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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased_fold_11_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_11_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_11_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T11:05:52+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\_fold\_11\_binary\_v1
=============================================
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: 1.8389
* F1: 0.8057
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: 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-to-speech | espnet | license: cc-by-4.0
--- | {"tags": ["espnet", "audio", "text-to-speech"]} | SYSPIN/Telugu_Male_TransformerTTS | null | [
"espnet",
"audio",
"text-to-speech",
"region:us"
] | null | 2022-08-03T11:12:28+00:00 | [] | [] | TAGS
#espnet #audio #text-to-speech #region-us
| license: cc-by-4.0
--- | [] | [
"TAGS\n#espnet #audio #text-to-speech #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_fold_12_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_12_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_12_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T11:20:05+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\_fold\_12\_binary\_v1
=============================================
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: 1.7046
* F1: 0.8165
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: 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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-ft1500_unnorm
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_unnorm", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_unnorm | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T11:24:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_unnorm
================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0557
* Mse: 205571.2188
* Mae: 74.8054
* R2: 0.0463
* Accuracy: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"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... |
null | null | git lfs install
git clone https://huggingface.co/Saraswati/ppo-CartPole-v2 | {} | Saraswati/ppo-CartPole-v2 | null | [
"region:us"
] | null | 2022-08-03T11:27:06+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | nemo |
# NVIDIA Conformer-Transducer Large (Croatian)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-archit... | {"language": ["hr"], "license": "cc-by-4.0", "library_name": "nemo", "tags": ["automatic-speech-recognition", "speech", "audio", "Transducer", "Conformer", "Transformer", "pytorch", "NeMo", "hf-asr-leaderboard"], "datasets": ["ParlaSpeech-HR-v1.0"]} | nvidia/stt_hr_conformer_transducer_large | null | [
"nemo",
"automatic-speech-recognition",
"speech",
"audio",
"Transducer",
"Conformer",
"Transformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"hr",
"dataset:ParlaSpeech-HR-v1.0",
"arxiv:2005.08100",
"license:cc-by-4.0",
"region:us"
] | null | 2022-08-03T11:30:37+00:00 | [
"2005.08100"
] | [
"hr"
] | TAGS
#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #hr #dataset-ParlaSpeech-HR-v1.0 #arxiv-2005.08100 #license-cc-by-4.0 #region-us
| NVIDIA Conformer-Transducer Large (Croatian)
============================================
img {
display: inline;
}
| 
| 
|  |
This model transcribes speech in lowercase Croatian alphabet including spaces, and is tra... | [
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nSimply do:",
"### Transcribing many audio files",
"### Input\n\n\nThis model accepts 16 kHz single-channel audio as input.",
"### Output\n\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n\nModel... | [
"TAGS\n#nemo #automatic-speech-recognition #speech #audio #Transducer #Conformer #Transformer #pytorch #NeMo #hf-asr-leaderboard #hr #dataset-ParlaSpeech-HR-v1.0 #arxiv-2005.08100 #license-cc-by-4.0 #region-us \n",
"### Automatically instantiate the model",
"### Transcribing using Python\n\n\nSimply do:",
"##... |
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. -->
# categorization-finetuned-20220721-164940-pruned-20220803-123018
This model is a fine-tuned version of [carted-nlp/categorization... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "categorization-finetuned-20220721-164940-pruned-20220803-123018", "results": []}]} | carted-nlp/categorization-finetuned-20220721-164940-pruned-20220803-123018 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T11:32:22+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| categorization-finetuned-20220721-164940-pruned-20220803-123018
===============================================================
This model is a fine-tuned version of carted-nlp/categorization-finetuned-20220721-164940 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5476
* Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 314\n* gradient\\_accumulation\\_steps: 6\n* total\\_train\\_batch\\_size: 288\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 48\n* eval\\_batch\\_si... |
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_fold_13_binary_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert-base-uncased_fold_13_binary_v1", "results": []}]} | elopezlopez/distilbert-base-uncased_fold_13_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T11:34:31+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\_fold\_13\_binary\_v1
=============================================
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: 1.7433
* F1: 0.8138
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: 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
null | null |
license: cc-by-4.0
--- | {} | SYSPIN/Telugu_Male_Tacotron2 | null | [
"region:us"
] | null | 2022-08-03T11:38:56+00:00 | [] | [] | TAGS
#region-us
|
license: cc-by-4.0
--- | [] | [
"TAGS\n#region-us \n"
] |
tabular-classification | sklearn |
## Baseline Model trained on arinfo_sample_dataset_finaltffwjv58 to apply classification on model
**Metrics of the best model:**
accuracy 0.930688
recall_macro 0.655991
precision_macro 0.640972
f1_macro 0.638021
Name: DecisionTreeClassifier(class_weight='balanced', max_depth=2249), d... | {"license": "apache-2.0", "library_name": "sklearn", "tags": ["tabular-classification", "baseline-trainer"]} | bhavesh/arinfo_sample_dataset_finaltffwjv58-model-classification | null | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] | null | 2022-08-03T11:40:39+00:00 | [] | [] | TAGS
#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us
|
## Baseline Model trained on arinfo_sample_dataset_finaltffwjv58 to apply classification on model
Metrics of the best model:
accuracy 0.930688
recall_macro 0.655991
precision_macro 0.640972
f1_macro 0.638021
Name: DecisionTreeClassifier(class_weight='balanced', max_depth=2249), dtype... | [
"## Baseline Model trained on arinfo_sample_dataset_finaltffwjv58 to apply classification on model\n\nMetrics of the best model:\n\naccuracy 0.930688\n\nrecall_macro 0.655991\n\nprecision_macro 0.640972\n\nf1_macro 0.638021\n\nName: DecisionTreeClassifier(class_weight='balanced', max_de... | [
"TAGS\n#sklearn #tabular-classification #baseline-trainer #license-apache-2.0 #region-us \n",
"## Baseline Model trained on arinfo_sample_dataset_finaltffwjv58 to apply classification on model\n\nMetrics of the best model:\n\naccuracy 0.930688\n\nrecall_macro 0.655991\n\nprecision_macro 0.64097... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | BekirTaha/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-08-03T11:55:41+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
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_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | sutd-ai/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T11:59:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5027
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | transformers |
# Chinese RoBERTa-Base Model for NER
## Model description
The model is used for named entity recognition. You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from the link [roberta-base-finetuned-cluener2020-chinese](... | {"language": "zh", "widget": [{"text": "\u6c5f\u82cf\u8b66\u65b9\u901a\u62a5\u7279\u65af\u62c9\u51b2\u8fdb\u5e97\u94fa"}]} | canIjoin/datafun | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T12:10:26+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #zh #autotrain_compatible #endpoints_compatible #region-us
|
# Chinese RoBERTa-Base Model for NER
## Model description
The model is used for named entity recognition. You can download the model either from the UER-py Modelzoo page (in UER-py format), or via HuggingFace from the link roberta-base-finetuned-cluener2020-chinese.
## How to use
You can use this model directly wi... | [
"# Chinese RoBERTa-Base Model for NER",
"## Model description\n\nThe model is used for named entity recognition. You can download the model either from the UER-py Modelzoo page (in UER-py format), or via HuggingFace from the link roberta-base-finetuned-cluener2020-chinese.",
"## How to use\n\nYou can use this m... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #zh #autotrain_compatible #endpoints_compatible #region-us \n",
"# Chinese RoBERTa-Base Model for NER",
"## Model description\n\nThe model is used for named entity recognition. You can download the model either from the UER-py Modelzoo page (in ... |
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... | jjjjjjjjjj/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-03T12:18:18+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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_finetuned_xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Mod... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5_finetuned_xsum", "results": []}]} | arinze/t5_finetuned_xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T12:27:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5_finetuned_xsum
This model is a fine-tuned version of t5-small on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followin... | [
"# t5_finetuned_xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5_finetuned_xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
"## ... |
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-ft1500_norm500
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_norm500", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm500 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T12:53:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_norm500
=================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8852
* Mse: 2.9505
* Mae: 1.0272
* R2: 0.4233
* Accuracy: 0.4914... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | jjjjjjjjjj/ppo-LunarLander-v3 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-03T13:03:03+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... |
text-generation | transformers | # GPTJ Booksum model
Model for hierarchical booksum stuff. | {} | ghomasHudson/booksum | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T13:12:02+00:00 | [] | [] | TAGS
#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us
| # GPTJ Booksum model
Model for hierarchical booksum stuff. | [
"# GPTJ Booksum model\n\nModel for hierarchical booksum stuff."
] | [
"TAGS\n#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPTJ Booksum model\n\nModel for hierarchical booksum stuff."
] |
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. -->
# temp
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikigold_splits data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikigold_splits"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "temp", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikigold_splits", "type": "wik... | DOOGLAK/wikigold_trained_no_DA | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikigold_splits",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T13:25:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| temp
====
This model is a fine-tuned version of bert-base-cased on the wikigold\_splits dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1322
* Precision: 0.8517
* Recall: 0.875
* F1: 0.8632
* Accuracy: 0.9607
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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-wikigold_splits #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* le... |
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. -->
# deberta-v3-base_fine_tuned_food_ner
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/mic... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "deberta-v3-base_fine_tuned_food_ner", "results": []}]} | davanstrien/deberta-v3-base_fine_tuned_food_ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-03T13:39:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| deberta-v3-base\_fine\_tuned\_food\_ner
=======================================
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4164
* Precision: 0.9268
* Recall: 0.9446
* F1: 0.9356
* Accuracy: 0.9197
Model de... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
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": []}]} | Petros89/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T13:56:57+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... |
fill-mask | transformers |
# LSG model
**Transformers >= 4.36.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https:... | {"language": "en", "tags": ["bert", "long context"], "pipeline_tag": "fill-mask"} | ccdv/lsg-bert-base-uncased-4096 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"long context",
"custom_code",
"en",
"arxiv:2210.15497",
"arxiv:1810.04805",
"autotrain_compatible",
"region:us"
] | null | 2022-08-03T14:04:22+00:00 | [
"2210.15497",
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #long context #custom_code #en #arxiv-2210.15497 #arxiv-1810.04805 #autotrain_compatible #region-us
|
# LSG model
Transformers >= 4.36.1\
This model relies on a custom modeling file, you need to add trust_remote_code=True\
See \#13467
LSG ArXiv paper. \
Github/conversion script is available at this link.
* Usage
* Parameters
* Sparse selection type
* Tasks
* Training global tokens
This model is adapted from BERT-b... | [
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \\\nGithub/conversion script is available at this link.\n\n* Usage\n* Parameters\n* Sparse selection type\n* Tasks\n* Training global tokens\n\nThis model ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #long context #custom_code #en #arxiv-2210.15497 #arxiv-1810.04805 #autotrain_compatible #region-us \n",
"# LSG model \nTransformers >= 4.36.1\\\nThis model relies on a custom modeling file, you need to add trust_remote_code=True\\\nSee \\#13467\n\nLSG ArXiv paper. \... |
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"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | yasnunsal/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T14:08:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the ... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | bhaskar75/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-03T14:08:41+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
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. -->
# Centrum
Centrum is a pretrained model for multi-document summarization, trained with centroid-based pretraining objective on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["NewSHead"], "model-index": [{"name": "Centrum", "results": []}]} | ratishsp/Centrum | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"generated_from_trainer",
"dataset:NewSHead",
"arxiv:2208.01006",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T14:15:49+00:00 | [
"2208.01006"
] | [] | TAGS
#transformers #pytorch #led #text2text-generation #generated_from_trainer #dataset-NewSHead #arxiv-2208.01006 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Centrum
=======
Centrum is a pretrained model for multi-document summarization, trained with centroid-based pretraining objective on the NewSHead dataset. It is initialized from allenai/led-base-16384. The details of the approach are mentioned in the preprint Multi-Document Summarization with Centroid-Based Pretraini... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n*... | [
"TAGS\n#transformers #pytorch #led #text2text-generation #generated_from_trainer #dataset-NewSHead #arxiv-2208.01006 #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: 3... |
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. -->
# Centrum-multinews
This model is a fine-tuned version of [Centrum](https://huggingface.co/ratishsp/Centrum) on the multi_news dat... | {"tags": ["generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "model-index": [{"name": "Centrum-multinews", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "multi_news", "type": "multi_news", "args": "default"}, "metrics": [{"type": "rouge", "value":... | ratishsp/Centrum-multinews | null | [
"transformers",
"pytorch",
"led",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"arxiv:2208.01006",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T14:32:09+00:00 | [
"2208.01006"
] | [] | TAGS
#transformers #pytorch #led #text2text-generation #generated_from_trainer #dataset-multi_news #arxiv-2208.01006 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Centrum-multinews
=================
This model is a fine-tuned version of Centrum on the multi\_news dataset. The details of the model are mentioned in the preprint Multi-Document Summarization with Centroid-Based Pretraining (Ratish Puduppully and Mark Steedman).
It achieves the following results on the evaluation s... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n*... | [
"TAGS\n#transformers #pytorch #led #text2text-generation #generated_from_trainer #dataset-multi_news #arxiv-2208.01006 #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: 3e-05\... |
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. -->
# distilbert-base-cased_fine_tuned_food_ner
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-cased", "model-index": [{"name": "distilbert-base-cased_fine_tuned_food_ner", "results": []}]} | davanstrien/distilbert-base-cased_fine_tuned_food_ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T14:46:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-cased\_fine\_tuned\_food\_ner
=============================================
This model is a fine-tuned version of distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6129
* Precision: 0.9080
* Recall: 0.9328
* F1: 0.9203
* Accuracy: 0.9095
... | [
"### 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: 50",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-cased #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* ... |
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. -->
# layoutlmv3-finetuned-wildreceipt
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/micros... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["wildreceipt"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "layoutlmv3-finetuned-wildreceipt", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "w... | NitishKarra/layoutlmv3-finetuned-wildreceipt | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:wildreceipt",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T15:06:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-wildreceipt #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv3-finetuned-wildreceipt
================================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the wildreceipt dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3154
* Precision: 0.8693
* Recall: 0.8753
* F1: 0.8723
* Accuracy: 0.9240
Model descripti... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 4000",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-wildreceipt #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #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
```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... | apurva19/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-03T15:45:50+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\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... |
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. -->
# Bio_ClinicalBERT_fold_1_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_1_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_1_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T16:12:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_1\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7063
* F1: 0.8114
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-thai-informal-to-formal
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-thai-informal-to-formal", "results": []}]} | farofang/t5-small-finetuned-thai-informal-to-formal | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T16:23:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-thai-informal-to-formal
==========================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3091
* Bleu: 20.5964
* Gen Len: 19.9981
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 300\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT_fold_2_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_2_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_2_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T16:38:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_2\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9317
* F1: 0.7921
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1216046004
- CO2 Emissions (in grams): 2.4365
## Validation Metrics
- Loss: 0.094
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: ... | {"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["BenWord/autotrain-data-APMv2Multiclass"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 2.4364900803769225}} | BenWord/autotrain-APMv2Multiclass-1216046004 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:BenWord/autotrain-data-APMv2Multiclass",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T17:03:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-BenWord/autotrain-data-APMv2Multiclass #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1216046004
- CO2 Emissions (in grams): 2.4365
## Validation Metrics
- Loss: 0.094
- Accuracy: 1.000
- Macro F1: 1.000
- Micro F1: 1.000
- Weighted F1: 1.000
- Macro Precision: 1.000
- Micro Precision: 1.000
- Weighted Precision: ... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1216046004\n- CO2 Emissions (in grams): 2.4365",
"## Validation Metrics\n\n- Loss: 0.094\n- Accuracy: 1.000\n- Macro F1: 1.000\n- Micro F1: 1.000\n- Weighted F1: 1.000\n- Macro Precision: 1.000\n- Micro Precision: 1.000\n-... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-BenWord/autotrain-data-APMv2Multiclass #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1216046004\n- CO2 Emissi... |
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. -->
# Bio_ClinicalBERT_fold_3_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_3_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_3_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T17:03:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_3\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8860
* F1: 0.8051
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT_fold_4_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_4_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_4_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T17:29:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_4\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4627
* F1: 0.8342
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | Rushikesh/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T17:45:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6893
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-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. -->
# categorization-finetuned-20220721-164940-pruned-20220803-184651
This model is a fine-tuned version of [carted-nlp/categorization... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "categorization-finetuned-20220721-164940-pruned-20220803-184651", "results": []}]} | carted-nlp/categorization-finetuned-20220721-164940-pruned-20220803-184651 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T17:49:03+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| categorization-finetuned-20220721-164940-pruned-20220803-184651
===============================================================
This model is a fine-tuned version of carted-nlp/categorization-finetuned-20220721-164940 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4673
* Acc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 314\n* gradient\\_accumulation\\_steps: 6\n* total\\_train\\_batch\\_size: 288\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 48\n* eval\\_batch\\_si... |
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. -->
# Bio_ClinicalBERT_fold_5_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_5_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_5_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T17:55:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_5\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6689
* F1: 0.8148
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# facebook_large_CV_bn2
This model is a fine-tuned version of [Sameen53/facebook_large_CV_bn3](https://huggingface.co/Sameen53/fac... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "facebook_large_CV_bn2", "results": []}]} | Sameen53/facebook_large_CV_bn2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T18:08:51+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| facebook\_large\_CV\_bn2
========================
This model is a fine-tuned version of Sameen53/facebook\_large\_CV\_bn3 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3172
* Wer: 0.2524
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_... |
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. -->
# Bio_ClinicalBERT_fold_6_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_6_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_6_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T18:20:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_6\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7858
* F1: 0.8079
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
summarization | transformers | # long-t5-tglobal-small-dutch-cnn
A Long-T5 tglobal small model pre-trained from scratch on [cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), and fine-tuned on CNN news summarization in Dutch.
See https://wandb.ai/yepster/long-t5-tglobal-small-dutch-cnn... | {"language": ["nl"], "license": "apache-2.0", "tags": ["summarization", "longt5", "seq2seq"], "datasets": ["yhavinga/mc4_nl_cleaned", "ml6team/cnn_dailymail_nl"], "pipeline_tag": "summarization", "widget": [{"text": "Het Van Goghmuseum in Amsterdam heeft vier kostbare prenten verworven van Mary Cassatt, de Amerikaanse ... | yhavinga/long-t5-tglobal-small-dutch-cnn | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"seq2seq",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"dataset:ml6team/cnn_dailymail_nl",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible"... | null | 2022-08-03T18:21:43+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #longt5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-ml6team/cnn_dailymail_nl #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| # long-t5-tglobal-small-dutch-cnn
A Long-T5 tglobal small model pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4, and fine-tuned on CNN news summarization in Dutch.
See URL
| [
"# long-t5-tglobal-small-dutch-cnn\n\nA Long-T5 tglobal small model pre-trained from scratch on cleaned Dutch 🇳🇱🇧🇪 mC4 and cleaned English 🇬🇧 C4, and fine-tuned on CNN news summarization in Dutch.\n\nSee URL"
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #longt5 #text2text-generation #summarization #seq2seq #nl #dataset-yhavinga/mc4_nl_cleaned #dataset-ml6team/cnn_dailymail_nl #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# long-t5-tglobal-small-dutch-cnn\... |
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. -->
# mal_tls-bert-base-relu-w1q8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalua... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal_tls-bert-base-relu-w1q8", "results": []}]} | SharpAI/mal-tls-bert-base-relu-w1q8 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T18:37:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal_tls-bert-base-relu-w1q8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tra... | [
"# mal_tls-bert-base-relu-w1q8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore info... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal_tls-bert-base-relu-w1q8\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
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. -->
# Bio_ClinicalBERT_fold_7_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_7_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_7_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T18:46:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_7\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6855
* F1: 0.8169
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the kl... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "model-index": [{"name": "bert-base-finetuned-ynat", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "config": "ynat", "split": "train", "args": "ynat"}, "metric... | bash1130/bert-base-finetuned-ynat | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T18:50:38+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-ynat
========================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3609
* F1: 0.8712
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: 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: 5",
"### Trai... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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. -->
# output
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xl... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "output", "results": []}]} | Talha/URDU-ASR | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T18:50:46+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| output
======
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2822
* Wer: 0.2423
* Cer: 0.0842
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 192\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 48\n* eval\\_b... |
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. -->
# story_spanish_category
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/g... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "story_spanish_category", "results": []}]} | dquisi/story_spanish_category | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T19:01:25+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
|
# story_spanish_category
This model is a fine-tuned version of datificate/gpt2-small-spanish 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 hyp... | [
"# story_spanish_category\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish 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 p... | [
"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",
"# story_spanish_category\n\nThis model is a fine-tuned version of datificate/gpt2-small-spanish on the None dataset.... |
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. -->
# mini-phobert-v2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mini-phobert-v2", "results": []}]} | keepitreal/mini-phobert-v2 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T19:07:20+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mini-phobert-v2
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information neede... | [
"# mini-phobert-v2\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.3293",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mini-phobert-v2\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 6.3293",
"## Model desc... |
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. -->
# Bio_ClinicalBERT_fold_8_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_8_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_8_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T19:12:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_8\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5821
* F1: 0.8265
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# prot_bert-finetuned-sp6
This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on t... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "prot_bert-finetuned-sp6", "results": []}]} | pc2976/prot_bert-finetuned-sp6 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T19:30:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| prot\_bert-finetuned-sp6
========================
This model is a fine-tuned version of Rostlab/prot\_bert on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4070
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_s... |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-mask-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) librar... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-mask-prompt-a-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics... | research-backup/roberta-large-conceptnet-mask-prompt-a-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T19:34:22+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-mask-prompt-a-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full resu... | [
"# relbert/roberta-large-conceptnet-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (dataset... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-mask-prompt-a-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done vi... |
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. -->
# Bio_ClinicalBERT_fold_9_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_9_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_9_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T19:38:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_9\_binary\_v1
======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6976
* F1: 0.8065
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="RayS2022/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional att... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | RayS2022/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-03T19:47:04+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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/1576151844383993856/o1cf... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/yeshuaissavior/1665185642178/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/yeshuaissavior | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T19:57:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
The A.I. Whisperer DJ ?= Desire
@yeshuaissavior
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.
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="RayS2022/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | RayS2022/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-03T19:58:15+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# Bio_ClinicalBERT_fold_10_binary_v1
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.c... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "Bio_ClinicalBERT_fold_10_binary_v1", "results": []}]} | elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T20:03:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Bio\_ClinicalBERT\_fold\_10\_binary\_v1
=======================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5504
* F1: 0.8243
Model description
-----------------
More information ne... | [
"### 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# legal-bert-base-uncased-finetuned-filtered-cuad
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["alex-apostolo/filtered-cuad"], "model-index": [{"name": "legal-bert-base-uncased-finetuned-filtered-cuad", "results": []}]} | alex-apostolo/legal-bert-base-filtered-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:alex-apostolo/filtered-cuad",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T20:20:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| legal-bert-base-uncased-finetuned-filtered-cuad
===============================================
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the filtered-cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0436
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
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-tiny-finetuned-legal-definitions
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-tiny-finetuned-legal-definitions", "results": []}]} | muhtasham/bert-tiny-finetuned-legal-definitions | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T20:36:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-tiny-finetuned-legal-definitions
=====================================
This model is a fine-tuned version of google/bert\_uncased\_L-2\_H-128\_A-2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5125
Model description
-----------------
More information needed
Inten... | [
"### 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... |
text-classification | transformers | # Fake News Classification Distilbert 🤗
This model was trained on 32,326 news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news.
0 : Fake News, 1 : Real News
# Sources
Dataset used: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
Base D... | {"license": "mit", "widget": [{"text": "Health and Human Services Secretary Xavier Becerra declared the monkeypox outbreak a public health emergency on Thursday in an effort to galvanize awareness and unlock additional flexibility and funding to fight the virus\u2019s spread."}]} | therealcyberlord/fake-news-classification-distilbert | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T20:45:25+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # Fake News Classification Distilbert
This model was trained on 32,326 news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news.
0 : Fake News, 1 : Real News
# Sources
Dataset used: URL
Base Distilbert: URL
| [
"# Fake News Classification Distilbert \nThis model was trained on 32,326 news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news from real news. \n\n0 : Fake News, 1 : Real News",
"# Sources\nDataset used: URL\n\nBase Distilbert: URL"
] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fake News Classification Distilbert \nThis model was trained on 32,326 news articles from CLÉMENT BISAILLON's dataset on Kaggle. The goal is to classify fake news... |
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/1552061223864127488/Y-7S... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-srinithyananda-yeshuaissavior | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T20:57:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Feather of the One & Elon Musk & KAILASA's SPH Nithyananda
@elonmusk-srinithyananda-yeshuaissavior
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 ho... | [] | [
"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. -->
# protBERTbfd_AAV2_regressor
This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_ber... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "protBERTbfd_AAV2_regressor", "results": []}]} | avuhong/protBERTbfd_AAV2_regressor | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T21:15:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| protBERTbfd\_AAV2\_regressor
============================
This model is a fine-tuned version of Rostlab/prot\_bert\_bfd on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0327
* Mse: 0.0327
* Rmse: 0.1808
* Mae: 0.0618
* R2: 0.8691
* Smape: 101.2324
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 4096\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| na... | {"library_name": "keras"} | khabiri/test_keras_model_elham | null | [
"keras",
"region:us"
] | null | 2022-08-03T21:23:36+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
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/1529956155937759233/Nyn1... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-srinithyananda | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T21:27:29+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & KAILASA's SPH Nithyananda
@elonmusk-srinithyananda
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 ... | [] | [
"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. -->
# distilbert-base-uncased-finetuned-zindi_tweets2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-zindi_tweets2", "results": []}]} | okite97/distilbert-base-uncased-finetuned-zindi_tweets2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T21:34:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-zindi\_tweets2
================================================
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: 0.3185
* Accuracy: 0.9216
* F1: 0.9216
Model description
-----------... | [
"### 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: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers | ## About
This model takes in a word as an input and splits it into syllables. I did this by pre-training a T5 model from a syllables dataset I scraped from the internet. I'm using a custom tokenizer that is effectively character-based. It seems to work okay in my limited tests, but the output may be unpredictable when ... | {} | imjeffhi/syllabizer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-03T22:14:33+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## About
This model takes in a word as an input and splits it into syllables. I did this by pre-training a T5 model from a syllables dataset I scraped from the internet. I'm using a custom tokenizer that is effectively character-based. It seems to work okay in my limited tests, but the output may be unpredictable when ... | [
"## About\nThis model takes in a word as an input and splits it into syllables. I did this by pre-training a T5 model from a syllables dataset I scraped from the internet. I'm using a custom tokenizer that is effectively character-based. It seems to work okay in my limited tests, but the output may be unpredictable... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## About\nThis model takes in a word as an input and splits it into syllables. I did this by pre-training a T5 model from a syllables dataset I scraped from th... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **EnduroNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **EnduroNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable B... | {"library_name": "stable-baselines3", "tags": ["EnduroNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "EnduroNoFrameskip-v4", "ty... | mrm8488/dqn-EnduroNoFrameskip-v4 | null | [
"stable-baselines3",
"EnduroNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-03T22:19:07+00:00 | [] | [] | TAGS
#stable-baselines3 #EnduroNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing EnduroNoFrameskip-v4
This is a trained model of a DQN agent playing EnduroNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
##... | [
"# DQN Agent playing EnduroNoFrameskip-v4\nThis is a trained model of a DQN agent playing EnduroNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents in... | [
"TAGS\n#stable-baselines3 #EnduroNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing EnduroNoFrameskip-v4\nThis is a trained model of a DQN agent playing EnduroNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
fill-mask | transformers |
# bert-tiny-finetuned-legal-contracts-longer
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/google/bert_uncased_L-2_H-128_A-2) on the portion of legal_contracts dataset but for longer epochs.
# Note
The model was not trained on the whole dataset which is arou... | {"datasets": ["albertvillanova/legal_contracts"], "base_model": "google/bert_uncased_L-2_H-128_A-2"} | muhtasham/bert-tiny-finetuned-legal-contracts-longer | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"dataset:albertvillanova/legal_contracts",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T22:19:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #dataset-albertvillanova/legal_contracts #base_model-google/bert_uncased_L-2_H-128_A-2 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-tiny-finetuned-legal-contracts-longer
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the portion of legal_contracts dataset but for longer epochs.
# Note
The model was not trained on the whole dataset which is around 9.5 GB, but only
## The first 10% of 'train' + the last 10% of... | [
"# bert-tiny-finetuned-legal-contracts-longer\n\nThis model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the portion of legal_contracts dataset but for longer epochs.",
"# Note \nThe model was not trained on the whole dataset which is around 9.5 GB, but only",
"## The first 10% of 'train' + t... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #dataset-albertvillanova/legal_contracts #base_model-google/bert_uncased_L-2_H-128_A-2 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-tiny-finetuned-legal-contracts-longer\n\nThis model is a fine-tuned version of google/... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "co... | RayK/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T23:05:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6949
* Matthews Correlation: 0.5410
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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... |
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. -->
# wikigold_trained_no_DA_small
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikigold_splits"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "wikigold_trained_no_DA_small", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikigo... | DOOGLAK/wikigold_trained_no_DA_small | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikigold_splits",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-03T23:47:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| wikigold\_trained\_no\_DA\_small
================================
This model is a fine-tuned version of bert-base-cased on the wikigold\_splits dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6066
* Precision: 0.3429
* Recall: 0.5455
* F1: 0.4211
* Accuracy: 0.8530
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: 32",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #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* le... |
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. -->
# my_bert-base-cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "my_bert-base-cased", "results": []}]} | jerryw/my_bert-base-cased | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T00:34:19+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# my_bert-base-cased
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information neede... | [
"# my_bert-base-cased\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\n... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# my_bert-base-cased\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",... |
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. -->
# scibert-lm-v1-finetuned-20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model-index": [{"name": "scibert-lm-v1-finetuned-20", "results": []}]} | ariesutiono/scibert-lm-v1-finetuned-20 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T00:57:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us
| scibert-lm-v1-finetuned-20
==========================
This model is a fine-tuned version of allenai/scibert\_scivocab\_cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 22.6145
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #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: 4\n*... |
null | null | Mateo Martínez, argentinian
license: afl-3.0
---
| {} | Mateopablo/Futur | null | [
"region:us"
] | null | 2022-08-04T01:26:46+00:00 | [] | [] | TAGS
#region-us
| Mateo Martínez, argentinian
license: afl-3.0
---
| [] | [
"TAGS\n#region-us \n"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jjjjjjjjjj/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to ad... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | jjjjjjjjjj/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-04T02:13:43+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | RayS2022/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-04T02:16:11+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# wikigold_trained_no_DA_testing
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikigold_splits"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "wikigold_trained_no_DA_testing", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wiki... | DOOGLAK/wikigold_trained_no_DA_testing | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikigold_splits",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T03:04:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| wikigold\_trained\_no\_DA\_testing
==================================
This model is a fine-tuned version of bert-base-cased on the wikigold\_splits dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1490
* Precision: 0.8380
* Recall: 0.8490
* F1: 0.8435
* Accuracy: 0.9564
Model descriptio... | [
"### 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-wikigold_splits #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* le... |
text-generation | transformers |
# Introduction
We have scrapped all the collected works of Mohandas Karamchand Gandhi (aka Mahatma Gandhi) from [here](http://www.gandhiashramsevagram.org/gandhi-literature/collected-works-of-mahatma-gandhi-volume-1-to-98.php). Cleaned the text so that it contains only the writings of Gandhi without footnotes, titles... | {"license": "gpl"} | ritwikm/gandhi-gpt | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-04T04:01:23+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #license-gpl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Introduction
We have scrapped all the collected works of Mohandas Karamchand Gandhi (aka Mahatma Gandhi) from here. Cleaned the text so that it contains only the writings of Gandhi without footnotes, titles, and other texts.
We observed that (after cleaning), Gandhi wrote 755468 sentences.
We first fine-tuned g... | [
"# Introduction\n\nWe have scrapped all the collected works of Mohandas Karamchand Gandhi (aka Mahatma Gandhi) from here. Cleaned the text so that it contains only the writings of Gandhi without footnotes, titles, and other texts.\n\n\nWe observed that (after cleaning), Gandhi wrote 755468 sentences.\n\n\nWe first ... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #license-gpl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Introduction\n\nWe have scrapped all the collected works of Mohandas Karamchand Gandhi (aka Mahatma Gandhi) from here. Cleaned the text so that i... |
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-cased-distilled-squad-finetuned-squad
This model is a fine-tuned version of [distilbert-base-cased-distilled-squ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2_yash"], "model-index": [{"name": "distilbert-base-cased-distilled-squad-finetuned-squad", "results": []}]} | kuberpmu/distilbert-base-cased-distilled-squad-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2_yash",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T04:20:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2_yash #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-cased-distilled-squad-finetuned-squad
=====================================================
This model is a fine-tuned version of distilbert-base-cased-distilled-squad on the squad\_v2\_yash dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0088
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2_yash #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
null | null |
SqueezeNet model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia, [... | {"license": "mit"} | FluxML/squeezenet | null | [
"license:mit",
"region:us"
] | null | 2022-08-04T04:50:38+00:00 | [] | [] | TAGS
#license-mit #region-us
|
SqueezeNet model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
DenseNet121 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia, ... | {"license": "mit"} | FluxML/densenet121 | null | [
"license:mit",
"region:us"
] | null | 2022-08-04T05:12:25+00:00 | [] | [] | TAGS
#license-mit #region-us
|
DenseNet121 model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
DenseNet161 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia, ... | {"license": "mit"} | FluxML/densenet161 | null | [
"license:mit",
"region:us"
] | null | 2022-08-04T05:16:19+00:00 | [] | [] | TAGS
#license-mit #region-us
|
DenseNet161 model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
DenseNet169 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia, ... | {"license": "mit"} | FluxML/densenet169 | null | [
"license:mit",
"region:us"
] | null | 2022-08-04T05:20:00+00:00 | [] | [] | TAGS
#license-mit #region-us
|
DenseNet169 model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
DenseNet201 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef).
To use this model in Julia, ... | {"license": "mit"} | FluxML/densenet201 | null | [
"license:mit",
"region:us"
] | null | 2022-08-04T05:22:23+00:00 | [] | [] | TAGS
#license-mit #region-us
|
DenseNet201 model ported from torchvision for use with URL. The scripts for creating this file can be found at this gist.
To use this model in Julia, add the URL package to your environment. Then execute:
| [] | [
"TAGS\n#license-mit #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
```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... | ajpapa/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-04T05:26:57+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\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... |
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. -->
# tonality
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "tonality", "results": []}]} | isaacaderogba/tonality | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T06:33:36+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# tonality
This model is a fine-tuned version of distilbert-base-cased 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 fo... | [
"# tonality\n\nThis model is a fine-tuned version of distilbert-base-cased 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",
"### Tr... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# tonality\n\nThis model is a fine-tuned version of distilbert-base-cased on an unknown dataset.",
"## Model description\n\nMore information nee... |
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-XLSR-ft-10
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-XLSR-ft-10", "results": []}]} | KaranChand/wav2vec2-XLSR-ft-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T06:37:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-XLSR-ft-10
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# wav2vec2-XLSR-ft-10\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english 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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-XLSR-ft-10\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on an unknown dataset.",
"## Model de... |
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. -->
# distilroberta-base-finetuned-marktextepoch_200
This model was trained from scratch on an unknown dataset.
## Model description
... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-finetuned-marktextepoch_200", "results": []}]} | yochen/distilroberta-base-finetuned-marktextepoch_200 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T06:42:55+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# distilroberta-base-finetuned-marktextepoch_200
This model was trained from scratch 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 hyperpara... | [
"# distilroberta-base-finetuned-marktextepoch_200\n\nThis model was trained from scratch 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 procedu... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilroberta-base-finetuned-marktextepoch_200\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"... |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846144
- CO2 Emissions (in grams): 3198.3977
## Validation Metrics
- Loss: 2.449
- Rouge1: 38.839
- Rouge2: 10.865
- RougeL: 21.994
- RougeLsum: 33.794
- Gen Len: 120.994
## Usage
You can use cURL to access this model:
```
$ curl -X PO... | {"language": ["unk"], "tags": ["autotrain", "summarization"], "datasets": ["L-macc/autotrain-data-Biomedical_sc_summ"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 3198.3976606503647}, "model-index": [{"name": "L-macc/autotrain-Biomedical_sc_summ-1217846144", "results": [{"t... | L-macc/autotrain-Biomedical_sc_summ-1217846144 | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:L-macc/autotrain-data-Biomedical_sc_summ",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T06:45:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-L-macc/autotrain-data-Biomedical_sc_summ #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846144
- CO2 Emissions (in grams): 3198.3977
## Validation Metrics
- Loss: 2.449
- Rouge1: 38.839
- Rouge2: 10.865
- RougeL: 21.994
- RougeLsum: 33.794
- Gen Len: 120.994
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1217846144\n- CO2 Emissions (in grams): 3198.3977",
"## Validation Metrics\n\n- Loss: 2.449\n- Rouge1: 38.839\n- Rouge2: 10.865\n- RougeL: 21.994\n- RougeLsum: 33.794\n- Gen Len: 120.994",
"## Usage\n\nYou can use cURL to access this... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-L-macc/autotrain-data-Biomedical_sc_summ #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 121... |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846142
- CO2 Emissions (in grams): 16.2112
## Validation Metrics
- Loss: 2.159
- Rouge1: 40.236
- Rouge2: 12.161
- RougeL: 23.255
- RougeLsum: 35.138
- Gen Len: 121.504
## Usage
You can use cURL to access this model:
```
$ curl -X POST... | {"language": ["unk"], "tags": ["autotrain", "summarization"], "datasets": ["L-macc/autotrain-data-Biomedical_sc_summ"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 16.211223325053414}, "model-index": [{"name": "L-macc/autotrain-Biomedical_sc_summ-1217846142", "results": [{"t... | L-macc/autotrain-Biomedical_sc_summ-1217846142 | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:L-macc/autotrain-data-Biomedical_sc_summ",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T06:45:06+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-L-macc/autotrain-data-Biomedical_sc_summ #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846142
- CO2 Emissions (in grams): 16.2112
## Validation Metrics
- Loss: 2.159
- Rouge1: 40.236
- Rouge2: 12.161
- RougeL: 23.255
- RougeLsum: 35.138
- Gen Len: 121.504
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1217846142\n- CO2 Emissions (in grams): 16.2112",
"## Validation Metrics\n\n- Loss: 2.159\n- Rouge1: 40.236\n- Rouge2: 12.161\n- RougeL: 23.255\n- RougeLsum: 35.138\n- Gen Len: 121.504",
"## Usage\n\nYou can use cURL to access this m... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-L-macc/autotrain-data-Biomedical_sc_summ #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 121... |
summarization | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846148
- CO2 Emissions (in grams): 13.6520
## Validation Metrics
- Loss: 2.503
- Rouge1: 38.768
- Rouge2: 10.791
- RougeL: 21.946
- RougeLsum: 33.780
- Gen Len: 123.331
## Usage
You can use cURL to access this model:
```
$ curl -X POST... | {"language": ["unk"], "tags": ["autotrain", "summarization"], "datasets": ["L-macc/autotrain-data-Biomedical_sc_summ"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 13.651986586580765}, "model-index": [{"name": "L-macc/autotrain-Biomedical_sc_summ-1217846148", "results": [{"t... | L-macc/autotrain-Biomedical_sc_summ-1217846148 | null | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:L-macc/autotrain-data-Biomedical_sc_summ",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-04T06:45:21+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-L-macc/autotrain-data-Biomedical_sc_summ #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1217846148
- CO2 Emissions (in grams): 13.6520
## Validation Metrics
- Loss: 2.503
- Rouge1: 38.768
- Rouge2: 10.791
- RougeL: 21.946
- RougeLsum: 33.780
- Gen Len: 123.331
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 1217846148\n- CO2 Emissions (in grams): 13.6520",
"## Validation Metrics\n\n- Loss: 2.503\n- Rouge1: 38.768\n- Rouge2: 10.791\n- RougeL: 21.946\n- RougeLsum: 33.780\n- Gen Len: 123.331",
"## Usage\n\nYou can use cURL to access this m... | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #autotrain #summarization #unk #dataset-L-macc/autotrain-data-Biomedical_sc_summ #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Mo... |
text-generation | transformers |
# Lyem Ningthou DialoGPT Model | {"tags": ["conversational"]} | Lyem/LyemBotv3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-04T06:54:39+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Lyem Ningthou DialoGPT Model | [
"# Lyem Ningthou DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Lyem Ningthou DialoGPT Model"
] |
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. -->
# mini-phobert-v3
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "mini-phobert-v3", "results": []}]} | keepitreal/mini-phobert-v3 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T07:08:28+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# mini-phobert-v3
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0510
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information neede... | [
"# mini-phobert-v3\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0510",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# mini-phobert-v3\n\nThis model is a fine-tuned version of [](URL on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0510",
"## Model desc... |
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-ft1500_class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ft1500_class", "results": []}]} | dminiotas05/distilbert-base-uncased-finetuned-ft1500_class | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T07:18:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ft1500\_class
===============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9779
* Accuracy: 0.2357
* F1: 0.2352
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"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... |
question-answering | transformers |
# INT8 DistilBERT base uncased finetuned on Squad
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
Th... | {"license": "apache-2.0", "tags": ["neural-compressor", "8-bit", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic", "onnx"], "datasets": ["squad"], "metrics": ["f1"]} | Intel/distilbert-base-uncased-distilled-squad-int8-static | null | [
"transformers",
"pytorch",
"onnx",
"distilbert",
"question-answering",
"neural-compressor",
"8-bit",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T07:18:57+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #distilbert #question-answering #neural-compressor #8-bit #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| INT8 DistilBERT base uncased finetuned on Squad
===============================================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 ... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model distilbert-base-uncased-distilled-squad.\n\n\nThe calibration dataloader is the train dataloader. The default calibrati... | [
"TAGS\n#transformers #pytorch #onnx #distilbert #question-answering #neural-compressor #8-bit #int8 #Intel® Neural Compressor #PostTrainingStatic #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel throug... |
text2text-generation | transformers | # Romanian paraphrase
Fine-tune t5-base model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v1). The dataset contains ~60k examples.
### H... | {"language": ["ro"], "license": ["apache-2.0"], "tags": [], "annotations_creators": [], "language_creators": ["machine-generated"], "multilinguality": ["monolingual"], "pretty_name": "BlackKakapo/t5-base-paraphrase-ro", "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-g... | BlackKakapo/t5-base-paraphrase-ro | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ro",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-04T07:27:22+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Romanian paraphrase
!v1.0
Fine-tune t5-base model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~60k examples.
### How to use
### Or
### Generate
### Output
| [
"# Romanian paraphrase\n\n!v1.0\n\nFine-tune t5-base model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own dataset. The dataset contains ~60k examples.",
"### How to use",
"### Or",
"### Generate",
"### Output"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ro #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Romanian paraphrase\n\n!v1.0\n\nFine-tune t5-base model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my ... |
null | null | First try HF | {"license": "apache-2.0"} | pesl98/HF_01 | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-04T07:39:24+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| First try HF | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
null | null | ## COGMEN; Official Pytorch Implementation
[](https://paperswithcode.com/sota/multimodal-emotion-recognition-on-iemocap?p=cogmen-contextualized-gnn-based-m... | {"license": "cc-by-nc-4.0"} | sagar122/xperimentalilst_hackathon_2022 | null | [
"arxiv:2205.02455",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-08-04T07:49:20+00:00 | [
"2205.02455"
] | [] | TAGS
#arxiv-2205.02455 #license-cc-by-nc-4.0 #region-us
| ## COGMEN; Official Pytorch Implementation
![PWC](URL
COntextualized GNN based Multimodal Emotion recognitioN
!Teaser image
Picture: *My sample picture for logo*
This repository contains the official Pytorch implementation of the following paper:
> COGMEN: COntextualized GNN based Multimodal Emotion recognitioN<br>
... | [
"## COGMEN; Official Pytorch Implementation\n![PWC](URL\n\nCOntextualized GNN based Multimodal Emotion recognitioN\n!Teaser image\nPicture: *My sample picture for logo*\n\nThis repository contains the official Pytorch implementation of the following paper:\n> COGMEN: COntextualized GNN based Multimodal Emotion reco... | [
"TAGS\n#arxiv-2205.02455 #license-cc-by-nc-4.0 #region-us \n",
"## COGMEN; Official Pytorch Implementation\n![PWC](URL\n\nCOntextualized GNN based Multimodal Emotion recognitioN\n!Teaser image\nPicture: *My sample picture for logo*\n\nThis repository contains the official Pytorch implementation of the following p... |
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | ligerre/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T08:09:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7778
* Accuracy: 0.9171
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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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* lea... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-filtered-cuad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the cuad... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["alex-apostolo/filtered-cuad"], "model-index": [{"name": "roberta-base-filtered-cuad", "results": []}]} | alex-apostolo/roberta-base-filtered-cuad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:alex-apostolo/filtered-cuad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T08:12:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #license-mit #endpoints_compatible #region-us
| roberta-base-filtered-cuad
==========================
This model is a fine-tuned version of roberta-base on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0396
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-alex-apostolo/filtered-cuad #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... |
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"} | nondevs/simcse-model-thai-v0 | null | [
"sentence-transformers",
"pytorch",
"camembert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T08:20:48+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #camembert #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 #camembert #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 ... |
feature-extraction | transformers | # SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
paper available at [https://arxiv.org/pdf/2207.02578](https://arxiv.org/pdf/2207.02578)
code available at [https://github.com/microsoft/unilm/tree/master/simlm](https://github.com/microsoft/unilm/tree/master/simlm)
## Paper abstract
I... | {"language": ["en"], "license": "mit"} | intfloat/simlm-base-msmarco-finetuned | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2207.02578",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-04T08:21:28+00:00 | [
"2207.02578"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2207.02578 #license-mit #endpoints_compatible #region-us
| SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
==============================================================================
paper available at URL
code available at URL
Paper abstract
--------------
In this paper, we propose SimLM (Similarity matching with Language Model pre-tr... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2207.02578 #license-mit #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **RecurrentPPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **RecurrentPPO** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework fo... | {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "RecurrentPPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "t... | Chris1/ppo_lstm-BipedalWalker-v3-1 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-04T08:58:17+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# RecurrentPPO Agent playing BipedalWalker-v3
This is a trained model of a RecurrentPPO agent playing BipedalWalker-v3
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents inc... | [
"# RecurrentPPO Agent playing BipedalWalker-v3\nThis is a trained model of a RecurrentPPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained... | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# RecurrentPPO Agent playing BipedalWalker-v3\nThis is a trained model of a RecurrentPPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
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