{ // 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--------------------------------------------------------------------------------------------------\n\n

Introduction

\n\nThis model is a lightweight and uncased version of MiniLM [1] for the Italian language. Its 17M parameters and 67MB size make it\n85% lighter than a typical mono-lingual BERT model. It is ideal when memory consumption and execution speed are critical while maintaining high-quality results.\n\n\n

Model description

\n\nThe model builds on mMiniLMv2 [1] (from Microsoft: [L6xH384 mMiniLMv2](https://github.com/microsoft/unilm/tree/master/minilm)) as a starting point, \nfocusing it on the Italian language while at the same time turning it into an uncased model by modifying the embedding layer \n(as in [2], but computing document-level frequencies over the Wikipedia dataset and setting a frequency threshold of 0.1%), which brings a considerable\nreduction in the number of parameters.\n\nTo compensate for the deletion of cased tokens, which now forces the model to exploit lowercase representations of words previously capitalized, \nthe model has been further pre-trained on the Italian split of the [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset, using the whole word masking [3] technique to make it more robust \nto the new uncased representations.\n\nThe resulting model has 17M parameters, a vocabulary of 14.610 tokens, and a size of 67MB, which makes it 85% lighter than a typical mono-lingual BERT model and\n75% lighter than a standard mono-lingual DistilBERT model.\n\n\n

Training procedure

\n\nThe model has been trained for masked language modeling on the Italian Wikipedia (~3GB) dataset for 10K steps, using the AdamW optimizer, with a batch size of 512 \n(obtained through 128 gradient accumulation steps),\na sequence length of 512, and a linearly decaying learning rate starting from 5e-5. The training has been performed using dynamic masking between epochs and\nexploiting the whole word masking technique.\n\n\n

Performances

\n\nThe following metrics have been computed on the Part of Speech Tagging and Named Entity Recognition tasks, using the UD Italian ISDT and WikiNER datasets, respectively. \nThe PoST model has been trained for 5 epochs, and the NER model for 3 epochs, both with a constant learning rate, fixed at 1e-5. For Part of Speech Tagging, the metrics have been computed on the default test set\nprovided with the dataset, while for Named Entity Recognition the metrics have been computed with a 5-fold cross-validation\n\n| Task | Recall | Precision | F1 |\n| ------ | ------ | ------ | ------ |\n| Part of Speech Tagging | 95.64 | 95.32 | 95.45 |\n| Named Entity Recognition | 82.27 | 80.64 | 81.29 |\n\nThe metrics have been computed at the token level and macro-averaged over the classes.\n\n

Demo

\n\nYou can try the model online (fine-tuned on named entity recognition) using this web app: https://huggingface.co/spaces/osiria/flare-it-demo\n\n

Quick usage

\n\n```python\nfrom transformers import AutoTokenizer, XLMRobertaForMaskedLM\nfrom transformers import pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"osiria/flare-it\")\nmodel = XLMRobertaForMaskedLM.from_pretrained(\"osiria/flare-it\")\npipeline_mlm = pipeline(task=\"fill-mask\", model=model, tokenizer=tokenizer)\n```\n\n\n

Limitations

\n\nThis lightweight model has been further pre-trained on Wikipedia, so it's particularly suitable as an agile analyzer for large volumes of natively digital text \nfrom the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions\n(like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).\n\n

References

\n\n[1] https://arxiv.org/abs/2012.15828\n\n[2] https://arxiv.org/abs/2010.05609\n\n[3] https://arxiv.org/abs/1906.08101\n\n

License

\n\nThe model is released under MIT license"}}},{"rowIdx":28078,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy"},"tags":{"kind":"list like","value":["pytorch","tf","bert","token-classification","ar","arxiv:2103.06678","transformers","license:apache-2.0","autotrain_compatible"],"string":"[\n \"pytorch\",\n \"tf\",\n \"bert\",\n \"token-classification\",\n \"ar\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\",\n \"autotrain_compatible\"\n]"},"pipeline_tag":{"kind":"string","value":"token-classification"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForTokenClassification\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":32,"string":"32"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\ninference: \n parameters: \n do_sample: true\n max_length: 384\n top_p: 0.9\n repetition_penalty: 1.0\nlanguage: \n - en\nlicense: mit\ntags: \n - \"text2text generation\"\ntask: \n name: \"lyrics interpretation\"\n type: \"text2text generation\"\nwidget: \n - \n text: \"Explain: \\nLoving him is like driving a new Maserati down a dead end street\\nFaster than the wind, passionate as sin, ending so suddenly\\nLoving him is like trying to change your mind\\nOnce you're already flying through the free fall\\nLike the colors in autumn, so bright, just before they lose it all\\n\\nLosing him was blue, like I'd never known\\nMissing him was dark gray, all alone\\nForgetting him was like trying to know\\nSomebody you never met\\nBut loving him was red\\nLoving him was red\\n\\nTouching him was like realizing all you ever wanted\\nWas right there in front of you\\nMemorizing him was as easy as knowing all the words\\nTo your old favorite song\\nFighting with him was like trying to solve a crossword\\nAnd realizing there's no right answer\\nRegretting him was like wishing you never found out\\nThat love could be that strong\\n\\nLosing him was blue, like I'd never known\\nMissing him was dark gray, all alone\\nForgetting him was like trying to know\\nSomebody you never met\\nBut loving him was red\\nOh, red\\nBurning red\\n\\nRemembering him comes in flashbacks and echoes\\nTell myself it's time now gotta let go\\nBut moving on from him is impossible\\nWhen I still see it all in my head\\nIn burning red\\nBurning, it was red\\n\\nOh, losing him was blue, like I'd never\\nnown\\nMissing him was dark gray, all alone\\nForgetting him was like trying to know\\nSomebody you never met\\n'Cause loving him was red\\nYeah, yeah, red\\nBurning red\\n\\nAnd that's why he's spinning 'round in my head\\nComes back to me, burning red\\nYeah, yeah\\nHis love was like driving a new Maserati down a dead end street\"\n example_title: \"Red - Taylor Swift\"\n---\n\n\n# Overview\nThis pilot hub aims to test whether a flan-t5-base can effectively automate poem interpretation. \nTo use the hub, simply paste in any poem of interest and see its meaning. Please begin your request with the prompt, 'Explain: '."}}},{"rowIdx":28079,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa"},"tags":{"kind":"list like","value":["pytorch","tf","bert","token-classification","ar","arxiv:2103.06678","transformers","license:apache-2.0","autotrain_compatible"],"string":"[\n \"pytorch\",\n \"tf\",\n \"bert\",\n \"token-classification\",\n \"ar\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\",\n \"autotrain_compatible\"\n]"},"pipeline_tag":{"kind":"string","value":"token-classification"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForTokenClassification\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":27,"string":"27"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\ninference: \n parameters: \n do_sample: true\n max_length: 384\n top_p: 0.9\n repetition_penalty: 1.0\nlanguage: \n - en\nlicense: mit\ntags: \n - \"text2text generation\"\ntask: \n name: \"poem interpretation\"\n type: \"text2text generation\"\nwidget: \n - \n text: \"Explain: \\nThe Lost Boy\\n\\nBoy it really stinks in here\\nThe dumpster is not the place\\nTo get the food you need each day\\nJust to feed your face.\\n\\nA ten-year-old with a dirty face\\nCrawls out with his daily meal\\nWhat is he doing in this place\\nHow am I suppose to feel?\\n\\nHis mother cradles a baby \\nThe child's been dead three weeks\\nHer mind is gone from drug abuse\\nAnd now she hardly speaks.\\n\\nGrandma is a drunkard\\nWith men who come to town\\nBringing her a bottle\\nJust to go a round.\\n\\nDrugs out on the table \\nA line or two is good\\nThat should carry her over \\nNo one ever understood.\\n\\nThe little boy with dirty face\\nHas not been schooled in years\\nHe fights the streets alone\\nLong since lost his fears.\\n\\nA stale sandwich, and watered coke\\nHis meal for this day\\nWhatever tomorrow may bring\\nHe knows not the word play.\\n\\nEmaciated with distant eyes\\nNo one really sees him\\nJust one of the lost boys\\nHis life completely grim.\\n\\nGod bless the children!\\n\\n\"\n example_title: \"The Lost Boy - pattyann4500 (allpoetry.com/920731)\"\n - \n text: \"Explain: \\nLet your breath be the air I need,\\nwhen I drown in your eyes as I see.\\nLet yourself fall into my arms that bleed,\\nwhen the world shows you no mercy.\\n\\nLet your sad past bury down in the core,\\nwhen you walk with your heart close to me.\\nLet there be your lovely face at the door,\\nWhen I return from the war no matter how long it be.\\n\\nLet your love nourish my frozen heart,\\nwhen it lies under the snow capped tree.\\nLet me be enslaved with you forever from the start,\\nwhen the time comes, together we shall flee.\\n\\nLet your presence enlighten my dark,\\nwhen your smile reflects in the sea.\\nLet the words of love be thy spark,\\nwhen you come out of dreams to kiss me.\\n\\nI wish we were together... my princess... \\n\"\n example_title: \"Princess... - Soulhealer95 (allpoetry.com/11038949)\"\n---\n\n\n# Overview\nThe aim of this pilot hub is to test whether a Flan-T5-Base model, when pre-trained with a lyrics interpretation task, can better interpret poems.\nTo use the hub, simply paste in any poem of interest and see its meaning. Please begin your request with the prompt, 'Explain: '."}}},{"rowIdx":28080,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-da"},"tags":{"kind":"list like","value":["pytorch","tf","jax","bert","fill-mask","ar","arxiv:2103.06678","transformers","license:apache-2.0","autotrain_compatible"],"string":"[\n \"pytorch\",\n \"tf\",\n \"jax\",\n \"bert\",\n \"fill-mask\",\n \"ar\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\",\n \"autotrain_compatible\"\n]"},"pipeline_tag":{"kind":"string","value":"fill-mask"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForMaskedLM\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":449,"string":"449"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\nlibrary_name: stable-baselines3\ntags:\n- LunarLander-v2\n- deep-reinforcement-learning\n- reinforcement-learning\n- stable-baselines3\nmodel-index:\n- name: PPO\n results:\n - task:\n type: reinforcement-learning\n name: reinforcement-learning\n dataset:\n name: LunarLander-v2\n type: LunarLander-v2\n metrics:\n - type: mean_reward\n value: 257.28 +/- 16.62\n name: mean_reward\n verified: false\n---\n\n# **PPO** Agent playing **LunarLander-v2**\nThis is a trained model of a **PPO** agent playing **LunarLander-v2**\nusing the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).\n\n## Usage (with Stable-baselines3)\nTODO: Add your code\n\n\n```python\nfrom stable_baselines3 import ...\nfrom huggingface_sb3 import load_from_hub\n\n...\n```\n"}}},{"rowIdx":28081,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6"},"tags":{"kind":"list like","value":["pytorch","tf","bert","text-classification","ar","arxiv:2103.06678","transformers","license:apache-2.0"],"string":"[\n \"pytorch\",\n \"tf\",\n \"bert\",\n \"text-classification\",\n \"ar\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForSequenceClassification\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":34,"string":"34"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\ndatasets:\n- logo-wizard/modern-logo-dataset\ntags:\n- text-to-image\n- lora\n- stable-diffusion\npipeline_tag: text-to-image\nlicense: creativeml-openrail-m\n---\n# LoRA text2image fine-tuning - eewwann/logo-diffusion-lora-v10\nThese are LoRA with Hadamard Product (LoHa) adaption weights for [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.safetensors). The weights were fine-tuned on the [logo-wizard/modern-logo-dataset](https://huggingface.co/datasets/logo-wizard/modern-logo-dataset) dataset. You can find some example images in the following. \n\n\n\n\n"}}},{"rowIdx":28082,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry"},"tags":{"kind":"list like","value":["pytorch","tf","bert","text-classification","ar","arxiv:1905.05700","arxiv:2103.06678","transformers","license:apache-2.0"],"string":"[\n \"pytorch\",\n \"tf\",\n \"bert\",\n \"text-classification\",\n \"ar\",\n \"arxiv:1905.05700\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForSequenceClassification\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":31,"string":"31"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\ntags:\n- Taxi-v3\n- q-learning\n- reinforcement-learning\n- custom-implementation\nmodel-index:\n- name: q-Taxi-v3\n results:\n - task:\n type: reinforcement-learning\n name: reinforcement-learning\n dataset:\n name: Taxi-v3\n type: Taxi-v3\n metrics:\n - type: mean_reward\n value: 7.50 +/- 2.76\n name: mean_reward\n verified: false\n---\n\n # **Q-Learning** Agent playing1 **Taxi-v3**\n This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .\n\n ## Usage\n\n ```python\n \n model = load_from_hub(repo_id=\"yasndr/q-Taxi-v3\", filename=\"q-learning.pkl\")\n\n # Don't forget to check if you need to add additional attributes (is_slippery=False etc)\n env = gym.make(model[\"env_id\"])\n ```\n "}}},{"rowIdx":28083,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa"},"tags":{"kind":"list like","value":["pytorch","tf","bert","token-classification","ar","arxiv:2103.06678","transformers","license:apache-2.0","autotrain_compatible"],"string":"[\n \"pytorch\",\n \"tf\",\n \"bert\",\n \"token-classification\",\n \"ar\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\",\n \"autotrain_compatible\"\n]"},"pipeline_tag":{"kind":"string","value":"token-classification"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForTokenClassification\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":1862,"string":"1,862"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"## Psychology-Alpaca-RM\n- PEFT adapter layers for a reward model based on ``decapoda-research/llama-7b-hf``. \n- Trained with a small subset (110 data points) of ``samhog/cgpt-pairs`` with 10K prompts, each with two answers (one 'good', one 'bad')"}}},{"rowIdx":28084,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-mix"},"tags":{"kind":"list like","value":["pytorch","tf","jax","bert","fill-mask","ar","arxiv:2103.06678","transformers","Arabic","Dialect","Egyptian","Gulf","Levantine","Classical Arabic","MSA","Modern Standard Arabic","license:apache-2.0","autotrain_compatible"],"string":"[\n \"pytorch\",\n \"tf\",\n \"jax\",\n \"bert\",\n \"fill-mask\",\n \"ar\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"Arabic\",\n \"Dialect\",\n \"Egyptian\",\n \"Gulf\",\n \"Levantine\",\n \"Classical Arabic\",\n \"MSA\",\n \"Modern Standard Arabic\",\n \"license:apache-2.0\",\n \"autotrain_compatible\"\n]"},"pipeline_tag":{"kind":"string","value":"fill-mask"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForMaskedLM\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":20880,"string":"20,880"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\nlibrary_name: stable-baselines3\ntags:\n- LunarLander-v2\n- deep-reinforcement-learning\n- reinforcement-learning\n- stable-baselines3\nmodel-index:\n- name: PPO\n results:\n - task:\n type: reinforcement-learning\n name: reinforcement-learning\n dataset:\n name: LunarLander-v2\n type: LunarLander-v2\n metrics:\n - type: mean_reward\n value: 259.59 +/- 19.34\n name: mean_reward\n verified: false\n---\n\n# **PPO** Agent playing **LunarLander-v2**\nThis is a trained model of a **PPO** agent playing **LunarLander-v2**\nusing the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).\n\n## Usage (with Stable-baselines3)\nTODO: Add your code\n\n\n```python\nfrom stable_baselines3 import ...\nfrom huggingface_sb3 import load_from_hub\n\n...\n```\n"}}},{"rowIdx":28085,"cells":{"modelId":{"kind":"string","value":"CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry"},"tags":{"kind":"list like","value":["pytorch","tf","bert","text-classification","ar","arxiv:1905.05700","arxiv:2103.06678","transformers","license:apache-2.0"],"string":"[\n \"pytorch\",\n \"tf\",\n \"bert\",\n \"text-classification\",\n \"ar\",\n \"arxiv:1905.05700\",\n \"arxiv:2103.06678\",\n \"transformers\",\n \"license:apache-2.0\"\n]"},"pipeline_tag":{"kind":"string","value":"text-classification"},"config":{"kind":"string","value":"{\n \"architectures\": [\n \"BertForSequenceClassification\"\n ],\n \"model_type\": \"bert\",\n \"task_specific_params\": {\n \"conversational\": {\n \"max_length\": null\n },\n \"summarization\": {\n \"early_stopping\": null,\n \"length_penalty\": null,\n \"max_length\": null,\n \"min_length\": null,\n \"no_repeat_ngram_size\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"text-generation\": {\n \"do_sample\": null,\n \"max_length\": null\n },\n \"translation_en_to_de\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_fr\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n },\n \"translation_en_to_ro\": {\n \"early_stopping\": null,\n \"max_length\": null,\n \"num_beams\": null,\n \"prefix\": null\n }\n }\n}"},"downloads":{"kind":"number","value":25,"string":"25"},"first_commit":{"kind":"null"},"card":{"kind":"string","value":"---\nlanguage: en\nthumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true\ntags:\n- huggingtweets\nwidget:\n- text: \"My dream is\"\n---\n\n\n \n