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| 0 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Ekami/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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| 38,156 | 2023-04-20T06:34:31Z |
---
license: afl-3.0
datasets:
- huggan/few-shot-pokemon
language:
- en
library_name: diffusers
---
## Training data
huggan/few-shot-pokemon
### Training hyperparameters
The following hyperparameters were used during training:
--checkpointing_steps=1000 \
--dataset_name="huggan/few-shot-pokemon" \
--resolution=128 \
--output_dir="ddpm-ema-pokemon-128" \
--train_batch_size=16 \
--eval_batch_size=16 \
--num_epochs=100 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=800 \
--mixed_precision="fp16" \
--push_to_hub
### Training results
📈 [TensorBoard logs]()
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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| 341 | 2023-04-20T06:38:04Z |
---
license: cc-by-sa-4.0
language:
- en
- zh
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- dreambooth
---
# asian-role
Welcome to asian-role model, this is a Chinese gorgeous antique style game role model. This model is intended to produce high-quality,
highly detailed anime style with just a few prompts.
e.g. **_1girl, white hair, beautiful blue eyes, red lips, detailed sky, garden_**
This model is a merged model, it has [GuoFeng3](https://huggingface.co/xiaolxl/GuoFeng3) and [Chilloutmix](https://huggingface.co/TASUKU2023/Chilloutmix) in it.
## Spaces
We support a Gradio Web UI to run it:
[https://huggingface.co/spaces/shibing624/asian-role](https://huggingface.co/spaces/shibing624/asian-role)
## 🧨 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 = "shibing624/asian-role"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.safety_checker = lambda images, **kwargs: (images, False)
prompt = "1girl"
negative_prompt = """(((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly, pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation, deformed, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs, bad feet, loli, little girl"""
image = pipe(prompt, height=512, width=512, num_inference_steps=30, guidance_scale=6,
negative_prompt=negative_prompt, num_images_per_prompt=1).images[0]
image.save("./1girl.png")
```
## NovelAI/stable-diffusion-webui
This model can used in [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
Just put the model file [asian-role.safetensors](https://huggingface.co/shibing624/asian-role/resolve/main/asian-role.safetensors) to [stable-diffusion-webui/models/Stable-diffusion](https://github.com/AUTOMATIC1111/stable-diffusion-webui/tree/master/models/Stable-diffusion), it is done, No extra VAE model need, the model contains VAE.
## Examples
Below are some examples of images generated using this model:
**Anime Girl:**
```
{{{masterpiece}}}, {{best quality, super fine illustration , beautiful and delicate water,The finest grass}}. ((beautiful eyes)),{ very delicate light, perfect and delicate limbs}, {nature, painting, water spray},{{ fine luminescence ,very fine 8K CG wallpaper}},Lavender eyes, pink pupils, whole body, white hair, bright eyes,( (an extremely delicate and beautiful girl)), ((1 girl)), medium bust, dynamic angle, (white dress with gold decoration), (long hair flowing with the wind, beautiful hair ornaments, delicate wet skirt, nsfw, breeze, long bangs between eyes), wrinkled skirt, (staring blankly, lovely big eyes),messy_hair,payot,Lateral braid,(Tulle lace white skirt),flowers and grass meadow, near the water edge, ((sunset, starry sky in a circle), randomly distributed clouds, (((river))), splashing water, falling petals
Negative prompt: (((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly, pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs, bad feet, loli, little girl
Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 1, Size: 618x768, Model: asian-role
```
**Real Girl**:
```
(Masterpiece),(best quality),((masterpiece)),(highres), original, portrait of a beautiful teenager, small breasts, formal dress, soft smile, red lips, nice hair, beauty eyes, 1girl, solo, realism, {{{{drawn by Xi Zhang}}}}
Negative prompt: (((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly, pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs, bad feet, loli, little girl
Steps: 23, Sampler: Euler, CFG scale: 7, Seed: 1, Size: 618x768, Model: asian-role
```
**Real Boy**:
```
(Masterpiece),(best quality),((masterpiece)),(highres), original, portrait of a beautiful young man, handsome, smile, short hair, beauty eyes, 1boy, solo, realism, formal dress, chinese face, {{{{drawn by Ralph Steadman}}}}
Negative prompt: (((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly, pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs, bad feet, loli, little girl
Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 1, Size: 618x768, Model hash: 60dbd0f982, Model: asian-role
```
**Scene**:
```
(extremely detailed CG unity 8k wallpaper),(((masterpiece))), (((best quality))), ((ultra-detailed)), (best illustration),(best shadow), ((an extremely delicate and beautiful)),dynamic angle,floating, fairyland,dynamic angle,sea of flowers,beautiful detailed garden,wind,classic,spring, (detailed light),feather, nature, (sunlight), river, forest,(((floating palace))),((the best building)),beautiful and delicate water,(painting),(sketch),(bloom),(shine)
Negative prompt: (((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly, pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs, bad feet, loli, little girl
Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 1, Size: 618x768, Model: asian-role
```
## How to use
Recommand settings:
- **prompts:**
```
{best quality}, {{masterpiece}}, {highres}, {an extremely delicate and beautiful}, original, extremely detailed wallpaper, 1girl
```
- **Negative prompts:**
```
(((simple background))),monochrome ,lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, lowres, bad anatomy, bad hands, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly,pregnant,vore,duplicate,morbid,mut ilated,tran nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,bad anatomy,bad proportions,malformed limbs,extra limbs,cloned face,disfigured,gross proportions, (((missing arms))),(((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error legs,username,blurry,bad feet
```
- Sampling steps:**30 or 50**
- Sampler:**DPM++ SDE Karras**
- The size of the picture should be at least **768**
- suggest **prompts keywords:**
```
strapless dress, smile, chinese dress, dress, hair ornament, necklace, jewelry, long hair, earrings, chinese clothes
```
## License
This model is open access and available to all, with a cc-by-sa-4.0 license further specifying rights and usage.
The cc-by-sa-4.0 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)
|
albert-xlarge-v2
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"AlbertForMaskedLM"
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}
| 2,973 | 2023-04-20T06:39:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
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. -->
# bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on an unknown dataset.
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
bert-base-cased-finetuned-mrpc
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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| 11,644 | 2023-04-20T06:43:13Z |
---
license: bsd-3-clause
---
This is a finetuned CodeT5-base checkpoint on CodeXGLUE code summarization Go data.
Pretrained model: https://huggingface.co/Salesforce/codet5-base
Finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the Go split)
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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}
| 3,377,486 | 2023-04-20T06:45:09Z |
---
license: bsd-3-clause
---
This is a finetuned CodeT5-base checkpoint on CodeXGLUE code summarization Java data.
Pretrained model: https://huggingface.co/Salesforce/codet5-base
Finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the Java split)
|
bert-base-german-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
| 175,983 | null |
---
license: bsd-3-clause
---
This is a finetuned CodeT5-base checkpoint on CodeXGLUE code summarization JavaScript data.
Pretrained model: https://huggingface.co/Salesforce/codet5-base
Finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the JavaScript split)
|
bert-base-german-dbmdz-cased
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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"task_specific_params": {
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| 1,814 | 2023-04-20T06:47:16Z |
---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
model-index:
- name: whisper-base-vinzalo-v1-60k
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. -->
# whisper-base-vinzalo-v1-60k
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0841
## 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: 4.323e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 60000
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 2.1.0a0+git4ab1588
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 68,305 | null |
---
license: bsd-3-clause
---
This is a finetuned CodeT5-base checkpoint on CodeXGLUE code summarization PHP data.
Pretrained model: https://huggingface.co/Salesforce/codet5-base
Finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the PHP split)
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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},
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"prefix": null
}
}
}
| 4,749,504 | 2023-04-20T06:49:29Z |
---
license: bsd-3-clause
---
This is a finetuned CodeT5-base checkpoint on CodeXGLUE code summarization Ruby data.
Pretrained model: https://huggingface.co/Salesforce/codet5-base
Finetuning dataset: https://huggingface.co/datasets/code_x_glue_ct_code_to_text (only the Ruby split)
|
bert-base-multilingual-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 328,585 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 42.10 +/- 33.58
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bert-base-uncased
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
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},
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},
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"num_beams": null,
"prefix": null
}
}
}
| 59,663,489 | 2023-04-20T06:53:04Z |
“พลอย เฌอมาลย์” ไม่ปิดแล้ว เปิดตัวคบ “โต้ง ทูพี”
หลังจากให้คนเดาความสัมพันธ์กันอยู่นานว่าระหว่าง “พลอย เฌอมาลย์ บุญยศักดิ์” กับ “โต้ง พิทวัส พฤกษกิจ” หรือ “โต้ง ทูพี” นั้นเป็นอะไรกันแน่ ล่าสุดทางนางเอกสาวได้ออกมาโพสต์ภาพคู่ พร้อมกับตนเองด้วยชื่อของแร๊ปเปอร์หนุ่ม เปิดคัวความสัมพันธ์ว่านี่คือรักครั้งใหม่ของทั้งคู่ หลังจากที่ฝ่ายชายเพิ่งเลิกรากับ “ปราง กัญญ์ณรัณ วงศ์ขจรไกล” และ พลอย ที่เพิ่งเลิกรากับแฟนสาว “คลอดีน อทิตยา เครก”
งานนี้เพื่อนๆเข้ามาแสดงความยินดีให้กับการปลูกต้นรักครั้งใหม่ของทั้งคู่กันเพียบทีเดียว
<p>► <a href="https://golden678.com/" rel="noopener nofollow">“พลอย เฌอมาลย์” ไม่ปิดแล้ว เปิดตัวคบ “โต้ง ทูพี”</a></p>
<p>► <a href="https://golden678.com/" rel="noopener nofollow">“พลอย เฌอมาลย์” ไม่ปิดแล้ว เปิดตัวคบ “โต้ง ทูพี”</a></p>
<“พลอย เฌอมาลย์” ไม่ปิดแล้ว เปิดตัวคบ “โต้ง ทูพี”
หลังจากให้คนเดาความสัมพันธ์กันอยู่นานว่าระหว่าง “พลอย เฌอมาลย์ บุญยศักดิ์” กับ “โต้ง พิทวัส พฤกษกิจ” หรือ “โต้ง ทูพี” นั้นเป็นอะไรกันแน่ ล่าสุดทางนางเอกสาวได้ออกมาโพสต์ภาพคู่ พร้อมกับตนเองด้วยชื่อของแร๊ปเปอร์หนุ่ม เปิดคัวความสัมพันธ์ว่านี่คือรักครั้งใหม่ของทั้งคู่ หลังจากที่ฝ่ายชายเพิ่งเลิกรากับ “ปราง กัญญ์ณรัณ วงศ์ขจรไกล” และ พลอย ที่เพิ่งเลิกรากับแฟนสาว “คลอดีน อทิตยา เครก”
งานนี้เพื่อนๆเข้ามาแสดงความยินดีให้กับการปลูกต้นรักครั้งใหม่ของทั้งคู่กันเพียบทีเดียว
|
bert-large-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
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}
}
}
| 1,058,496 | 2023-04-20T07:05:23Z |
# Info
Checkpoint 256
Wandb details: https://wandb.ai/bangnbx/text-to-sql-2/runs/b8ce44g0
Exact Match: 31.62% (max 45.84%)
Quite stable from step 768 (44.87%)
|
ctrl
|
[
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null |
{
"architectures": null,
"model_type": "ctrl",
"task_specific_params": {
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},
"summarization": {
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},
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},
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},
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},
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}
}
}
| 17,007 | 2023-04-20T07:07:36Z |
---
license: mit
language:
- en
---
# bert-uncased-L2-H512-A8
This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking)
released by [google-research/bert](https://github.com/google-research/bert).
These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch.
More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch).
## Evaluation
Here are the evaluation scores (F1/Accuracy) for the MPRC task.
|Model|MRPC|
|-|:-:|
|BERT-Tiny|81.22/68.38|
|BERT-Mini|81.43/69.36|
|BERT-Small|81.41/70.34|
|BERT-Medium|83.33/73.53|
|BERT-Base|85.62/78.19|
### References
```
@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}
}
```
|
distilgpt2
|
[
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"transformers",
"exbert",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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"max_length": null,
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}
}
}
| 1,611,668 | 2023-04-20T07:19:12Z |
# Info
Checkpoint 512
Wandb details: https://wandb.ai/bangnbx/text-to-sql-2/runs/b8ce44g0
Exact Match: 40.81% (max 45.84%)
Quite stable from step 768 (44.87%)
|
gpt2-medium
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
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"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
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"early_stopping": null,
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}
}
}
| 759,601 | 2023-04-20T07:20:35Z |
# Info
Checkpoint 2048
Wandb details: https://wandb.ai/bangnbx/text-to-sql-2/runs/b8ce44g0
Exact Match: 44.2% (max 45.84%)
Quite stable from step 768 (44.87%)
|
275Gameplay/test
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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},
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},
"translation_en_to_de": {
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},
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}
}
}
| 5 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# nikcheerla/nooks-amd-detection-full-v3
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("nikcheerla/nooks-amd-detection-full-v3")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
AAli/distilgpt2-finetuned-wikitext2
|
[] | null |
{
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| 0 | 2023-04-20T09:46:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: images
split: train
args: images
metrics:
- name: Accuracy
type: accuracy
value: 0.9609375
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1211
- Accuracy: 0.9609
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.4862 | 0.8516 |
| No log | 2.0 | 8 | 0.4103 | 0.8828 |
| 0.4518 | 3.0 | 12 | 0.3210 | 0.8984 |
| 0.4518 | 4.0 | 16 | 0.2053 | 0.9375 |
| 0.2909 | 5.0 | 20 | 0.1675 | 0.9453 |
| 0.2909 | 6.0 | 24 | 0.1439 | 0.9531 |
| 0.2909 | 7.0 | 28 | 0.1448 | 0.9297 |
| 0.1492 | 8.0 | 32 | 0.1798 | 0.9531 |
| 0.1492 | 9.0 | 36 | 0.1360 | 0.9453 |
| 0.1161 | 10.0 | 40 | 0.1670 | 0.9531 |
| 0.1161 | 11.0 | 44 | 0.1637 | 0.9531 |
| 0.1161 | 12.0 | 48 | 0.1298 | 0.9531 |
| 0.1053 | 13.0 | 52 | 0.1162 | 0.9531 |
| 0.1053 | 14.0 | 56 | 0.1353 | 0.9531 |
| 0.0839 | 15.0 | 60 | 0.1211 | 0.9609 |
| 0.0839 | 16.0 | 64 | 0.1113 | 0.9609 |
| 0.0839 | 17.0 | 68 | 0.1145 | 0.9609 |
| 0.0689 | 18.0 | 72 | 0.1239 | 0.9531 |
| 0.0689 | 19.0 | 76 | 0.1280 | 0.9531 |
| 0.0581 | 20.0 | 80 | 0.1533 | 0.9531 |
| 0.0581 | 21.0 | 84 | 0.1323 | 0.9609 |
| 0.0581 | 22.0 | 88 | 0.1327 | 0.9531 |
| 0.0545 | 23.0 | 92 | 0.1529 | 0.9531 |
| 0.0545 | 24.0 | 96 | 0.1357 | 0.9531 |
| 0.046 | 25.0 | 100 | 0.1333 | 0.9531 |
| 0.046 | 26.0 | 104 | 0.1466 | 0.9531 |
| 0.046 | 27.0 | 108 | 0.1300 | 0.9531 |
| 0.0421 | 28.0 | 112 | 0.1077 | 0.9609 |
| 0.0421 | 29.0 | 116 | 0.0985 | 0.9609 |
| 0.0371 | 30.0 | 120 | 0.1186 | 0.9531 |
| 0.0371 | 31.0 | 124 | 0.1123 | 0.9531 |
| 0.0371 | 32.0 | 128 | 0.1144 | 0.9531 |
| 0.0348 | 33.0 | 132 | 0.1276 | 0.9531 |
| 0.0348 | 34.0 | 136 | 0.1488 | 0.9531 |
| 0.0211 | 35.0 | 140 | 0.1560 | 0.9531 |
| 0.0211 | 36.0 | 144 | 0.1477 | 0.9531 |
| 0.0211 | 37.0 | 148 | 0.1488 | 0.9531 |
| 0.0274 | 38.0 | 152 | 0.1467 | 0.9531 |
| 0.0274 | 39.0 | 156 | 0.1401 | 0.9531 |
| 0.0259 | 40.0 | 160 | 0.1379 | 0.9531 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AbhinavSaiTheGreat/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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| 10 | 2023-04-20T12:54:46Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -146.75 +/- 65.98
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Periramm/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
AdapterHub/bert-base-uncased-pf-copa
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/copa"
] | null |
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}
| 4 | null |
---
tags:
- t5
- adapter-transformers
datasets:
- hotpot_qa
---
# Adapter `carnival13/t5-small-hpqa-ia3lo` for mrm8488/t5-small-finetuned-squadv2
An [adapter](https://adapterhub.ml) for the `mrm8488/t5-small-finetuned-squadv2` model that was trained on the [hotpot_qa](https://huggingface.co/datasets/hotpot_qa/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("mrm8488/t5-small-finetuned-squadv2")
adapter_name = model.load_adapter("carnival13/t5-small-hpqa-ia3lo", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AdapterHub/bert-base-uncased-pf-qnli
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/qnli"
] |
text-classification
|
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| 2 | 2023-04-20T14:18:39Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - erkam/sd-clevr-sg2im-nocap-nodesonly
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the erkam/clevr-with-depth-full-v2 dataset. You can find some example images in the following.
|
AidenGO/KDXF_Bert4MaskedLM
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
| 5 | null |
# First Millennium Babylonian model for [BabyLemmatizer](https://github.com/asahala/BabyLemmatizer)
Total data set size ca. 1.3M words (including lacunae). Consists of all Oracc texts labeled as any variant of Babylonian or Akkadian in the first millennium BCE. Neo-Assyrian excluded. OOV rate is fairly low but the data set is very varied and comprises all different text genres.
## Evaluation
This model is MODEL8.
|
AimB/mT5-en-kr-opus
|
[] | null |
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| 0 | null |
---
language:
- en
tags:
- openvino
---
# bert-large-uncased-whole-word-masking-finetuned-squad
This is the [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/bert-large-uncased-whole-word-masking-finetuned-squad-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForQuestionAnswering.from_pretrained(model_id)
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
result = pipe("What is OpenVINO?", "OpenVINO is a framework that accelerates deep learning inferencing")
print(result)
```
|
Aimendo/Triage
|
[] | null |
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},
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},
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},
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}
| 0 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: yingzhi/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ajaykannan6/autonlp-manthan-16122692
|
[
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:Ajaykannan6/autonlp-data-manthan",
"transformers",
"autonlp",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": null
},
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"max_length": null
},
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},
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}
| 4 | 2023-04-20T17:35:49Z |
---
language:
- en
tags:
- openvino
---
# distilbert-base-uncased-distilled-squad
This is the [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/distilbert-base-uncased-distilled-squad-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForQuestionAnswering.from_pretrained(model_id)
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
result = pipe("What is OpenVINO?", "OpenVINO is a framework that accelerates deep learning inferencing")
print(result)
```
|
Akbarariza/Anjar
|
[] | null |
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"max_length": null
},
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},
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}
}
}
| 0 | null |
---
language:
- en
tags:
- openvino
---
# assemblyai/bert-large-uncased-sst2
This is the [assemblyai/bert-large-uncased-sst2](https://huggingface.co/assemblyai/bert-large-uncased-sst2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/assemblyai-bert-large-uncased-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
|
AkshatSurolia/DeiT-FaceMask-Finetuned
|
[
"pytorch",
"deit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
image-classification
|
{
"architectures": [
"DeiTForImageClassification"
],
"model_type": "deit",
"task_specific_params": {
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},
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},
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},
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}
| 46 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="srinivasvl81/FrozenLake-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AkshatSurolia/ViT-FaceMask-Finetuned
|
[
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
image-classification
|
{
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"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
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}
}
| 40 | null |
---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
pipeline_tag: image-to-text
duplicated_from: Salesforce/blip2-opt-2.7b
---
# BLIP-2, OPT-2.7b, pre-trained only
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
which bridge the gap between the embedding space of the image encoder and the large language model.
The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
This allows the model to be used for tasks like:
- image captioning
- visual question answering (VQA)
- chat-like conversations by feeding the image and the previous conversation as prompt to the model
## Direct Use and Downstream Use
You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
fine-tuned versions on a task that interests you.
## Bias, Risks, Limitations, and Ethical Considerations
BLIP2-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card.
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
>
BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, Blip2ForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In 8-bit precision (`int8`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate bitsandbytes
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
|
AkshayDev/BERT_Fine_Tuning
|
[] | null |
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}
| 0 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: vldnechai/pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AkshaySg/LanguageIdentification
|
[
"multilingual",
"dataset:VoxLingua107",
"LID",
"spoken language recognition",
"license:apache-2.0"
] | null |
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| 0 | null |
---
language:
- en
tags:
- openvino
---
# assemblyai/distilbert-base-uncased-sst2
This is the [assemblyai/distilbert-base-uncased-sst2](https://huggingface.co/assemblyai/distilbert-base-uncased-sst2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/assemblyai-distilbert-base-uncased-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
|
AkshaySg/langid
|
[
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] |
audio-classification
|
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}
| 2 | null |
---
language:
- en
tags:
- openvino
---
# echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid
This is the [echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid](https://huggingface.co/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/echarlaix-bert-base-uncased-sst2-acc91.1-d37-hybrid-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
|
AlErysvi/Erys
|
[] | null |
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},
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},
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},
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},
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"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
language:
- en
tags:
- openvino
---
# Alireza1044/albert-base-v2-sst2
This is the [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/Alireza1044-albert-base-v2-sst2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
```
|
AlbertHSU/BertTEST
|
[
"pytorch"
] | null |
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}
| 8 | null |
huggyllama/llama-30b merged with serpdotai/llama-oasst-lora-30B. Both 4bit-128g and 4bit non-groupsize versions are on my repo as well.
|
AlchemistDude/DialoGPT-medium-Gon
|
[] | null |
{
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}
| 0 | 2023-04-20T18:32:14Z |
---
library_name: stable-baselines3
tags:
- reinforce
- Pixelcopter-PLE-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 57.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **A2C** agent playing **Pixelcopter-PLE-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Aleksandar1932/distilgpt2-rock
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
| 11 | null |
# `vocabtrimmer/xlm-v-base-xnli-es`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[xnli](https://huggingface.co/datasets/xnli) (es).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(es).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 80.72 | 80.72 | 80.72 | 80.71 | 80.72 | 81.19 | 80.72 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-xnli-es/raw/main/eval.json).
|
Aleksandar1932/gpt2-country
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
}
| 12 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.50 +/- 5.13
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Nake/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Aleksandar1932/gpt2-hip-hop
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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"text-generation": {
"do_sample": true,
"max_length": 50
},
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| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5619
- Matthews Correlation: 0.5295
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5238 | 1.0 | 535 | 0.5285 | 0.4003 |
| 0.3493 | 2.0 | 1070 | 0.4934 | 0.4960 |
| 0.2357 | 3.0 | 1605 | 0.5619 | 0.5295 |
| 0.1793 | 4.0 | 2140 | 0.7578 | 0.5189 |
| 0.137 | 5.0 | 2675 | 0.8105 | 0.5199 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
Aleksandar1932/gpt2-pop
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 8 | null |
---
datasets:
- yahma/alpaca_cleaned
- lksy/ru_instruct_gpt4
language:
- ru
pipeline_tag: text2text-generation
inference: false
---
Based on [LLaMA 30B](https://huggingface.co/huggyllama/llama-30b).
Trained on 4 LoRA modules.
Parameters:
```
{
"base_model_name_or_path": "./llama-30b-hf",
"bias": "none",
"enable_lora": null,
"fan_in_fan_out": false,
"inference_mode": true,
"lora_alpha": 16,
"lora_dropout": 0.05,
"merge_weights": false,
"modules_to_save": null,
"peft_type": "LORA",
"r": 16,
"target_modules": [
"q_proj",
"v_proj",
"k_proj",
"o_proj"
],
"task_type": "CAUSAL_LM"
}
```
Cutoff length set to 512
```
Prompt template:
{
"description": "A shorter template to experiment with.",
"prompt_input": "### Задание:\n{instruction}\n\n### Вход:\n{input}\n\n### Ответ:\n",
"prompt_no_input": "### Задание:\n{instruction}\n\n### Ответ:\n",
"response_split": "### Ответ:"
}
```
Epochs: 3
Loss: 0.774 (Might be overfit a bit, try to use a checkpoint)
|
AlekseyKulnevich/Pegasus-Summarization
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"PegasusForConditionalGeneration"
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"model_type": "pegasus",
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| 7 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="arvindsg/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Alerosae/SocratesGPT-2
|
[
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
] |
text-generation
|
{
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"GPT2Model"
],
"model_type": "gpt2",
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"max_length": 50
},
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}
| 7 | null |
---
tags:
- conversational
---
# Palpatine DialoGPT Model
|
Alexandru/creative_copilot
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1891.77 +/- 44.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AlexeyIgnatov/albert-xlarge-v2-squad-v2
|
[] | null |
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}
| 0 | null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Helsinki-NLP-finetuned-ru-to-en
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. -->
# Helsinki-NLP-finetuned-ru-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-en](https://huggingface.co/Helsinki-NLP/opus-mt-ru-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1061
- Bleu: 36.9394
- Gen Len: 20.6363
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
Amalq/distilroberta-base-finetuned-MentalHealth
|
[] | null |
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},
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}
}
}
| 0 | 2023-04-20T21:37:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ossib/kho-lex-fi-sv
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ossib/kho-lex-fi-sv
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-fi-sv](https://huggingface.co/Helsinki-NLP/opus-mt-fi-sv) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6010
- Validation Loss: 0.7602
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1800, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0714 | 0.8481 | 0 |
| 0.7317 | 0.7788 | 1 |
| 0.6010 | 0.7602 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Anamika/autonlp-Feedback1-479512837
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"unk",
"dataset:Anamika/autonlp-data-Feedback1",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
{
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"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
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}
| 34 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- juanArevalo/autotrain-data-classificacion
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
co2_eq_emissions:
emissions: 0.7664598785084752
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51168121452
- CO2 Emissions (in grams): 0.7665
## Validation Metrics
- Loss: 0.248
- Accuracy: 0.920
- Macro F1: 0.920
- Micro F1: 0.920
- Weighted F1: 0.920
- Macro Precision: 0.921
- Micro Precision: 0.920
- Weighted Precision: 0.921
- Macro Recall: 0.920
- Micro Recall: 0.920
- Weighted Recall: 0.920
|
Ann2020/rubert-base-cased-finetuned-ner
|
[] | null |
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}
| 0 | null |
---
license: unknown
---
개인용 모델 모음
2023년 04월 21일까지의 모은 하드의 모델들 / 로라들 / VAE들이 모아져있음
|
Anomic/DialoGPT-medium-loki
|
[] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Tq-axi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jadetiger88/Tq-axi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/AR_rule_based_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
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}
| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi-vit-model-Santiago-Garcia
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9774436090225563
---
<!-- 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. -->
# platzi-vit-model-Santiago-Garcia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0881
- Accuracy: 0.9774
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1371 | 3.85 | 500 | 0.0881 | 0.9774 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/AR_rule_based_only_classfn_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 1 | null |
# `vocabtrimmer/xlm-v-base-trimmed-es-xnli-es`
This model is a fine-tuned version of [vocabtrimmer/xlm-v-base-trimmed-es](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-es) on the
[xnli](https://huggingface.co/datasets/xnli) (es).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(es).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 80.58 | 80.58 | 80.58 | 80.56 | 80.58 | 81.19 | 80.58 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-es-xnli-es/raw/main/eval.json).
|
AnonymousSub/AR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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| 2 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: amazon-roberta
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. -->
# amazon-roberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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 |
|:-------------:|:-----:|:------:|:---------------:|
| 0.0001 | 1.0 | 102078 | 0.0000 |
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0
|
AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
| 4 | 2023-05-02T19:08:44Z |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# These are Stable Diffusion v1.5 type models and compatible ControlNet v1.1 models that have been converted to Apple's CoreML format
## For use with a Swift app or the SwiftCLI
The SD models are all "original" (not split-einsum) and built for CPU and GPU. They are each for the output size noted. They are fp16, with the standard SD-1.5 VAE embedded.
The Stable Diffusion v1.5 model and the other SD 1.5 type models now contain both the standard Unet and the ControlledUnet used for the ControlNet pipeline. The correct one will be used automatically based on whether ControlNet is enabled or not.
They also should have VAEEncoder.mlmodelc bundles that allow Image2Image to operate correctly at all resolutions, with a current Swift CLI pipeline or a current GUI built with ml-stable-diffusion 0.4.0.
All the ControlNet models are also "original" ones, built for CPU and GPU compute units (cpuAndGPU) and for SD-1.5 type models. The smaller files are only 512x512. The larger files each have a set of 4 resolutions. They will not work with split-einsum models or with SD-2.1 type models.
All of the models in this repo will only work with Swift and the current ml-stable-diffusion pipeline (0.4.0). They were not built for a python diffusers pipeline. They need apple/ml-stable-diffusion (from GitHub) for command line use or a Swift app (currently in a closed beta test at https://github.com/godly-devotion/MochiDiffusion) that supports ControlNet.
The full SD models are in the "SD" folder here. They are individually zipped and need to be unzipped after downloading.
The ControlNet model files are in the "CN" folder here. They are also zipped and need to be unzipped after downloading. Note that there are 2 sizes containing either 1 512x512 model or a set of 4: 512x512, 512x768, 768x512, 768x768.
There is also a MISC folder that has text files with my notes and a screencap of my directory structure.
For command line use, it all runs in a miniconda3 environment, covered in one of the notes. If you are using the command line, please read the notes concerning naming and placement of your ControlNet model folder. If you are using a GUI, it will guide you to the correct location/arrangement.
## * * * DYSLEXIA ALERT * * * Many for the initially uploaded model files reversed the names on the 512x768 and 768x512 models.
**You can just rename them yourself, or download them again as the file names have been corrected.**
**The sizes are always meant to be WIDTH x HEIGHT. A 512x768 is "portrait" orientation and a 768x512 is "landscape" orientation.**
**Sorry if my early transposing of sizes messed with your mind**
## Notes
- There ia also a branch to main here called "For-Mochi-Model-Env".
- It was going to be a shortcut version of the conversion and generation pipelines for people who already have a setup for converting models per the Wiki at Mochi Diffusion. Development of a new version of Mochi Diffusion, with ControlNet included, is moving along very quickly, so I don't plan to spend more time on the CLI instructions.
- If you downloaded Stable Diffusion v1.5 Orignal 768x768 For ControlNet before 4/27/23, or Stable Diffusion v1.5 Original 512x768 before 5/4/23, please re-download. Those models were not supporting all intended features.
- If you encounter any models that do not work fully with image2image and ControlNet using the current CLI pipeline or Mochi Diffusion 3.2, please leave a report in the Community area here.
## Model List
**Each zip fles contains a single model for the output size indicated: 512x512, 512x768, 768x512 or 768x768**
- Stable Diffusion v1.5, original, for ControlNet & Standard
- MyMerge of 8 1.5-type NSFW models, original, for ControlNet & Standard
- MeinaMix9 1.5-type anime model, original, for ControlNet & Standard
- GhostMix v1.1, 1.5-type anime model, original, for ControlNet & Standard
- Realistic Vision v2.0, 1.5-type model, original, for ControlNet & Standard
- DreamShaper v5.0, 1.5-type model, original, for ControlNet & Standard <<<=== NEW <<<=== NEW
## ControlNet List
**The smaller files are 512x512 only. The larger files are a set of 4 resolutions zipped together: 512x512, 512x768, 768x512, 768x768**
- Canny -- Edge Detection, Outlines As Input
- Scribble -- Freehand Sketch As Input
- InstrP2P -- Instruct Picture2Picture, Modified By Text ("change dog to cat")
- MLSD -- Find And Reuse Straight Lines And Edges
- InPaint -- Modify An Indicated Area Of An Image (not sure how this works)
- LineArt -- Find And Reuse Small Outlines
- OpenPose -- Copy Body Poses
- SoftEdge -- Find And Reuse Soft Edges
- Tile -- Subtle Variations In Batch Runs
- Depth -- Reproduces Depth Relationships From An Image
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
| 5 | null |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
use to learn
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
| 1 | null |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1613774119534792704/kufnfHiX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ricky</div>
<div style="text-align: center; font-size: 14px;">@rickyedit</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ricky.
| Data | ricky |
| --- | --- |
| Tweets downloaded | 3147 |
| Retweets | 28 |
| Short tweets | 827 |
| Tweets kept | 2292 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qwdtadg0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rickyedit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jpbekp6t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jpbekp6t/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/rickyedit')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AnonymousSub/EManuals_RoBERTa_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
}
| 4 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - brathief/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
AnonymousSub/SR_EManuals-BERT
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 6 | null |
---
license: apache-2.0
---
## Quickstart
```python
!pip install diffusers
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"YouLiXiya/yl-consistency",
custom_pipeline="YouLiXiya/yl-consistency",
)
from PIL import Image
def make_grid(images, rows, cols):
w, h = images[0].size
grid = Image.new('RGB', size=(cols * w, rows * h))
for i, image in enumerate(images):
grid.paste(image, box=(i % cols * w, i // cols * h))
return grid
import matplotlib.pyplot as plt
images = pipeline(batch_size=64, num_inference_steps=10).images
grid = make_grid(images, 8, 8)
plt.imshow(grid)
plt.axis('off')
plt.show()
```
<div>
<a href="https://colab.research.google.com/github/Youlixiya/ylcm/blob/master/scripts/unconditional_cm_cifar10_32_pipeline.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
</div>
|
AnonymousSub/SR_declutr
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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},
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}
| 6 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1685.68 +/- 229.74
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_rule_based_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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| 1 | null |
Access to model europaa/apejakovich is restricted and you are not in the authorized list. Visit https://huggingface.co/europaa/apejakovich to ask for access.
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 2 | 2023-04-21T03:36:25Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/gpl-e5-base-unsupervised-arguana-1
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.
<!--- Describe your model here -->
## 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('rithwik-db/gpl-e5-base-unsupervised-arguana-1')
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('rithwik-db/gpl-e5-base-unsupervised-arguana-1')
model = AutoModel.from_pretrained('rithwik-db/gpl-e5-base-unsupervised-arguana-1')
# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/gpl-e5-base-unsupervised-arguana-1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5654 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1696,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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
<!--- Describe where people can find more information -->
|
AnonymousSub/SR_rule_based_roberta_only_classfn_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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| 2 | null |
# `vocabtrimmer/xlm-v-base-trimmed-en-xnli-en`
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[xnli](https://huggingface.co/datasets/xnli) (en).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(en).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 84.57 | 84.57 | 84.57 | 84.58 | 84.57 | 84.73 | 84.57 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-en-xnli-en/raw/main/eval.json).
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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},
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}
}
| 8 | null |
Access to model jesperchou/autogpt is restricted and you are not in the authorized list. Visit https://huggingface.co/jesperchou/autogpt to ask for access.
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
}
| 4 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.20 +/- 2.90
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r ashishj20/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
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},
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},
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}
| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi-vit-model-Santiago-Garcia-Solarte
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- 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. -->
# platzi-vit-model-Santiago-Garcia-Solarte
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0276
- Accuracy: 0.9925
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1434 | 3.85 | 500 | 0.0276 | 0.9925 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
}
| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-medical-specialty-model
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. -->
# finetuning-medical-specialty-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4732
- Accuracy: 0.338
- F1: 0.2195
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/cline-emanuals-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
}
| 4 | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - BigBri/sd-pokemon-model-lora
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
AnonymousSub/declutr-s10-SR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
| 36 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.55 +/- 22.96
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
}
| 4 | null |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM-6B
<p align="center">
🌐 <a href="https://chatglm.cn/blog" target="_blank">Blog</a> • 💻 <a href="https://github.com/THUDM/ChatGLM-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1th2q5u69-7tURzFuOPanmuHy9hsZnKA" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
</p>
## 介绍
ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 [ChatGLM](https://chatglm.cn) 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。
ChatGLM-6B is an open bilingual language model based on [General Language Model (GLM)](https://github.com/THUDM/GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese QA and dialogue. The model is trained for about 1T tokens of Chinese and English corpus, supplemented by supervised fine-tuning, feedback bootstrap, and reinforcement learning wit human feedback. With only about 6.2 billion parameters, the model is able to generate answers that are in line with human preference.
## 软件依赖
```shell
pip install protobuf==3.20.0 transformers==4.27.1 icetk cpm_kernels
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM-6B)。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM-6B).
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
```
@inproceedings{
zeng2023glm-130b,
title={{GLM}-130B: An Open Bilingual Pre-trained Model},
author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=-Aw0rrrPUF}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
```
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
| 8 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Gptdetect
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. -->
# Gptdetect
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2608
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1037 | 1.0 | 5000 | 0.2125 |
| 0.6868 | 2.0 | 10000 | 0.6931 |
| 0.6931 | 3.0 | 15000 | 0.6931 |
| 0.7143 | 4.0 | 20000 | 0.6934 |
| 0.6881 | 5.0 | 25000 | 0.6931 |
| 0.6797 | 6.0 | 30000 | 0.6934 |
| 0.6965 | 7.0 | 35000 | 0.6939 |
| 0.6923 | 8.0 | 40000 | 0.7373 |
| 0.5338 | 9.0 | 45000 | 0.5631 |
| 0.2927 | 10.0 | 50000 | 0.2608 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 4 | null |
---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: m2m100_pruned_ta-freeze100-finetuned-mcsw-to-en-smallmcswbitext
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. -->
# m2m100_pruned_ta-freeze100-finetuned-mcsw-to-en-smallmcswbitext
This model is a fine-tuned version of [Elaine/m2m100_pruned_ta](https://huggingface.co/Elaine/m2m100_pruned_ta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7877
- Bleu: 31.7539
- Gen Len: 27.4
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.4228 | 0.8 | 2500 | 1.7877 | 31.7539 | 27.4 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 3 | null |
---
license: creativeml-openrail-m
language:
- aa
library_name: diffusers
tags:
- art
pipeline_tag: text-to-image
---
use to learn
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 8 | null |
---
license:
- cc-by-sa-4.0
inference: false
tags:
- ggml
- causal-lm
---
[StableLM-Base-Alpha 3B model card](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
}
| 6 | 2023-04-21T07:58:11Z |
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet34
- cifar100
datasets: cifar100
metrics:
- accuracy
model-index:
- name: resnet34_simclr_cifar100
results:
- task:
type: image-classification
dataset:
name: CIFAR-100
type: cifar100
metrics:
- type: accuracy
value: 0.5496
---
# Model Card for Model ID
This model is a small resnet34 trained on cifar100.
- **Test Accuracy:** 0.5496
- **License:** MIT
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import detectors
import timm
model = timm.create_model("resnet34_simclr_cifar100", pretrained=True)
```
## Training Data
Training data is cifar100.
## Training Hyperparameters
- **config**: `None`
- **model**: `resnet34_simclr_cifar100`
- **batch_size**: `512`
- **epochs**: `501`
- **lr**: `0.5`
- **warmup_epochs**: `10`
- **validation_frequency**: `50`
- **output_features_dim**: `128`
- **seed**: `1`
- **debug**: `False`
- **dataset**: `cifar100`
- **training_mode**: `simclr`
## Testing Data
Testing data is cifar100.
---
This model card was created by Eduardo Dadalto.
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
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},
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"prefix": null
},
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet50
- cifar10
datasets: cifar10
metrics:
- accuracy
model-index:
- name: resnet50_simclr_cifar10
results:
- task:
type: image-classification
dataset:
name: CIFAR-10
type: cifar10
metrics:
- type: accuracy
value: 0.9012
---
# Model Card for Model ID
This model is a small resnet50 trained on cifar10.
- **Test Accuracy:** 0.9012
- **License:** MIT
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import detectors
import timm
model = timm.create_model("resnet50_simclr_cifar10", pretrained=True)
```
## Training Data
Training data is cifar10.
## Training Hyperparameters
- **config**: `None`
- **model**: `resnet50_simclr_cifar10`
- **batch_size**: `512`
- **epochs**: `501`
- **lr**: `0.5`
- **warmup_epochs**: `10`
- **validation_frequency**: `50`
- **output_features_dim**: `128`
- **seed**: `1`
- **debug**: `False`
- **dataset**: `cifar10`
- **training_mode**: `simclr`
## Testing Data
Testing data is cifar10.
---
This model card was created by Eduardo Dadalto.
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
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},
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},
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}
}
}
| 6 | null |
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet50
- cifar100
datasets: cifar100
metrics:
- accuracy
model-index:
- name: resnet50_simclr_cifar100
results:
- task:
type: image-classification
dataset:
name: CIFAR-100
type: cifar100
metrics:
- type: accuracy
value: 0.5715
---
# Model Card for Model ID
This model is a small resnet50 trained on cifar100.
- **Test Accuracy:** 0.5715
- **License:** MIT
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import detectors
import timm
model = timm.create_model("resnet50_simclr_cifar100", pretrained=True)
```
## Training Data
Training data is cifar100.
## Training Hyperparameters
- **config**: `None`
- **model**: `resnet50_simclr_cifar100`
- **batch_size**: `512`
- **epochs**: `501`
- **lr**: `0.5`
- **warmup_epochs**: `10`
- **validation_frequency**: `50`
- **output_features_dim**: `128`
- **seed**: `1`
- **debug**: `False`
- **dataset**: `cifar100`
- **training_mode**: `simclr`
## Testing Data
Testing data is cifar100.
---
This model card was created by Eduardo Dadalto.
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
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},
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},
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}
}
}
| 4 | null |
---
license: openrail
base_model: hf-internal-testing/tiny-stable-diffusion-pipe-no-safety
tags:
- art
- controlnet
- stable-diffusion
- image-to-image
---
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 24 | null |
---
license: bsd
datasets:
- anon8231489123/ShareGPT_Vicuna_unfiltered
language:
- am
metrics:
- character
library_name: diffusers
tags:
- art
---
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"num_beams": null,
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},
"translation_en_to_fr": {
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},
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}
}
}
| 7 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -127.75 +/- 76.44
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'taoist/ppo-LunarLander-v2-2'
'batch_size': 512
'minibatch_size': 128}
```
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
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| 2 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/gpl-e5-base-unsupervised-scifact-k10
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.
<!--- Describe your model here -->
## 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('rithwik-db/gpl-e5-base-unsupervised-scifact-k10')
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('rithwik-db/gpl-e5-base-unsupervised-scifact-k10')
model = AutoModel.from_pretrained('rithwik-db/gpl-e5-base-unsupervised-scifact-k10')
# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/gpl-e5-base-unsupervised-scifact-k10)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 12063 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MarginMSELoss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3618,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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
<!--- Describe where people can find more information -->
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 2 | null |
---
license: openrail
---
# 本仓库为备份仓库，模型来源于网络
# 命令下载格式：
git lfs clone https://huggingface.co/用户名/项目
(下载全部)
aria2c https://huggingface.co/用户名/项目/resolve/main/目录/文件名
(下载单个文件)
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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| 1 | null |
---
license:
- apache-2.0
inference: false
tags:
- ggml
- causal-lm
---
[Open-Assistant StableLM-7B SFT-7 model card](https://huggingface.co/OpenAssistant/stablelm-7b-sft-v7-epoch-3)
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
],
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| 24 | null |
---
tags:
- conversational
---
# Barry. B DialoGPT Model
|
AnonymousSub/specter-emanuals-model
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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| 6 | 2023-04-21T09:28:14Z |
---
license: mit
---
Trigger Words：ICONSMI <br/>
url:https://civitai.com/models/93?modelVersionId=105
|
AnonymousSub/unsup-consert-base
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
AnonymousSub/unsup-consert-base_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 6 | null |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Find your model_id: vovikdrg/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/unsup-consert-emanuals
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 2 | null |
# `vocabtrimmer/xlm-v-base-xnli-de`
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the
[xnli](https://huggingface.co/datasets/xnli) (de).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(de).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 78.9 | 78.9 | 78.9 | 78.85 | 78.9 | 79.29 | 78.9 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-xnli-de/raw/main/eval.json).
|
AnonymousSubmission/pretrained-model-1
|
[] | null |
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| 0 | null |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9516472935
- name: NER Recall
type: recall
value: 0.9483207676
- name: NER F Score
type: f_score
value: 0.9499811185
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.2,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (12 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CITY`, `COMPANY`, `DEGREE`, `DESIGNATION`, `DOB`, `EMAIL`, `EXPERIENCE`, `INSTITUTE`, `LINKEDIN`, `MOBILE NUMBER`, `NAME`, `SKILLS` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 95.00 |
| `ENTS_P` | 95.16 |
| `ENTS_R` | 94.83 |
| `TRANSFORMER_LOSS` | 448284.92 |
| `NER_LOSS` | 122909.16 |
|
Anorak/nirvana
|
[
"pytorch",
"pegasus",
"text2text-generation",
"unk",
"dataset:Anorak/autonlp-data-Niravana-test2",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
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| 7 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: miki030/pyramid-mlagents-test
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Anthos23/my-awesome-model
|
[
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
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}
| 30 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/43860/girls-frontline-zas-m21
|
Anthos23/test_trainer
|
[] | null |
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| 0 | null |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Apoorva/k2t-test
|
[
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"autotrain_compatible"
] |
text2text-generation
|
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"prefix": "translate English to German: "
},
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"prefix": "translate English to French: "
},
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 7 | 2023-04-21T10:28:20Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Sergendel/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ArBert/albert-base-v2-finetuned-ner-gmm-twitter
|
[
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"AlbertForTokenClassification"
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| 8 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.10 +/- 32.37
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Aravinth/test
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 585.00 +/- 200.51
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga prepsyched -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga prepsyched -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga prepsyched
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Arnold/wav2vec2-hausa2-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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| 9 | null |
---
datasets:
- GreeneryScenery/SheepsDiffusionNet
- poloclub/diffusiondb
pipeline_tag: image-to-image
tags:
- art
- ControlNet
---
# V8
Similar to V7. 🤗 Try it [here](https://replicate.com/greeneryscenery/sheeps-controlnet-sketch-2-image)
<img src = 'https://huggingface.co/GreeneryScenery/SheepsControlV7/resolve/main/overview.png'>
|
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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| 5 | null |
---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
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. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## 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: 1e-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
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
|
ArshdeepSekhon050/DialoGPT-medium-RickAndMorty
|
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| 0 | null |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0134
- Rouge1: 15.9126
- Rouge2: 7.2527
- Rougel: 15.474
- Rougelsum: 15.4686
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.6983 | 1.0 | 1209 | 3.2107 | 16.6172 | 8.5236 | 15.898 | 15.9553 |
| 3.6379 | 2.0 | 2418 | 3.0731 | 16.4382 | 7.6292 | 15.8204 | 15.9041 |
| 3.4344 | 3.0 | 3627 | 3.0456 | 17.4139 | 8.5795 | 16.8805 | 16.9012 |
| 3.3049 | 4.0 | 4836 | 3.0279 | 17.3026 | 8.2542 | 16.8521 | 16.8026 |
| 3.2357 | 5.0 | 6045 | 3.0383 | 16.6803 | 7.4959 | 16.2268 | 16.2583 |
| 3.1688 | 6.0 | 7254 | 3.0127 | 16.641 | 7.6987 | 16.2072 | 16.2232 |
| 3.1377 | 7.0 | 8463 | 3.0172 | 16.1591 | 7.5274 | 15.7203 | 15.7465 |
| 3.1104 | 8.0 | 9672 | 3.0134 | 15.9126 | 7.2527 | 15.474 | 15.4686 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.0.dev0
|
Ashim/dga-transformer
|
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| 0 | null |
---
license: other
---
# 聲明 Disclaimer
本資料夾中的模型不是我所製作，版權歸原作者所有（各模型版權詳見 http://www.civitai.com 所示）。我上傳至本資料夾僅爲方便在綫抽取資源，并非盈利。
The models in this folder are not made by me, and the copyright belongs to the original author (see http://www.civitai.com for details on the copyright of each model). I uploaded to this folder only for the convenience of extracting resources online, not for profit.
# 模型列表 List of Models
本資料夾中所有模型詳見下表。
All the models in this folder are detailed in the table below.
| 模型名稱 Model Name | Civitai 頁面鏈接 Civitai Page Link | Civitai 下載鏈接 Civitai Download Link |
|----------------------|--------------------|--------------------|
|artErosAerosATribute_aerosNovae.safetensors |https://civitai.com/models/3950?modelVersionId=5180 |https://civitai.com/api/download/models/5180 |
|artErosAerosATribute_aerosPruned.safetensors |https://civitai.com/models/3950?modelVersionId=4396 |https://civitai.com/api/download/models/4396 |
## artErosAerosATribute_aerosNovae
<img src="https://img1.wsimg.com/isteam/ip/062334e1-a8fb-4784-b30a-5b8d15b1aaeb/aerosNovae_15.png" width="768" height="">
## artErosAerosATribute_aerosPruned
<img src="https://img1.wsimg.com/isteam/ip/062334e1-a8fb-4784-b30a-5b8d15b1aaeb/aerosPruned_09.png" width="768" height="">
|
Ashl3y/model_name
|
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| 0 | null |
---
language:
- en
tags:
- openvino
---
# xlm-roberta-large-finetuned-conll03-english
This is the [xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/xlm-roberta-large-finetuned-conll03-english) model converted to [OpenVINO](https://openvino.ai), for accellerated inference.
An example of how to do inference on this model:
```python
from optimum.intel.openvino import OVModelForTokenClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/xlm-roberta-large-finetuned-conll03-english-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForTokenClassification.from_pretrained(model_id)
pipe = pipeline("token-classification", model=model, tokenizer=tokenizer)
result = pipe("hello world")
print(result)
```
|
Ateeb/asd
|
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| 0 | 2023-04-21T13:30:33Z |
---
license: cc-by-4.0
language:
- en
tags:
- ' ANIME'
- ' CHARACTER'
- PHOTOREALISTIC
---
***All in one***
This model gives you the ability to create whatever you want.</br>
Attention, the model requires VAE from Stability AI: vae-ft-ema-560000</br>
or you can use VAE from Stability AI: vae-ft-mse-840000</br>
****Who is this model for?****
- NSFW Art Designers
- Character designers
- Professional prompters
- Art designers
I hope you enjoy the results of my efforts.
Thank you very much XpucT. My first knowledge came from this man.
This model is based on his Deliberate V2 model.
Civitai: https://civitai.com/models/35403/hodgepodge
|
Augustvember/WokkaBot
|
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| 0 | 2023-04-21T13:36:56Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xmlRoberta_Ger
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. -->
# xmlRoberta_Ger
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
## 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: 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
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Augustvember/WokkaBot6
|
[] | null |
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| 0 | 2023-04-21T13:44:59Z |
---
license: creativeml-openrail-m
pipeline_tag: text-to-image
tags:
- ' stable-diffusion'
- ' stable-diffusion-diffusers '
---
# MechaDream
### A Stable Diffusion model for Mecha
---
## Available Models
### MechaDream-V1_lora
**Base model:** Counterfeit-V2.5
**The Road to this Lora**
Step 1: Fine-tuned on a dataset consisting of 130,000+ images of mecha/robots from various sources
Step 2: Mix the Lora models trained using different parameters
Step 3: Test different mixing ratios to find the best model
**Examples**
```
((masterpiece)), ((chibi)), a ((mecha)) with metallic hues and neon lights,symmetric body, full body, sky background
<lora:mechaDream-v1_lora:1>
Negative prompt: EasyNegative, bad-hands-5, human
Steps: 20, Sampler: UniPC, CFG scale: 15, Seed: 3779625873, Size: 680x456, Model hash: a074b8864e, Model: Counterfeit-V2.5_pruned, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
```
((masterpiece)), ((chibi)), a ((mecha)) with metallic hues and neon lights,symmetric body, full body, sky background
<lora:mecha_v2-1_u04t10_mecha_v1-2_u08t03:1>
Negative prompt: EasyNegative, bad-hands-5, human
Steps: 20, Sampler: UniPC, CFG scale: 15, Seed: 3779625858, Size: 680x456, Model hash: a074b8864e, Model: Counterfeit-V2.5_pruned, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 2, Hires upscaler: R-ESRGAN 4x+ Anime6B
```
```
((masterpiece)), a (((robot))) with sleek and menacing design, glowing eyes, full body, highly detailed
<lora:mecha_v2-1_u04t10_mecha_v1-2_u08t03:0.8>
Negative prompt: EasyNegative, bad-hands-5, signature, boarder, word
Steps: 20, Sampler: UniPC, CFG scale: 15, Seed: 3274412811, Size: 720x408, Model hash: a074b8864e, Model: Counterfeit-V2.5_pruned, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 2, Hires steps: 30, Hires upscaler: Latent (nearest)
```
```
((masterpiece)), a metallic crimson (((robot))) with sleek and menacing design, glowing eyes, full body, highly detailed
<lora:mecha_v2-1_u04t10_mecha_v1-2_u08t03:0.8>
Negative prompt: EasyNegative, bad-hands-5, signature, boarder
Steps: 20, Sampler: UniPC, CFG scale: 15, Seed: 1562808839, Size: 720x408, Model hash: a074b8864e, Model: Counterfeit-V2.5_pruned, Denoising strength: 0.7, Clip skip: 2, Hires upscale: 2, Hires steps: 30, Hires upscaler: Latent (nearest)
```
### MechaDream-V1
🚧 Pre-merged model is comming soon 🚧
---
## Recommended Setting
**Checkpoint:** Counterfeit-V2.5
**VAE:** mecha_v2_e3-pruned.ckpt
**Sampler:** UniPC (Personal preference)
**CFG Scale:** 15
**Negative Embeddings:** EasyNegative, bad-hands-5
**Hires.fix with scale >= 2 is highly recommend**
---
## My workflow
Step 1: Batch generate images
Step 2: Select the best image and inpaint any flaws
Step 3: Upscale using MultiDiffusion
*🡳 Some comparison images between original image and after the above workflow 🡳*
https://imgsli.com/MTcyNDM2
https://imgsli.com/MTcyNDM3
https://imgsli.com/MTcyNDM5
|
Augustvember/wokka
|
[
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 4 | null |
Access to model nuljon/hugbface is restricted and you are not in the authorized list. Visit https://huggingface.co/nuljon/hugbface to ask for access.
|
Augustvember/wokka2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 12 | null |
Access to model estrategista/livro is restricted and you are not in the authorized list. Visit https://huggingface.co/estrategista/livro to ask for access.
|
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