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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | SebastianS/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T13:39:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information... |
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... | zezafa/deep_rl_class | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T13:49:36+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... |
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... | umbertospazio/1500000_PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T14:02:54+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## 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... | traxes/repos | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T14:03:31+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",
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-tr-fine-tuning-00
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-tr-fine-tuning", "results": []}]} | bekirbakar/wav2vec2-large-xlsr-53-tr-fine-tuning | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T14:16:33+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-tr-fine-tuning-00
========================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3974
* Wer: 0.4784
Training Procedure
------------------
### Tr... | [
"### Training Hyper-parameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training Hyper-parameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch... |
fill-mask | transformers |
# CoReNer
## Demo
We released an online demo so you can easily play with the model. Check it out: [http://corener-demo.aiola-lab.com](http://corener-demo.aiola-lab.com).
The demo uses the [aiola/roberta-base-corener](https://huggingface.co/aiola/roberta-base-corener) model.
## Model description
A multi-task model... | {"language": ["en"], "license": "apache-2.0", "tags": ["NER", "named entity recognition", "RE", "relation extraction", "entity mention detection", "EMD", "coreference resolution"], "datasets": ["Ontonotes", "CoNLL04"]} | aiola/roberta-base-corener | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"NER",
"named entity recognition",
"RE",
"relation extraction",
"entity mention detection",
"EMD",
"coreference resolution",
"en",
"dataset:Ontonotes",
"dataset:CoNLL04",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compa... | null | 2022-05-15T14:18:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #NER #named entity recognition #RE #relation extraction #entity mention detection #EMD #coreference resolution #en #dataset-Ontonotes #dataset-CoNLL04 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CoReNer
## Demo
We released an online demo so you can easily play with the model. Check it out: URL.
The demo uses the aiola/roberta-base-corener model.
## Model description
A multi-task model for named-entity recognition, relation extraction, entity mention detection, and coreference resolution.
We model NER ... | [
"# CoReNer",
"## Demo\n\nWe released an online demo so you can easily play with the model. Check it out: URL. \nThe demo uses the aiola/roberta-base-corener model.",
"## Model description\n\nA multi-task model for named-entity recognition, relation extraction, entity mention detection, and coreference resolutio... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #NER #named entity recognition #RE #relation extraction #entity mention detection #EMD #coreference resolution #en #dataset-Ontonotes #dataset-CoNLL04 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CoReNer",
"## Dem... |
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/1472740175130230784/L7Xc... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/dclblogger-loopifyyy/1652628765621/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/dclblogger-loopifyyy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-15T14:28:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Matty & Loopify ️
@dclblogger-loopifyyy
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.
Trai... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# consumer_super
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown d... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "consumer_super", "results": []}]} | Tititun/consumer_super | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T14:31:47+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# consumer_super
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# consumer_super\n\nThis model is a fine-tuned version of xlm-roberta-base 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 procedure",
"### T... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# consumer_super\n\nThis model is a fine-tuned version of xlm-roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** 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": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | KrusHan/DQN-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T14:57:14+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing LunarLander-v2
This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing LunarLander-v2\n This is a trained model of a DQN 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",
"# DQN Agent playing LunarLander-v2\n This is a trained model of a DQN 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... | umbertospazio/2000000_PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T15:26:24+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 |
# Model bertin_base_sentiment_analysis_es
## **A finetuned model for Sentiment analysis in Spanish**
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **Bertin base** which is a RoBERTa-base model pre-trained on the Spanish portion of mC4 using Flax.
I... | {"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "bertin", "TextClassification", "SentimentAnalysis"], "datasets": ["IMDbreviews_es"], "metrics": ["accuracy"], "widget": [{"text": "Se trata de una pel\u00edcula interesante, con un solido argumento y un gran interpretaci\u00f3n de su actor principal"}],... | edumunozsala/bertin_base_sentiment_analysis_es | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"sagemaker",
"bertin",
"TextClassification",
"SentimentAnalysis",
"es",
"dataset:IMDbreviews_es",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T15:40:29+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #sagemaker #bertin #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Model bertin_base_sentiment_analysis_es
## A finetuned model for Sentiment analysis in Spanish
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is Bertin base which is a RoBERTa-base model pre-trained on the Spanish portion of mC4 using Flax.
It was tr... | [
"# Model bertin_base_sentiment_analysis_es",
"## A finetuned model for Sentiment analysis in Spanish\n\nThis model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,\nThe base model is Bertin base which is a RoBERTa-base model pre-trained on the Spanish portion of mC4 using Flax... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #sagemaker #bertin #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model bertin_base_sentiment_analysis_es",
"## A finetuned... |
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. -->
# t5vi-finetuned-en-to-vi
This model is a fine-tuned version of [imthanhlv/t5vi](https://huggingface.co/imthanhlv/t5vi) on the mt_... | {"tags": ["generated_from_trainer"], "datasets": ["mt_eng_vietnamese"], "metrics": ["bleu"], "model-index": [{"name": "t5vi-finetuned-en-to-vi", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "mt_eng_vietnamese", "type": "mt_eng_vietnamese", ... | nttoanh/t5vi-finetuned-en-to-vi | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:mt_eng_vietnamese",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-15T16:03:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-mt_eng_vietnamese #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5vi-finetuned-en-to-vi
=======================
This model is a fine-tuned version of imthanhlv/t5vi on the mt\_eng\_vietnamese dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3827
* Bleu: 13.547
* Gen Len: 17.3719
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 20\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 #t5 #text2text-generation #generated_from_trainer #dataset-mt_eng_vietnamese #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\... |
text-classification | transformers |
# Model beto_sentiment_analysis_es
## **A finetuned model for Sentiment analysis in Spanish**
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **BETO** which is a BERT-base model pre-trained on a spanish corpus. BETO is of size similar to a BERT-Base ... | {"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "beto", "TextClassification", "SentimentAnalysis"], "datasets": ["IMDbreviews_es"], "metrics": ["accuracy"], "widget": [{"text": "Se trata de una pel\u00edcula interesante, con un solido argumento y un gran interpretaci\u00f3n de su actor principal"}], "... | edumunozsala/beto_sentiment_analysis_es | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sagemaker",
"beto",
"TextClassification",
"SentimentAnalysis",
"es",
"dataset:IMDbreviews_es",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T16:06:52+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #beto #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Model beto_sentiment_analysis_es
## A finetuned model for Sentiment analysis in Spanish
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is BETO which is a BERT-base model pre-trained on a spanish corpus. BETO is of size similar to a BERT-Base and was ... | [
"# Model beto_sentiment_analysis_es",
"## A finetuned model for Sentiment analysis in Spanish\n\nThis model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,\nThe base model is BETO which is a BERT-base model pre-trained on a spanish corpus. BETO is of size similar to a BERT-Ba... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sagemaker #beto #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model beto_sentiment_analysis_es",
"## A finetuned model for S... |
text-classification | transformers |
# Model roberta_bne_sentiment_analysis_es
## **A finetuned model for Sentiment analysis in Spanish**
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **RoBERTa-base-bne** which is a RoBERTa base model and has been pre-trained using the largest Spanish ... | {"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "roberta-bne", "TextClassification", "SentimentAnalysis"], "datasets": ["IMDbreviews_es"], "metrics": ["accuracy"], "widget": [{"text": "Se trata de una pel\u00edcula interesante, con un solido argumento y un gran interpretaci\u00f3n de su actor principa... | edumunozsala/roberta_bne_sentiment_analysis_es | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"sagemaker",
"roberta-bne",
"TextClassification",
"SentimentAnalysis",
"es",
"dataset:IMDbreviews_es",
"arxiv:2107.07253",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
... | null | 2022-05-15T16:18:15+00:00 | [
"2107.07253"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #sagemaker #roberta-bne #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #arxiv-2107.07253 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Model roberta_bne_sentiment_analysis_es
## A finetuned model for Sentiment analysis in Spanish
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is RoBERTa-base-bne which is a RoBERTa base model and has been pre-trained using the largest Spanish corpus k... | [
"# Model roberta_bne_sentiment_analysis_es",
"## A finetuned model for Sentiment analysis in Spanish\n\nThis model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,\nThe base model is RoBERTa-base-bne which is a RoBERTa base model and has been pre-trained using the largest Spani... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #sagemaker #roberta-bne #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #arxiv-2107.07253 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model roberta_bne_sentiment_analysis... |
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": []}]} | gkss/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T16:35:56+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description... |
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... | send-it/TEST5ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T17:30:25+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... |
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. -->
# mbart-large-cc25-ge-hi-to-en
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/m... | {"tags": ["generated_from_trainer"], "datasets": ["hindi_english_machine_translation"], "metrics": ["bleu"], "model-index": [{"name": "mbart-large-cc25-ge-hi-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "hindi_english_machine_transl... | prashanth/mbart-large-cc25-ge-hi-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T17:42:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-hindi_english_machine_translation #model-index #autotrain_compatible #endpoints_compatible #region-us
| mbart-large-cc25-ge-hi-to-en
============================
This model is a fine-tuned version of facebook/mbart-large-cc25 on the hindi\_english\_machine\_translation dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1000
* Bleu: 0.1823
* Gen Len: 1023.383
Model description
--------------... | [
"### 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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-hindi_english_machine_translation #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* lea... |
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. -->
# mateotyz/tf-xml-r-base-ape-swm
This model is a fine-tuned version of [jplu/tf-xlm-roberta-base](https://huggingface.co/jplu/tf-xlm-rob... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mateotyz/tf-xml-r-base-ape-swm", "results": []}]} | mateotyz/tf-xml-r-base-ape-swm | null | [
"transformers",
"tf",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T17:47:41+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #xlm-roberta #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| mateotyz/tf-xml-r-base-ape-swm
==============================
This model is a fine-tuned version of jplu/tf-xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.1811
* Validation Loss: 1.0441
* Epoch: 2
Model description
-----------------
More informat... | [
"### 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 #tensorboard #xlm-roberta #fill-mask #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': 'AdamWeightDecay', 'learning\\_rate': {'cl... |
text-classification | transformers |
# Funnel Transformer small (B4-4-4 with decoder) fine-tuned on IMDB for Sentiment Analysis
These are the model weights for the Funnel Transformer small model fine-tuned on the IMDB dataset for performing Sentiment Analysis with `max_position_embeddings=1024`.
The original model weights for English language are from ... | {"language": "en", "license": "apache-2.0", "tags": ["sentiment-analysis"], "datasets": ["imdb"], "widget": [{"text": "In the garden of wonderment that is the body of work by the animation master Hayao Miyazaki, his 2001 gem 'Spirited Away' is at once one of his most accessible films to a Western audience and the one m... | Sreevishnu/funnel-transformer-small-imdb | null | [
"transformers",
"pytorch",
"funnel",
"text-classification",
"sentiment-analysis",
"en",
"dataset:imdb",
"arxiv:2006.03236",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T17:48:18+00:00 | [
"2006.03236"
] | [
"en"
] | TAGS
#transformers #pytorch #funnel #text-classification #sentiment-analysis #en #dataset-imdb #arxiv-2006.03236 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Funnel Transformer small (B4-4-4 with decoder) fine-tuned on IMDB for Sentiment Analysis
========================================================================================
These are the model weights for the Funnel Transformer small model fine-tuned on the IMDB dataset for performing Sentiment Analysis with 'ma... | [] | [
"TAGS\n#transformers #pytorch #funnel #text-classification #sentiment-analysis #en #dataset-imdb #arxiv-2006.03236 #license-apache-2.0 #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... | maglagla/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T18:01:19+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... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924
## Validation Metrics
- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890... | {"language": "tr", "tags": "autotrain", "datasets": ["emre/autotrain-data-turkish-sentiment-analysis"], "widget": [{"text": "Bu \u00fcr\u00fcn ger\u00e7ekten g\u00fczel \u00e7\u0131kt\u0131"}], "co2_eq_emissions": 120.82460124309924} | emre/turkish-sentiment-analysis | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"tr",
"dataset:emre/autotrain-data-turkish-sentiment-analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-15T19:05:07+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain #tr #dataset-emre/autotrain-data-turkish-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 870727732
- CO2 Emissions (in grams): 120.82460124309924
## Validation Metrics
- Loss: 0.1098366305232048
- Accuracy: 0.9697853317600073
- Macro F1: 0.9482820974460786
- Micro F1: 0.9697853317600073
- Weighted F1: 0.9695237873890... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 870727732\n- CO2 Emissions (in grams): 120.82460124309924",
"## Validation Metrics\n\n- Loss: 0.1098366305232048\n- Accuracy: 0.9697853317600073\n- Macro F1: 0.9482820974460786\n- Micro F1: 0.9697853317600073\n- Weighted F... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #autotrain #tr #dataset-emre/autotrain-data-turkish-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Mo... |
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... | Fandaymon/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T19:41:54+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Ad... |
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. -->
# quales-iberlef-squad_2
This model is a fine-tuned version of [jamarju/roberta-large-bne-squad-2.0-es](https://huggingface.co/jam... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "quales-iberlef-squad_2", "results": []}]} | stevemobs/quales-iberlef-squad_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T20:02:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# quales-iberlef-squad_2
This model is a fine-tuned version of jamarju/roberta-large-bne-squad-2.0-es on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# quales-iberlef-squad_2\n\nThis model is a fine-tuned version of jamarju/roberta-large-bne-squad-2.0-es 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",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# quales-iberlef-squad_2\n\nThis model is a fine-tuned version of jamarju/roberta-large-bne-squad-2.0-es on the None dataset.",
"## Model description\n\nMore information needed"... |
text-classification | transformers | # all-mpnet-base-v2-tasky-classification | {"widget": [{"text": "Satellites chart unlit territory and poverty hotspots."}]} | khalidalt/all-mpnet-base-v2-tasky-classification | null | [
"transformers",
"pytorch",
"mpnet",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-15T20:30:10+00:00 | [] | [] | TAGS
#transformers #pytorch #mpnet #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # all-mpnet-base-v2-tasky-classification | [
"# all-mpnet-base-v2-tasky-classification"
] | [
"TAGS\n#transformers #pytorch #mpnet #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# all-mpnet-base-v2-tasky-classification"
] |
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... | Sicko-Code/PPO-LunarLander-v2-Try | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T20:37:47+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 **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | vukpetar/ppo-CarRacing-v0-v2 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T20:41:44+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | nadirbekovnadir/LunarLander-64_128_tanh | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-15T21:15:21+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 |
<!-- 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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | Gnosky/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T03:06:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6421
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 #gpt2 #text-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: 2... |
text-classification | transformers | <!DOCTYPE html>
<html>
<body>
<h1><b>Financial-RoBERTa</b></h1>
<p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p>
<ul style="PADDING-LEFT: 40px">
<li>Financial Statements,</li>
<li>Earnings Announcements,</li>
<li>Earnings Call Transcripts,</li>
<li>Co... | {"language": ["eng"], "license": "apache-2.0", "tags": ["text-classification", "Sentiment", "RoBERTa", "Financial Statements", "Accounting", "Finance", "Business", "ESG", "CSR Reports", "Financial News", "Earnings Call Transcripts", "Sustainability", "Corporate governance"]} | soleimanian/financial-roberta-large-sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"Sentiment",
"RoBERTa",
"Financial Statements",
"Accounting",
"Finance",
"Business",
"ESG",
"CSR Reports",
"Financial News",
"Earnings Call Transcripts",
"Sustainability",
"Corporate governance",
"eng",
"license:apache-2.... | null | 2022-05-16T03:09:10+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #roberta #text-classification #Sentiment #RoBERTa #Financial Statements #Accounting #Finance #Business #ESG #CSR Reports #Financial News #Earnings Call Transcripts #Sustainability #Corporate governance #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us... | <!DOCTYPE html>
<html>
<body>
<h1><b>Financial-RoBERTa</b></h1>
<p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p>
<ul style="PADDING-LEFT: 40px">
<li>Financial Statements,</li>
<li>Earnings Announcements,</li>
<li>Earnings Call Transcripts,</li>
<li>Co... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #Sentiment #RoBERTa #Financial Statements #Accounting #Finance #Business #ESG #CSR Reports #Financial News #Earnings Call Transcripts #Sustainability #Corporate governance #eng #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #reg... |
text-classification | transformers |
# Hate Speech Target Classifier for Social Media Content in Dutch
A monolingual model for hate speech target classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on the pre-train... | {"language": ["nl"], "license": "mit"} | IMSyPP/hate_speech_targets_nl | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"nl",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T03:23:10+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #distilbert #text-classification #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Hate Speech Target Classifier for Social Media Content in Dutch
A monolingual model for hate speech target classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on the pre-train... | [
"# Hate Speech Target Classifier for Social Media Content in Dutch\n\nA monolingual model for hate speech target classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on the pre... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Hate Speech Target Classifier for Social Media Content in Dutch\n\nA monolingual model for hate speech target classification of social media content in Dutch. The model wa... |
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. -->
# akmal2500/bert-finetuned-squad
This model is a fine-tuned version of [akmal2500/bert-finetuned-squad](https://huggingface.co/akmal2500... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "akmal2500/bert-finetuned-squad", "results": []}]} | akmal2500/bert-finetuned-squad | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T03:56:54+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| akmal2500/bert-finetuned-squad
==============================
This model is a fine-tuned version of akmal2500/bert-finetuned-squad on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5715
* Epoch: 0
Model description
-----------------
More information needed
Intende... | [
"### 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': 5546, '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': ... |
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... | fancyerii/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 | 2022-05-16T04:00:21+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: 0.0592
* Precision: 0.9388
* Recall: 0.9522
* F1: 0.9454
* Accuracy: 0.9870
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... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-finetuned-xsum-v2
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/euge... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-paraphrase-finetuned-xsum-v2", "results": []}]} | yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T04:06:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-paraphrase-finetuned-xsum-v2
=================================
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2329
* Rouge1: 100.0
* Rouge2: 100.0
* Rougel: 100.0
* Rougelsum: 100.0
* Gen Len: 9.2619
M... | [
"### 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\... |
text-generation | transformers |
# How to use
```python
from transformers import pipeline
generator = pipeline('text-generation', model="DedsecurityAI/dpt-125mb")
generator("Hello Simon")
[{'generated_text': 'Hello Simon :) Welcome aboard aboard :) :) :) :) :) :) :) :) :) :) :) :) :) :)'}]
``` | {"license": "mit"} | DedsecurityAI/dpt-125mb | null | [
"transformers",
"pytorch",
"opt",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T04:51:26+00:00 | [] | [] | TAGS
#transformers #pytorch #opt #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# How to use
| [
"# How to use"
] | [
"TAGS\n#transformers #pytorch #opt #text-generation #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# How to use"
] |
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... | devtrent/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T05:08:36+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... |
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_256 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T05:48:33+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... |
image-segmentation | null |
# Welcome to the demo of
- **Task**: Image Segmentation / Semantic Segmentation
- **Paper**: The preprint of our paper is available on [arXiv](https://arxiv.org/pdf/2111.06693.pdf)
- **Data**: The cFO... | {"license": "apache-2.0", "tags": ["image-segmentation", "semantic-segmentation", "deepflash2"], "datasets": ["cFOS in HC"], "library_tag": "deepflash2"} | matjesg/cFOS_in_HC | null | [
"onnx",
"image-segmentation",
"semantic-segmentation",
"deepflash2",
"arxiv:2111.06693",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-05-16T06:28:51+00:00 | [
"2111.06693"
] | [] | TAGS
#onnx #image-segmentation #semantic-segmentation #deepflash2 #arxiv-2111.06693 #license-apache-2.0 #has_space #region-us
|
# Welcome to the demo of
!deepflash2
- Task: Image Segmentation / Semantic Segmentation
- Paper: The preprint of our paper is available on arXiv
- Data: The cFOS in HC dataset (Article, Data) describes the indirect immunofluorescent labeling of the transcription factor cFOS in different subregions of the hippocampus... | [
"# Welcome to the demo of\n\n!deepflash2\n\n- Task: Image Segmentation / Semantic Segmentation\n- Paper: The preprint of our paper is available on arXiv\n- Data: The cFOS in HC dataset (Article, Data) describes the indirect immunofluorescent labeling of the transcription factor cFOS in different subregions of the h... | [
"TAGS\n#onnx #image-segmentation #semantic-segmentation #deepflash2 #arxiv-2111.06693 #license-apache-2.0 #has_space #region-us \n",
"# Welcome to the demo of\n\n!deepflash2\n\n- Task: Image Segmentation / Semantic Segmentation\n- Paper: The preprint of our paper is available on arXiv\n- Data: The cFOS in HC data... |
feature-extraction | 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. -->
# XpCoDir2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the XpCoDataset ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["XpCo"], "model-index": [{"name": "XpCoDir2", "results": []}]} | Yotta/XpCoDir2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"generated_from_trainer",
"dataset:XpCo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T06:43:43+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #generated_from_trainer #dataset-XpCo #license-apache-2.0 #endpoints_compatible #region-us
|
# XpCoDir2
This model is a fine-tuned version of bert-base-uncased on the XpCoDataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# XpCoDir2\n\nThis model is a fine-tuned version of bert-base-uncased on the XpCoDataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #generated_from_trainer #dataset-XpCo #license-apache-2.0 #endpoints_compatible #region-us \n",
"# XpCoDir2\n\nThis model is a fine-tuned version of bert-base-uncased on the XpCoDataset dataset.",
"## Model description\n\nMore information needed",
"## In... |
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-sqac-finetuned-recores
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne-sqac](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-sqac-finetuned-recores", "results": []}]} | nandezgarcia/roberta-base-bne-sqac-finetuned-recores | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T06:52:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| roberta-base-bne-sqac-finetuned-recores
=======================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne-sqac on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4624
* Accuracy: 0.3691
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #multiple-choice #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: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batc... |
null | null | Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/355
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
The model was trained on full [Aidatatang... | {} | luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2 | null | [
"has_space",
"region:us"
] | null | 2022-05-16T07:24:41+00:00 | [] | [] | TAGS
#has_space #region-us
| Note: This recipe is trained with the codes from this PR URL
And the SpecAugment codes from this PR URL
Pre-trained Transducer-Stateless2 models for the Aidatatang\_200zh dataset with icefall.
========================================================================================
The model was trained on full Aida... | [] | [
"TAGS\n#has_space #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-finetuned-cord
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micro... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-cord", "results": []}]} | jsunster/layoutlmv2-finetuned-cord | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T07:58:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-cord
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# layoutlmv2-finetuned-cord\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-cord\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",... |
automatic-speech-recognition | transformers | Thiss project is translated and documented for an internship to gain experince in XLS-R model and Wav2Vec2 architectures. You can read the Turkish documentation on medium.com
https://medium.com/loudest-machine-learning/wav2vec2-xls-r-ile-t%C3%BCrk%C3%A7e-sesten-metine-%C3%A7eviri-25212fdce0d8 | {} | hasanalay/wav2vec2-large-xls-r-300m-turkish-colab-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T07:59:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Thiss project is translated and documented for an internship to gain experince in XLS-R model and Wav2Vec2 architectures. You can read the Turkish documentation on URL
URL | [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | SreyanG-NVIDIA/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T09:15:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6408
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 #gpt2 #text-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: 2... |
text2text-generation | transformers |
# pko-t5-small
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 ... | {"language": "ko", "license": "cc-by-4.0"} | paust/pko-t5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T09:26:56+00:00 | [
"2105.09680"
] | [
"ko"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ko #arxiv-2105.09680 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pko-t5-small
============
Source Code
pko-t5 는 한국어 전용 데이터로 학습한 t5 v1.1 모델입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (나무위키, 위키피디아, 모두의말뭉치 등..) 를 T5 의 span corruption task 를 사용해서 unsupervised learning 만 적용하여 학습을 진행했습니다.
pko-t5 를 사용하실 때는 대상 task 에 파인튜닝하여 사용하시기 바랍니다.
Usage
-----
tr... | [
"### Example\n\n\nKlue 평가 (dev)\n-------------\n\n\n\n* FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝\n* Baseline: KLUE 논문에서 소개된 dev set 에 대한 SOTA 점수\n\n\nLicense\n-------\n\n\nPAUST에서 만든 pko-t5는 MIT license 하에 공개되어 있습니다."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ko #arxiv-2105.09680 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Example\n\n\nKlue 평가 (dev)\n-------------\n\n\n\n* FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝\n* Baseline: KLUE 논문에서 소개된 dev set 에 대한 SO... |
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. -->
# nouman10/robertabase-finetuned-claim-ltp-full-prompt
This model is a fine-tuned version of [roberta-base](https://huggingface.co/rober... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nouman10/robertabase-finetuned-claim-ltp-full-prompt", "results": []}]} | nouman10/robertabase-finetuned-claim-ltp-full-prompt | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T09:45:36+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nouman10/robertabase-finetuned-claim-ltp-full-prompt
====================================================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0233
* Validation Loss: 0.0231
* Epoch: 4
Model description
-... | [
"### 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 #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\... |
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. -->
# ar-kd-XLM-minilmv2-32
This model is a fine-tuned version of [subhasisj/ar-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/ar-T... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ar-kd-XLM-minilmv2-32", "results": []}]} | subhasisj/ar-kd-XLM-minilmv2-32 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T09:49:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# ar-kd-XLM-minilmv2-32
This model is a fine-tuned version of subhasisj/ar-TAPT-MLM-MiniLM on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyper... | [
"# ar-kd-XLM-minilmv2-32\n\nThis model is a fine-tuned version of subhasisj/ar-TAPT-MLM-MiniLM on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# ar-kd-XLM-minilmv2-32\n\nThis model is a fine-tuned version of subhasisj/ar-TAPT-MLM-MiniLM on the None dataset.",
"## Model description\n\nMore information needed",
"## Intend... |
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. -->
# aragpt2-base-finetuned-wikitext2
This model is a fine-tuned version of [aubmindlab/aragpt2-base](https://huggingface.co/aubmindl... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "aragpt2-base-finetuned-wikitext2", "results": []}]} | anes-saidi/aragpt2-base-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T09:51:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| aragpt2-base-finetuned-wikitext2
================================
This model is a fine-tuned version of aubmindlab/aragpt2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.0307
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 #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **CarRacing-v0**
This is a trained model of a **RecurrentPPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
Using recurrent PPO implementation from... | {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "C... | araffin/RecurrentPPO-CarRacing-v0_2 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T09:55:12+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing CarRacing-v0
This is a trained model of a RecurrentPPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
Using recurrent PPO implementation from SB3 contrib: URL
| [
"# RecurrentPPO Agent playing CarRacing-v0\n This is a trained model of a RecurrentPPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code\n \n Using recurrent PPO implementation from SB3 contrib: URL"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing CarRacing-v0\n This is a trained model of a RecurrentPPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | SreyanG-NVIDIA/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T10:23:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1085
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | leumastai/CarRacing-v0-TestModel | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T10:59:10+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
text2text-generation | transformers |
# pko-t5-base
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (... | {"language": "ko", "license": "cc-by-4.0"} | paust/pko-t5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T10:59:13+00:00 | [
"2105.09680"
] | [
"ko"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ko #arxiv-2105.09680 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pko-t5-base
===========
Source Code
pko-t5 는 한국어 전용 데이터로 학습한 t5 v1.1 모델입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (나무위키, 위키피디아, 모두의말뭉치 등..) 를 T5 의 span corruption task 를 사용해서 unsupervised learning 만 적용하여 학습을 진행했습니다.
pko-t5 를 사용하실 때는 대상 task 에 파인튜닝하여 사용하시기 바랍니다.
Usage
-----
tran... | [
"### Example\n\n\nKlue 평가 (dev)\n-------------\n\n\n\n* FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝\n* Baseline: KLUE 논문에서 소개된 dev set 에 대한 SOTA 점수\n\n\nLicense\n-------\n\n\nPAUST에서 만든 pko-t5는 MIT license 하에 공개되어 있습니다."
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ko #arxiv-2105.09680 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Example\n\n\nKlue 평가 (dev)\n-------------\n\n\n\n* FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝\n* Baseline: KLUE 논문에서 소개된 dev set 에 대한 SO... |
text2text-generation | transformers |
# pko-t5-large
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 ... | {"language": "ko", "license": "cc-by-4.0"} | paust/pko-t5-large | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T10:59:52+00:00 | [
"2105.09680"
] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #ko #arxiv-2105.09680 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| pko-t5-large
============
Source Code
pko-t5 는 한국어 전용 데이터로 학습한 t5 v1.1 모델입니다.
한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (나무위키, 위키피디아, 모두의말뭉치 등..) 를 T5 의 span corruption task 를 사용해서 unsupervised learning 만 적용하여 학습을 진행했습니다.
pko-t5 를 사용하실 때는 대상 task 에 파인튜닝하여 사용하시기 바랍니다.
Usage
-----
tr... | [
"### Example\n\n\nKlue 평가 (dev)\n-------------\n\n\n\n* FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝\n* Baseline: KLUE 논문에서 소개된 dev set 에 대한 SOTA 점수\n\n\nLicense\n-------\n\n\nPAUST에서 만든 pko-t5는 MIT license 하에 공개되어 있습니다."
] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #ko #arxiv-2105.09680 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Example\n\n\nKlue 평가 (dev)\n-------------\n\n\n\n* FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝\n* Baseline: KLUE ... |
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. -->
# syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5
This model is a fine-tuned version of [monologg/koelectra-base-v3-generat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5", "results": []}]} | syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5 | null | [
"transformers",
"tf",
"electra",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T11:36:12+00:00 | [] | [] | TAGS
#transformers #tf #electra #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5
============================================================
This model is a fine-tuned version of monologg/koelectra-base-v3-generator on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.1280
* Validation Loss:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #electra #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': 2... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-urdu-roberta
This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuning-sentiment-model-urdu-roberta", "results": []}]} | maazmikail/finetuning-sentiment-model-urdu-roberta | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T11:46:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-urdu-roberta
This model is a fine-tuned version of urduhack/roberta-urdu-small on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# finetuning-sentiment-model-urdu-roberta\n\nThis model is a fine-tuned version of urduhack/roberta-urdu-small 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",... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-urdu-roberta\n\nThis model is a fine-tuned version of urduhack/roberta-urdu-small on an unknown dataset.",
"## M... |
text-generation | transformers |
# JARVIS DialoGPT Model | {"tags": ["conversational"]} | Varick/dialo-jarvis | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T11:48:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# JARVIS DialoGPT Model | [
"# JARVIS DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# JARVIS DialoGPT Model"
] |
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... | Manaranjan/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T11:49:17+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 |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533
## Validation Metrics
- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959... | {"language": "unk", "tags": "autotrain", "datasets": ["Yarn007/autotrain-data-Napkin"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.020162211418903533} | Yarn007/autotrain-Napkin-872827783 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:Yarn007/autotrain-data-Napkin",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T11:59:13+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn007/autotrain-data-Napkin #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533
## Validation Metrics
- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 872827783\n- CO2 Emissions (in grams): 0.020162211418903533",
"## Validation Metrics\n\n- Loss: 0.25198695063591003\n- Accuracy: 0.9325714285714286\n- Macro F1: 0.9254931094274171\n- Micro F1: 0.9325714285714286\n- Weighte... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn007/autotrain-data-Napkin #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 872827783\n- CO2 Emissions (in g... |
token-classification | transformers |
# BiodivBERT
## Model description
* BiodivBERT is a domain-specific BERT based cased model for the biodiversity literature.
* It uses the tokenizer from BERTT base cased model.
* BiodivBERT is pre-trained on abstracts and full text from biodiversity literature.
* BiodivBERT is fine-tuned on two down stream tasks for ... | {"language": ["en"], "license": "apache-2.0", "tags": ["bert-base-cased", "biodiversity", "token-classification", "sequence-classification"], "metrics": ["f1", "precision", "recall", "accuracy"], "thumbnail": "https://www.fusion.uni-jena.de/fusionmedia/fusionpictures/fusion-service/fusion-transp.png?height=383&width=68... | NoYo25/BiodivBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"bert-base-cased",
"biodiversity",
"token-classification",
"sequence-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T12:02:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #bert-base-cased #biodiversity #token-classification #sequence-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BiodivBERT
## Model description
* BiodivBERT is a domain-specific BERT based cased model for the biodiversity literature.
* It uses the tokenizer from BERTT base cased model.
* BiodivBERT is pre-trained on abstracts and full text from biodiversity literature.
* BiodivBERT is fine-tuned on two down stream tasks for ... | [
"# BiodivBERT",
"## Model description\n* BiodivBERT is a domain-specific BERT based cased model for the biodiversity literature.\n* It uses the tokenizer from BERTT base cased model.\n* BiodivBERT is pre-trained on abstracts and full text from biodiversity literature.\n* BiodivBERT is fine-tuned on two down strea... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #bert-base-cased #biodiversity #token-classification #sequence-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BiodivBERT",
"## Model description\n* BiodivBERT is a domain-specific BERT based cased model for the... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ThoDum/PPO-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T12:56:13+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text2text-generation | transformers |
# Modern French normalisation model
Normalisation model from Modern (17th c.) French to contemporary French. It was introduced in [this paper](https://hal.inria.fr/hal-03540226/) (see citation below). The main research repository can be found [here](https://github.com/rbawden/ModFr-Norm). If you use this model, plea... | {"language": "fr", "license": "cc-by-4.0", "inference": false} | rbawden/modern_french_normalisation | null | [
"transformers",
"pytorch",
"safetensors",
"fsmt",
"text2text-generation",
"fr",
"license:cc-by-4.0",
"autotrain_compatible",
"region:us"
] | null | 2022-05-16T12:56:36+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #fsmt #text2text-generation #fr #license-cc-by-4.0 #autotrain_compatible #region-us
|
# Modern French normalisation model
Normalisation model from Modern (17th c.) French to contemporary French. It was introduced in this paper (see citation below). The main research repository can be found here. If you use this model, please cite our research paper (see below).
## Model description
The normalisatio... | [
"# Modern French normalisation model \n\nNormalisation model from Modern (17th c.) French to contemporary French. It was introduced in this paper (see citation below). The main research repository can be found here. If you use this model, please cite our research paper (see below).",
"## Model description\n\nThe ... | [
"TAGS\n#transformers #pytorch #safetensors #fsmt #text2text-generation #fr #license-cc-by-4.0 #autotrain_compatible #region-us \n",
"# Modern French normalisation model \n\nNormalisation model from Modern (17th c.) French to contemporary French. It was introduced in this paper (see citation below). The main resea... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Dutch-Large-ft-CGN-3hrs
A Dutch Wav2Vec2 model. This model is created by fine-tuning [`GroNLP/wav2vec2-dutch-large`](https://huggingface.co/GroNLP/wav2vec2-dutch-large) model on 3 hours of Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-ne... | {"language": "nl", "tags": ["speech"]} | bartelds/wav2vec2-dutch-large-ft-cgn-3hrs | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:00:54+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us
|
# Wav2Vec2-Dutch-Large-ft-CGN-3hrs
A Dutch Wav2Vec2 model. This model is created by fine-tuning 'GroNLP/wav2vec2-dutch-large' model on 3 hours of Dutch speech from Het Corpus Gesproken Nederlands. | [
"# Wav2Vec2-Dutch-Large-ft-CGN-3hrs\n\nA Dutch Wav2Vec2 model. This model is created by fine-tuning 'GroNLP/wav2vec2-dutch-large' model on 3 hours of Dutch speech from Het Corpus Gesproken Nederlands."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us \n",
"# Wav2Vec2-Dutch-Large-ft-CGN-3hrs\n\nA Dutch Wav2Vec2 model. This model is created by fine-tuning 'GroNLP/wav2vec2-dutch-large' model on 3 hours of Dutch speech from Het Corpus Gesproken Neder... |
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... | jespern/TEST2ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T13:11:05+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. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emot... | juliensimon/sagemaker-distilbert-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:22:55+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-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: 0.2402
* Accuracy: 0.919
Model description
-----------------
More information needed
Intended uses & ... | [
"### 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: 64\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 #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
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. -->
# mt5-small-finetuned-multilingual-xlsum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt... | {"license": "apache-2.0", "tags": ["multilingual model", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-multilingual-xlsum", "results": []}]} | ankitkupadhyay/mt5-small-finetuned-multilingual-xlsum | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"multilingual model",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T13:25:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #multilingual model #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-multilingual-xlsum
======================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7979
* Rouge1: 9.2017
* Rouge2: 2.3976
* Rougel: 7.7055
* Rougelsum: 7.7347
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #multilingual model #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... |
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... | Vvek/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T13:33:45+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 |
#harrypotter | {"tags": ["conversational"]} | Robinsd/HarryBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T13:35:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#harrypotter | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-ft-CGN-3hrs
An English Wav2Vec2 model fine-tuned on Dutch. This model is created by fine-tuning [`facebook/wav2vec2-large`](https://huggingface.co/facebook/wav2vec2-large) model on 3 hours of Dutch speech from [Het Corpus Gesproken Nederlands](https://taalmaterialen.ivdnt.org/download/tstc-corpus-ges... | {"language": "nl", "tags": ["speech"]} | bartelds/wav2vec2-large-ft-cgn-3hrs | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"nl",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:38:39+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us
|
# Wav2Vec2-Large-ft-CGN-3hrs
An English Wav2Vec2 model fine-tuned on Dutch. This model is created by fine-tuning 'facebook/wav2vec2-large' model on 3 hours of Dutch speech from Het Corpus Gesproken Nederlands. | [
"# Wav2Vec2-Large-ft-CGN-3hrs\n\nAn English Wav2Vec2 model fine-tuned on Dutch. This model is created by fine-tuning 'facebook/wav2vec2-large' model on 3 hours of Dutch speech from Het Corpus Gesproken Nederlands."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #nl #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-ft-CGN-3hrs\n\nAn English Wav2Vec2 model fine-tuned on Dutch. This model is created by fine-tuning 'facebook/wav2vec2-large' model on 3 hours of Dutch speech from Het Corpus Ge... |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {"license": "other"} | huawei-noah/AutoTinyBERT-S1 | null | [
"transformers",
"pytorch",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:39:19+00:00 | [] | [] | TAGS
#transformers #pytorch #license-other #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #license-other #endpoints_compatible #region-us \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. -->
# syp1229/bert-base-finetuned-koidiom-epoch5
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-bas... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "syp1229/bert-base-finetuned-koidiom-epoch5", "results": []}]} | syp1229/bert-base-finetuned-koidiom-epoch5 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:43:06+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| syp1229/bert-base-finetuned-koidiom-epoch5
==========================================
This model is a fine-tuned version of klue/bert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.8275
* Validation Loss: 1.7743
* Epoch: 4
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #fill-mask #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': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'be... |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {"license": "other"} | huawei-noah/AutoTinyBERT-S2 | null | [
"transformers",
"pytorch",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:48:46+00:00 | [] | [] | TAGS
#transformers #pytorch #license-other #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #license-other #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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... | W42/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 | 2022-05-16T13:49:40+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: 0.2158
* Accuracy: 0.927
* F1: 0.9271
Model description
-----------------
Mor... | [
"### 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... |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {"license": "other"} | huawei-noah/AutoTinyBERT-S3 | null | [
"transformers",
"pytorch",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:52:15+00:00 | [] | [] | TAGS
#transformers #pytorch #license-other #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #license-other #endpoints_compatible #region-us \n"
] |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {"license": "other"} | huawei-noah/AutoTinyBERT-S4 | null | [
"transformers",
"pytorch",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:54:54+00:00 | [] | [] | TAGS
#transformers #pytorch #license-other #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #license-other #endpoints_compatible #region-us \n"
] |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {"license": "other"} | huawei-noah/AutoTinyBERT-KD-S1 | null | [
"transformers",
"pytorch",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T13:58:25+00:00 | [] | [] | TAGS
#transformers #pytorch #license-other #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #license-other #endpoints_compatible #region-us \n"
] |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {} | huawei-noah/AutoTinyBERT-KD-S2 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T14:00:10+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | {"license": "other"} | huawei-noah/AutoTinyBERT-KD-S4 | null | [
"transformers",
"pytorch",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T14:09:51+00:00 | [] | [] | TAGS
#transformers #pytorch #license-other #endpoints_compatible #region-us
| Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neura... | [] | [
"TAGS\n#transformers #pytorch #license-other #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-est
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-est", "results": []}]} | knurm/xlm-roberta-base-finetuned-est | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T14:14:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-est
==============================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8077
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/kyrgyz_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
.... | {"language": "ky", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/kyrgyz_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"ky",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-16T14:19:16+00:00 | [
"1804.00015"
] | [
"ky"
] | TAGS
#espnet #audio #automatic-speech-recognition #ky #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/kyrgyz\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon May 16 11:17:33 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 1... | [
"### 'espnet/kyrgyz\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon May 16 11:17:33 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #ky #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/kyrgyz\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nE... |
null | null | # A Text-Conditioned Diffusion-Prior
## Training Details
[Updated Reports Coming]
## Source Code
Models are diffusion prior trainers from https://github.com/lucidrains/DALLE2-pytorch
## Community: LAION
Join Us!: https://discord.gg/uPMftTmrvS
---
## Intro
A properly trained prior will allow you to translate betw... | {"license": "mit"} | nousr/conditioned-prior | null | [
"arxiv:2204.06125",
"license:mit",
"region:us"
] | null | 2022-05-16T14:43:14+00:00 | [
"2204.06125"
] | [] | TAGS
#arxiv-2204.06125 #license-mit #region-us
| # A Text-Conditioned Diffusion-Prior
## Training Details
[Updated Reports Coming]
## Source Code
Models are diffusion prior trainers from URL
## Community: LAION
Join Us!: URL
---
## Intro
A properly trained prior will allow you to translate between two embedding spaces. If you know *a priori* that two embedding... | [
"# A Text-Conditioned Diffusion-Prior",
"## Training Details\n\n[Updated Reports Coming]",
"## Source Code\nModels are diffusion prior trainers from URL",
"## Community: LAION\nJoin Us!: URL\n\n---",
"## Intro\n\nA properly trained prior will allow you to translate between two embedding spaces. If you know ... | [
"TAGS\n#arxiv-2204.06125 #license-mit #region-us \n",
"# A Text-Conditioned Diffusion-Prior",
"## Training Details\n\n[Updated Reports Coming]",
"## Source Code\nModels are diffusion prior trainers from URL",
"## Community: LAION\nJoin Us!: URL\n\n---",
"## Intro\n\nA properly trained prior will allow you... |
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... | APY/LunarLander | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T14:53:57+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 Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nouman10/robertabase-finetuned-claim-ltp-full-prompt_
This model is a fine-tuned version of [roberta-base](https://huggingface.co/robe... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nouman10/robertabase-finetuned-claim-ltp-full-prompt_", "results": []}]} | nouman10/robertabase-finetuned-claim-ltp-full-prompt_ | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T15:09:03+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
| nouman10/robertabase-finetuned-claim-ltp-full-prompt\_
======================================================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0334
* Validation Loss: 0.0237
* Epoch: 1
Model descripti... | [
"### 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 #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\... |
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)
Using this model becomes easy when you have stable-baselines3 and huggingface... | {"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... | kingabzpro/Full-Force-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T15:21:21+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)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
Then, you can use the model like this:
| [
"# 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 Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\nThen, you can use the model like this:... | [
"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 Using th... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** 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": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ThoDum/DQN-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T15:26:33+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing LunarLander-v2
This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing LunarLander-v2\n This is a trained model of a DQN 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",
"# DQN Agent playing LunarLander-v2\n This is a trained model of a DQN 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 **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | vukpetar/ppo-CarRacing-v0-v3 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T15:49:29+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | mariastull/unit_1 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T15:55:05+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... |
translation | transformers |
## [t5-small](https://huggingface.co/t5-small) exported to the ONNX format
## Model description
[T5](https://huggingface.co/docs/transformers/model_doc/t5#t5) is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text ... | {"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]} | echarlaix/t5-small-onnx | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"summarization",
"translation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T15:58:04+00:00 | [
"1910.10683"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | TAGS
#transformers #onnx #t5 #text2text-generation #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## t5-small exported to the ONNX format
## Model description
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
For more information, please take a look at the original paper.
Paper: Exploring the ... | [
"## t5-small exported to the ONNX format",
"## Model description\n\nT5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.\n\nFor more information, please take a look at the original paper.\n\nPaper: ... | [
"TAGS\n#transformers #onnx #t5 #text2text-generation #summarization #translation #en #fr #ro #de #multilingual #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## t5-small exported to the ONNX format",
"## Model description... |
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. -->
# bert-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-finetuned-mrpc", "results": []}]} | BobBraico/bert-finetuned-mrpc | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T16:04:54+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-mrpc
===================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1719
* Train Accuracy: 0.9359
* Validation Loss: 0.4050
* Validation Accuracy: 0.8382
* Epoch: 2
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 1374, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'cla... |
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... | nazariinyzhnyk/PPO-lunar | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T16:21:06+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... |
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-to-distilbert-NER
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) o... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-to-distilbert-NER", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conl... | importsmart/bert-to-distilbert-NER | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T16:45:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-to-distilbert-NER
======================
This model is a fine-tuned version of dslim/bert-base-NER on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 44.0386
* Precision: 0.0145
* Recall: 0.0185
* F1: 0.0163
* Accuracy: 0.7597
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
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. -->
# xlm-roberta-base-finetuned-est
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-finetuned-est", "results": []}]} | eglesaks/xlm-roberta-base-finetuned-est | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T17:30:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-est
==============================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6781
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585
## Validation Metrics
- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.82... | {"language": "unk", "tags": "autotrain", "datasets": ["Amalq/autotrain-data-smm4h_large_roberta_clean"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 9.123490454955585} | Amalq/autotrain-smm4h_large_roberta_clean-874027878 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:Amalq/autotrain-data-smm4h_large_roberta_clean",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T17:39:21+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-Amalq/autotrain-data-smm4h_large_roberta_clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585
## Validation Metrics
- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.82... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 874027878\n- CO2 Emissions (in grams): 9.123490454955585",
"## Validation Metrics\n\n- Loss: 0.35724225640296936\n- Accuracy: 0.8571428571428571\n- Precision: 0.7637362637362637\n- Recall: 0.8910256410256411\n- AUC: 0.926755536... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-Amalq/autotrain-data-smm4h_large_roberta_clean #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 874027878\n- CO2 ... |
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-emotion-climateChange
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-emotion-climateChange", "results": []}]} | Suhong/distilbert-base-uncased-emotion-climateChange | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T17:42:26+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-emotion-climateChange
=============================================
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: 0.7189
* Accuracy: 0.8416
* F1: 0.7735
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\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",
"### Trai... | [
"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... |
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. -->
# syp1229/roberta-base-finetuned-koidiom-epoch5
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/ro... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "syp1229/roberta-base-finetuned-koidiom-epoch5", "results": []}]} | syp1229/roberta-base-finetuned-koidiom-epoch5 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T17:42:53+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| syp1229/roberta-base-finetuned-koidiom-epoch5
=============================================
This model is a fine-tuned version of klue/roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.9099
* Validation Loss: 1.8647
* Epoch: 4
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #roberta #fill-mask #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': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
| {"library_name": "stable-baselines3", "tags": ["CarRacing-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CarRacing-v0", "type": "CarRacing-... | KhariotnovKK/Car_racing_v0 | null | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T17:45:52+00:00 | [] | [] | TAGS
#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CarRacing-v0
This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CarRacing-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CarRacing-v0\n This is a trained model of a PPO agent playing CarRacing-v0 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your... |
null | null | This is my first model in rl | {} | Blueshell/TEST2ppo-LunarLander-v2 | null | [
"region:us"
] | null | 2022-05-16T17:53:23+00:00 | [] | [] | TAGS
#region-us
| This is my first model in rl | [] | [
"TAGS\n#region-us \n"
] |
null | transformers |
# PIXEL (Pixel-based Encoder of Language)
PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can be typeset... | {"language": ["en"], "license": "apache-2.0", "tags": ["pretraining", "pixel"], "datasets": ["Team-PIXEL/rendered-bookcorpus", "Team-PIXEL/rendered-wikipedia-english"]} | Team-PIXEL/pixel-base | null | [
"transformers",
"pytorch",
"pixel",
"pretraining",
"en",
"dataset:Team-PIXEL/rendered-bookcorpus",
"dataset:Team-PIXEL/rendered-wikipedia-english",
"arxiv:2207.06991",
"arxiv:2111.06377",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-05-16T17:54:48+00:00 | [
"2207.06991",
"2111.06377"
] | [
"en"
] | TAGS
#transformers #pytorch #pixel #pretraining #en #dataset-Team-PIXEL/rendered-bookcorpus #dataset-Team-PIXEL/rendered-wikipedia-english #arxiv-2207.06991 #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# PIXEL (Pixel-based Encoder of Language)
PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can be typeset... | [
"# PIXEL (Pixel-based Encoder of Language)\n\nPIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can be t... | [
"TAGS\n#transformers #pytorch #pixel #pretraining #en #dataset-Team-PIXEL/rendered-bookcorpus #dataset-Team-PIXEL/rendered-wikipedia-english #arxiv-2207.06991 #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# PIXEL (Pixel-based Encoder of Language)\n\nPIXEL is a language mo... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-FR-EN-ST
`s2t-small-covost2-fr-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["fr", "en"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | joaogante/test_audio | null | [
"transformers",
"pytorch",
"safetensors",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"fr",
"en",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T17:55:08+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"fr",
"en"
] | TAGS
#transformers #pytorch #safetensors #speech_to_text #automatic-speech-recognition #audio #speech-translation #fr #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-FR-EN-ST
's2t-small-covost2-fr-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-FR-EN-ST\n\n's2t-small-covost2-fr-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #safetensors #speech_to_text #automatic-speech-recognition #audio #speech-translation #fr #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-FR-EN-ST\n\n's2t-small-covost2-fr-en-st' is a S... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | evolvingstuff/gpt2-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-05-16T19:30:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1128
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | evolvingstuff/bert-base-cased-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-05-16T20:26:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8574
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 #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\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... | ATH0/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-05-16T20:43:12+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... |
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