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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... | antonionieto/ppo-LunarLander-v1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T12:47:11+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. -->
# F_Roberta_classifier2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "F_Roberta_classifier2", "results": []}]} | James-kc-min/F_Roberta_classifier2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T12:59:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| F\_Roberta\_classifier2
=======================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1317
* Accuracy: 0.9751
* F1: 0.9751
* Precision: 0.9751
* Recall: 0.9751
* C Report: precision recall f1-score support
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batc... |
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-ft35ep
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-base-timit-demo-google-colab-ft30ep_v4", "results": []}]} | danieleV9H/hubert-base-timit-demo-google-colab-ft30ep_v4 | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T13:05:20+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-ft35ep
==========================================
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: 0.4602
* Wer: 0.3466
Model description
-----------------
More informat... | [
"### 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... |
null | null | this is a test that will be deleted | {} | gtsherman/test_model | null | [
"region:us"
] | null | 2022-05-11T13:14:19+00:00 | [] | [] | TAGS
#region-us
| this is a test that will be deleted | [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1** 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": ["FrozenLake-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1", "type": "FrozenLa... | DBusAI/ppo-FrozenLake-v1 | null | [
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T13:19:20+00:00 | [] | [] | TAGS
#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing FrozenLake-v1
This is a trained model of a PPO agent playing FrozenLake-v1 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing FrozenLake-v1\n This is a trained model of a PPO agent playing FrozenLake-v1 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #FrozenLake-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing FrozenLake-v1\n This is a trained model of a PPO agent playing FrozenLake-v1 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add y... |
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. -->
# distilbart-cnn-arxiv-pubmed-pubmed-earlystopping
This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-arxiv-pubmed-pubmed-earlystopping", "results": []}]} | theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed-earlystopping | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T13:38:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbart-cnn-arxiv-pubmed-pubmed-earlystopping
================================================
This model is a fine-tuned version of theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8596
* Rouge1: 53.4491
* Rouge2: 35.0041
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_preci... | [
"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\... |
image-classification | transformers |
# MobileViT (small-sized model)
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](ht... | {"license": "other", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_titl... | Matthijs/mobilevit-small | null | [
"transformers",
"pytorch",
"coreml",
"mobilevit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2110.02178",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T13:39:15+00:00 | [
"2110.02178"
] | [] | TAGS
#transformers #pytorch #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #region-us
| MobileViT (small-sized model)
=============================
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first released in this repository. The license... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe MobileViT model was pretrained on ImageNet-1k, a dataset consistin... | [
"TAGS\n#transformers #pytorch #coreml #mobilevit #image-classification #vision #dataset-imagenet-1k #arxiv-2110.02178 #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 Im... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1424546909104926720/g4pT... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/alice_lbl-lotrbookquotes/1652280261416/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/alice_lbl-lotrbookquotes | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-11T13:43:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Alice in Wonderland & Looking-Glass (line by line) & Lord of the Rings quotes
@alice\_lbl-lotrbookquotes
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To unde... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | bansals10/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T13:43:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-turkish-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_... |
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. -->
# vi-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": "vi-TAPT-MLM-MiniLM", "results": []}]} | subhasisj/vi-TAPT-MLM-MiniLM | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T13:57:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# vi-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... | [
"# vi-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",
"# vi-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",... |
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... | jabot/PPPO_LunarLanderV2_2000000Steps_schLR_schCR | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T14:36:08+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... | format37/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T14:56:24+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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eduardopds/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eduardopds/bert-finetuned-ner", "results": []}]} | eduardopds/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T15:03:33+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| eduardopds/bert-finetuned-ner
=============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0269
* Validation Loss: 0.0545
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers | precision recall f1-score support
negative 0.681957 0.675758 0.678843 660
neutral 0.707845 0.735019 0.721176 1068
positive 0.596591 0.652174 0.623145 483
skip 0.583062 0.485095 0.529586 369
speech 0.827160 0.676768 0.744444 ... | {} | sismetanin/rubert-rusentitweet | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T15:07:24+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| precision recall f1-score support
negative 0.681957 0.675758 0.678843 660
neutral 0.707845 0.735019 0.721176 1068
positive 0.596591 0.652174 0.623145 483
skip 0.583062 0.485095 0.529586 369
speech 0.827160 0.676768 0.744444 ... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #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... | ironbar/the-eagle-has-landed | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T15:20:35+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... | vukpetar/ppo-LunarLander-v3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T15:45:27+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... |
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_512 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T15:45:45+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... |
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... | kaeldric/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T15:48:25+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... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.3,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Auth... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | Vnven25/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-05-11T16:14:48+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
##NE
View label scheme (6 labels for 1 components)
### Accuracy
| [
"### Label Scheme",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme",
"### Accuracy"
] |
fill-mask | transformers |
# CXR-BERT-specialized
[CXR-BERT](https://arxiv.org/abs/2204.09817) is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization, and text augmentations. The resulting model demonstrates improved performance on radiology natural lan... | {"language": "en", "license": "mit", "tags": ["exbert"], "pipeline_tag": "fill-mask", "widget": [{"text": "Left pleural effusion with adjacent [MASK].", "example_title": "Radiology 1"}, {"text": "Heart size normal and lungs are [MASK].", "example_title": "Radiology 2"}], "inference": false} | microsoft/BiomedVLP-CXR-BERT-specialized | null | [
"transformers",
"pytorch",
"cxr-bert",
"feature-extraction",
"exbert",
"fill-mask",
"custom_code",
"en",
"arxiv:2204.09817",
"arxiv:2103.00020",
"arxiv:2002.05709",
"license:mit",
"region:us"
] | null | 2022-05-11T16:20:52+00:00 | [
"2204.09817",
"2103.00020",
"2002.05709"
] | [
"en"
] | TAGS
#transformers #pytorch #cxr-bert #feature-extraction #exbert #fill-mask #custom_code #en #arxiv-2204.09817 #arxiv-2103.00020 #arxiv-2002.05709 #license-mit #region-us
| CXR-BERT-specialized
====================
CXR-BERT is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization, and text augmentations. The resulting model demonstrates improved performance on radiology natural language inference, ... | [
"### Intended Use\n\n\nThis model is intended to be used solely for (I) future research on visual-language processing and (II) reproducibility of the experimental results reported in the reference paper.",
"#### Primary Intended Use\n\n\nThe primary intended use is to support AI researchers building on top of thi... | [
"TAGS\n#transformers #pytorch #cxr-bert #feature-extraction #exbert #fill-mask #custom_code #en #arxiv-2204.09817 #arxiv-2103.00020 #arxiv-2002.05709 #license-mit #region-us \n",
"### Intended Use\n\n\nThis model is intended to be used solely for (I) future research on visual-language processing and (II) reproduc... |
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. -->
# single_label_N_max_long_training
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "single_label_N_max_long_training", "results": []}]} | mcurmei/single_label_N_max_long_training | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T16:22:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| single\_label\_N\_max\_long\_training
=====================================
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: 2.8288
Model description
-----------------
More information needed
Intended uses & li... | [
"### 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 #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: 2e-05\n* train\\_batch\\_s... |
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_mnr_256 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T16:53:31+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. -->
# NER-for-female-names
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "NER-for-female-names", "results": []}]} | Xiaoman/NER-for-female-names | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T17:10:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| NER-for-female-names
====================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2606
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.961395091713594e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.961395091713594e-05\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eduardopds/distilbert-base-uncase-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eduardopds/distilbert-base-uncase-finetuned-imdb", "results": []}]} | eduardopds/distilbert-base-uncase-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T17:19:41+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| eduardopds/distilbert-base-uncase-finetuned-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: 2.8482
* Validation Loss: 2.5792
* Epoch: 0
Model descriptio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** 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": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | DBusAI/DQN-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T17:21:27+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\n This is a trained model of a DQN 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",
"# DQN Agent playing MountainCar-v0\n This is a trained model of a DQN agent playing MountainCar-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
token-classification | transformers |
# Model Card for sbb_ner
<!-- Provide a quick summary of what the model is/does. [Optional] -->
A BERT model trained on three German corpora containing contemporary and historical texts for named entity recognition tasks. It predicts the classes `PER`, `LOC` and `ORG`.
The model was developed by the Berlin Stat... | {"language": "de", "license": "apache-2.0", "tags": ["pytorch", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003", "germeval_14"]} | SBB/sbb_ner | null | [
"transformers",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"dataset:germeval_14",
"arxiv:1910.09700",
"doi:10.57967/hf/0403",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T17:25:53+00:00 | [
"1910.09700"
] | [
"de"
] | TAGS
#transformers #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #dataset-germeval_14 #arxiv-1910.09700 #doi-10.57967/hf/0403 #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for sbb_ner
A BERT model trained on three German corpora containing contemporary and historical texts for named entity recognition tasks. It predicts the classes 'PER', 'LOC' and 'ORG'.
The model was developed by the Berlin State Library (SBB) in the QURATOR project.
# Table of Contents
- Mo... | [
"# Model Card for sbb_ner\n\n\nA BERT model trained on three German corpora containing contemporary and historical texts for named entity recognition tasks. It predicts the classes 'PER', 'LOC' and 'ORG'. \nThe model was developed by the Berlin State Library (SBB) in the QURATOR project.",
"# Table of Contents\n... | [
"TAGS\n#transformers #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #dataset-germeval_14 #arxiv-1910.09700 #doi-10.57967/hf/0403 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for sbb_ner\n\n\nA BERT model trained on three German corpora containing contempora... |
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... | gleberof/ll_rl | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T17:36:34+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... |
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/mt5-small-mt5-small-finetuned-billsum-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "alk/mt5-small-mt5-small-finetuned-billsum-en-es", "results": []}]} | alk/mt5-small-mt5-small-finetuned-billsum-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-11T17:40:38+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| alk/mt5-small-mt5-small-finetuned-billsum-en-es
===============================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1897
* Validation Loss: 1.0147
* Epoch: 7
Model description
-------... | [
"### 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': 18944, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle... | [
"TAGS\n#transformers #tf #mt5 #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': 'Adam... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081
## Validation Metrics
- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.973518793409936... | {"language": "unk", "tags": "autotrain", "datasets": ["Yarn/autotrain-data-Traimn"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.712176860015081} | Yarn/autotrain-Traimn-853827191 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Yarn/autotrain-data-Traimn",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T17:46:11+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn/autotrain-data-Traimn #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081
## Validation Metrics
- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.973518793409936... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 853827191\n- CO2 Emissions (in grams): 1.712176860015081",
"## Validation Metrics\n\n- Loss: 0.10257730633020401\n- Accuracy: 0.973421926910299\n- Macro F1: 0.9735224586288418\n- Micro F1: 0.973421926910299\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn/autotrain-data-Traimn #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 853827191\n- CO2 Emissions (in gram... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-deletion-squad-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-deletion-squad-10", "results": []}]} | huxxx657/roberta-base-finetuned-deletion-squad-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T17:51:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| roberta-base-finetuned-deletion-squad-10
========================================
This model is a fine-tuned version of roberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0246
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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 #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #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\\_size: 16\n*... |
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_mnr_128 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T17:53:34+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... |
token-classification | flair | ## Biomedical Entity Recognition in Bahasa Indonesia
Summary:
- Trained using manually annotated data from alodokter.com (online health QA platform) using UMLS guideline (see https://rdcu.be/cNxV3)
- Recognize disorders (DISO) and anatomy (ANAT) entities
- Achieve best F1 macro score 0.81
- Based on XLM-Roberta. So, ... | {"language": ["id", "en"], "license": "bsd-3-clause", "tags": ["flair", "token-classification", "sequence-tagger-model"], "widget": [{"text": "Dok saya mau tanya kenapa ya kulit saya kering bersisik gitu dok. Apalagi bagian tumit sampai nglupas terus gatal. Penyebabnya apa y dok terus cara mengobatinya gimana? Terima k... | abid/indonesia-bioner | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"id",
"en",
"license:bsd-3-clause",
"has_space",
"region:us"
] | null | 2022-05-11T17:55:55+00:00 | [] | [
"id",
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #id #en #license-bsd-3-clause #has_space #region-us
| ## Biomedical Entity Recognition in Bahasa Indonesia
Summary:
- Trained using manually annotated data from URL (online health QA platform) using UMLS guideline (see URL
- Recognize disorders (DISO) and anatomy (ANAT) entities
- Achieve best F1 macro score 0.81
- Based on XLM-Roberta. So, cross lingual recognition mig... | [
"## Biomedical Entity Recognition in Bahasa Indonesia\n\nSummary:\n- Trained using manually annotated data from URL (online health QA platform) using UMLS guideline (see URL \n- Recognize disorders (DISO) and anatomy (ANAT) entities\n- Achieve best F1 macro score 0.81\n- Based on XLM-Roberta. So, cross lingual reco... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #id #en #license-bsd-3-clause #has_space #region-us \n",
"## Biomedical Entity Recognition in Bahasa Indonesia\n\nSummary:\n- Trained using manually annotated data from URL (online health QA platform) using UMLS guideline (see URL \n- Recognize d... |
reinforcement-learning | stable-baselines3 |
# **PP0** Agent playing **LunarLander-v2**
This is a trained model of a **PP0** 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": "PP0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | RaphaelReinauer/LunarLander-v7 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T18:30:45+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PP0 Agent playing LunarLander-v2
This is a trained model of a PP0 agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PP0 Agent playing LunarLander-v2\n This is a trained model of a PP0 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",
"# PP0 Agent playing LunarLander-v2\n This is a trained model of a PP0 agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([docume... | {"tags": ["fastai"]} | kurianbenoy/course_v5_lesson2_pets_convnext_base_in22k | null | [
"fastai",
"region:us"
] | null | 2022-05-11T18:41:20+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the ... | [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai co... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentatio... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** 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": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | DBusAI/DQN-MountainCar-v0-v2 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T18:42:54+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\n This is a trained model of a DQN 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",
"# DQN Agent playing MountainCar-v0\n This is a trained model of a DQN agent playing MountainCar-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
text-generation | transformers |
# ChemGPT 4.7M
ChemGPT is based on the GPT-Neo model and was introduced in the paper [Neural Scaling of Deep Chemical Models](https://chemrxiv.org/engage/chemrxiv/article-details/627bddd544bdd532395fb4b5).
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on ... | {"tags": ["chemistry"]} | ncfrey/ChemGPT-4.7M | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"chemistry",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-11T18:54:55+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #chemistry #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ChemGPT 4.7M
ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.
## Intended uses & limitations
### How to use
You can u... | [
"# ChemGPT 4.7M\nChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.",
"## Model description\nChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.",
"## Intended uses & limitations",
"### Ho... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #chemistry #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ChemGPT 4.7M\nChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.",
"## Model description\nChemGPT is a transform... |
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_mnr_64 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T18:58:34+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... |
reinforcement-learning | stable-baselines3 |
# parameters <br>
model = A2C(policy = "MlpPolicy", <br>
env = env, <br>
n_steps = 256, <br>
learning_rate = 0.001, <br>
gamma = 0.99, <br>
verbose=1) <br> | {"library_name": "stable-baselines3", "tags": ["BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalkerHardcore-... | SimingSiming/ppo-BipedalWalkerHardcore-v3 | null | [
"stable-baselines3",
"BipedalWalkerHardcore-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T18:59:27+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# parameters <br>
model = A2C(policy = "MlpPolicy", <br>
env = env, <br>
n_steps = 256, <br>
learning_rate = 0.001, <br>
gamma = 0.99, <br>
verbose=1) <br> | [
"# parameters <br>\n model = A2C(policy = \"MlpPolicy\", <br>\n env = env, <br>\n n_steps = 256, <br>\n learning_rate = 0.001, <br>\n gamma = 0.99, <br>\n verbose=1) <br>"
] | [
"TAGS\n#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# parameters <br>\n model = A2C(policy = \"MlpPolicy\", <br>\n env = env, <br>\n n_steps = 256, <br>\n learning_rate = 0.001, <br>\n g... |
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... | AndrewK/ppo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T18:59:43+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... |
text-generation | transformers |
# ChemGPT 19M
ChemGPT is based on the GPT-Neo model and was introduced in the paper [Neural Scaling of Deep Chemical Models](https://chemrxiv.org/engage/chemrxiv/article-details/627bddd544bdd532395fb4b5).
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on t... | {"tags": ["chemistry"]} | ncfrey/ChemGPT-19M | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"chemistry",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T19:02:27+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #chemistry #autotrain_compatible #endpoints_compatible #region-us
|
# ChemGPT 19M
ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.
## Intended uses & limitations
### How to use
You can us... | [
"# ChemGPT 19M\nChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.",
"## Model description\nChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.",
"## Intended uses & limitations",
"### How... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #chemistry #autotrain_compatible #endpoints_compatible #region-us \n",
"# ChemGPT 19M\nChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.",
"## Model description\nChemGPT is a transformers model fo... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-deletion-squad-15
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-deletion-squad-15", "results": []}]} | huxxx657/roberta-base-finetuned-deletion-squad-15 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T19:04:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
| roberta-base-finetuned-deletion-squad-15
========================================
This model is a fine-tuned version of roberta-base on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1057
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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 #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #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\\_size: 16\n*... |
text-generation | transformers |
# ChemGPT 1.2B
ChemGPT is based on the GPT-Neo model and was introduced in the paper [Neural Scaling of Deep Chemical Models](https://chemrxiv.org/engage/chemrxiv/article-details/627bddd544bdd532395fb4b5).
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on ... | {"tags": ["chemistry"]} | ncfrey/ChemGPT-1.2B | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"chemistry",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-11T19:16:48+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #chemistry #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ChemGPT 1.2B
ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.
## Intended uses & limitations
### How to use
You can u... | [
"# ChemGPT 1.2B\nChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.",
"## Model description\nChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.",
"## Intended uses & limitations",
"### Ho... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #chemistry #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ChemGPT 1.2B\nChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.",
"## Model description\nChemGPT is a transform... |
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. -->
# eduardopds/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/He... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eduardopds/marian-finetuned-kde4-en-to-fr", "results": []}]} | eduardopds/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T19:52:37+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| eduardopds/marian-finetuned-kde4-en-to-fr
=========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6855
* Validation Loss: 0.8096
* Epoch: 2
Model description
---------... | [
"### 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': 5e-05, 'decay\\_steps': 17733, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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... | jabot/PPO_LunarLanderV2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T20:01:39+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... |
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... | RebeccaJeffers/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T20:02:58+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... |
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_mnr_32 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T20:21:52+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... |
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-cnn-pubmed-arxiv-pubmed-arxiv-earlystopping
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubme... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-earlystopping", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-earlystopping | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T20:46:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-earlystopping
================================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8793
* Rouge1: 56.2055
* Rouge2: 41.9231
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1000\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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:... |
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/LunarLander-v8 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T21:25:23+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 |
# vBERT-2021-BASE
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: April, 2022
<li> Model version: 2021-base
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), techni... | {"language": ["eng"], "license": "apache-2.0", "tags": ["pytorch", "tensorflow"], "thumbnail": "url to a thumbnail used in social sharing"} | VMware/vbert-2021-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"tensorflow",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T21:33:48+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #tensorflow #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vBERT-2021-BASE
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: April, 2022
<li> Model version: 2021-base
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), techni... | [
"# vBERT-2021-BASE",
"### Model Info:\n<ul>\n<li> Authors: R&D AI Lab, VMware Inc.\n<li> Model date: April, 2022\n<li> Model version: 2021-base\n<li> Model type: Pretrained language model\n<li> License: Apache 2.0\n</ul>",
"#### Motivation\nTraditional BERT models struggle with VMware-specific words (Tanzu, vSp... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #tensorflow #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# vBERT-2021-BASE",
"### Model Info:\n<ul>\n<li> Authors: R&D AI Lab, VMware Inc.\n<li> Model date: April, 2022\n<li> Model version: 2021-base\n<li> M... |
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/LunarLander-v10 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T21:37:09+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... | RaphaelReinauer/LunarLander-v6 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T21:44:59+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 |
# vBERT-2021-LARGE
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: April, 2022
<li> Model version: 2021-base
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), techn... | {"language": ["eng"], "license": "apache-2.0", "tags": ["PyTorch", "tensorflow"], "thumbnail": "URL to a thumbnail used in social sharing"} | VMware/vbert-2021-large | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"PyTorch",
"tensorflow",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-11T21:58:24+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #PyTorch #tensorflow #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# vBERT-2021-LARGE
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: April, 2022
<li> Model version: 2021-base
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), techn... | [
"# vBERT-2021-LARGE",
"### Model Info:\n<ul>\n<li> Authors: R&D AI Lab, VMware Inc.\n<li> Model date: April, 2022\n<li> Model version: 2021-base\n<li> Model type: Pretrained language model\n<li> License: Apache 2.0\n</ul>",
"#### Motivation\nTraditional BERT models struggle with VMware-specific words (Tanzu, vS... | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #PyTorch #tensorflow #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# vBERT-2021-LARGE",
"### Model Info:\n<ul>\n<li> Authors: R&D AI Lab, VMware Inc.\n<li> Model date: April, 2022\n<li> Model version: 2021-ba... |
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/mt5-small-finetuned-cnn_dailymail-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "alk/mt5-small-finetuned-cnn_dailymail-en-es", "results": []}]} | alk/mt5-small-finetuned-cnn_dailymail-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-11T22:49:04+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| alk/mt5-small-finetuned-cnn\_dailymail-en-es
============================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9490
* Validation Loss: 1.6920
* Epoch: 7
Model description
-------------... | [
"### 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': 287112, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycl... | [
"TAGS\n#transformers #tf #mt5 #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': 'Adam... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1487841586541215745/J1Y6... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nft_redlist/1652316177890/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nft_redlist | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-11T23:16:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
TON Animals Red List
@nft\_redlist
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training dat... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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... | pirchavez/PPO-FirstModel | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-11T23:26:51+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... |
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. -->
# eduardopds/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "eduardopds/mt5-small-finetuned-amazon-en-es", "results": []}]} | eduardopds/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-11T23:39:53+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| eduardopds/mt5-small-finetuned-amazon-en-es
===========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 4.0870
* Validation Loss: 3.3925
* Epoch: 7
Model description
---------------... | [
"### 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': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #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': 'Adam... |
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... | tjscollins/ppo-LunarLander-v2-tuned | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T00:07:36+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... |
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-urdu-finetuned-urdu-arabic
This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-urdu](https://hu... | {"license": "apache-2.0", "tags": ["summarization", "arabic", "ar", "mt5", "Abstractive Summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "mt5-base-finetuned-urdu-finetuned-urdu-arabic", "results": []}]} | eslamxm/mt5-base-finetuned-urdu-arabic | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"arabic",
"ar",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T00:15:19+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #arabic #ar #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-urdu-finetuned-urdu-arabic
=============================================
This model is a fine-tuned version of eslamxm/mt5-base-finetuned-urdu on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3744
* Rouge-1: 22.77
* Rouge-2: 10.15
* Rouge-l: 20.71
* Gen Len... | [
"### 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 #Abstractive Summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpar... |
text-classification | transformers |
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Manag... | {"language": "en", "tags": ["financial-text-analysis", "forward-looking-statement"], "widget": [{"text": "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. "}]} | yiyanghkust/finbert-fls | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-text-analysis",
"forward-looking-statement",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-12T00:33:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #financial-text-analysis #forward-looking-statement #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Manag... | [
"# How to use \nYou can use this model with Transformers pipeline for forward-looking statement classification.\n\n\nVisit FinBERT.AI for more details on the recent development of FinBERT."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #financial-text-analysis #forward-looking-statement #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# How to use \nYou can use this model with Transformers pipeline for forward-looking statement classification.\n\n\nVisit FinBERT... |
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-poem_key_words
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves t... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-poem_key_words", "results": []}]} | guhuawuli/gpt2-poem_key_words | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T00:51:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-poem\_key\_words
=====================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5370
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
question-answering | transformers | src/models/0511_hard_dataset | {} | Khyeyoon/MRC_roberta | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T01:32:58+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| src/models/0511_hard_dataset | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us \n"
] |
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. -->
# madatnlp/rob-large-krmath
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an ... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/rob-large-krmath", "results": []}]} | madatnlp/rob-large-krmath | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T02:07:10+00:00 | [] | [] | TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| madatnlp/rob-large-krmath
=========================
This model is a fine-tuned version of klue/roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2249
* Validation Loss: 0.1952
* Epoch: 3
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-04, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #roberta #text-classification #generated_from_keras_callback #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': 1e-04, 'decay': 0.0, '... |
text-classification | transformers | # Erlangshen-MegatronBert-1.3B-Similarity
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个相似度任务上微调后的版本
This is the fine-tuned version of the Chinese BERT model on several sim... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "NLU", "Similarity"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]} | IDEA-CCNL/Erlangshen-MegatronBert-1.3B-Similarity | null | [
"transformers",
"pytorch",
"megatron-bert",
"text-classification",
"bert",
"NLU",
"Similarity",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-12T02:08:36+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #megatron-bert #text-classification #bert #NLU #Similarity #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Erlangshen-MegatronBert-1.3B-Similarity
=======================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个相似度任务上微调后的版本
This is the fine-tuned version of the Chinese BERT model on several similarity datasets, w... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文：\n\n\nIf you are using the resource for your work, please cite the our paper for this model:\n\n\n如果您在您的工作中使用了我们的模型，也可以引用我们的总论文：\n\n\nIf you are using the resource for your work, please cite the our o... | [
"TAGS\n#transformers #pytorch #megatron-bert #text-classification #bert #NLU #Similarity #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的对该模型... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | sp02/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T02:18:19+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2103
* Accuracy: 0.9245
* F1: 0.9245
Model description
-----------------
Mo... | [
"### 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: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #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\\_rate: 2... |
text-classification | transformers | # Erlangshen-MegatronBert-1.3B-NLI
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个推理任务微调后的版本
This is the fine-tuned version of the Chinese BERT model on several NLI datasets... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "NLU", "NLI"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]} | IDEA-CCNL/Erlangshen-MegatronBert-1.3B-NLI | null | [
"transformers",
"pytorch",
"megatron-bert",
"text-classification",
"bert",
"NLU",
"NLI",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T03:02:48+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #megatron-bert #text-classification #bert #NLU #NLI #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Erlangshen-MegatronBert-1.3B-NLI
================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个推理任务微调后的版本
This is the fine-tuned version of the Chinese BERT model on several NLI datasets, which topped FewCLUE and... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #megatron-bert #text-classification #bert #NLU #NLI #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are us... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | SherlockGuo/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T03:42:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7677
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #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: 2e-05\n* train\\_batch\\_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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["or"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_9_0", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_9_0"], "metrics": ["wer"], "model-index": [{"name": "XLS-R-300M - Odia", "results": [{"task": {"type": "automatic-speech-... | anuragshas/wav2vec2-xls-r-300m-or-cv9-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"or",
"dataset:mozilla-foundation/common_voice_9_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T04:05:41+00:00 | [] | [
"or"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_9_0 #generated_from_trainer #or #dataset-mozilla-foundation/common_voice_9_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_9\_0 - OR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7886
* Wer: 0.5495
* Cer: 0.1311
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_9_0 #generated_from_trainer #or #dataset-mozilla-foundation/common_voice_9_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameter... |
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_32 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T04:17:33+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... |
text-generation | transformers | # KoGPT2-emotion-chatbot
kogpt2 on hugging face Transformers for Psychological Counseling
- [full project link](https://github.com/jiminAn/Capstone_2022)
## how to use
```
from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast
model = GPT2LMHeadModel.from_pretrained("withU/kogpt2-emotion-chatbot")
tokenize... | {} | withU/kogpt2-emotion-chatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T04:21:44+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # KoGPT2-emotion-chatbot
kogpt2 on hugging face Transformers for Psychological Counseling
- full project link
## how to use
## dataset finetuned on
- wellness dataset
- emotion corpus of conversations
- chatbot data
## references
- WelllnessConversation-LanguageModel
- KoGPT2: SKT-AI | [
"# KoGPT2-emotion-chatbot\nkogpt2 on hugging face Transformers for Psychological Counseling\n- full project link",
"## how to use",
"## dataset finetuned on\n- wellness dataset\n- emotion corpus of conversations\n- chatbot data",
"## references\n- WelllnessConversation-LanguageModel\n- KoGPT2: SKT-AI"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# KoGPT2-emotion-chatbot\nkogpt2 on hugging face Transformers for Psychological Counseling\n- full project link",
"## how to use",
"## dataset finetuned on\n- wellness d... |
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. -->
# Laikokwei/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Laikokwei/bert-finetuned-squad", "results": []}]} | Laikokwei/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T04:42:28+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| Laikokwei/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: 0.4662
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 44364, '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 | # Koelectra-five-sentiment-classification
Koelectra on hugging face Transformers for Psychological Counseling
- [full project link](https://github.com/jiminAn/Capstone_2022)
## how to use
```
from transformers import ElectraModel, ElectraTokenizer
model = ElectraModel.from_pretrained("withU/Koelectra-five-sentiment-c... | {} | withU/Koelectra-five-sentiment-classification | null | [
"region:us"
] | null | 2022-05-12T04:44:01+00:00 | [] | [] | TAGS
#region-us
| # Koelectra-five-sentiment-classification
Koelectra on hugging face Transformers for Psychological Counseling
- full project link
## how to use
## dataset finetuned on
- wellness dataset
- chatbot data
- korean-hate-speech
## references
- WelllnessConversation-LanguageModel
- KoELECTRA
| [
"# Koelectra-five-sentiment-classification\nKoelectra on hugging face Transformers for Psychological Counseling\n- full project link",
"## how to use",
"## dataset finetuned on\n\n- wellness dataset\n- chatbot data\n- korean-hate-speech",
"## references\n\n- WelllnessConversation-LanguageModel\n- KoELECTRA"
] | [
"TAGS\n#region-us \n",
"# Koelectra-five-sentiment-classification\nKoelectra on hugging face Transformers for Psychological Counseling\n- full project link",
"## how to use",
"## dataset finetuned on\n\n- wellness dataset\n- chatbot data\n- korean-hate-speech",
"## references\n\n- WelllnessConversation-Lang... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | ahujaniharika95/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T05:14:07+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### T... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased 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",
"##... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.",
"## Model description\n\nMore informa... |
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... | sagerpascal/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 | 2022-05-12T05:30:48+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: 0.0646
* Precision: 0.9349
* Recall: 0.9498
* F1: 0.9423
* Accuracy: 0.9859
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... |
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-Arabizi-gpu-colab-similar-to-german-param
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-Arabizi-gpu-colab-similar-to-german-param", "results": []}]} | ali-issa/FYP_ARABIZI | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T05:34:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-Arabizi-gpu-colab-similar-to-german-param
==================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5609
* Wer: 0.4042
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 6\n* total\\_train\\_batch\\_size: 12\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2... |
text-classification | transformers |
ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-ESG is a FinBERT model fine-tuned on 2,000 manually annotated sentences from firms' ESG reports and annual reports.
**Input**: A financial text.
**Output**: Environmental, Social, Governance or Non... | {"language": "en", "tags": ["financial-text-analysis", "esg", "environmental-social-corporate-governance"], "widget": [{"text": "Rhonda has been volunteering for several years for a variety of charitable community programs. "}]} | yiyanghkust/finbert-esg | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-text-analysis",
"esg",
"environmental-social-corporate-governance",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-12T05:53:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #financial-text-analysis #esg #environmental-social-corporate-governance #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-ESG is a FinBERT model fine-tuned on 2,000 manually annotated sentences from firms' ESG reports and annual reports.
Input: A financial text.
Output: Environmental, Social, Governance or None.
# Ho... | [
"# How to use \nYou can use this model with Transformers pipeline for ESG classification.\n\n\nVisit FinBERT.AI for more details on the recent development of FinBERT.\n\nIf you use the model in your academic work, please cite the following paper:\n\nHuang, Allen H., Hui Wang, and Yi Yang. \"FinBERT: A Large Languag... | [
"TAGS\n#transformers #pytorch #bert #text-classification #financial-text-analysis #esg #environmental-social-corporate-governance #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# How to use \nYou can use this model with Transformers pipeline for ESG classification.\n\n\nVisit FinBERT.... |
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-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | yogeshchandrasekharuni/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T05:56:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# distilroberta-base-wiki
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-wiki", "results": []}]} | uhlenbeckmew/distilroberta-base-wiki | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T06:02:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-wiki
=======================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0961
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #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: ... |
null | null |
- Test
---
license: apache-2.0
---
| {} | shoyano372/test | null | [
"region:us"
] | null | 2022-05-12T06:17:37+00:00 | [] | [] | TAGS
#region-us
|
- Test
---
license: apache-2.0
---
| [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# Based on bert-base-chinese
基于bert-base-chinese在`message80W`数据集（垃圾邮件二分类）上做了5个epoch的fine-tune
```python
# evaluate
with torch.no_grad():
model.eval()
eval_steps = 0
pred_list = []
label_list = []
for i, batch in enumerate(tqdm(test_loader)):
input_ids, attention_mask, label = ba... | {"language": "zh"} | cocoshe/bert-base-chinese-finetune-5-trash-email | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T06:25:12+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us
|
# Based on bert-base-chinese
基于bert-base-chinese在'message80W'数据集（垃圾邮件二分类）上做了5个epoch的fine-tune
80W数据，shuffled，8：3分train eval
下面是eval结果
!image-20220512153415505
| [
"# Based on bert-base-chinese\n\n基于bert-base-chinese在'message80W'数据集（垃圾邮件二分类）上做了5个epoch的fine-tune\n\n\n\n\n\n\n\n80W数据，shuffled，8：3分train eval\n\n下面是eval结果\n\n!image-20220512153415505"
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# Based on bert-base-chinese\n\n基于bert-base-chinese在'message80W'数据集（垃圾邮件二分类）上做了5个epoch的fine-tune\n\n\n\n\n\n\n\n80W数据，shuffled，8：3分train eval\n\n下面是eval结果\n\n!image-20220512153415505"
] |
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. -->
# madatnlp/prefix-ket5-scratch
This model is a fine-tuned version of [madatnlp/ke-t5-math-py](https://huggingface.co/madatnlp/ke-t5-math... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "madatnlp/prefix-ket5-scratch", "results": []}]} | madatnlp/prefix-ket5-scratch | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T06:49:21+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| madatnlp/prefix-ket5-scratch
============================
This model is a fine-tuned version of madatnlp/ke-t5-math-py on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7214
* Validation Loss: 0.8747
* Epoch: 98
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 1e-04, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #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': 'Adam', 'learning\\_rate':... |
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-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-earlystopping
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-earlystopping", "results": []}]} | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-earlystopping | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T07:17:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-earlystopping
======================================================
This model is a fine-tuned version of theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8347
* Rouge1: 53.9049
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 200\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #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:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2-shake-wiki-update-shuffle
This model is a fine-tuned version of [distilbert-base-uncased]... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2-shake-wiki-update-shuffle", "results": []}]} | DioLiu/distilbert-base-uncased-finetuned-sst2-shake-wiki-update-shuffle | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T07:35:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2-shake-wiki-update-shuffle
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0284
* Accuracy: 0.9971
Model desc... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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... | Einbauch/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T07:50:34+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... |
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": []}]} | Sumedha/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 | 2022-05-12T08:07:29+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: 2.4726
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... |
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... | iceicenice2ice/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T08:16:19+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... |
sentence-similarity | sentence-transformers | # distilbert-base-uncased trained for Semantic Textual Similarity in Catalan
This is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) in order to understand and benchmark STS models.
## Model and training data descrip... | {"language": "ca", "tags": ["sentence-similarity", "sentence-transformers"], "datasets": ["stsb_multi_mt"]} | driwnet/stsb-m-mt-ca-distilbert-base-uncased | null | [
"sentence-transformers",
"sentence-similarity",
"ca",
"dataset:stsb_multi_mt",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T08:29:27+00:00 | [] | [
"ca"
] | TAGS
#sentence-transformers #sentence-similarity #ca #dataset-stsb_multi_mt #endpoints_compatible #region-us
| # distilbert-base-uncased trained for Semantic Textual Similarity in Catalan
This is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.
## Model and training data description
This model was built taking 'distilbert-bas... | [
"# distilbert-base-uncased trained for Semantic Textual Similarity in Catalan\n\nThis is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.",
"## Model and training data description\n\nThis model was built taking 'd... | [
"TAGS\n#sentence-transformers #sentence-similarity #ca #dataset-stsb_multi_mt #endpoints_compatible #region-us \n",
"# distilbert-base-uncased trained for Semantic Textual Similarity in Catalan\n\nThis is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order... |
summarization | transformers |
# ViT5-large Finetuned on `vietnews` Abstractive Summarization
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
[]... | {"language": "vi", "license": "mit", "tags": ["summarization"], "datasets": ["cc100"], "widget": [{"text": "vietnews: VietAI l\u00e0 t\u1ed5 ch\u1ee9c phi l\u1ee3i nhu\u1eadn v\u1edbi s\u1ee9 m\u1ec7nh \u01b0\u01a1m m\u1ea7m t\u00e0i n\u0103ng v\u1ec1 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o v\u00e0 x\u00e2y d\u1ef1ng m\u1... | VietAI/vit5-large-vietnews-summarization | null | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"summarization",
"vi",
"dataset:cc100",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T09:09:43+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #summarization #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# ViT5-large Finetuned on 'vietnews' Abstractive Summarization
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
![PWC](URL
## How to use
For more details, do check out our Github repo and eval script.
| [
"# ViT5-large Finetuned on 'vietnews' Abstractive Summarization\n\n\nState-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.\n![PWC](URL",
"## How to use\nFor more details, do check out our Github repo and eval script."
] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #summarization #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# ViT5-large Finetuned on 'vietnews' Abstractive Summarization\n\n\nState-of-the-art pretrained Transformer-... |
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. -->
# vanichandna/muril-finetuned-squadv1
This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muri... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vanichandna/muril-finetuned-squadv1", "results": []}]} | vanichandna/muril-finetuned-squadv1 | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T09:51:51+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| vanichandna/muril-finetuned-squadv1
===================================
This model is a fine-tuned version of google/muril-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7246
* Epoch: 2
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 1e-05, 'decay\\... | [
"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: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'confi... |
null | null | dataset containing | {} | Ericblancosf/attribute | null | [
"region:us"
] | null | 2022-05-12T09:53:44+00:00 | [] | [] | TAGS
#region-us
| dataset containing | [] | [
"TAGS\n#region-us \n"
] |
audio-classification | transformers |
# DistilWav2Vec2 Adult/Child Indonesian Speech Classifier 52M
DistilWav2Vec2 Adult/Child Indonesian Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-id-cls](https://huggingface.co/bo... | {"language": "id", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distil-wav2vec2-adult-child-id-cls-52m", "results": []}]} | bookbot/distil-wav2vec2-adult-child-id-cls-52m | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"id",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T09:54:26+00:00 | [
"2006.11477"
] | [
"id"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #id #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| DistilWav2Vec2 Adult/Child Indonesian Speech Classifier 52M
===========================================================
DistilWav2Vec2 Adult/Child Indonesian Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-id-cls on a ... | [
"### 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.... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #id #arxiv-2006.11477 #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\\... |
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... | alisonbrwn/ppo-LunarLander_doubled_steps | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T09:59:24+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 |
**Hyperparameters:**
- learning rate: 2e-5
- weight decay: 0.01
- per_device_train_batch_size: 16
- per_device_eval_batch_size: 16
- gradient_accumulation_steps:1
- eval steps: 5000
- max_length: 128
- num_epochs: 3
**Dataset version:**
- “craffel/tasky_or_not”, “10xp3_10xc4”, “15f88c8”
**Checkpoint:** ... | {"language": ["en"], "license": "mit", "datasets": ["craffel/tasky_or_not"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "text-classification"} | taskydata/deberta-v3-base_10xp3_10xc4_128 | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:craffel/tasky_or_not",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-12T10:05:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #deberta-v2 #text-classification #en #dataset-craffel/tasky_or_not #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Hyperparameters:
* learning rate: 2e-5
* weight decay: 0.01
* per\_device\_train\_batch\_size: 16
* per\_device\_eval\_batch\_size: 16
* gradient\_accumulation\_steps:1
* eval steps: 5000
* max\_length: 128
* num\_epochs: 3
Dataset version:
* “craffel/tasky\_or\_not”, “10xp3\_10xc4”, “15f88c8”
Checkpoint:
* 1... | [] | [
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #en #dataset-craffel/tasky_or_not #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... | CrispyAlbumArt/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T10:24:02+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... | MikhailKon/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T10:31:06+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... | alisonbrwn/ppo-LunarLander_doubled_steps_wyth_hptune | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T10:42:28+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... |
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-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T11:37:22+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... | strangetcy/PPO-LunarLander-v2_experiments | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T11:50:38+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... | fgmckee/ppo_lunar_lander_1m | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T11:51:34+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... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1522032150358511616/83U7... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/_is_is_are-newscollected/1652362282720/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/_is_is_are-newscollected | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-12T12:29:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
del co & angelicism01 滲み出るエロス
@\_is\_is\_are-newscollected
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/first_ppo-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-12T12:30:42+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... |
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