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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | vukpetar/ppo-MountainCar-v1 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T12:44:56+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCar-v0\n This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\n This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | turhancan97/second_ppo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T12:54:54+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing MountainCar-v0\n This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing MountainCar-v0\n This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | Kire/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T12:55:46+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# roberta-base-bne-finetuned-detests
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "BSC-TeMU/roberta-base-bne", "model-index": [{"name": "roberta-base-bne-finetuned-detests", "results": []}]} | Pablo94/roberta-base-bne-finetuned-detests | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-TeMU/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T13:18:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-detests
==================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1686
* Accuracy: 0.8494
* F1-score: 0.7869
* Precision: 0.7855
* Recall: 0.7883
* Auc: 0.7883
... | [
"### 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 #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #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* ... |
null | null | Private sample code for running categorisation on the mT5X | {} | pere/north-t5-base-deuncaser | null | [
"region:us"
] | null | 2022-05-12T13:31:11+00:00 | [] | [] | TAGS
#region-us
| Private sample code for running categorisation on the mT5X | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# German BERT base fine-tuned to predict educational requirements
This is a fine-tuned version of the German BERT base language model [deepset/gbert-base](https://huggingface.co/deepset/gbert-base). The multilabel task this model was trained on was to predict education requirements from job ad texts. The dataset used... | {"language": "de", "license": "mit"} | gonzpen/gbert-base-ft-edu-redux | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T13:40:27+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
| German BERT base fine-tuned to predict educational requirements
===============================================================
This is a fine-tuned version of the German BERT base language model deepset/gbert-base. The multilabel task this model was trained on was to predict education requirements from job ad texts.... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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
| {"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... | damianr13/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T14:06:51+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | vukpetar/ppo-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T14:43:44+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | SusBioRes-UBC/ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T14:47:34+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
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
| {"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... | jgerbscheid/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T15:07:02+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | CrispyAlbumArt/ppo-LunarLander-v4 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T15:17:02+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
fill-mask | 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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | vives/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T15:33:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
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: 2.4721
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | mybot/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T15:50:44+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | vukpetar/ppo-BipedalWalker-v3-v1 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T16:20:30+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
text-generation | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | Dedemg1988/DialoGPT-small-michaelscott | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T16:31:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Michael Scott DialoGPT Model | [
"# Michael Scott DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT Model"
] |
null | null | # This model is a clone of https://huggingface.co/EleutherAI/gpt-j-6B in which I have simply increased the max response size.
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the clas... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]} | deepparag/gpt-j-6B-longer-generation | null | [
"pytorch",
"causal-lm",
"en",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"region:us"
] | null | 2022-05-12T16:32:17+00:00 | [
"2104.09864",
"2101.00027"
] | [
"en"
] | TAGS
#pytorch #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #region-us
| This model is a clone of URL in which I have simply increased the max response size.
====================================================================================
GPT-J 6B
========
Model Description
-----------------
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" ref... | [
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unkn... | [
"TAGS\n#pytorch #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #region-us \n",
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nThe core functionality of GPT-J is taking a string of text and predicting... |
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
| {"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... | jgerbscheid/lander-go-fast | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T16:36:19+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | robsoneng/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T17:00:00+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
null | null | ## Identificación de retinopatías
El Propósito del siguiente trabajo es identificar los pacientes que tienen complicaciones diabéticas, como lo son la neuropatía, nefropatía y retinopatía de notas médicas. Es el trabajo final del curso Clinical Natural Language Processing impartido en Coursera. Las notas medicas se en... | {} | LazaroAGM/Complicaciones_Diabetes | null | [
"region:us"
] | null | 2022-05-12T17:32:36+00:00 | [] | [] | TAGS
#region-us
| Identificación de retinopatías
------------------------------
El Propósito del siguiente trabajo es identificar los pacientes que tienen complicaciones diabéticas, como lo son la neuropatía, nefropatía y retinopatía de notas médicas. Es el trabajo final del curso Clinical Natural Language Processing impartido en Cour... | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers |
# RoBERTa for Single Language Classification
## Training
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
| data source | language |
|-----------------|----------------|
| open_subtitles | ka, he, en, de |
| oscar | be, kk, az, hu |
... | {"language": ["ru", "uk", "be", "kk", "az", "hy", "ka", "he", "en", "de", "multilingual"], "tags": ["language classification"], "datasets": ["open_subtitles", "tatoeba", "oscar"]} | nikitast/lang-classifier-roberta | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"language classification",
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"multilingual",
"dataset:open_subtitles",
"dataset:tatoeba",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible"... | null | 2022-05-12T18:10:25+00:00 | [] | [
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #language classification #ru #uk #be #kk #az #hy #ka #he #en #de #multilingual #dataset-open_subtitles #dataset-tatoeba #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa for Single Language Classification
==========================================
Training
--------
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
Validation
----------
The metrics obtained from validation on the another part of dataset (~1k samp... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #language classification #ru #uk #be #kk #az #hy #ka #he #en #de #multilingual #dataset-open_subtitles #dataset-tatoeba #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | This project is made in the Epitech Tek4 cursus for the MLops project
---
title: IOT
emoji: 🐢
colorFrom: pink
colorTo: pink
sdk: streamlit
sdk_version: 1.2.0
app_file: model.py
pinned: false
---
Check out the configuration reference at
https://huggingface.co/docs/hub/spaces#reference
| {} | David-Tedesco/MLops | null | [
"region:us"
] | null | 2022-05-12T18:25:54+00:00 | [] | [] | TAGS
#region-us
| This project is made in the Epitech Tek4 cursus for the MLops project
---
title: IOT
emoji:
colorFrom: pink
colorTo: pink
sdk: streamlit
sdk_version: 1.2.0
app_file: URL
pinned: false
---
Check out the configuration reference at
URL
| [] | [
"TAGS\n#region-us \n"
] |
fill-mask | 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. -->
# de-TAPT-MLM-MiniLM
This model is a fine-tuned version of [subhasisj/MiniLMv2-qa-encoder](https://huggingface.co/subhasisj/MiniLM... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "de-TAPT-MLM-MiniLM", "results": []}]} | subhasisj/de-TAPT-MLM-MiniLM | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T18:29:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# de-TAPT-MLM-MiniLM
This model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# de-TAPT-MLM-MiniLM\n\nThis model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# de-TAPT-MLM-MiniLM\n\nThis model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder on an unknown dataset.",
"## Model description\n\nMore information needed",... |
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. -->
# eduardopds/distilbert-base-uncased-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eduardopds/distilbert-base-uncased-imdb", "results": []}]} | eduardopds/distilbert-base-uncased-imdb | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T18:40:15+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| eduardopds/distilbert-base-uncased-imdb
=======================================
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: 0.0638
* Validation Loss: 0.2317
* Epoch: 2
Model description
----------------... | [
"### 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': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #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', 'learning\\_rate':... |
fill-mask | 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. -->
# es-TAPT-MLM-MiniLM
This model is a fine-tuned version of [subhasisj/MiniLMv2-qa-encoder](https://huggingface.co/subhasisj/MiniLM... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "es-TAPT-MLM-MiniLM", "results": []}]} | subhasisj/es-TAPT-MLM-MiniLM | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T18:46:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# es-TAPT-MLM-MiniLM
This model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# es-TAPT-MLM-MiniLM\n\nThis model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# es-TAPT-MLM-MiniLM\n\nThis model is a fine-tuned version of subhasisj/MiniLMv2-qa-encoder on an unknown dataset.",
"## Model description\n\nMore information needed",... |
text-classification | transformers |
# RoBERTa for Multilabel Language Classification
## Training
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
Implemented heuristic algorithm for multilingual training data creation - https://github.com/n1kstep/lang-classifier
| data source | langua... | {"language": ["ru", "uk", "be", "kk", "az", "hy", "ka", "he", "en", "de", "multilingual"], "tags": ["language classification"], "datasets": ["open_subtitles", "tatoeba", "oscar"]} | nikitast/multilang-classifier-roberta | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"language classification",
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"multilingual",
"dataset:open_subtitles",
"dataset:tatoeba",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible"... | null | 2022-05-12T18:55:55+00:00 | [] | [
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #language classification #ru #uk #be #kk #az #hy #ka #he #en #de #multilingual #dataset-open_subtitles #dataset-tatoeba #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa for Multilabel Language Classification
==============================================
Training
--------
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
Implemented heuristic algorithm for multilingual training data creation - URL
Validation
--... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #language classification #ru #uk #be #kk #az #hy #ka #he #en #de #multilingual #dataset-open_subtitles #dataset-tatoeba #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ## bert-ascii-base
A BERT base Language Model pre-trained by predicting the summation of the **ASCII** code values of the characters in a masked token as a pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective... | {"license": "cc-by-4.0", "tags": ["bert"]} | aajrami/bert-ascii-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"bert",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:06:43+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us
| ## bert-ascii-base
A BERT base Language Model pre-trained by predicting the summation of the ASCII code values of the characters in a masked token as a pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affe... | [
"## bert-ascii-base\nA BERT base Language Model pre-trained by predicting the summation of the ASCII code values of the characters in a masked token as a pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objectiv... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"## bert-ascii-base\nA BERT base Language Model pre-trained by predicting the summation of the ASCII code values of the characters in a masked token as a pre-training objective. For more detai... |
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. -->
# de-finetuned-squad-qa-minilmv2-16
This model is a fine-tuned version of [subhasisj/de-TAPT-MLM-MiniLM](https://huggingface.co/su... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "de-finetuned-squad-qa-minilmv2-16", "results": []}]} | subhasisj/de-finetuned-squad-qa-minilmv2-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:12:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| de-finetuned-squad-qa-minilmv2-16
=================================
This model is a fine-tuned version of subhasisj/de-TAPT-MLM-MiniLM on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5756
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 16\n* see... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ceggian/sbert_pt_reddit_softmax_64 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:13:34+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
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 you can u... | [
"# {MODEL_NAME}\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:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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 se... |
feature-extraction | transformers | ## bert-sr-base
A BERT base Language Model with a **shuffle + random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclant... | {"license": "cc-by-4.0", "tags": ["bert"]} | aajrami/bert-sr-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"bert",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:19:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us
| ## bert-sr-base
A BERT base Language Model with a shuffle + random pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?
## License
CC BY 4.... | [
"## bert-sr-base\nA BERT base Language Model with a shuffle + random pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?",
"## Licens... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"## bert-sr-base\nA BERT base Language Model with a shuffle + random pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refe... |
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-base-timit-demo-google-colab-ft30ep_v5
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-base-timit-demo-google-colab-ft30ep_v5", "results": []}]} | danieleV9H/hubert-base-timit-demo-google-colab-ft30ep_v5 | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:23:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| hubert-base-timit-demo-google-colab-ft30ep\_v5
==============================================
This model is a fine-tuned version of facebook/hubert-base-ls960 on the timit-asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4763
* Wer: 0.3322
Model description
-----------------
More ... | [
"### 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 #tensorboard #hubert #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... |
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. -->
# es-finetuned-squad-qa-minilmv2-16
This model is a fine-tuned version of [subhasisj/es-TAPT-MLM-MiniLM](https://huggingface.co/su... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "es-finetuned-squad-qa-minilmv2-16", "results": []}]} | subhasisj/es-finetuned-squad-qa-minilmv2-16 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:30:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| es-finetuned-squad-qa-minilmv2-16
=================================
This model is a fine-tuned version of subhasisj/es-TAPT-MLM-MiniLM on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2304
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 16\n* see... |
token-classification | transformers |
# RoBERTa for Multilabel Language Segmentation
## Training
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
Implemented heuristic algorithm for multilingual training data creation with generation of target masks- https://github.com/n1kstep/lang-classifier
| ... | {"language": ["ru", "uk", "be", "kk", "az", "hy", "ka", "he", "en", "de", "multilingual"], "tags": ["language classification", "text segmentation"], "datasets": ["open_subtitles", "tatoeba", "oscar"]} | nikitast/lang-segmentation-roberta | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"language classification",
"text segmentation",
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"multilingual",
"dataset:open_subtitles",
"dataset:tatoeba",
"dataset:oscar",
"autotrain_compatible",
... | null | 2022-05-12T19:32:02+00:00 | [] | [
"ru",
"uk",
"be",
"kk",
"az",
"hy",
"ka",
"he",
"en",
"de",
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #language classification #text segmentation #ru #uk #be #kk #az #hy #ka #he #en #de #multilingual #dataset-open_subtitles #dataset-tatoeba #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa for Multilabel Language Segmentation
============================================
Training
--------
RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
Implemented heuristic algorithm for multilingual training data creation with generation of target ... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #language classification #text segmentation #ru #uk #be #kk #az #hy #ka #he #en #de #multilingual #dataset-open_subtitles #dataset-tatoeba #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | ## bert-fc-base
A BERT base Language Model with a **first character** prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](http... | {"license": "cc-by-4.0", "tags": ["bert"]} | aajrami/bert-fc-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"bert",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:32:41+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us
| ## bert-fc-base
A BERT base Language Model with a first character prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?
## Licens... | [
"## bert-fc-base\nA BERT base Language Model with a first character prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?",
... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"## bert-fc-base\nA BERT base Language Model with a first character prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, p... |
feature-extraction | transformers | ## bert-mlm-base
A BERT base Language Model with an **MLM** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclanthology.org/... | {"license": "cc-by-4.0", "tags": ["bert"]} | aajrami/bert-mlm-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"bert",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T19:46:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us
| ## bert-mlm-base
A BERT base Language Model with an MLM pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?
## License
CC BY 4.0
If you u... | [
"## bert-mlm-base\nA BERT base Language Model with an MLM pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?",
"## License\nCC BY 4.... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"## bert-mlm-base\nA BERT base Language Model with an MLM pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How do... |
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
| {"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... | RaphaelReinauer/TEST-6-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T19:46:44+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# alk/t5-small-finetuned-cnn_dailymail-en-es
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "alk/t5-small-finetuned-cnn_dailymail-en-es", "results": []}]} | alk/t5-small-finetuned-cnn_dailymail-en-es | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T19:51:21+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| alk/t5-small-finetuned-cnn\_dailymail-en-es
===========================================
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: 1.9163
* Validation Loss: 1.7610
* Epoch: 3
Model description
-----------------
Mor... | [
"### 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': 5.6e-05, 'decay\\_steps': 71776, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle... | [
"TAGS\n#transformers #tf #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: {'name': 'AdamW... |
feature-extraction | transformers | ## bert-rand-base
A BERT base Language Model with a **random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclanthology.o... | {"license": "cc-by-4.0", "tags": ["bert"]} | aajrami/bert-rand-base | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"bert",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T20:10:17+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us
| ## bert-rand-base
A BERT base Language Model with a random pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?
## License
CC BY 4.0
If yo... | [
"## bert-rand-base\nA BERT base Language Model with a random pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How does the pre-training objective affect what large language models learn about linguistic properties?",
"## License\nCC BY... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #bert #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"## bert-rand-base\nA BERT base Language Model with a random pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to How... |
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
| {"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... | kRo0T/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T20:16:27+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | eijnuhs/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T20:33:28+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | pedrobaiainin/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T20:59:30+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
image-to-image | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras", "pipeline_tag": "image-to-image"} | Jorgvt/CycleGAN_GTA_REAL | null | [
"keras",
"image-to-image",
"has_space",
"region:us"
] | null | 2022-05-12T22:26:10+00:00 | [] | [] | TAGS
#keras #image-to-image #has_space #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #image-to-image #has_space #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summ... |
feature-extraction | transformers | # Fixed-roberta-base
[roberta-base](https://huggingface.co/roberta-base) but with a resized embedding matrix and an extra dim in the token type embedding matrix for better sharding/partitioning. | {} | hamishivi/fixed-roberta-base | null | [
"transformers",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T22:57:50+00:00 | [] | [] | TAGS
#transformers #jax #roberta #feature-extraction #endpoints_compatible #region-us
| # Fixed-roberta-base
roberta-base but with a resized embedding matrix and an extra dim in the token type embedding matrix for better sharding/partitioning. | [
"# Fixed-roberta-base\n\nroberta-base but with a resized embedding matrix and an extra dim in the token type embedding matrix for better sharding/partitioning."
] | [
"TAGS\n#transformers #jax #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# Fixed-roberta-base\n\nroberta-base but with a resized embedding matrix and an extra dim in the token type embedding matrix for better sharding/partitioning."
] |
null | null | license:apache-2.0 | {} | quantity/super-cool-model | null | [
"region:us"
] | null | 2022-05-12T23:12:40+00:00 | [] | [] | TAGS
#region-us
| license:apache-2.0 | [] | [
"TAGS\n#region-us \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. -->
# distilbert-base-uncased-finetuned-jumbling-squad-15
This model is a fine-tuned version of [distilbert-base-uncased](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-jumbling-squad-15", "results": []}]} | huxxx657/distilbert-base-uncased-finetuned-jumbling-squad-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T23:19:11+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-jumbling-squad-15
===================================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3345
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-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 #question-answering #generated_from_trainer #dataset-squad #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: 7e-05\n* train\\_batch\\_s... |
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_zu_ukuxhumana_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/o... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "en_zu_ukuxhumana_model", "results": []}]} | kabelomalapane/en_zu_ukuxhumana_model | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T23:21:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# en_zu_ukuxhumana_model
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0772
- Bleu: 7.6322
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training ... | [
"# en_zu_ukuxhumana_model\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.0772\n- Bleu: 7.6322",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# en_zu_ukuxhumana_model\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-mul on the None dataset.\nIt achi... |
null | k2 | # SPGISpeech
SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective
transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in
length to allow easy training for speech recognition systems. Calls represent a broad
cross-section of international busin... | {"language": ["en"], "license": "mit", "tags": ["k2", "icefall"], "datasets": ["SPGISpeech"]} | desh2608/icefall-asr-spgispeech-pruned-transducer-stateless2 | null | [
"k2",
"tensorboard",
"icefall",
"en",
"dataset:SPGISpeech",
"arxiv:2104.02014",
"license:mit",
"region:us"
] | null | 2022-05-13T00:09:34+00:00 | [
"2104.02014"
] | [
"en"
] | TAGS
#k2 #tensorboard #icefall #en #dataset-SPGISpeech #arxiv-2104.02014 #license-mit #region-us
| SPGISpeech
==========
SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective
transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in
length to allow easy training for speech recognition systems. Calls represent a broad
cross-section of internationa... | [] | [
"TAGS\n#k2 #tensorboard #icefall #en #dataset-SPGISpeech #arxiv-2104.02014 #license-mit #region-us \n"
] |
audio-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. -->
# wav2vec2-base-finetuned-manthan_base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["new_dataset"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-manthan_base", "results": []}]} | manthan40/wav2vec2-base-finetuned-manthan_base | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:new_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T00:24:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-new_dataset #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-manthan\_base
=====================================
This model is a fine-tuned version of facebook/wav2vec2-base on the new\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2246
* Accuracy: 0.9691
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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 #wav2vec2 #audio-classification #generated_from_trainer #dataset-new_dataset #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: 3e-05\n* train\\_bat... |
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. -->
# eduardopds/distilbert-base-uncased-tweets
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/disti... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eduardopds/distilbert-base-uncased-tweets", "results": []}]} | eduardopds/distilbert-base-uncased-tweets | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T00:38:51+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| eduardopds/distilbert-base-uncased-tweets
=========================================
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: 0.7428
* Validation Loss: 0.9322
* Epoch: 9
Model description
------------... | [
"### 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': 310, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name... | [
"TAGS\n#transformers #tf #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', 'learning\\_rate':... |
audio-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. -->
# wav2vec2-base-finetuned-manthan-gujarati-digits
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["new_dataset"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-manthan-gujarati-digits", "results": []}]} | manthan40/wav2vec2-base-finetuned-manthan-gujarati-digits | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:new_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T00:47:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-new_dataset #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-manthan-gujarati-digits
===============================================
This model is a fine-tuned version of facebook/wav2vec2-base on the new\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5613
* Accuracy: 0.9923
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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 #wav2vec2 #audio-classification #generated_from_trainer #dataset-new_dataset #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: 3e-05\n* train\\_bat... |
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-large-data-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-data-seed-0", "results": []}]} | anas-awadalla/roberta-large-data-seed-0 | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T00:47:50+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# roberta-large-data-seed-0
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# roberta-large-data-seed-0\n\nThis model is a fine-tuned version of roberta-large 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",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# roberta-large-data-seed-0\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"#... |
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
| {"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... | Sidahmed/RLcourse | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T00:54:54+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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).
Used default settings but for 1511424 timesteps | {"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... | Ambiwlans/Default_ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T01:09:15+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.
Used default settings but for 1511424 timesteps | [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n Used default settings but for 1511424 timesteps"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n Used default settings but for 1511424 timeste... |
image-classification | timm |
# my-cool-model-with-card
## Model description
This isn't really a model, it's just a test repo to see if the [modelcards](https://github.com/nateraw/modelcards) package works!
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and ... | {"language": "en", "license": "mit", "library_name": "timm", "tags": ["image-classification", "resnet"], "datasets": "beans", "metrics": ["acc", "f1"]} | nateraw/my-cool-model-with-card | null | [
"timm",
"image-classification",
"resnet",
"en",
"dataset:beans",
"license:mit",
"region:us"
] | null | 2022-05-13T01:13:22+00:00 | [] | [
"en"
] | TAGS
#timm #image-classification #resnet #en #dataset-beans #license-mit #region-us
|
# my-cool-model-with-card
## Model description
This isn't really a model, it's just a test repo to see if the modelcards package works!
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you... | [
"# my-cool-model-with-card",
"## Model description\n\nThis isn't really a model, it's just a test repo to see if the modelcards package works!",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training d... | [
"TAGS\n#timm #image-classification #resnet #en #dataset-beans #license-mit #region-us \n",
"# my-cool-model-with-card",
"## Model description\n\nThis isn't really a model, it's just a test repo to see if the modelcards package works!",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations... |
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-small-finetuned-spider
This model was trained from scratch on an unknown dataset.
It achieves the following results on the ev... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-spider", "results": []}]} | tomhavy/t5-small-finetuned-spider | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-13T01:16:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-spider
=========================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1914
* Rouge2 Precision: 0.6349
* Rouge2 Recall: 0.3964
* Rouge2 Fmeasure: 0.4619
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 5\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: 15",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 5e-05\n* train\\_b... |
fill-mask | transformers | ## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain
For Chinese natural language processing in specific domains, we provide **Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model)** for the medical domain named **pai-dkplm-bert-zh**, from our AAAI 20... | {"language": "zh", "license": "apache-2.0", "tags": ["bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "\u611f\u5192\u9700\u8981\u5403[MASK]"}, {"text": "\u4eba\u7c7b\u7684[MASK]\u6e29\u662f37\u5ea6"}]} | alibaba-pai/pai-dkplm-medical-base-zh | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2205.00258",
"arxiv:2112.01047",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T01:38:37+00:00 | [
"2205.00258",
"2112.01047"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #arxiv-2205.00258 #arxiv-2112.01047 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain
For Chinese natural language processing in specific domains, we provide Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain named pai-dkplm-bert-zh, from our AAAI 2021 paper... | [
"## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain\nFor Chinese natural language processing in specific domains, we provide Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain named pai-dkplm-bert-zh, from our AAAI 2021... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #arxiv-2205.00258 #arxiv-2112.01047 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain\nFor Chinese natural language processing in s... |
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. -->
# filipino-wav2vec2-l-xls-r-300m-official
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["filipino_voice"], "model-index": [{"name": "filipino-wav2vec2-l-xls-r-300m-official", "results": []}]} | Khalsuu/filipino-wav2vec2-l-xls-r-300m-official | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:filipino_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T02:24:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-filipino_voice #license-apache-2.0 #endpoints_compatible #region-us
| filipino-wav2vec2-l-xls-r-300m-official
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the filipino\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4672
* Wer: 0.2922
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-filipino_voice #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*... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | Nurr/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T02:48:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hype... | [
"# wav2vec2-base-finetuned-ks\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pr... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-finetuned-ks\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.",
"## Model descriptio... |
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. -->
# language-detection-Bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "language-detection-Bert-base-uncased", "results": []}]} | jkhan447/language-detection-Bert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T03:02:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# language-detection-Bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2231
- Accuracy: 0.9512
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
"# language-detection-Bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2231\n- Accuracy: 0.9512",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore infor... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# language-detection-Bert-base-uncased\n\nThis model is a fine-tuned version of bert-base-uncased on the None dataset.\nIt achieves the fol... |
text-classification | transformers |
# Model description
A BertForSequenceClassification model that is finetuned on Wikipedia for zero-shot text classification. For details, see our NAACL'22 paper.
# Usage
Concatenate the text sentence with each of the candidate labels as input to the model. The model will output a score for each label. Below is an ... | {"license": "apache-2.0"} | CogComp/ZeroShotWiki | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T03:04:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model description
A BertForSequenceClassification model that is finetuned on Wikipedia for zero-shot text classification. For details, see our NAACL'22 paper.
# Usage
Concatenate the text sentence with each of the candidate labels as input to the model. The model will output a score for each label. Below is an ... | [
"# Model description\n\nA BertForSequenceClassification model that is finetuned on Wikipedia for zero-shot text classification. For details, see our NAACL'22 paper.",
"# Usage\n\nConcatenate the text sentence with each of the candidate labels as input to the model. The model will output a score for each label. Be... | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\n\nA BertForSequenceClassification model that is finetuned on Wikipedia for zero-shot text classification. For details, see our NAACL'22 paper.",
"# Usage... |
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-large-data-seed-2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-data-seed-2", "results": []}]} | anas-awadalla/roberta-large-data-seed-2 | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T03:10:19+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# roberta-large-data-seed-2
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# roberta-large-data-seed-2\n\nThis model is a fine-tuned version of roberta-large 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",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# roberta-large-data-seed-2\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"#... |
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-large-data-seed-4
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-data-seed-4", "results": []}]} | anas-awadalla/roberta-large-data-seed-4 | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T03:13:10+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# roberta-large-data-seed-4
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters... | [
"# roberta-large-data-seed-4\n\nThis model is a fine-tuned version of roberta-large 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",
"## Training procedure",
... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# roberta-large-data-seed-4\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information needed",
"#... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ceggian/sbert_pt_reddit_softmax_128 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T03:35:58+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
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 you can u... | [
"# {MODEL_NAME}\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:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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 se... |
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | jasonyim2/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T03:58:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1440
* F1: 0.8632
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #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\\_... |
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
| {"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... | whimsical/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T03:59:39+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text2text-generation | transformers |
# Maeve - SAMSum
Maeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer).
This allows the model to learn to create long-form text from short entries with high degrees of control and coherence that are impossible to achieve with traditio... | {"language": ["en"], "license": "gpl-3.0", "tags": ["text2text-generation", "pytorch"], "datasets": ["samsum"], "widget": [{"text": "Ruben has forgotten what the homework was. Alex tells him to ask the teacher.", "example_title": "I forgot my homework"}, {"text": "Mac is lost at the zoo. Frank says he is at the gorilla... | aiko/maeve-12-6-samsum | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T04:42:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-samsum #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Maeve - SAMSum
Maeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer).
This allows the model to learn to create long-form text from short entries with high degrees of control and coherence that are impossible to achieve with traditio... | [
"# Maeve - SAMSum\n\nMaeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer).\n\nThis allows the model to learn to create long-form text from short entries with high degrees of control and coherence that are impossible to achieve with ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-samsum #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Maeve - SAMSum\n\nMaeve is a language model that is similar to BART in structure but trained specially using a CAT (Conditionally Adversarial Transformer).\... |
question-answering | transformers |
## モデル詳細
- [cl-tohoku/bert-base-japanese](https://huggingface.co/cl-tohoku/bert-base-japanese) を JaQuAD で fine-tuning した [SkelterLabsInc/bert-base-japanese-jaquad](https://huggingface.co/SkelterLabsInc/bert-base-japanese-jaquad) に対して [TextPruner](https://github.com/airaria/TextPruner) を使って
Transformer Pruning したモデル。 ... | {"widget": [{"text": "\u30c9\u30af\u30a6\u30c4\u30dc\u306f\u30a4\u30f3\u30c9\u6d0b\u3068\u3069\u306e\u6d77\u57df\u306e\u71b1\u5e2f\u57df\u306b\u5206\u5e03\u3057\u307e\u3059\u304b?", "context": "\u30c9\u30af\u30a6\u30c4\u30dc(\u6bd2\u9c53)Gymnothoraxjavanicus(Bleeker,1859)\u306f\u4f53\u95773\u30e1\u30fc\u30c8\u30eb\u306... | misawann/bert-base-jaquad-ffn2150-head-10 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T04:50:42+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
|
## モデル詳細
- cl-tohoku/bert-base-japanese を JaQuAD で fine-tuning した SkelterLabsInc/bert-base-japanese-jaquad に対して TextPruner を使って
Transformer Pruning したモデル。
- 枝刈りには，JaQuAD の訓練データのうち1024件を使用し，10イテレーションで実施。
- FFNのサイズを30%，attention head の数を 10 % 削減 (ffn: 3072, head: 12 -> ffn: 2150, head: 10)。
- ※ JaQuAD の実験コードと同じ前処理... | [
"## モデル詳細\n- cl-tohoku/bert-base-japanese を JaQuAD で fine-tuning した SkelterLabsInc/bert-base-japanese-jaquad に対して TextPruner を使って\nTransformer Pruning したモデル。 \n- 枝刈りには，JaQuAD の訓練データのうち1024件を使用し，10イテレーションで実施。 \n- FFNのサイズを30%，attention head の数を 10 % 削減 (ffn: 3072, head: 12 -> ffn: 2150, head: 10)。 \n- ※ JaQuAD の実験... | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n",
"## モデル詳細\n- cl-tohoku/bert-base-japanese を JaQuAD で fine-tuning した SkelterLabsInc/bert-base-japanese-jaquad に対して TextPruner を使って\nTransformer Pruning したモデル。 \n- 枝刈りには，JaQuAD の訓練データのうち1024件を使用し，10イテレーションで実施。 \n- FFNのサイ... |
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. -->
# bart-paraphrase-finetuned-xsum
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenes... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-paraphrase-finetuned-xsum", "results": []}]} | yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T05:12:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-finetuned-xsum
==============================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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\\_batch\... |
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. -->
# language-detection-RoBert-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "language-detection-RoBert-base", "results": []}]} | jkhan447/language-detection-RoBert-base | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T05:37:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# language-detection-RoBert-base
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1398
- Accuracy: 0.9865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and... | [
"# language-detection-RoBert-base\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1398\n- Accuracy: 0.9865",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information need... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# language-detection-RoBert-base\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results ... |
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.
## Model descript... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | shenyi/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-13T06:00:51+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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyper... | [
"# gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperp... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-wikitext2\n\nThis model is a fine-tuned version of gpt2 on the None dataset.",
"## Model description\n\nMore infor... |
fill-mask | 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-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | shenyi/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T06:22:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 7.0721
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: 48\n* eval\\_batch\\_size: 48\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",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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\\_batch\\_size: 48\... |
null | null | ## Test Project
---
license: mit
---
| {} | anorprogrammer/Test | null | [
"region:us"
] | null | 2022-05-13T06:52:05+00:00 | [] | [] | TAGS
#region-us
| ## Test Project
---
license: mit
---
| [
"## Test Project\n\n---\nlicense: mit\n---"
] | [
"TAGS\n#region-us \n",
"## Test Project\n\n---\nlicense: mit\n---"
] |
question-answering | 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. -->
# chanifrusydi/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "chanifrusydi/bert-finetuned-squad", "results": []}]} | chanifrusydi/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T07:05:44+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| chanifrusydi/bert-finetuned-squad
=================================
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: 5.4528
* Epoch: 0
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': 0.0002, 'decay\\_steps': 11091, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/330
It has random combiner inside.
| {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-2022-05-13 | null | [
"tensorboard",
"has_space",
"region:us"
] | null | 2022-05-13T08:10:54+00:00 | [] | [] | TAGS
#tensorboard #has_space #region-us
| # Introduction
See URL
It has random combiner inside.
| [
"# Introduction\n\nSee URL\n\nIt has random combiner inside."
] | [
"TAGS\n#tensorboard #has_space #region-us \n",
"# Introduction\n\nSee URL\n\nIt has random combiner inside."
] |
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": ... | beltran/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T08:41:36+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: 0.3185
- Accuracy: 0.8567
- F1: 0.8571
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# 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: 0.3185\n- Accuracy: 0.8567\n- F1: 0.8571",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"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... |
null | null | # Introduction
See https://github.com/k2-fsa/icefall/pull/330
It has random combiner inside.
| {} | csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-05-13 | null | [
"tensorboard",
"region:us"
] | null | 2022-05-13T08:44:03+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Introduction
See URL
It has random combiner inside.
| [
"# Introduction\n\nSee URL\n\nIt has random combiner inside."
] | [
"TAGS\n#tensorboard #region-us \n",
"# Introduction\n\nSee URL\n\nIt has random combiner inside."
] |
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
| {"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... | GhadeerElmkaiel/LunarLander-v2-Test | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T09:02:34+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
image-classification | transformers |
# amgerindaf
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/huggingpi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | gaganpathre/amgerindaf | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T09:06:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# amgerindaf
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
#### african
!african
#### american
!american
#### german
!german
#### indian
!indian | [
"# amgerindaf\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",
"#### african\n\n!african",
"#### american\n\n!american",
"#### german\n\n!german",
"#### ind... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# amgerindaf\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues wi... |
text-classification | transformers |
# German BERT large fine-tuned to predict educational requirements
This is a fine-tuned version of the German BERT large language model [deepset/gbert-large](https://huggingface.co/deepset/gbert-large). The multilabel task this model was trained on was to predict education requirements from job ad texts. The dataset ... | {"language": "de", "license": "mit"} | gonzpen/gbert-large-ft-edu-redux | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T09:44:39+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #bert #text-classification #de #license-mit #autotrain_compatible #endpoints_compatible #region-us
| German BERT large fine-tuned to predict educational requirements
================================================================
This is a fine-tuned version of the German BERT large language model deepset/gbert-large. The multilabel task this model was trained on was to predict education requirements from job ad te... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #de #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# madatnlp/sk-kogptv2-kormath-causal
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/sk-kogptv2-kormath-causal", "results": []}]} | madatnlp/sk-kogptv2-kormath-causal | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-13T10:28:16+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| madatnlp/sk-kogptv2-kormath-causal
==================================
This model is a fine-tuned version of skt/kogpt2-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3184
* Validation Loss: 1.4046
* Epoch: 15
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 2.2999999e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### F... | [
"TAGS\n#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #license-cc-by-nc-sa-4.0 #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': 'Ada... |
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. -->
# vi-finetuned-squad-qa-minilmv2-8
This model is a fine-tuned version of [subhasisj/vi-TAPT-MLM-MiniLM](https://huggingface.co/sub... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "vi-finetuned-squad-qa-minilmv2-8", "results": []}]} | subhasisj/vi-finetuned-squad-qa-minilmv2-8 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T10:30:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
| vi-finetuned-squad-qa-minilmv2-8
================================
This model is a fine-tuned version of subhasisj/vi-TAPT-MLM-MiniLM on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3335
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### 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* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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\\_size: 8\n* seed:... |
summarization | 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. -->
# mt5-base-finetuned-english-finetuned-english-arabic
This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-english](h... | {"license": "apache-2.0", "tags": ["summarization", "arabic", "ar", "en", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mt5-base-finetuned-english-finetuned-english-arabic", "results": []}]} | eslamxm/mt5-base-finetuned-english-finetuned-english-arabic | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"arabic",
"ar",
"en",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"re... | null | 2022-05-13T10:40:25+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #arabic #ar #en #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| mt5-base-finetuned-english-finetuned-english-arabic
===================================================
This model is a fine-tuned version of eslamxm/mt5-base-finetuned-english on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4788
* Rouge-1: 22.55
* Rouge-2: 9.84
* Rouge-l: 2... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #arabic #ar #en #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe fol... |
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-xlsr-53_full_train_full_train
This model was trained from scratch on the None dataset.
It achieves the following ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_full_train_full_train", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_full_train_full_train | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T10:57:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_full\_train\_full\_train
================================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8369
* Wer: 0.5052
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 16\n* eval\\_batch\\_... |
token-classification | transformers | Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 7.961395091713594e-05
train_batch_size: 32
eval_batch_size: 32
seed: 27
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
| {} | Xiaoman/NER-CoNLL2003-V2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T11:14:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 7.961395091713594e-05
train_batch_size: 32
eval_batch_size: 32
seed: 27
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | michojan/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T11:43:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0622
* Precision: 0.9324
* Recall: 0.9495
* F1: 0.9409
* Accuracy: 0.9864
Model description
-----------------
More information ... | [
"### 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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* learning... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | DBusAI/PPO-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T11:53:48+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
multiple-choice | 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. -->
# bertin-roberta-base-spanish-finetuned-recores
This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish]... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bertin-roberta-base-spanish-finetuned-recores", "results": []}]} | versae/bertin-roberta-base-spanish-finetuned-recores | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"multiple-choice",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T12:01:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us
| bertin-roberta-base-spanish-finetuned-recores
=============================================
This model is a fine-tuned version of bertin-project/bertin-roberta-base-spanish on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.2985
* Accuracy: 0.3581
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch... |
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
| {"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... | tobyych/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T12:35:32+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | DBusAI/PPO-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T12:36:41+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
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. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-cased-finetuned-squad", "results": []}]} | SreyanG-NVIDIA/bert-base-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T12:39:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-cased-finetuned-squad
===============================
This model is a fine-tuned version of bert-base-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0848
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_size: 1... |
text-generation | transformers | # Fairseq-dense 2.7B - Nerys
## Model Description
Fairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most ... | {"language": "en", "license": "mit"} | KoboldAI/fairseq-dense-2.7B-Nerys | null | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-13T12:40:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Fairseq-dense 2.7B - Nerys
## Model Description
Fairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most ... | [
"# Fairseq-dense 2.7B - Nerys",
"## Model Description\nFairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.",
"## Training data\nThe training data contains around 2500 ebooks in various genres (the \"Pike\" dataset), a CYOA dataset called \"CYS\" and 50 Asian \"Light Novels\" (the \"M... | [
"TAGS\n#transformers #pytorch #xglm #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Fairseq-dense 2.7B - Nerys",
"## Model Description\nFairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.",
"## Training data\nThe training ... |
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. -->
# vit-base-patch16-224-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/go... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["cifar10"], "model-index": [{"name": "vit-base-patch16-224-cifar10", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cifar10", "type": "cifar10", "config": "plai... | karthiksv/vit-base-patch16-224-cifar10 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T12:41:59+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vit-base-patch16-224-cifar10
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# vit-base-patch16-224-cifar10\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 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 #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit-base-patch16-224-cifar10\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 d... |
fill-mask | 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. -->
# closure_system_door_inne-roberta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "closure_system_door_inne-roberta-base", "results": []}]} | Davincilee/closure_system_door_inne-roberta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T12:57:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| closure\_system\_door\_inne-roberta-base
========================================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6038
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #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\\_batch\\_size: 6\n* ev... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | DBusAI/PPO-BipedalWalker-v3-v1 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T13:32:01+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
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-large-initialization-seed-0
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-large-initialization-seed-0", "results": []}]} | anas-awadalla/roberta-large-initialization-seed-0 | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T13:36:47+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# roberta-large-initialization-seed-0
This model is a fine-tuned version of roberta-large on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# roberta-large-initialization-seed-0\n\nThis model is a fine-tuned version of roberta-large 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",
"## Training pro... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# roberta-large-initialization-seed-0\n\nThis model is a fine-tuned version of roberta-large on the squad dataset.",
"## Model description\n\nMore information nee... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | vukpetar/ppo-CarRacing-v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T13:53:49+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
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 | {"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... | N18/lunar-lander | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T14:10:49+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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
| {"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... | aleks0309/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T14:38:50+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | Rietta/CycleGAN_WoW | null | [
"keras",
"region:us"
] | null | 2022-05-13T14:57:23+00:00 | [] | [] | TAGS
#keras #region-us
|
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
!Model Image
</details> | [
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summary>\n\n!Model Image\n\n</details>... | [
"TAGS\n#keras #region-us \n",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed\n\n ## Training Metrics\nModel history needed\n ## Model Plot\n\n<details>\n<summary>View Model Plot</summar... |
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. -->
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://h... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["cifar10"], "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-cifar10", "results": []}]} | karthiksv/vit-base-patch16-224-in21k-finetuned-cifar10 | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-13T15:21:13+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Trainin... | [
"# vit-base-patch16-224-in21k-finetuned-cifar10\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informat... | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit-base-patch16-224-in21k-finetuned-cifar10\n\nThis model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar1... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | DBusAI/PPO-BipedalWalker-v3-v2 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-13T15:40:07+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\n This is a trained model of a PPO agent playing BipedalWalker-v3 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TO... |
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