبك فحسب.")) # expected output: Just follow your heart. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | ara-eng | tatoeba-test-v2021-08-07 | 0.63477 | 47.3 | 10305 | 76975 | | ara-eng | flores101-devtest | 0.66987 | 42.6 | 1012 | 24721 | | ara-eng | tico19-test | 0.68521 | 44.4 | 2100 | 56323 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 18:17:57 EEST 2022 * port machine: LM0-400-22516.local
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finiteautomata/bertweet-base-emotion-analysis
finiteautomata
2023-03-20T14:47:04Z
18,262
11
transformers
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "emotion-analysis", "en", "arxiv:2106.09462", "endpoints_compatible", "has_space", "region:us" ]
text-classification
2022-03-02T23:29:05Z
--- language: - en tags: - emotion-analysis --- # Emotion Analysis in English ## bertweet-base-emotion-analysis Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/) Model trained with EmoEvent corpus for Emotion detection in English. Base model is [BerTweet](https://huggingface.co/vinai/bertweet-base). ## License `pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. 1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php) 2. [SEMEval 2017 Dataset license]() ## Citation If you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462) ``` @misc{perez2021pysentimiento, title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks}, author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque}, year={2021}, eprint={2106.09462}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` and also the dataset related paper ``` @inproceedings{del2020emoevent, title={EmoEvent: A multilingual emotion corpus based on different events}, author={del Arco, Flor Miriam Plaza and Strapparava, Carlo and Lopez, L Alfonso Urena and Mart{\'\i}n-Valdivia, M Teresa}, booktitle={Proceedings of the 12th Language Resources and Evaluation Conference}, pages={1492--1498}, year={2020} } ``` Enjoy! 🤗
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JeremiahZ/bert-base-uncased-rte
JeremiahZ
2023-09-24T22:17:57Z
18,256
1
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:bert-base-uncased", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
text-classification
2022-06-21T12:40:10Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy base_model: bert-base-uncased model-index: - name: bert-base-uncased-rte results: - task: type: text-classification name: Text Classification dataset: name: GLUE RTE type: glue args: rte metrics: - type: accuracy value: 0.6895306859205776 name: Accuracy - task: type: natural-language-inference name: Natural Language Inference dataset: name: glue type: glue config: rte split: validation metrics: - type: accuracy value: 0.6823104693140795 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTc4OTUzYmQ0NWRkMjdmYjI3ZDhmNWU5MTQ1YTM4ZTQzOWM5MTJjYzlmMjM3ZTE2Y2ZhMGJlNTI0OTJhYjNmMCIsInZlcnNpb24iOjF9.siPkmQhZKOZ1k_SyS1xIMavpK_CQ8tHTm39McCIEjiF7G1x62lbuKfrZsLoKoPf9XpNJqXoaXIRPCpHBKlfwCA - type: precision value: 0.7047619047619048 name: Precision verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzdmMjk0ZjRkNzM4ZWE1ZGUyOWUxZTFjNmEyNTRmNjZmMDUxOTJlZmUxNjUzMWFhZTYzZTM1ZGNkMDg1YTMzYyIsInZlcnNpb24iOjF9.Cm2kMSTsWVPU9mBv8xAyvo7msTHdG3SECIYZ4kQ5RpN4NV3WE1k0EqmcGzAedwYNfSEg1qXL-qWDKOeoXDAnCw - type: recall value: 0.5648854961832062 name: Recall verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjc1NTMzYWI5OGRjODdhYmJjZDQ3ODdiMWE3ODYzZjNhZDg1MGIyNjA1YzQwNzcwYTQzNjJkNGVjNmNjMWJmNSIsInZlcnNpb24iOjF9.MwRAu1AKhCt__2vBjhvEqU0gvXaJ5EMOOotKmwGXsuF3eGJEEDDuiWBgu9y291aqndTTwWvuH9CNQjGKLCoNCw - type: auc value: 0.7394646031580048 name: AUC verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGQwMWVhZjlhNjhhZDRhNGYwNWUyOTZjMTgwMDQ5ZWM3MDcxNDc5OGJiNDE5NTNhOTU0MDMwYTdkYjcxNzFiZiIsInZlcnNpb24iOjF9.ZLyE_ZDWyVAr_GL_3lSqBmuIip7C13oj5kT1lI9JQOidt-IXsRHYUmJt_f7HWUNU1-FBD5lzHYcstF6WQ0VrAw - type: f1 value: 0.6271186440677967 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmU0YjI4Y2MyMzU5ZTYzMmE0YzhjOTgwM2UwYTNhZDFmZWE3NTdhODMzYmU1ZWUxYTc2Yjk3Nzg3MjAyOThiOSIsInZlcnNpb24iOjF9.GJhnKNSHN5Sv9W3gl8fgPAAbM5EtMlhOHoOFcj_O65FLtcTi_ANpyv7gi41fPbMjS2TG4fgdVHQ_UZg7M6W2Cw - type: loss value: 0.7001310586929321 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjMwMTY5OGVkMzVhMzdjMmY1YmIzYTViOTRlNzcxMTI4NWNiOTQ5ZjMwMjRlODE4ZWUwMGVhZjhiMmIzM2ViOSIsInZlcnNpb24iOjF9.IsJhfeeqnVZFn10sOkCW7vAzfw1WQMwR8b99B3-hct_lrI1xodt5ySGltDvx2Q8ufD6hzfQ7YaDeHytDmeeFBw --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-rte This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6972 - Accuracy: 0.6895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 156 | 0.6537 | 0.6318 | | No log | 2.0 | 312 | 0.6383 | 0.6534 | | No log | 3.0 | 468 | 0.6972 | 0.6895 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
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jjzha/jobbert_skill_extraction
jjzha
2023-10-26T10:25:11Z
18,164
4
transformers
[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
token-classification
2023-04-06T13:41:51Z
This is a demo using the models from: ``` @inproceedings{zhang-etal-2022-skillspan, title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings", author = "Zhang, Mike and Jensen, Kristian and Sonniks, Sif and Plank, Barbara", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.366", doi = "10.18653/v1/2022.naacl-main.366", pages = "4962--4984", abstract = "Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning.", } ``` Note that there is another endpoint, namely `jjzha/jobbert_knowledge_extraction`. Knowledge can be seen as hard skills and skills are both soft and applied skills.
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