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text-classification | transformers |
<!-- 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/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | Malanga/finetuning-sentiment-model-3000-samples | null | [
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] | null | 2022-07-19T08:30:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3104
- Accuracy: 0.87
- F1: 0.8713
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3104\n- Accuracy: 0.87\n- F1: 0.8713",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
reinforcement-learning | stable-baselines3 |
# **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 framewor... | {"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": "SpaceInvadersNoFram... | spacestar1705/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-19T08:41:17+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
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text-classification | transformers |
<!-- 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. -->
# hf-model-full-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "hf-model-full-0", "results": []}]} | semy/hf-model-full-0 | null | [
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"text-classification",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T09:07:46+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| hf-model-full-0
===============
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4295
* Accuracy: 0.802
* F1: 0.802
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text2text-generation | transformers |
# Knight-errant
Knight is a text style transfer model for knight-errant style. This model is for Chinese Knight-errant style transfer.
paper link: [To be a Knight-errant Novel Master: Knight-errant Style Transfer via Contrastive Learning](https://openreview.net/forum?id=FDw2hdpiWNO)
```python
#inference
from tran... | {"language": ["multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "u... | Anonymous-TST/knight-errant-TST-zh | null | [
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"af",
"az",
... | null | 2022-07-19T09:14:27+00:00 | [] | [
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"m... | TAGS
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# Knight-errant
Knight is a text style transfer model for knight-errant style. This model is for Chinese Knight-errant style transfer.
paper link: To be a Knight-errant Novel Master: Knight-errant Style Transfer via Contrastive Learning
''' | [
"# Knight-errant\n\nKnight is a text style transfer model for knight-errant style. This model is for Chinese Knight-errant style transfer.\n\npaper link: To be a Knight-errant Novel Master: Knight-errant Style Transfer via Contrastive Learning\n\n\n\n\n\n\n\n'''"
] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #mbart-50 #multilingual #ar #cs #de #en #es #et #fi #fr #gu #hi #it #ja #kk #ko #lt #lv #my #ne #nl #ro #ru #si #tr #vi #zh #af #az #bn #fa #he #hr #id #ka #km #mk #ml #mn #mr #pl #ps #pt #sv #sw #ta #te #th #tl #uk #ur #xh #gl #sl #license-mit #autotrain_c... |
text2text-generation | transformers |
# ByT5 Song Lyrics
This is a Seq2Seq model trained on a karaoke dataset to predict syllables with pitch and timing from song lyrics.
As of writing, the model has only been trained on 1/2 of the full dataset. Expect the quality to improve later.
The Huggingface demo seems to produce outputs with a small sequence len... | {"language": ["en"], "license": "isc", "tags": ["music", "t5", "byt5"], "metrics": ["accuracy"]} | nev/byt5-song-lyrics | null | [
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"safetensors",
"t5",
"text2text-generation",
"music",
"byt5",
"en",
"license:isc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T09:30:21+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #music #byt5 #en #license-isc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ByT5 Song Lyrics
This is a Seq2Seq model trained on a karaoke dataset to predict syllables with pitch and timing from song lyrics.
As of writing, the model has only been trained on 1/2 of the full dataset. Expect the quality to improve later.
The Huggingface demo seems to produce outputs with a small sequence len... | [
"# ByT5 Song Lyrics\n\nThis is a Seq2Seq model trained on a karaoke dataset to predict syllables with pitch and timing from song lyrics.\n\nAs of writing, the model has only been trained on 1/2 of the full dataset. Expect the quality to improve later.\n\nThe Huggingface demo seems to produce outputs with a small se... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #music #byt5 #en #license-isc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ByT5 Song Lyrics\n\nThis is a Seq2Seq model trained on a karaoke dataset to predict syllables with pitch and timing from song... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-cat-256 | null | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:42:07+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #safetensors #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
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"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-celebahq-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:42:22+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
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"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-ema-celebahq-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:42:32+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
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"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-church-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:42:51+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
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unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-bedroom-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"has_space",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:43:04+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
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"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-ema-church-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:43:19+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We prese... |
unconditional-image-generation | diffusers |
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variabl... | {"license": "apache-2.0", "tags": ["pytorch", "diffusers", "unconditional-image-generation"]} | google/ddpm-ema-cat-256 | null | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-19T09:45:53+00:00 | [
"2006.11239"
] | [] | TAGS
#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# Denoising Diffusion Probabilistic Models (DDPM)
Paper: Denoising Diffusion Probabilistic Models
Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel
Abstract:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequi... | [
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations ... | [
"TAGS\n#diffusers #pytorch #unconditional-image-generation #arxiv-2006.11239 #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# Denoising Diffusion Probabilistic Models (DDPM)\n\nPaper: Denoising Diffusion Probabilistic Models\n\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n\nAbstract:\n\n*We prese... |
text2text-generation | transformers |
This is the t5 model, fine-tuned using the KorQuAD dataset. It's been trained on question-answer pairs for the task of Question Answering.
# KorQuAD MT5 Model | {"tags": ["question answering"]} | mingu/mt5-base-finetuned-korquad | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"question answering",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T10:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #question answering #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is the t5 model, fine-tuned using the KorQuAD dataset. It's been trained on question-answer pairs for the task of Question Answering.
# KorQuAD MT5 Model | [
"# KorQuAD MT5 Model"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #question answering #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# KorQuAD MT5 Model"
] |
translation | transformers |
<!-- 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. -->
# Nso-En_update
This model is a fine-tuned version of [kabelomalapane/En-Nso](https://huggingface.co/kabelomalapane/En-Nso) on the... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Nso-En_update", "results": []}]} | kabelomalapane/Nso-En_update | null | [
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"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T10:31:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Nso-En\_update
==============
This model is a fine-tuned version of kabelomalapane/En-Nso on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9219
* Bleu: 0.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text2text-generation | transformers | English-Simile-Generation is a seq2seq paraphrase model which can transform sentence A to sentence B containing figurative or simile expression.
A： Now I feel sad to see your scientific research progress is so slow.
B： Now I feel sad to see your scientific research progress is as slow as snail.
**To our knowledge, o... | {} | figurative-nlp/English-Simile-Generation | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T10:41:07+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| English-Simile-Generation is a seq2seq paraphrase model which can transform sentence A to sentence B containing figurative or simile expression.
A： Now I feel sad to see your scientific research progress is so slow.
B： Now I feel sad to see your scientific research progress is as slow as snail.
To our knowledge, our... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_modified_for_t5_qg"], "model-index": [{"name": "t5-end2end-questions-generation", "results": []}]} | Tahsin-Mayeesha/t5-end2end-questions-generation | null | [
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"dataset:squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T10:58:20+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-end2end-questions-generation
This model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6143
- eval_runtime: 96.0898
- eval_samples_per_second: 21.511
- eval_steps_per_second: 5.38
- epoch: 2.03
- step: 600
## ... | [
"# t5-end2end-questions-generation\n\nThis model is a fine-tuned version of t5-base on the squad_modified_for_t5_qg dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.6143\n- eval_runtime: 96.0898\n- eval_samples_per_second: 21.511\n- eval_steps_per_second: 5.38\n- epoch: 2.03\n- ste... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-squad_modified_for_t5_qg #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-end2end-questions-generation\n\nThis model is a fine-tuned version of t5-base on the sq... |
translation | transformers |
<!-- 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. -->
# En-Nso_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "En-Nso_update", "results": []}]} | kabelomalapane/En-Nso_update | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T11:12:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| En-Nso\_update
==============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-nso on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8782
* Bleu: 31.2967
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
reinforcement-learning | ml-agents |
# **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 comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | spacestar1705/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-19T11:20:02+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
null | null | # this is a test | {"license": "apache-2.0"} | liyangbing/dp-library | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-07-19T11:41:13+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| # this is a test | [
"# this is a test"
] | [
"TAGS\n#license-apache-2.0 #region-us \n",
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] |
text2text-generation | transformers | # Training Data
**Autochart:** Zhu, J., Ran, J., Lee, R. K. W., Choo, K., & Li, Z. (2021). AutoChart: A Dataset for Chart-to-Text Generation Task. arXiv preprint arXiv:2108.06897.
**Gitlab Link for the data**: https://gitlab.com/bottle_shop/snlg/chart/autochart
Train split for this model: Train 23336, Validation 1297... | {} | saadob12/t5_autochart_2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T11:54:20+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Training Data
=============
Autochart: Zhu, J., Ran, J., Lee, R. K. W., Choo, K., & Li, Z. (2021). AutoChart: A Dataset for Chart-to-Text Generation Task. arXiv preprint arXiv:2108.06897.
Gitlab Link for the data: URL
Train split for this model: Train 23336, Validation 1297, Test 1296
Example use:
============
... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
question-answering | transformers |
# DistilBERT base cased distilled SQuAD
> Note: This model is a clone of [`distilbert-base-cased-distilled-squad`](https://huggingface.co/distilbert-base-cased-distilled-squad) for internal testing.
This model is a fine-tune checkpoint of [DistilBERT-base-cased](https://huggingface.co/distilbert-base-cased), fine-tu... | {"language": "en", "license": "apache-2.0", "datasets": ["squad"], "metrics": ["squad"]} | autoevaluate/distilbert-base-cased-distilled-squad | null | [
"transformers",
"pytorch",
"tf",
"rust",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T12:08:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #rust #distilbert #question-answering #en #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# DistilBERT base cased distilled SQuAD
> Note: This model is a clone of 'distilbert-base-cased-distilled-squad' for internal testing.
This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.
This model reaches a F1 score of 87.1 on the ... | [
"# DistilBERT base cased distilled SQuAD\n\n> Note: This model is a clone of 'distilbert-base-cased-distilled-squad' for internal testing.\n\nThis model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1.\nThis model reaches a F1 score of 87.... | [
"TAGS\n#transformers #pytorch #tf #rust #distilbert #question-answering #en #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DistilBERT base cased distilled SQuAD\n\n> Note: This model is a clone of 'distilbert-base-cased-distilled-squad' for internal testing.\n\nThis model is a fine-tu... |
question-answering | transformers |
<!-- 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. -->
# roberta-base-spanish-squades-modelo-robertav3
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://h... | {"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "roberta-base-spanish-squades-becasv3", "results": []}]} | Evelyn18/roberta-base-spanish-squades-becasv3 | null | [
"transformers",
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"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T12:20:41+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #has_space #region-us
| roberta-base-spanish-squades-modelo-robertav3
=============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6939
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 11\n* eval\\_batch\\_size: 11\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 11\n* eval\\_batch... |
null | null |
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo corresponds to Megatron-Deespeed checkpoints. Use them together with our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed). For a normal Hugging Face Transformers checkpoint please go [here](https://huggingface... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | bigscience/bloom-optimizer-states | null | [
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... | null | 2022-07-19T12:22:48+00:00 | [
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... | TAGS
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| **WARNING:** The checkpoints on this repo corresponds to Megatron-Deespeed checkpoints. Use them together with our fork of Megatron-DeepSpeed. For a normal Hugging Face Transformers checkpoint please go here instead.
=======================================================================================================... | [
"### Model Architecture and Objective\n\n\n* Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):\n* Decoder-only architecture\n* Layer normalization applied to word embeddings layer ('StableEmbedding'; see code, paper)\n* ALiBI positional encodings (see paper), with GeLU activation functions\n* 176 bil... | [
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"##... |
question-answering | transformers |
# roberta-base for QA
> Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing.
This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been train... | {"language": "en", "license": "cc-by-4.0", "datasets": ["squad_v2"]} | autoevaluate/roberta-base-squad2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T12:30:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #roberta #question-answering #en #dataset-squad_v2 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# roberta-base for QA
> Note: this is a clone of 'roberta-base-squad2' for internal testing.
This is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
Language model: roberta... | [
"# roberta-base for QA \n\n> Note: this is a clone of 'roberta-base-squad2' for internal testing.\n\nThis is the roberta-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.",
"## Overview\nLanguage mod... | [
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"# roberta-base for QA \n\n> Note: this is a clone of 'roberta-base-squad2' for internal testing.\n\nThis is the roberta-base model, fine-tuned using the SQuAD2.... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# hubert-large-ll60k-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://h... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "hubert-large-ll60k-librispeech-clean-100h-demo-dist", "results": []}]} | zenkingsama/hubert-large-ll60k-librispeech-clean-100h-demo-dist | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T12:37:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #librispeech_asr #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| hubert-large-ll60k-librispeech-clean-100h-demo-dist
===================================================
This model is a fine-tuned version of facebook/hubert-base-ls960 on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1361
* Wer: 0.9769
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_... |
image-to-text | transformers |
# Donut (base-sized model, pre-trained only)
Donut model pre-trained-only. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Donut d... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-base | null | [
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"donut",
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"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T12:49:17+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us
|
# Donut (base-sized model, pre-trained only)
Donut model pre-trained-only. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been wri... | [
"# Donut (base-sized model, pre-trained only) \n\nDonut model pre-trained-only. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model card has ... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Donut (base-sized model, pre-trained only) \n\nDonut model pre-trained-only. It was introduced in the paper OCR-free Document Understanding Transfo... |
image-to-text | transformers |
# Donut (base-sized model, pre-trained only)
Donut model pre-trained only. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Donut d... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-proto | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T12:50:47+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #region-us
|
# Donut (base-sized model, pre-trained only)
Donut model pre-trained only. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been wri... | [
"# Donut (base-sized model, pre-trained only) \n\nDonut model pre-trained only. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model card has ... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #region-us \n",
"# Donut (base-sized model, pre-trained only) \n\nDonut model pre-trained only. It was introduced in the paper OCR-free Document Understanding Transformer by Gee... |
image-to-text | transformers |
# Donut (base-sized model, fine-tuned on RVL-CDIP)
Donut model fine-tuned on RVL-CDIP. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team relea... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-base-finetuned-rvlcdip | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T12:53:57+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us
|
# Donut (base-sized model, fine-tuned on RVL-CDIP)
Donut model fine-tuned on RVL-CDIP. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card ... | [
"# Donut (base-sized model, fine-tuned on RVL-CDIP) \n\nDonut model fine-tuned on RVL-CDIP. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this mod... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Donut (base-sized model, fine-tuned on RVL-CDIP) \n\nDonut model fine-tuned on RVL-CDIP. It was introduced in the paper OCR-free Document Understan... |
document-question-answering | transformers |
# Donut (base-sized model, fine-tuned on DocVQA)
Donut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"], "pipeline_tag": "document-question-answering", "widget": [{"text": "What is the invoice number?", "src": "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"}, {"text": "What is the purchase amount?"... | naver-clova-ix/donut-base-finetuned-docvqa | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"document-question-answering",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T12:58:22+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #document-question-answering #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us
|
# Donut (base-sized model, fine-tuned on DocVQA)
Donut model fine-tuned on DocVQA. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has ... | [
"# Donut (base-sized model, fine-tuned on DocVQA) \n\nDonut model fine-tuned on DocVQA. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model c... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #document-question-answering #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Donut (base-sized model, fine-tuned on DocVQA) \n\nDonut model fine-tuned on DocVQA. It was introduced in the paper OC... |
token-classification | transformers |
<!-- 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. -->
# aalogan/bert-ner-nsm1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "aalogan/bert-ner-nsm1", "results": []}]} | aalogan/bert-ner-nsm1 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T13:00:30+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| aalogan/bert-ner-nsm1
=====================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0366
* Validation Loss: 0.1607
* Epoch: 5
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2694, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
image-to-text | transformers |
# Donut (base-sized model, fine-tuned on CORD)
Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Don... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-base-finetuned-cord-v1-2560 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T13:01:21+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us
|
# Donut (base-sized model, fine-tuned on CORD)
Donut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been... | [
"# Donut (base-sized model, fine-tuned on CORD) \n\nDonut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model card ... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Donut (base-sized model, fine-tuned on CORD) \n\nDonut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Tra... |
image-to-text | transformers |
# Donut (base-sized model, fine-tuned on CORD, v1)
Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-base-finetuned-cord-v1 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T13:05:20+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #region-us
|
# Donut (base-sized model, fine-tuned on CORD, v1)
Donut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has ... | [
"# Donut (base-sized model, fine-tuned on CORD, v1) \n\nDonut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model so this model c... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #region-us \n",
"# Donut (base-sized model, fine-tuned on CORD, v1) \n\nDonut model fine-tuned on CORD. It was introduced in the paper OCR-free Document Understanding Transforme... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | juancopi81/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T13:08:04+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
image-to-text | transformers |
# Donut (base-sized model, fine-tuned on ZhTrainTicket)
Donut model fine-tuned on ZhTrainTicket. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The ... | {"license": "mit", "tags": ["donut", "image-to-text", "vision"]} | naver-clova-ix/donut-base-finetuned-zhtrainticket | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-19T13:09:36+00:00 | [
"2111.15664"
] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us
|
# Donut (base-sized model, fine-tuned on ZhTrainTicket)
Donut model fine-tuned on ZhTrainTicket. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this m... | [
"# Donut (base-sized model, fine-tuned on ZhTrainTicket) \n\nDonut model fine-tuned on ZhTrainTicket. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.\n\nDisclaimer: The team releasing Donut did not write a model card for this model s... | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #donut #image-to-text #vision #arxiv-2111.15664 #license-mit #endpoints_compatible #has_space #region-us \n",
"# Donut (base-sized model, fine-tuned on ZhTrainTicket) \n\nDonut model fine-tuned on ZhTrainTicket. It was introduced in the paper OCR-free Document... |
text-classification | transformers |
<!-- 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. -->
# jonaskoenig/topic_classification_02
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jonaskoenig/topic_classification_02", "results": []}]} | jonaskoenig/topic_classification_02 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T13:37:24+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| jonaskoenig/topic\_classification\_02
=====================================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0189
* Train Binary Crossentropy: 0.3299
* Epoch: 5
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 3e-05, 'deca... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portugu... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["harem"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {... | jordyvl/bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:harem",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T13:51:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-harem #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased\_harem-selective-lowC-sm-first-ner
=============================================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the harem dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1160
* Precision: 0.8
* Rec... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-harem #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portug... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["harem"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner", "results": []}]} | jordyvl/bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:harem",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T14:10:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-harem #license-mit #endpoints_compatible #region-us
| bert-base-portuguese-cased\_harem-selective-lowC-CRF-first-ner
==============================================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the harem dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0687
* Precision: 0.8030
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: ... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-harem #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\... |
text-generation | transformers |
# CodeParrot 🦜 small for text-t-code generation
This model is [CodeParrot-small](https://huggingface.co/codeparrot/codeparrot-small) (from `branch megatron`) fine-tuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text), a dataset where the samples are a successi... | {"language": ["code"], "license": "apache-2.0", "tags": ["code", "gpt2", "generation"], "datasets": ["codeparrot/codeparrot-clean", "codeparrot/github-jupyter-code-to-text"]} | codeparrot/codeparrot-small-code-to-text | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"code",
"generation",
"dataset:codeparrot/codeparrot-clean",
"dataset:codeparrot/github-jupyter-code-to-text",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T14:34:24+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean #dataset-codeparrot/github-jupyter-code-to-text #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# CodeParrot small for text-t-code generation
This model is CodeParrot-small (from 'branch megatron') fine-tuned on github-jupyter-code-to-text, a dataset where the samples are a succession of Python code and its explanation as a docstring, originally extracted from Jupyter notebooks parsed in this dataset. | [
"# CodeParrot small for text-t-code generation\n\nThis model is CodeParrot-small (from 'branch megatron') fine-tuned on github-jupyter-code-to-text, a dataset where the samples are a succession of Python code and its explanation as a docstring, originally extracted from Jupyter notebooks parsed in this dataset."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #code #generation #dataset-codeparrot/codeparrot-clean #dataset-codeparrot/github-jupyter-code-to-text #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# CodeParrot small for text-t-code gen... |
fill-mask | transformers |
<!-- 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. -->
# oscarth_54321
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "oscarth_54321", "results": []}]} | bigmorning/oscarth_54321 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T14:49:28+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| oscarth\_54321
==============
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.5784
* Validation Loss: 4.5266
* Epoch: 1
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
fill-mask | transformers |
# Twitter June 2022 (RoBERTa-base, 132M)
This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For ... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-jun2022 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T15:39:20+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter June 2022 (RoBERTa-base, 132M)
This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to com... | [
"# Twitter June 2022 (RoBERTa-base, 132M)\n\nThis is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suit... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter June 2022 (RoBERTa-base, 132M)\n\nThis is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.\nM... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-emo20q-classification
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the N... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-emo20q-classification", "results": []}]} | abecode/t5-base-finetuned-emo20q-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T16:02:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-emo20q-classification
=======================================
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3759
* Rouge1: 70.3125
* Rouge2: 0.0
* Rougel: 70.2083
* Rougelsum: 70.2083
* Gen Len: 2.0
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-classification | transformers |
<!-- 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. -->
# xlm-kabita
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-emotion](https://huggingface.co/cardiffnlp/twi... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-kabita", "results": []}]} | sam34738/xlm-kabita | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T16:15:52+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| xlm-kabita
==========
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-emotion on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4984
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
image-classification | transformers |
<!-- 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](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | himal/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T16:17:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0738
* Accuracy: 0.9756
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
sentence-similarity | sentence-transformers |
# gemasphi/laprador_pt
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 ea... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | gemasphi/laprador_pt_pb | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T16:23:09+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# gemasphi/laprador_pt
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then y... | [
"# gemasphi/laprador_pt\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# gemasphi/laprador_pt\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clusteri... |
automatic-speech-recognition | k2-sherpa |
# Introduction
See https://github.com/k2-fsa/icefall/pull/363 | {"language": ["en"], "license": "apache-2.0", "library_name": "k2-sherpa", "tags": ["automatic-speech-recognition", "k2", "k2-sherpa"], "datasets": ["librispeech"], "metric": ["wer", "cer"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-tra... | jtrmal/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13 | null | [
"k2-sherpa",
"tensorboard",
"automatic-speech-recognition",
"k2",
"en",
"dataset:librispeech",
"license:apache-2.0",
"region:us"
] | null | 2022-07-19T17:11:56+00:00 | [] | [
"en"
] | TAGS
#k2-sherpa #tensorboard #automatic-speech-recognition #k2 #en #dataset-librispeech #license-apache-2.0 #region-us
|
# Introduction
See URL | [
"# Introduction\nSee URL"
] | [
"TAGS\n#k2-sherpa #tensorboard #automatic-speech-recognition #k2 #en #dataset-librispeech #license-apache-2.0 #region-us \n",
"# Introduction\nSee URL"
] |
text-generation | transformers |
<!-- 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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | rapid3/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T17:29:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1100
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
question-answering | transformers |
<!-- 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. -->
# roberta-base-spanish-squades-modelo-robertav1b3
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https:/... | {"tags": ["generated_from_trainer"], "datasets": ["becasv3"], "model-index": [{"name": "roberta-base-spanish-squades-modelo-robertav1b3", "results": []}]} | Evelyn18/roberta-base-spanish-squades-modelo-robertav1b3 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv3",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T17:43:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv3 #endpoints_compatible #region-us
| roberta-base-spanish-squades-modelo-robertav1b3
===============================================
This model is a fine-tuned version of IIC/roberta-base-spanish-squades on the becasv3 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3537
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 11\n* eval\\_batch\\_size: 11\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-becasv3 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 11\n* eval\\_bat... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1154042510
- CO2 Emissions (in grams): 39.98165454365982
## Validation Metrics
- Loss: 0.7440880537033081
- Accuracy: 0.6724677090414684
- Macro F1: 0.5448715054903115
- Micro F1: 0.6724677090414684
- Weighted F1: 0.6355527198056... | {"language": "en", "tags": "autotrain", "datasets": ["snap/autotrain-data-argument-feedback"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 39.98165454365982} | snap/autotrain-argument-feedback-1154042510 | null | [
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"bert",
"text-classification",
"autotrain",
"en",
"dataset:snap/autotrain-data-argument-feedback",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T17:50:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-snap/autotrain-data-argument-feedback #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1154042510
- CO2 Emissions (in grams): 39.98165454365982
## Validation Metrics
- Loss: 0.7440880537033081
- Accuracy: 0.6724677090414684
- Macro F1: 0.5448715054903115
- Micro F1: 0.6724677090414684
- Weighted F1: 0.6355527198056... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1154042510\n- CO2 Emissions (in grams): 39.98165454365982",
"## Validation Metrics\n\n- Loss: 0.7440880537033081\n- Accuracy: 0.6724677090414684\n- Macro F1: 0.5448715054903115\n- Micro F1: 0.6724677090414684\n- Weighted F... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-snap/autotrain-data-argument-feedback #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1154042510\n- CO2 Emissio... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715
## Validation Metrics
- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.62833369992878... | {"language": "en", "tags": "autotrain", "datasets": ["snap/autotrain-data-argument-feedback"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 50.942111222257715} | snap/autotrain-argument-feedback-1154042511 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:snap/autotrain-data-argument-feedback",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T17:53:33+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-snap/autotrain-data-argument-feedback #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1154042511
- CO2 Emissions (in grams): 50.942111222257715
## Validation Metrics
- Loss: 0.7391096353530884
- Accuracy: 0.672331747110809
- Macro F1: 0.5302988889038903
- Micro F1: 0.672331747110809
- Weighted F1: 0.62833369992878... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1154042511\n- CO2 Emissions (in grams): 50.942111222257715",
"## Validation Metrics\n\n- Loss: 0.7391096353530884\n- Accuracy: 0.672331747110809\n- Macro F1: 0.5302988889038903\n- Micro F1: 0.672331747110809\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-snap/autotrain-data-argument-feedback #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1154042511\n- CO2 Emissio... |
text-classification | transformers |
<!-- 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. -->
# topic_classification_03
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsof... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "topic_classification_03", "results": []}]} | jonaskoenig/topic_classification_03 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T18:33:22+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| topic\_classification\_03
=========================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0459
* Train Sparse Categorical Accuracy: 0.6535
* Validation Loss: 1.1181
* Validation Spa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'deca... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# T5-as-chat-bot
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "T5-as-chat-bot", "results": []}]} | Ahmed007/T5-as-chat-bot | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T18:36:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-as-chat-bot
==============
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2717
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
null | null | Floral Diffusion V1
Floral diffusion is a trained model set of 10 K floral sets of 512 kb size images that have been trained on 256 x 256 diffusion model.
custom model settings
model_config.update({
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
... | {"license": "mit"} | jags/floraldiffusion | null | [
"license:mit",
"region:us"
] | null | 2022-07-19T18:43:01+00:00 | [] | [] | TAGS
#license-mit #region-us
| Floral Diffusion V1
Floral diffusion is a trained model set of 10 K floral sets of 512 kb size images that have been trained on 256 x 256 diffusion model.
custom model settings
model_config.update({
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
... | [] | [
"TAGS\n#license-mit #region-us \n"
] |
reinforcement-learning | ml-agents |
# **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 comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | QuickSilver007/MLAgents-Pyramids_v2 | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-19T18:59:03+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# run1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "run1", "results": []}]} | Siyong/MT_RN | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T19:53:19+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| run1
====
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6666
* Wer: 0.6375
* Cer: 0.3170
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
text2text-generation | transformers |
# Sumerian and Akkadian Cuneiform Language Translator
This is a translation network that understands Sumerian and Akkadian languages written in cuneiform.
It was trained on cuneiform transcribed in the CDLI ATF format. For example:
```text
translate Akkadian to English: 1(disz){d}szul3-ma-nu-_sag man gal?_-u2 _man_... | {"license": "mit", "tags": ["cuneiform", "akkadian", "sumerian"]} | praeclarum/cuneiform | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"cuneiform",
"akkadian",
"sumerian",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T20:06:21+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #cuneiform #akkadian #sumerian #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sumerian and Akkadian Cuneiform Language Translator
This is a translation network that understands Sumerian and Akkadian languages written in cuneiform.
It was trained on cuneiform transcribed in the CDLI ATF format. For example:
The network was trained to translate from the ancient languages:
* Akkadian
* Sum... | [
"# Sumerian and Akkadian Cuneiform Language Translator\n\nThis is a translation network that understands Sumerian and Akkadian languages written in cuneiform.\n\nIt was trained on cuneiform transcribed in the CDLI ATF format. For example:\n\n\n\nThe network was trained to translate from the ancient languages:\n\n* ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #cuneiform #akkadian #sumerian #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sumerian and Akkadian Cuneiform Language Translator\n\nThis is a translation network that understands Sumerian and Akkadian ... |
fill-mask | transformers |
<!-- 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. -->
# distilbert_oscarth_0020
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_oscarth_0020", "results": []}]} | bigmorning/distilbert_oscarth_0020 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T20:34:19+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_oscarth\_0020
=========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.4909
* Validation Loss: 1.4161
* Epoch: 19
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
text-generation | transformers |
# Bushcat DialoGPT-small Model
A smaller personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.
Smaller model based on DialoGPT-small. Recommended to use the **TeaTM/DialoGPT-large-bushcat** model on my Hugging Face page.
The large model is bigger in size but also significantly sm... | {"language": ["en"], "tags": ["conversational", "DialoGPT"]} | TeaTM/DialoGPT-small-bushcat | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"DialoGPT",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T21:25:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #DialoGPT #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Bushcat DialoGPT-small Model
A smaller personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.
Smaller model based on DialoGPT-small. Recommended to use the TeaTM/DialoGPT-large-bushcat model on my Hugging Face page.
The large model is bigger in size but also significantly smarte... | [
"# Bushcat DialoGPT-small Model\n\nA smaller personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.\n\nSmaller model based on DialoGPT-small. Recommended to use the TeaTM/DialoGPT-large-bushcat model on my Hugging Face page.\nThe large model is bigger in size but also significant... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #DialoGPT #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Bushcat DialoGPT-small Model\n\nA smaller personified DialoGPT fork for a side project. Conversational for an entertainment chatbot.\n\nSmall... |
text-generation | transformers |
<!-- 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. -->
# distilgpt_oscarth_0020
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_oscarth_0020", "results": []}]} | bigmorning/distilgpt_oscarth_0020 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-19T21:35:51+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_oscarth\_0020
========================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.2188
* Validation Loss: 3.0982
* Epoch: 19
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **mujoco_halfcheetah** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"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": "mujoco_halfcheetah", "type": "mujoco_halfcheetah"}, "me... | andrewzhang505/sample-factory-2-mujoco-halfcheetah | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-19T22:14:18+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the mujoco_halfcheetah environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# DEREXP
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the No... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "DEREXP", "results": []}]} | zluvolyote/DEREXP | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T23:49:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DEREXP
======
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1590
* Mse: 3.1590
* Mae: 1.3397
* R2: 0.4465
* Accuracy: 0.2528
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
image-classification | transformers |
# Check_Aligned_Teeth
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | steven123/Check_Aligned_Teeth | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T23:58:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Check_Aligned_Teeth
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Aligned Teeth
!Aligned Teeth
#### Crooked Teeth
!Crooked Teeth | [
"# Check_Aligned_Teeth\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### Aligned Teeth\n\n!Aligned Teeth",
"#### Crooked Teeth\n\n!Crooked Teeth"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Check_Aligned_Teeth\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any ... |
text-generation | transformers |
# GPT2 Fine-Tuned Banking 77
This is a fine-tuned version of the GPT2 model. It's best suited for text-generation.
## Model Description
Kwaku/gpt2-finetuned-banking77 was fine tuned on the [banking77](https://huggingface.co/datasets/banking77) dataset, which is "composed of online banking queries annotated with their... | {"language": "eng", "datasets": ["banking77"]} | Kwaku/gpt2-finetuned-banking77 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"eng",
"dataset:banking77",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T00:06:19+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #eng #dataset-banking77 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2 Fine-Tuned Banking 77
This is a fine-tuned version of the GPT2 model. It's best suited for text-generation.
## Model Description
Kwaku/gpt2-finetuned-banking77 was fine tuned on the banking77 dataset, which is "composed of online banking queries annotated with their corresponding intents."
## Intended Uses an... | [
"# GPT2 Fine-Tuned Banking 77\nThis is a fine-tuned version of the GPT2 model. It's best suited for text-generation.",
"## Model Description\nKwaku/gpt2-finetuned-banking77 was fine tuned on the banking77 dataset, which is \"composed of online banking queries annotated with their corresponding intents.\"",
"## ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #eng #dataset-banking77 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 Fine-Tuned Banking 77\nThis is a fine-tuned version of the GPT2 model. It's best suited for text-generation.",
"## Model Description\nKwaku/... |
fill-mask | transformers |
<!-- 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. -->
# distilbert_oscarth_0040
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_oscarth_0040", "results": []}]} | bigmorning/distilbert_oscarth_0040 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T00:27:11+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_oscarth\_0040
=========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.2890
* Validation Loss: 1.2296
* Epoch: 39
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
feature-extraction | transformers |
## Main repository
https://github.com/martiansideofthemoon/rankgen
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from a... | {"language": ["en"], "license": "apache-2.0", "tags": ["t5", "contrastive learning", "ranking", "decoding", "metric learning", "pytorch", "text generation", "retrieval"], "datasets": ["Wikipedia", "PG19", "C4", "relic", "ChapterBreak", "HellaSwag", "ROCStories"], "metrics": ["MAUVE", "human"], "thumbnail": "https://pbs... | kalpeshk2011/rankgen-t5-base-all | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"contrastive learning",
"ranking",
"decoding",
"metric learning",
"text generation",
"retrieval",
"custom_code",
"en",
"dataset:Wikipedia",
"dataset:PG19",
"dataset:C4",
"dataset:relic",
"dataset:ChapterBreak",
"dataset:HellaSw... | null | 2022-07-20T00:35:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-inferen... |
## Main repository
URL
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorporated as a s... | [
"## Main repository\n\nURL",
"## What is RankGen?\n\nRankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorpo... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-i... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# run1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "run1", "results": []}]} | Siyong/MT_RN_LM | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T00:38:19+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| run1
====
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6666
* Wer: 0.6375
* Cer: 0.3170
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_ba... |
text-generation | null |
# RWKV-3 430M
## Model Description
RWKV-3 430M is a L24-D1024 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it.
ctx_len = 768
n_layer = 24
n_embd = 1024
Final checkpo... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["The Pile"]} | BlinkDL/rwkv-3-pile-430m | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-07-20T00:53:29+00:00 | [] | [
"en"
] | TAGS
#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us
|
# RWKV-3 430M
## Model Description
RWKV-3 430M is a L24-D1024 causal language model trained on the Pile. See URL for details.
At this moment you have to use my Github code (URL to run it.
ctx_len = 768
n_layer = 24
n_embd = 1024
Final checkpoint: URL : Trained on the Pile for 326B tokens.
* Pile loss 2.288
* LAMB... | [
"# RWKV-3 430M",
"## Model Description\n\nRWKV-3 430M is a L24-D1024 causal language model trained on the Pile. See URL for details.\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = 768\nn_layer = 24\nn_embd = 1024\n\nFinal checkpoint: URL : Trained on the Pile for 326B tokens.\n* Pil... | [
"TAGS\n#pytorch #text-generation #causal-lm #rwkv #en #license-apache-2.0 #has_space #region-us \n",
"# RWKV-3 430M",
"## Model Description\n\nRWKV-3 430M is a L24-D1024 causal language model trained on the Pile. See URL for details.\n\nAt this moment you have to use my Github code (URL to run it.\n\nctx_len = ... |
feature-extraction | transformers |
## Main repository
https://github.com/martiansideofthemoon/rankgen
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from a... | {"language": ["en"], "license": "apache-2.0", "tags": ["t5", "contrastive learning", "ranking", "decoding", "metric learning", "pytorch", "text generation", "retrieval"], "datasets": ["Wikipedia", "PG19", "C4", "relic", "ChapterBreak", "HellaSwag", "ROCStories"], "metrics": ["MAUVE", "human"], "thumbnail": "https://pbs... | kalpeshk2011/rankgen-t5-large-all | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"contrastive learning",
"ranking",
"decoding",
"metric learning",
"text generation",
"retrieval",
"custom_code",
"en",
"dataset:Wikipedia",
"dataset:PG19",
"dataset:C4",
"dataset:relic",
"dataset:ChapterBreak",
"dataset:HellaSw... | null | 2022-07-20T00:56:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-inferen... |
## Main repository
URL
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorporated as a s... | [
"## Main repository\n\nURL",
"## What is RankGen?\n\nRankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorpo... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-i... |
token-classification | transformers |
<!-- 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. -->
# bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://hugg... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["bc2gm_corpus"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "b... | commanderstrife/bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:bc2gm_corpus",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T01:00:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-bc2gm_corpus #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bc2gm\_corpus-Bio\_ClinicalBERT-finetuned-ner
=============================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the bc2gm\_corpus dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1505
* Precision: 0.7854
* Recall: 0.8158
* F1: 0.8003
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-bc2gm_corpus #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
feature-extraction | transformers |
## Main repository
https://github.com/martiansideofthemoon/rankgen
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from a... | {"language": ["en"], "license": "apache-2.0", "tags": ["t5", "contrastive learning", "ranking", "decoding", "metric learning", "pytorch", "text generation", "retrieval"], "datasets": ["Wikipedia", "PG19", "C4", "relic", "ChapterBreak", "HellaSwag", "ROCStories"], "metrics": ["MAUVE", "human"], "thumbnail": "https://pbs... | kalpeshk2011/rankgen-t5-xl-all | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"contrastive learning",
"ranking",
"decoding",
"metric learning",
"text generation",
"retrieval",
"custom_code",
"en",
"dataset:Wikipedia",
"dataset:PG19",
"dataset:C4",
"dataset:relic",
"dataset:ChapterBreak",
"dataset:HellaSw... | null | 2022-07-20T01:30:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-inferen... |
## Main repository
URL
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorporated as a s... | [
"## Main repository\n\nURL",
"## What is RankGen?\n\nRankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorpo... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-i... |
null | null | import requests
API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
headers = {"Authorization": "Bearer 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"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Can you... | {"license": "bigscience-bloom-rail-1.0"} | Willaim/Bl00m | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-07-20T01:32:19+00:00 | [] | [] | TAGS
#license-bigscience-bloom-rail-1.0 #region-us
| import requests
API_URL = "URL
headers = {"Authorization": "Bearer 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"}
def query(payload):
response = URL(API_URL, headers=headers, json=payload)
return URL()
output = query({
"inputs": "Can you please let us know more details about your ",
}) | [] | [
"TAGS\n#license-bigscience-bloom-rail-1.0 #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- 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. -->
# wav2vec2-large-xls-r-300m-en-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-en-colab", "results": []}]} | tsrivatsav/wav2vec2-large-xls-r-300m-en-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T01:32:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-en-colab
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7541
* Wer: 1.0
* Cer: 0.9877
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n*... |
fill-mask | transformers |
<!-- 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. -->
# nlp-esg-scoring/bert-base-finetuned-cleaned-esg-plus
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlp-esg-scoring/bert-base-finetuned-cleaned-esg-plus", "results": []}]} | nlp-esg-scoring/bert-base-finetuned-cleaned-esg-plus | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T01:42:16+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| nlp-esg-scoring/bert-base-finetuned-cleaned-esg-plus
====================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7242
* Validation Loss: 2.5107
* Epoch: 9
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
text-classification | transformers |
<!-- 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-wikiandmark_epoch20
This model is a fine-tuned version of [distilbert-base-uncased](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wikiandmark_epoch20", "results": []}]} | leokai/distilbert-base-uncased-finetuned-wikiandmark_epoch20 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T01:43:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-wikiandmark\_epoch20
======================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0561
* Accuracy: 0.9944
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
reinforcement-learning | stable-baselines3 |
# **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 ... | {"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": "LunarL... | Sily/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-20T01:48:20+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
question-answering | transformers |
<!-- 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. -->
# bart-base-finetuned-squad2-finetuned-squad2
This model is a fine-tuned version of [ChuVN/bart-base-finetuned-squad2](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "bart-base-finetuned-squad2-finetuned-squad2", "results": []}]} | ChuVN/bart-base-finetuned-squad2-finetuned-squad2 | null | [
"transformers",
"pytorch",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T02:32:31+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
|
# bart-base-finetuned-squad2-finetuned-squad2
This model is a fine-tuned version of ChuVN/bart-base-finetuned-squad2 on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training... | [
"# bart-base-finetuned-squad2-finetuned-squad2\n\nThis model is a fine-tuned version of ChuVN/bart-base-finetuned-squad2 on the squad_v2 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informati... | [
"TAGS\n#transformers #pytorch #bart #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bart-base-finetuned-squad2-finetuned-squad2\n\nThis model is a fine-tuned version of ChuVN/bart-base-finetuned-squad2 on the squad_v2 dataset.",
"## Mode... |
text-generation | transformers |
<!-- 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. -->
# distilgpt_oscarth_0040
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_oscarth_0040", "results": []}]} | bigmorning/distilgpt_oscarth_0040 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T02:34:17+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_oscarth\_0040
========================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.0004
* Validation Loss: 2.8864
* Epoch: 39
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
fill-mask | transformers |
## RoBERTa Greek small model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of RoBERTa base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of [CC-100/el](https://data.statmt.org/cc-... | {"language": "el", "license": "cc-by-sa-4.0", "datasets": ["cc100", "oscar", "wikipedia"], "widget": [{"text": "\u0394\u03b5\u03bd \u03c4\u03b7\u03bd \u03ad\u03c7\u03c9 <mask> \u03c0\u03bf\u03c4\u03ad."}, {"text": "\u0388\u03c7\u03b5\u03b9 \u03c0\u03bf\u03bb\u03cd \u03ba\u03b1\u03b9\u03c1\u03cc \u03c0\u03bf\u03c5 \u03b... | ClassCat/roberta-small-greek | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"el",
"dataset:cc100",
"dataset:oscar",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T02:51:52+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #roberta #fill-mask #el #dataset-cc100 #dataset-oscar #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
## RoBERTa Greek small model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of RoBERTa base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of CC-100/el : Monolingual Datasets from W... | [
"## RoBERTa Greek small model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses approximately half the size of RoBERTa base model parameters.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data\n\n* Subset of CC-100/el :... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #el #dataset-cc100 #dataset-oscar #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Greek small model (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model ... |
text-generation | transformers |
## Greek GPT2 small model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of GPT2 base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of [CC-100/el](https://data.statmt.or... | {"language": "el", "license": "cc-by-sa-4.0", "datasets": ["cc100", "oscar", "wikipedia"], "widget": [{"text": "\u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1"}, {"text": "\u0391\u03bd\u03bf\u03b9\u03be\u03b1 \u03c4\u03b7\u03bd"}, {"text": "\u0395\u03c5\u03c7\u03b1\u03c1\u03b9\u03c3\u03c4\u0... | ClassCat/gpt2-small-greek-v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"el",
"dataset:cc100",
"dataset:oscar",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T03:20:58+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #el #dataset-cc100 #dataset-oscar #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Greek GPT2 small model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses approximately half the size of GPT2 base model parameters.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* Subset of CC-100/el : Monolingual Datasets f... | [
"## Greek GPT2 small model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"### Model architecture\n\nThis model uses approximately half the size of GPT2 base model parameters.",
"### Tokenizer\n\nUsing BPE tokenizer with vocabulary size 50,000.",
"### Training Data \n\n* Subset of CC-100... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #el #dataset-cc100 #dataset-oscar #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Greek GPT2 small model Version 2 (Uncased)",
"### Prerequisites\n\ntransformers==4.19.2",
"... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/2158957823960c84c7890b8fa5e6d47... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/rage-against-the-machine"], "widget": [{"text": "I am"}]} | huggingartists/rage-against-the-machine | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/rage-against-the-machine",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T03:21:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/rage-against-the-machine #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from Rage Against the Machine.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pip... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/rage-against-the-machine #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, chec... |
question-answering | transformers |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | lqdisme/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T03:25:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
feature-extraction | transformers |
## Main repository
https://github.com/martiansideofthemoon/rankgen
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from a... | {"language": ["en"], "license": "apache-2.0", "tags": ["t5", "contrastive learning", "ranking", "decoding", "metric learning", "pytorch", "text generation", "retrieval"], "datasets": ["Wikipedia", "PG19", "C4", "relic", "ChapterBreak", "HellaSwag", "ROCStories"], "metrics": ["MAUVE", "human"], "thumbnail": "https://pbs... | kalpeshk2011/rankgen-t5-xl-pg19 | null | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"contrastive learning",
"ranking",
"decoding",
"metric learning",
"text generation",
"retrieval",
"custom_code",
"en",
"dataset:Wikipedia",
"dataset:PG19",
"dataset:C4",
"dataset:relic",
"dataset:ChapterBreak",
"dataset:HellaSw... | null | 2022-07-20T03:40:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-inferen... |
## Main repository
URL
## What is RankGen?
RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorporated as a s... | [
"## Main repository\n\nURL",
"## What is RankGen?\n\nRankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorpo... | [
"TAGS\n#transformers #pytorch #t5 #feature-extraction #contrastive learning #ranking #decoding #metric learning #text generation #retrieval #custom_code #en #dataset-Wikipedia #dataset-PG19 #dataset-C4 #dataset-relic #dataset-ChapterBreak #dataset-HellaSwag #dataset-ROCStories #license-apache-2.0 #text-generation-i... |
fill-mask | transformers |
<!-- 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. -->
# distilbert_oscarth_0060
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert_oscarth_0060", "results": []}]} | bigmorning/distilbert_oscarth_0060 | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T04:20:36+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert\_oscarth\_0060
=========================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1876
* Validation Loss: 1.1378
* Epoch: 59
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
null | transformers |
<!-- 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. -->
# rule_learning_margin_1mm_many_negatives_spanpred_attention
This model is a fine-tuned version of [enoriega/rule_softmatching](ht... | {"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_margin_1mm_many_negatives_spanpred_attention", "results": []}]} | enoriega/rule_learning_margin_1mm_many_negatives_spanpred_attention | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T05:09:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us
| rule\_learning\_margin\_1mm\_many\_negatives\_spanpred\_attention
=================================================================
This model is a fine-tuned version of enoriega/rule\_softmatching on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2369
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_... |
text-generation | transformers | # NakaAI DialoGPT Model | {"tags": ["conversational"]} | ionite/DialoGPT-medium-NakaAI | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T05:38:17+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # NakaAI DialoGPT Model | [
"# NakaAI DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NakaAI DialoGPT Model"
] |
token-classification | transformers |
<!-- 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. -->
# bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://hugg... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["bc4chemd_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "b... | commanderstrife/bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:bc4chemd_ner",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T05:38:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-bc4chemd_ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bc4chemd\_ner-Bio\_ClinicalBERT-finetuned-ner
=============================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the bc4chemd\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0641
* Precision: 0.8944
* Recall: 0.8777
* F1: 0.8860
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-bc4chemd_ner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
text2text-generation | transformers |
<!-- 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. -->
# kapuska/t5-small-finetuned-on-800-records-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "kapuska/t5-small-finetuned-on-800-records-samsum", "results": []}]} | Ecosmob555/t5-small-finetuned-on-800-records-samsum | null | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T06:03:47+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| kapuska/t5-small-finetuned-on-800-records-samsum
================================================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7883
* Validation Loss: 2.3752
* Train Rouge1: 24.8093
* Train Rouge2: 8.88... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #tensorboard #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'... |
reinforcement-learning | stable-baselines3 |
# **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 ... | {"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": "LunarL... | RajSang/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-20T06:05:41+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
token-classification | transformers |
This is a token-classification model.
This model is AlephBert fine-tuned on detecting metaphors from Hebrew Piyutim
model-index:
- name: tokeron/alephbert-finetuned-metaphor-detection
results: []
# model
This model fine-tunes onlplab/alephbert-base model on Piyutim dataset.
### About Us
Created by Michael ... | {"language": ["he"], "license": "afl-3.0", "tags": ["token-classification"], "datasets": ["Piyutim"], "metrics": ["f1"], "model": ["onlplab/alephbert-base"], "widget": [{"text": "\u05e0\u05e9\u05d1\u05e8 \u05dc\u05d9 \u05d4\u05d2\u05d1", "example_title": "Broken back"}, {"text": "\u05e9 \u05dc\u05d5 \u05dc\u05d1 \u05d6... | tokeron/alephbert-finetuned-metaphor-detection | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"he",
"dataset:Piyutim",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T06:06:57+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #bert #token-classification #he #dataset-Piyutim #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a token-classification model.
This model is AlephBert fine-tuned on detecting metaphors from Hebrew Piyutim
model-index:
- name: tokeron/alephbert-finetuned-metaphor-detection
results: []
# model
This model fine-tunes onlplab/alephbert-base model on Piyutim dataset.
### About Us
Created by Michael ... | [
"# model\nThis model fine-tunes onlplab/alephbert-base model on Piyutim dataset.",
"### About Us\nCreated by Michael Toker in collaboration with Yonatan Belinkov, Benny Kornfeld, Oren Mishali, and Ophir Münz-Manor.\nFor more cooperation, please contact email: \ntok@URL"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #he #dataset-Piyutim #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# model\nThis model fine-tunes onlplab/alephbert-base model on Piyutim dataset.",
"### About Us\nCreated by Michael Toker in collaboration with Yonatan Bel... |
null | null | # Anime recommender system
A simple set of models which provides similar anime shows or predicts a user's rating on a anime show.
Names and rating system follows MyAnimeList.
---
title: Anime-Collaborative Filtering
emoji: 📚
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 3.0.24
app_file: anime.py
pinned: fal... | {} | NomiWai/anime-collaborative-filtering | null | [
"region:us"
] | null | 2022-07-20T06:21:01+00:00 | [] | [] | TAGS
#region-us
| # Anime recommender system
A simple set of models which provides similar anime shows or predicts a user's rating on a anime show.
Names and rating system follows MyAnimeList.
---
title: Anime-Collaborative Filtering
emoji:
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 3.0.24
app_file: URL
pinned: false
---
... | [
"# Anime recommender system\nA simple set of models which provides similar anime shows or predicts a user's rating on a anime show.\nNames and rating system follows MyAnimeList.\n\n---\ntitle: Anime-Collaborative Filtering\nemoji: \ncolorFrom: red\ncolorTo: yellow\nsdk: gradio\nsdk_version: 3.0.24\napp_file: URL\np... | [
"TAGS\n#region-us \n",
"# Anime recommender system\nA simple set of models which provides similar anime shows or predicts a user's rating on a anime show.\nNames and rating system follows MyAnimeList.\n\n---\ntitle: Anime-Collaborative Filtering\nemoji: \ncolorFrom: red\ncolorTo: yellow\nsdk: gradio\nsdk_version:... |
text-classification | transformers |
<!-- 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. -->
# topic_classification_04
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsof... | {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "microsoft/xtremedistil-l6-h256-uncased", "model-index": [{"name": "topic_classification_04", "results": []}]} | jonaskoenig/topic_classification_04 | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:microsoft/xtremedistil-l6-h256-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-20T06:26:43+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-microsoft/xtremedistil-l6-h256-uncased #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| topic\_classification\_04
=========================
This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.8325
* Train Sparse Categorical Accuracy: 0.7237
* Epoch: 9
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 5e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-microsoft/xtremedistil-l6-h256-uncased #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\... |
automatic-speech-recognition | transformers |
<!-- 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. -->
# korean-aihub-learning-2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "korean-aihub-learning-2", "results": []}]} | jaeyeon/korean-aihub-learning-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T06:38:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| korean-aihub-learning-2
=======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9945
* Wer: 0.9533
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4... |
text-classification | transformers |
<!-- 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. -->
# FAICAM/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "FAICAM/distilbert-base-uncased-finetuned-cola", "results": []}]} | FAICAM/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T06:47:13+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FAICAM/distilbert-base-uncased-finetuned-cola
=============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1871
* Validation Loss: 0.4889
* Train Matthews Correlation: 0.564... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2670, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'lear... |
reinforcement-learning | ml-agents |
# **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 comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | auriolar/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-20T06:55:36+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
token-classification | transformers |
<!-- 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. -->
# notmaineyy/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "notmaineyy/distilbert-base-uncased-finetuned-ner", "results": []}]} | notmaineyy/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T06:55:44+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| notmaineyy/distilbert-base-uncased-finetuned-ner
================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0344
* Validation Loss: 0.0633
* Train Precision: 0.9181
* T... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightD... |
null | null | 111
---
---
| {"license": "other"} | zxc/model_epoch40_50w | null | [
"license:other",
"has_space",
"region:us"
] | null | 2022-07-20T07:02:28+00:00 | [] | [] | TAGS
#license-other #has_space #region-us
| 111
---
---
| [] | [
"TAGS\n#license-other #has_space #region-us \n"
] |
token-classification | spacy | ### Details: https://spacy.io/models/hr#hr_core_news_sm
Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `hr_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.... | {"language": ["hr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/hr_core_news_sm | null | [
"spacy",
"token-classification",
"hr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-07-20T07:06:57+00:00 | [] | [
"hr"
] | TAGS
#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (1518 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nCroatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1518 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nCroatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1518 labels f... |
token-classification | spacy | ### Details: https://spacy.io/models/hr#hr_core_news_md
Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `hr_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.... | {"language": ["hr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/hr_core_news_md | null | [
"spacy",
"token-classification",
"hr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-07-20T07:07:10+00:00 | [] | [
"hr"
] | TAGS
#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (1518 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nCroatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1518 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nCroatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1518 labels f... |
token-classification | spacy | ### Details: https://spacy.io/models/hr#hr_core_news_lg
Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `hr_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.... | {"language": ["hr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/hr_core_news_lg | null | [
"spacy",
"token-classification",
"hr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-07-20T07:07:47+00:00 | [] | [
"hr"
] | TAGS
#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner.
### Label Scheme
View label scheme (1518 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nCroatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1518 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nCroatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1518 labels f... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinformce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [... | auriolar/Reinformce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-20T07:18:24+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-generation | transformers |
<!-- 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. -->
# distilgpt_oscarth_0060
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_oscarth_0060", "results": []}]} | bigmorning/distilgpt_oscarth_0060 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-20T07:30:47+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_oscarth\_0060
========================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8883
* Validation Loss: 2.7784
* Epoch: 59
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeig... |
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