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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": []}]} | x574chen/wav2vec2-common_voice-tr-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:04:34+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-tr-demo
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3815
* Wer: 0.3493
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | dkasti/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3996
* F1: 0.6886
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | JXL884/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 | 2022-06-02T01:05:53+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 ... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | dkasti/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:10:13+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1769
* F1: 0.8533
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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 #xlm-roberta #token-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: 5e-05\n* train\\_batch\\_size: 24\n*... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-19
WER 0.283
WER 0.129 with 2-Gram
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-19", "results": []}]} | chrisvinsen/wav2vec2-final-1-lm-1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:20:22+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-19
===========
WER 0.283
WER 0.129 with 2-Gram
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6305
* Wer: 0.4499
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_b... |
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. -->
# wav2vec2-19
WER 0.283
WER 0.126 with 3-Gram
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-19", "results": []}]} | chrisvinsen/wav2vec2-final-1-lm-2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:20:45+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-19
===========
WER 0.283
WER 0.126 with 3-Gram
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6305
* Wer: 0.4499
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_b... |
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. -->
# wav2vec2-19
WER 0.283
WER 0.126 with 4-Gram
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-19", "results": []}]} | chrisvinsen/wav2vec2-final-1-lm-3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:20:52+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-19
===========
WER 0.283
WER 0.126 with 4-Gram
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6305
* Wer: 0.4499
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_b... |
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. -->
# wav2vec2-19
WER 0.283
WER 0.126 with 5-Gram
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-19", "results": []}]} | chrisvinsen/wav2vec2-final-1-lm-4 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T01:21:02+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-19
===========
WER 0.283
WER 0.126 with 5-Gram
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6305
* Wer: 0.4499
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_b... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Andaf/chatbot-trvlk-finetuned-squad
This model is a fine-tuned version of [cahya/bert-base-indonesian-522M](https://huggingface.co/cah... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Andaf/chatbot-trvlk-finetuned-squad", "results": []}]} | Andaf/bert-uncased-finetuned-squad-indonesian | null | [
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T02:19:04+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #bert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us
| Andaf/chatbot-trvlk-finetuned-squad
===================================
This model is a fine-tuned version of cahya/bert-base-indonesian-522M on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.5335
* Validation Loss: 6.4566
* Epoch: 1
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 14444, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #bert #question-answering #generated_from_keras_callback #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'Poly... |
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. -->
# tiny_ktoto_punctuator
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalua... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_ktoto_punctuator", "results": []}]} | kktoto/tiny_ktoto_punctuator | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T02:34:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_ktoto\_punctuator
=======================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1342
* Precision: 0.6446
* Recall: 0.6184
* F1: 0.6312
* Accuracy: 0.9503
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-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: 4e-05\n* train\\_batch\\_size: 16\n* eval\\... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-fine-tuned-rajat
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bert-fine-tuned-rajat", "results": []}]} | gullu72/bert-fine-tuned-rajat | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T02:50:40+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-fine-tuned-rajat
=====================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1791
* Validation Loss: 0.4963
* Epoch: 2
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_r... |
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. -->
# tiny_bb_wd
This model is a fine-tuned version of [kktoto/tiny_bb_wd](https://huggingface.co/kktoto/tiny_bb_wd) on an unknown dat... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "tiny_bb_wd", "results": []}]} | kktoto/tiny_bb_wd | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T03:01:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| tiny\_bb\_wd
============
This model is a fine-tuned version of kktoto/tiny\_bb\_wd on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1331
* Precision: 0.6566
* Recall: 0.6502
* F1: 0.6533
* Accuracy: 0.9524
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\... |
tabular-classification | keras |
## Model description
This repo contains the model and the notebook on [how to build and train a Keras model for Collaborative Filtering for Movie Recommendations](https://keras.io/examples/structured_data/collaborative_filtering_movielens/).
Full credits to [Siddhartha Banerjee](https://twitter.com/sidd2006).
## I... | {"license": ["cc0-1.0"], "library_name": "keras", "tags": ["collaborative-filtering", "recommender", "tabular-classification"]} | keras-io/collaborative-filtering-movielens | null | [
"keras",
"tensorboard",
"collaborative-filtering",
"recommender",
"tabular-classification",
"license:cc0-1.0",
"has_space",
"region:us"
] | null | 2022-06-02T03:20:04+00:00 | [] | [] | TAGS
#keras #tensorboard #collaborative-filtering #recommender #tabular-classification #license-cc0-1.0 #has_space #region-us
| Model description
-----------------
This repo contains the model and the notebook on how to build and train a Keras model for Collaborative Filtering for Movie Recommendations.
Full credits to Siddhartha Banerjee.
Intended uses & limitations
---------------------------
Based on a user and movies they have rated... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #collaborative-filtering #recommender #tabular-classification #license-cc0-1.0 #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.... |
text2text-generation | transformers | ## 模型
- 基于中文[MengziT5](https://huggingface.co/Langboat/mengzi-t5-base)的新闻评论生成模型
- 数据集来源于论文[《Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model》](https://github.com/lancopku/Graph-to-seq-comment-generation)
## 生成评论
- 在线API只能生成一种评论，模型通过设置model.generate()参数是可以生成多种评论的
```Python
t5_tokenizer ... | {"language": ["zh"], "license": "apache-2.0", "datasets": ["TencentKuaibao"], "metrics": ["bleu", "rouge"]} | wawaup/MengziT5-Comment | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"zh",
"dataset:TencentKuaibao",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T03:44:50+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #zh #dataset-TencentKuaibao #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## 模型
- 基于中文MengziT5的新闻评论生成模型
- 数据集来源于论文《Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model》
## 生成评论
- 在线API只能生成一种评论，模型通过设置model.generate()参数是可以生成多种评论的
| [
"## 模型\n- 基于中文MengziT5的新闻评论生成模型\n- 数据集来源于论文《Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model》",
"## 生成评论\n- 在线API只能生成一种评论，模型通过设置model.generate()参数是可以生成多种评论的"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #zh #dataset-TencentKuaibao #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## 模型\n- 基于中文MengziT5的新闻评论生成模型\n- 数据集来源于论文《Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model... |
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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["precision", "recall"], "model-index": [{"name": "bert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emo... | ShoneRan/bert-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T03:55:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-emotion
============
This model is a fine-tuned version of distilbert-base-cased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1670
* Precision: 0.7262
* Recall: 0.7255
* Fscore: 0.7253
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* 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 #dataset-tweet_eval #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... |
null | null |
## File descriptions
| Filename | Description |
|----------|-------------|
|2022-06-02-danpovey.mov| <https://2022-live.baai.ac.cn/2022/live/?room_id=16989> |
|emformer-streaming-asr-demo.mov| <https://github.com/k2-fsa/sherpa/pull/6> |
|2023-06-08-vision-five-2-bilingual-zipformer-demo.mov|<https://www.bilibili.com/... | {} | csukuangfj/2022-next-gen-kaldi-videos | null | [
"region:us"
] | null | 2022-06-02T04:05:52+00:00 | [] | [] | TAGS
#region-us
| File descriptions
-----------------
| [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ThePixOne/SeconBERTa1 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T04:46:38+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #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 #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or... |
null | transformers | Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created an... | {"license": "mit"} | TencentGameMate/chinese-wav2vec2-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T05:17:07+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #license-mit #endpoints_compatible #region-us
| Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
python... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers | Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created an... | {"license": "mit"} | TencentGameMate/chinese-wav2vec2-large | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-02T05:20:03+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #license-mit #endpoints_compatible #has_space #region-us
| Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
python... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #license-mit #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created an... | {"license": "mit"} | TencentGameMate/chinese-hubert-base | null | [
"transformers",
"pytorch",
"hubert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-02T05:21:23+00:00 | [] | [] | TAGS
#transformers #pytorch #hubert #feature-extraction #license-mit #endpoints_compatible #has_space #region-us
| Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
python... | [] | [
"TAGS\n#transformers #pytorch #hubert #feature-extraction #license-mit #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created an... | {"license": "mit"} | TencentGameMate/chinese-hubert-large | null | [
"transformers",
"pytorch",
"hubert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T05:22:33+00:00 | [] | [] | TAGS
#transformers #pytorch #hubert #feature-extraction #license-mit #endpoints_compatible #region-us
| Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
python... | [] | [
"TAGS\n#transformers #pytorch #hubert #feature-extraction #license-mit #endpoints_compatible #region-us \n"
] |
text-generation | keras |
## Model description
This repo contains the model which showcases the learning capabilities of LSTM using a simple example. A single-layer LSTM is made to learn to add two numbers, provided as strings. The model has been trained for adding two numbers where each number can have maximum of 5 digits.
*Example:*
Inp... | {"library_name": "keras", "tags": ["text-generation"]} | keras-io/addition-lstm | null | [
"keras",
"tensorboard",
"text-generation",
"has_space",
"region:us"
] | null | 2022-06-02T05:40:24+00:00 | [] | [] | TAGS
#keras #tensorboard #text-generation #has_space #region-us
| Model description
-----------------
This repo contains the model which showcases the learning capabilities of LSTM using a simple example. A single-layer LSTM is made to learn to add two numbers, provided as strings. The model has been trained for adding two numbers where each number can have maximum of 5 digits.
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* optimizer: {'name': 'Adam', 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n* num\\_epochs:... | [
"TAGS\n#keras #tensorboard #text-generation #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* optimizer: {'name': 'Adam', 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1... |
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/1268155013882396672/Ev_5... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/contextmemlab-jeremyrmanning/1654153159177/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/contextmemlab-jeremyrmanning | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T05:55:41+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Jeremy Manning & Context Lab
@contextmemlab-jeremyrmanning
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | yannis95/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T05:57:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0665
* Precision: 0.9261
* Recall: 0.9455
* F1: 0.9357
* Accuracy: 0.9851
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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/1359906890340306950/s5cX... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/paxt0n4/1654155052782/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/paxt0n4 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T06:30:25+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Paxton Fitzpatrick
@paxt0n4
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# dialogue-bart-base-chinese
This is a seq2seq model fine-tuned on several Chinese dialogue datasets, from bart-base-chinese.
# Spaces
Now you can experience our model on HuggingFace Spaces [HIT-TMG/dialogue-bart-large-chinese](https://huggingface.co/spaces/HIT-TMG/dialogue-bart-large-chinese) .
# Datasets
We util... | {"language": ["zh"], "tags": ["bart-base-chinese"], "datasets": ["lccc", "kd_conv"], "thumbnail": "url to a thumbnail used in social sharing"} | HIT-TMG/dialogue-bart-base-chinese | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"bart-base-chinese",
"zh",
"dataset:lccc",
"dataset:kd_conv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T06:34:20+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bart #text2text-generation #bart-base-chinese #zh #dataset-lccc #dataset-kd_conv #autotrain_compatible #endpoints_compatible #region-us
| dialogue-bart-base-chinese
==========================
This is a seq2seq model fine-tuned on several Chinese dialogue datasets, from bart-base-chinese.
Spaces
======
Now you can experience our model on HuggingFace Spaces HIT-TMG/dialogue-bart-large-chinese .
Datasets
========
We utilize 4 Chinese dialogue data... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #bart-base-chinese #zh #dataset-lccc #dataset-kd_conv #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | # Face Detection using DEtection TRansformers from Facebook AI 🚀
This repository includes
* Training Pipeline for DETR on Custom dataset
* Wider Face Dataset annotaions ... | {} | nsa/detr_r50_ep15 | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T06:46:19+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| Face Detection using DEtection TRansformers from Facebook AI
============================================================
!PyTorch 1.5 +
!torch vision 0.6 +
This repository includes
* Training Pipeline for DETR on Custom dataset
* Wider Face Dataset annotaions and images
* Evaluation on test dataset
* Trained wei... | [] | [
"TAGS\n#transformers #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | dsghrg/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T07:00:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0646
* Precision: 0.9339
* Recall: 0.9510
* F1: 0.9424
* Accuracy: 0.9864
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers | ## CYBERT
BERT model dedicated to the domain of cyber security. The model has been trained on a corpus of high-quality cyber security and computer science text and is unlikely to work outside this domain.
##Model architecture
The model architecture used is original Roberta and tokenizer to train the corpus is Byte ... | {} | SynamicTechnologies/CYBERT | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-02T07:22:55+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## CYBERT
BERT model dedicated to the domain of cyber security. The model has been trained on a corpus of high-quality cyber security and computer science text and is unlikely to work outside this domain.
##Model architecture
The model architecture used is original Roberta and tokenizer to train the corpus is Byte ... | [
"## CYBERT\n\nBERT model dedicated to the domain of cyber security. The model has been trained on a corpus of high-quality cyber security and computer science text and is unlikely to work outside this domain."
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## CYBERT\n\nBERT model dedicated to the domain of cyber security. The model has been trained on a corpus of high-quality cyber security and computer science text and is unlikely to ... |
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="jcastanyo/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional at... | {"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": ... | jcastanyo/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-02T07:37:28+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"
] |
text2text-generation | transformers |
# dialogue-bart-large-chinese
This is a seq2seq model pre-trained on several Chinese dialogue datasets, from bart-large-chinese. It's better to fine-tune it on downstream tasks for better performance.
# Spaces
Now you can experience our model on HuggingFace Spaces [HIT-TMG/dialogue-bart-large-chinese](https://huggin... | {"language": ["zh"], "tags": ["bart-large-chinese"], "datasets": ["lccc", "kd_conv"], "thumbnail": "url to a thumbnail used in social sharing"} | HIT-TMG/dialogue-bart-large-chinese | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"bart-large-chinese",
"zh",
"dataset:lccc",
"dataset:kd_conv",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-06-02T07:42:16+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bart #text2text-generation #bart-large-chinese #zh #dataset-lccc #dataset-kd_conv #autotrain_compatible #endpoints_compatible #has_space #region-us
| dialogue-bart-large-chinese
===========================
This is a seq2seq model pre-trained on several Chinese dialogue datasets, from bart-large-chinese. It's better to fine-tune it on downstream tasks for better performance.
Spaces
======
Now you can experience our model on HuggingFace Spaces HIT-TMG/dialogue-b... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #bart-large-chinese #zh #dataset-lccc #dataset-kd_conv #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
## Overview
This folder contains a fully trained German speech recognition pipeline
consisting of an acoustic model using the new wav2vec 2.0 XLS-R 1B **TEVR** architecture
and a 5-gram KenLM language model.
For an explanation of the TEVR enhancements and their motivation, please see our paper:
[TEVR: Improving Spe... | {"language": "de", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "hf-asr-leaderboard"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "inference": false, "model-index": [{"name": "wav2vec 2.0 XLS-R 1B + TEVR tokens + 5-gram LM by Hajo Nils Krabbenh\u00f6ft", "results": ... | fxtentacle/wav2vec2-xls-r-1b-tevr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"de",
"dataset:common_voice",
"arxiv:2206.12693",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-06-02T08:09:53+00:00 | [
"2206.12693"
] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #de #dataset-common_voice #arxiv-2206.12693 #license-apache-2.0 #model-index #region-us
|
## Overview
This folder contains a fully trained German speech recognition pipeline
consisting of an acoustic model using the new wav2vec 2.0 XLS-R 1B TEVR architecture
and a 5-gram KenLM language model.
For an explanation of the TEVR enhancements and their motivation, please see our paper:
TEVR: Improving Speech R... | [
"## Overview\n\nThis folder contains a fully trained German speech recognition pipeline\nconsisting of an acoustic model using the new wav2vec 2.0 XLS-R 1B TEVR architecture\nand a 5-gram KenLM language model. \nFor an explanation of the TEVR enhancements and their motivation, please see our paper:\nTEVR: Improving... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #de #dataset-common_voice #arxiv-2206.12693 #license-apache-2.0 #model-index #region-us \n",
"## Overview\n\nThis folder contains a fully trained German speech recognition pipeline\nconsisting of an acoustic m... |
fill-mask | transformers | # Skimformer
A collaboration between [reciTAL](https://recital.ai/en/) & [MLIA](https://mlia.lip6.fr/) (ISIR, Sorbonne Université)
## Model description
Skimformer is a two-stage Transformer that replaces self-attention with Skim-Attention, a self-attention module that computes attention solely based on the 2D positio... | {"license": "apache-2.0"} | nglaura/skimformer | null | [
"transformers",
"pytorch",
"skimformer",
"fill-mask",
"arxiv:2109.01078",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T08:40:00+00:00 | [
"2109.01078"
] | [] | TAGS
#transformers #pytorch #skimformer #fill-mask #arxiv-2109.01078 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Skimformer
A collaboration between reciTAL & MLIA (ISIR, Sorbonne Université)
## Model description
Skimformer is a two-stage Transformer that replaces self-attention with Skim-Attention, a self-attention module that computes attention solely based on the 2D positions of tokens in the page. The model adopts a two-st... | [
"# Skimformer\n\nA collaboration between reciTAL & MLIA (ISIR, Sorbonne Université)"
] | [
"TAGS\n#transformers #pytorch #skimformer #fill-mask #arxiv-2109.01078 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Skimformer\n\nA collaboration between reciTAL & MLIA (ISIR, Sorbonne Université)"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2
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": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}... | Fulccrum/distilbert-base-uncased-finetuned-sst2 | 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 | 2022-06-02T08:56:04+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-sst2
======================================
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: 0.3739
* Accuracy: 0.9128
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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... |
text-classification | transformers | # `BERT-Banking77` Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 940131041
- CO2 Emissions (in grams): 0.03330651014155927
## Validation Metrics
- Loss: 0.3505457043647766
- Accuracy: 0.9263261296660118
- Macro F1: 0.9268371013605569
- Micro F1: 0.9263261296660118
- Weighted F... | {"language": "en", "tags": ["autotrain"], "datasets": ["banking77"], "widget": [{"text": "I am still waiting on my card?"}], "co2_eq_emissions": 0.03330651014155927, "model-index": [{"name": "BERT-Banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "BANKI... | philschmid/BERT-Banking77 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:banking77",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T09:37:57+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
| # 'BERT-Banking77' Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 940131041
- CO2 Emissions (in grams): 0.03330651014155927
## Validation Metrics
- Loss: 0.3505457043647766
- Accuracy: 0.9263261296660118
- Macro F1: 0.9268371013605569
- Micro F1: 0.9263261296660118
- Weighted F... | [
"# 'BERT-Banking77' Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 940131041\n- CO2 Emissions (in grams): 0.03330651014155927",
"## Validation Metrics\n\n- Loss: 0.3505457043647766\n- Accuracy: 0.9263261296660118\n- Macro F1: 0.9268371013605569\n- Micro F1: 0.92632612966... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# 'BERT-Banking77' Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 940131041\n- CO2 Emiss... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 940131045
- CO2 Emissions (in grams): 5.632805352029529
## Validation Metrics
- Loss: 0.3392622470855713
- Accuracy: 0.9199410609037328
- Macro F1: 0.9199390885956755
- Micro F1: 0.9199410609037327
- Weighted F1: 0.91981402950057... | {"language": "en", "tags": "autotrain", "datasets": ["banking77"], "widget": [{"text": "I am still waiting on my card?"}], "co2_eq_emissions": 5.632805352029529, "model-index": [{"name": "BERT-Banking77", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "BANKING77... | philschmid/DistilBERT-Banking77 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:banking77",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T09:38:18+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 940131045
- CO2 Emissions (in grams): 5.632805352029529
## Validation Metrics
- Loss: 0.3392622470855713
- Accuracy: 0.9199410609037328
- Macro F1: 0.9199390885956755
- Micro F1: 0.9199410609037327
- Weighted F1: 0.91981402950057... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 940131045\n- CO2 Emissions (in grams): 5.632805352029529",
"## Validation Metrics\n\n- Loss: 0.3392622470855713\n- Accuracy: 0.9199410609037328\n- Macro F1: 0.9199390885956755\n- Micro F1: 0.9199410609037327\n- Weighted F1... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-banking77 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 940131045\n- CO2 Emissions (in gra... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Lolaibrin/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T09:42:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2108
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **CartPole-v1**
This is a trained model of a **A2C** agent playing **CartPole-v1**
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 Baselines3
reinforc... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"... | sb3/a2c-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:48:51+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing CartPole-v1
This is a trained model of a A2C agent playing CartPole-v1
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.
## Usage (with SB3 R... | [
"# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\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 included.",
"## Us... | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
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="jcastanyo/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 +/... | jcastanyo/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-02T09:48:54+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"
] |
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="elfray/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"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": ... | elfray/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-02T09:55:09+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 |
# **TRPO** Agent playing **Walker2d-v3**
This is a trained model of a **TRPO** agent playing **Walker2d-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 for Stable Baselines3
reinfo... | {"library_name": "stable-baselines3", "tags": ["Walker2d-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2d-v3", "type": "Walker2d-v3... | sb3/trpo-Walker2d-v3 | null | [
"stable-baselines3",
"Walker2d-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:55:15+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Walker2d-v3
This is a trained model of a TRPO agent playing Walker2d-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 included.
## Usage (with SB3... | [
"# TRPO Agent playing Walker2d-v3\nThis is a trained model of a TRPO agent playing Walker2d-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 agents included.",
"## ... | [
"TAGS\n#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Walker2d-v3\nThis is a trained model of a TRPO agent playing Walker2d-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for St... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **LunarLander-v2**
This is a trained model of a **TRPO** agent playing **LunarLander-v2**
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 Baselines3
... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "Lunar... | sb3/trpo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:56:01+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing LunarLander-v2
This is a trained model of a TRPO agent playing LunarLander-v2
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.
## Usage (wi... | [
"# TRPO Agent playing LunarLander-v2\nThis is a trained model of a TRPO agent playing LunarLander-v2\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 included.",
... | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing LunarLander-v2\nThis is a trained model of a TRPO agent playing LunarLander-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framewo... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **Acrobot-v1**
This is a trained model of a **TRPO** agent playing **Acrobot-v1**
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 Baselines3
reinforc... | {"library_name": "stable-baselines3", "tags": ["Acrobot-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Acrobot-v1", "type": "Acrobot-v1"},... | sb3/trpo-Acrobot-v1 | null | [
"stable-baselines3",
"Acrobot-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:56:52+00:00 | [] | [] | TAGS
#stable-baselines3 #Acrobot-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Acrobot-v1
This is a trained model of a TRPO agent playing Acrobot-v1
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.
## Usage (with SB3 R... | [
"# TRPO Agent playing Acrobot-v1\nThis is a trained model of a TRPO agent playing Acrobot-v1\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 included.",
"## Us... | [
"TAGS\n#stable-baselines3 #Acrobot-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Acrobot-v1\nThis is a trained model of a TRPO agent playing Acrobot-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stabl... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **HalfCheetah-v3**
This is a trained model of a **TRPO** agent playing **HalfCheetah-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 for Stable Baselines3
... | {"library_name": "stable-baselines3", "tags": ["HalfCheetah-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetah-v3", "type": "HalfC... | sb3/trpo-HalfCheetah-v3 | null | [
"stable-baselines3",
"HalfCheetah-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:57:38+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetah-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing HalfCheetah-v3
This is a trained model of a TRPO agent playing HalfCheetah-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 included.
## Usage (wi... | [
"# TRPO Agent playing HalfCheetah-v3\nThis is a trained model of a TRPO agent playing HalfCheetah-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 agents included.",
... | [
"TAGS\n#stable-baselines3 #HalfCheetah-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing HalfCheetah-v3\nThis is a trained model of a TRPO agent playing HalfCheetah-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framewo... |
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="elfray/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"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": "7.46 +/... | elfray/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-06-02T09:58:19+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"
] |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **AntBulletEnv-v0**
This is a trained model of a **TRPO** agent playing **AntBulletEnv-v0**
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 Baselines... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "Ant... | sb3/trpo-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:58:24+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing AntBulletEnv-v0
This is a trained model of a TRPO agent playing AntBulletEnv-v0
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.
## Usage (... | [
"# TRPO Agent playing AntBulletEnv-v0\nThis is a trained model of a TRPO agent playing AntBulletEnv-v0\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 included."... | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing AntBulletEnv-v0\nThis is a trained model of a TRPO agent playing AntBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training fram... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **Swimmer-v3**
This is a trained model of a **TRPO** agent playing **Swimmer-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 for Stable Baselines3
reinforc... | {"library_name": "stable-baselines3", "tags": ["Swimmer-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Swimmer-v3", "type": "Swimmer-v3"},... | sb3/trpo-Swimmer-v3 | null | [
"stable-baselines3",
"Swimmer-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T09:59:24+00:00 | [] | [] | TAGS
#stable-baselines3 #Swimmer-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Swimmer-v3
This is a trained model of a TRPO agent playing Swimmer-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 included.
## Usage (with SB3 R... | [
"# TRPO Agent playing Swimmer-v3\nThis is a trained model of a TRPO agent playing Swimmer-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 agents included.",
"## Us... | [
"TAGS\n#stable-baselines3 #Swimmer-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Swimmer-v3\nThis is a trained model of a TRPO agent playing Swimmer-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stabl... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **Hopper-v3**
This is a trained model of a **TRPO** agent playing **Hopper-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 for Stable Baselines3
reinforcem... | {"library_name": "stable-baselines3", "tags": ["Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Hopper-v3", "type": "Hopper-v3"}, "m... | sb3/trpo-Hopper-v3 | null | [
"stable-baselines3",
"Hopper-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:00:16+00:00 | [] | [] | TAGS
#stable-baselines3 #Hopper-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Hopper-v3
This is a trained model of a TRPO agent playing Hopper-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 included.
## Usage (with SB3 RL ... | [
"# TRPO Agent playing Hopper-v3\nThis is a trained model of a TRPO agent playing Hopper-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 agents included.",
"## Usag... | [
"TAGS\n#stable-baselines3 #Hopper-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Hopper-v3\nThis is a trained model of a TRPO agent playing Hopper-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable B... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **ReacherBulletEnv-v0**
This is a trained model of a **TRPO** agent playing **ReacherBulletEnv-v0**
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": ["ReacherBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "ReacherBulletEnv-v0", "typ... | sb3/trpo-ReacherBulletEnv-v0 | null | [
"stable-baselines3",
"ReacherBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:01:11+00:00 | [] | [] | TAGS
#stable-baselines3 #ReacherBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing ReacherBulletEnv-v0
This is a trained model of a TRPO agent playing ReacherBulletEnv-v0
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.
##... | [
"# TRPO Agent playing ReacherBulletEnv-v0\nThis is a trained model of a TRPO agent playing ReacherBulletEnv-v0\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 #ReacherBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing ReacherBulletEnv-v0\nThis is a trained model of a TRPO agent playing ReacherBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a t... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **HopperBulletEnv-v0**
This is a trained model of a **TRPO** agent playing **HopperBulletEnv-v0**
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 Bas... | {"library_name": "stable-baselines3", "tags": ["HopperBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HopperBulletEnv-v0", "type"... | sb3/trpo-HopperBulletEnv-v0 | null | [
"stable-baselines3",
"HopperBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:02:07+00:00 | [] | [] | TAGS
#stable-baselines3 #HopperBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing HopperBulletEnv-v0
This is a trained model of a TRPO agent playing HopperBulletEnv-v0
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.
## U... | [
"# TRPO Agent playing HopperBulletEnv-v0\nThis is a trained model of a TRPO agent playing HopperBulletEnv-v0\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 incl... | [
"TAGS\n#stable-baselines3 #HopperBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing HopperBulletEnv-v0\nThis is a trained model of a TRPO agent playing HopperBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a trai... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **TRPO** agent playing **HalfCheetahBulletEnv-v0**
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 ... | {"library_name": "stable-baselines3", "tags": ["HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetahBulletEnv-v... | sb3/trpo-HalfCheetahBulletEnv-v0 | null | [
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:03:04+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing HalfCheetahBulletEnv-v0
This is a trained model of a TRPO agent playing HalfCheetahBulletEnv-v0
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 inclu... | [
"# TRPO Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a TRPO agent playing HalfCheetahBulletEnv-v0\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 a... | [
"TAGS\n#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a TRPO agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe R... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **Pendulum-v1**
This is a trained model of a **TRPO** agent playing **Pendulum-v1**
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 Baselines3
reinfo... | {"library_name": "stable-baselines3", "tags": ["Pendulum-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pendulum-v1", "type": "Pendulum-v1... | sb3/trpo-Pendulum-v1 | null | [
"stable-baselines3",
"Pendulum-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:04:04+00:00 | [] | [] | TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Pendulum-v1
This is a trained model of a TRPO agent playing Pendulum-v1
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.
## Usage (with SB3... | [
"# TRPO Agent playing Pendulum-v1\nThis is a trained model of a TRPO agent playing Pendulum-v1\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 included.",
"## ... | [
"TAGS\n#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Pendulum-v1\nThis is a trained model of a TRPO agent playing Pendulum-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for St... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **LunarLanderContinuous-v2**
This is a trained model of a **TRPO** agent playing **LunarLanderContinuous-v2**
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": ["LunarLanderContinuous-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLanderContinuous... | sb3/trpo-LunarLanderContinuous-v2 | null | [
"stable-baselines3",
"LunarLanderContinuous-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:11:26+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLanderContinuous-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing LunarLanderContinuous-v2
This is a trained model of a TRPO agent playing LunarLanderContinuous-v2
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... | [
"# TRPO Agent playing LunarLanderContinuous-v2\nThis is a trained model of a TRPO agent playing LunarLanderContinuous-v2\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 #LunarLanderContinuous-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing LunarLanderContinuous-v2\nThis is a trained model of a TRPO agent playing LunarLanderContinuous-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nTh... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **TRPO** 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 for Stable Baselin... | {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "B... | sb3/trpo-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:12:13+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing BipedalWalker-v3
This is a trained model of a TRPO 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 included.
## Usage... | [
"# TRPO Agent playing BipedalWalker-v3\nThis is a trained model of a TRPO 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 agents included... | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing BipedalWalker-v3\nThis is a trained model of a TRPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training f... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **TRPO** agent playing **Walker2DBulletEnv-v0**
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... | {"library_name": "stable-baselines3", "tags": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "t... | sb3/trpo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:13:05+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a TRPO agent playing Walker2DBulletEnv-v0
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.
... | [
"# TRPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a TRPO agent playing Walker2DBulletEnv-v0\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 ... | [
"TAGS\n#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a TRPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is ... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **TRPO** agent playing **MountainCarContinuous-v0**
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": ["MountainCarContinuous-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCarContinuous... | sb3/trpo-MountainCarContinuous-v0 | null | [
"stable-baselines3",
"MountainCarContinuous-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:13:59+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing MountainCarContinuous-v0
This is a trained model of a TRPO agent playing MountainCarContinuous-v0
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... | [
"# TRPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a TRPO agent playing MountainCarContinuous-v0\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 #MountainCarContinuous-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing MountainCarContinuous-v0\nThis is a trained model of a TRPO agent playing MountainCarContinuous-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nTh... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **CartPole-v1**
This is a trained model of a **TRPO** agent playing **CartPole-v1**
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 Baselines3
reinfo... | {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1... | sb3/trpo-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:14:44+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing CartPole-v1
This is a trained model of a TRPO agent playing CartPole-v1
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.
## Usage (with SB3... | [
"# TRPO Agent playing CartPole-v1\nThis is a trained model of a TRPO agent playing CartPole-v1\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 included.",
"## ... | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing CartPole-v1\nThis is a trained model of a TRPO agent playing CartPole-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for St... |
reinforcement-learning | stable-baselines3 |
# **TRPO** Agent playing **Ant-v3**
This is a trained model of a **TRPO** agent playing **Ant-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 for Stable Baselines3
reinforcement le... | {"library_name": "stable-baselines3", "tags": ["Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "TRPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Ant-v3", "type": "Ant-v3"}, "metrics": ... | sb3/trpo-Ant-v3 | null | [
"stable-baselines3",
"Ant-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:15:31+00:00 | [] | [] | TAGS
#stable-baselines3 #Ant-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# TRPO Agent playing Ant-v3
This is a trained model of a TRPO agent playing Ant-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 included.
## Usage (with SB3 RL Zoo)
... | [
"# TRPO Agent playing Ant-v3\nThis is a trained model of a TRPO agent playing Ant-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 agents included.",
"## Usage (wit... | [
"TAGS\n#stable-baselines3 #Ant-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# TRPO Agent playing Ant-v3\nThis is a trained model of a TRPO agent playing Ant-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **PongNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **PongNoFrameskip-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 Basel... | {"library_name": "stable-baselines3", "tags": ["PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PongNoFrameskip-v4", "type":... | sb3/ppo-PongNoFrameskip-v4 | null | [
"stable-baselines3",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:16:21+00:00 | [] | [] | TAGS
#stable-baselines3 #PongNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing PongNoFrameskip-v4
This is a trained model of a PPO agent playing PongNoFrameskip-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.
## Usa... | [
"# PPO Agent playing PongNoFrameskip-v4\nThis is a trained model of a PPO agent playing PongNoFrameskip-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 includ... | [
"TAGS\n#stable-baselines3 #PongNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing PongNoFrameskip-v4\nThis is a trained model of a PPO agent playing PongNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a traini... |
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. -->
# electra-base-discriminator-finetuned-filtered-0602
This model is a fine-tuned version of [google/electra-base-discriminator](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electra-base-discriminator-finetuned-filtered-0602", "results": []}]} | YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602 | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:16:49+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# electra-base-discriminator-finetuned-filtered-0602
This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1685
- Accuracy: 0.9720
- F1: 0.9721
## Model description
More information needed
## Intended uses... | [
"# electra-base-discriminator-finetuned-filtered-0602\n\nThis model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1685\n- Accuracy: 0.9720\n- F1: 0.9721",
"## Model description\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# electra-base-discriminator-finetuned-filtered-0602\n\nThis model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2d-v3**
This is a trained model of a **PPO** agent playing **Walker2d-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 for Stable Baselines3
reinforc... | {"library_name": "stable-baselines3", "tags": ["Walker2d-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2d-v3", "type": "Walker2d-v3"... | sb3/ppo-Walker2d-v3 | null | [
"stable-baselines3",
"Walker2d-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:17:27+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2d-v3
This is a trained model of a PPO agent playing Walker2d-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 included.
## Usage (with SB3 R... | [
"# PPO Agent playing Walker2d-v3\nThis is a trained model of a PPO agent playing Walker2d-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 agents included.",
"## Us... | [
"TAGS\n#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Walker2d-v3\nThis is a trained model of a PPO agent playing Walker2d-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stab... |
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)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
re... | {"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... | sb3/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:18:11+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library
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.
## Usage (with... | [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\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 included.",
... | [
"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\nand the RL Zoo.\n\nThe RL Zoo is a training framework... |
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-finetuned-Age
This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/b... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "bert-finetuned-Age", "results": []}]} | Abderrahim2/bert-finetuned-Age | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:26:19+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-Age
==================
This model is a fine-tuned version of dbmdz/bert-base-french-europeana-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4642
* F1: 0.7254
* Roc Auc: 0.7940
* Accuracy: 0.7249
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Training... | [
"TAGS\n#transformers #pytorch #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: 8\n* eval\\_b... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Acrobot-v1**
This is a trained model of a **PPO** agent playing **Acrobot-v1**
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 Baselines3
reinforcem... | {"library_name": "stable-baselines3", "tags": ["Acrobot-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Acrobot-v1", "type": "Acrobot-v1"}, ... | sb3/ppo-Acrobot-v1 | null | [
"stable-baselines3",
"Acrobot-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:35:25+00:00 | [] | [] | TAGS
#stable-baselines3 #Acrobot-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Acrobot-v1
This is a trained model of a PPO agent playing Acrobot-v1
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.
## Usage (with SB3 RL ... | [
"# PPO Agent playing Acrobot-v1\nThis is a trained model of a PPO agent playing Acrobot-v1\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 included.",
"## Usag... | [
"TAGS\n#stable-baselines3 #Acrobot-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Acrobot-v1\nThis is a trained model of a PPO agent playing Acrobot-v1\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable ... |
text2text-generation | transformers |
# MVP-summarization
The MVP-summarization model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation", "summarization"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", "example_title": "Example1"},... | RUCAIBox/mvp-summarization | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"summarization",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:49:40+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #summarization #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-summarization
The MVP-summarization model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-summarization is a prompt-based model t... | [
"# MVP-summarization\nThe MVP-summarization model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-summarization is a prompt-b... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #summarization #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-summarization\nThe MVP-summarization model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tian... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **HalfCheetah-v3**
This is a trained model of a **PPO** agent playing **HalfCheetah-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 for Stable Baselines3
re... | {"library_name": "stable-baselines3", "tags": ["HalfCheetah-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetah-v3", "type": "HalfCh... | sb3/ppo-HalfCheetah-v3 | null | [
"stable-baselines3",
"HalfCheetah-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:52:34+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetah-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing HalfCheetah-v3
This is a trained model of a PPO agent playing HalfCheetah-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 included.
## Usage (with... | [
"# PPO Agent playing HalfCheetah-v3\nThis is a trained model of a PPO agent playing HalfCheetah-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 agents included.",
... | [
"TAGS\n#stable-baselines3 #HalfCheetah-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing HalfCheetah-v3\nThis is a trained model of a PPO agent playing HalfCheetah-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **AntBulletEnv-v0**
This is a trained model of a **PPO** agent playing **AntBulletEnv-v0**
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 Baselines3
... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | sb3/ppo-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"has_space",
"region:us"
] | null | 2022-06-02T10:53:21+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #has_space #region-us
|
# PPO Agent playing AntBulletEnv-v0
This is a trained model of a PPO agent playing AntBulletEnv-v0
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.
## Usage (wi... | [
"# PPO Agent playing AntBulletEnv-v0\nThis is a trained model of a PPO agent playing AntBulletEnv-v0\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 included.",
... | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #has_space #region-us \n",
"# PPO Agent playing AntBulletEnv-v0\nThis is a trained model of a PPO agent playing AntBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a trai... |
text2text-generation | transformers |
# MVP-data-to-text
The MVP-data-to-text model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MV... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man", "example_title": "Example1"}, {"text": "Describe the follo... | RUCAIBox/mvp-data-to-text | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:53:26+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-data-to-text
The MVP-data-to-text model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-data-to-text is a prompt-based model that... | [
"# MVP-data-to-text\nThe MVP-data-to-text model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-data-to-text is a prompt-base... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-data-to-text\nThe MVP-data-to-text model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li... |
text2text-generation | transformers |
# MVP-open-dialog
The MVP-open-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP]... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation", "conversational"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce Lee was a cha cha dancer?", "example_title": "Example1"}, {"text": "Gi... | RUCAIBox/mvp-open-dialog | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"conversational",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:53:44+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #conversational #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-open-dialog
The MVP-open-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-open-dialog is a prompt-based model that MV... | [
"# MVP-open-dialog\nThe MVP-open-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-open-dialog is a prompt-based m... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #conversational #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-open-dialog\nThe MVP-open-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi ... |
text2text-generation | transformers |
# MVP-task-dialog
The MVP-task-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP]... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Given the task dialog: Belief state [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest.", "example_title": "Example1"}, {"... | RUCAIBox/mvp-task-dialog | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:53:57+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-task-dialog
The MVP-task-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-task-dialog is a prompt-based model that MV... | [
"# MVP-task-dialog\nThe MVP-task-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-task-dialog is a prompt-based m... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-task-dialog\nThe MVP-task-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Swimmer-v3**
This is a trained model of a **PPO** agent playing **Swimmer-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 for Stable Baselines3
reinforcem... | {"library_name": "stable-baselines3", "tags": ["Swimmer-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Swimmer-v3", "type": "Swimmer-v3"}, ... | sb3/ppo-Swimmer-v3 | null | [
"stable-baselines3",
"Swimmer-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:54:23+00:00 | [] | [] | TAGS
#stable-baselines3 #Swimmer-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Swimmer-v3
This is a trained model of a PPO agent playing Swimmer-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 included.
## Usage (with SB3 RL ... | [
"# PPO Agent playing Swimmer-v3\nThis is a trained model of a PPO agent playing Swimmer-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 agents included.",
"## Usag... | [
"TAGS\n#stable-baselines3 #Swimmer-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Swimmer-v3\nThis is a trained model of a PPO agent playing Swimmer-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable ... |
text2text-generation | transformers |
# MVP-question-generation
The MVP-question-generation model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.c... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing .", "example_title": "Examp... | RUCAIBox/mvp-question-generation | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:54:39+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-question-generation
The MVP-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-question-generation is a pr... | [
"# MVP-question-generation\nThe MVP-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-question-genera... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-question-generation\nThe MVP-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi ... |
text2text-generation | transformers |
# MVP-question-answering
The MVP-question-answering model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Answer the following question: From which country did Angola achieve independence in 1975?", "example_title": "Example1"}, {"text": "Answer the following questi... | RUCAIBox/mvp-question-answering | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:54:54+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-question-answering
The MVP-question-answering model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-question-answering is a promp... | [
"# MVP-question-answering\nThe MVP-question-answering model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-question-answerin... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-question-answering\nThe MVP-question-answering model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Ta... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **EnduroNoFrameskip-v4**
This is a trained model of a **PPO** 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": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "EnduroNoFrameskip-v4", "ty... | sb3/ppo-EnduroNoFrameskip-v4 | null | [
"stable-baselines3",
"EnduroNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:55:15+00:00 | [] | [] | TAGS
#stable-baselines3 #EnduroNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing EnduroNoFrameskip-v4
This is a trained model of a PPO 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.
##... | [
"# PPO Agent playing EnduroNoFrameskip-v4\nThis is a trained model of a PPO 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",
"# PPO Agent playing EnduroNoFrameskip-v4\nThis is a trained model of a PPO agent playing EnduroNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
text2text-generation | transformers |
# MVP-story
The MVP-story model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://git... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Given the story title: I think all public schools should have a uniform dress code.", "example_title": "Example1"}, {"text": "Given the story title: My girlfrie... | RUCAIBox/mvp-story | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T10:55:25+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MVP-story
The MVP-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MVP-story is a prompt-based model that MVP is further equip... | [
"# MVP-story\nThe MVP-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMVP-story is a prompt-based model that MVP is f... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MVP-story\nThe MVP-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zh... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Hopper-v3**
This is a trained model of a **PPO** agent playing **Hopper-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 for Stable Baselines3
reinforcemen... | {"library_name": "stable-baselines3", "tags": ["Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Hopper-v3", "type": "Hopper-v3"}, "me... | sb3/ppo-Hopper-v3 | null | [
"stable-baselines3",
"Hopper-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T10:59:26+00:00 | [] | [] | TAGS
#stable-baselines3 #Hopper-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Hopper-v3
This is a trained model of a PPO agent playing Hopper-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 included.
## Usage (with SB3 RL Zo... | [
"# PPO Agent playing Hopper-v3\nThis is a trained model of a PPO agent playing Hopper-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 agents included.",
"## Usage ... | [
"TAGS\n#stable-baselines3 #Hopper-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Hopper-v3\nThis is a trained model of a PPO agent playing Hopper-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Bas... |
text2text-generation | transformers |
# MTL-story
The MTL-story model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://git... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Given the story title: I think all public schools should have a uniform dress code.", "example_title": "Example1"}, {"text": "Given the story title: My girlfrie... | RUCAIBox/mtl-story | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:00:10+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-story
The MTL-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-story is supervised pre-trained using a mixture of labe... | [
"# MTL-story\nThe MTL-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-story is supervised pre-trained using a mix... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-story\nThe MTL-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zh... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **RoadRunnerNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **RoadRunnerNoFrameskip-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 ... | {"library_name": "stable-baselines3", "tags": ["RoadRunnerNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "RoadRunnerNoFrameskip-... | sb3/ppo-RoadRunnerNoFrameskip-v4 | null | [
"stable-baselines3",
"RoadRunnerNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:00:25+00:00 | [] | [] | TAGS
#stable-baselines3 #RoadRunnerNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing RoadRunnerNoFrameskip-v4
This is a trained model of a PPO agent playing RoadRunnerNoFrameskip-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 inclu... | [
"# PPO Agent playing RoadRunnerNoFrameskip-v4\nThis is a trained model of a PPO agent playing RoadRunnerNoFrameskip-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 a... | [
"TAGS\n#stable-baselines3 #RoadRunnerNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing RoadRunnerNoFrameskip-v4\nThis is a trained model of a PPO agent playing RoadRunnerNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe ... |
text2text-generation | transformers |
# MTL-question-answering
The MTL-question-answering model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Answer the following question: From which country did Angola achieve independence in 1975?", "example_title": "Example1"}, {"text": "Answer the following questi... | RUCAIBox/mtl-question-answering | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:00:27+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-question-answering
The MTL-question-answering model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-question-answering is supervi... | [
"# MTL-question-answering\nThe MTL-question-answering model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-question-answerin... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-question-answering\nThe MTL-question-answering model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Ta... |
text2text-generation | transformers |
# MTL-question-generation
The MTL-question-generation model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.c... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing .", "example_title": "Examp... | RUCAIBox/mtl-question-generation | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:00:54+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-question-generation
The MTL-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-question-generation is supe... | [
"# MTL-question-generation\nThe MTL-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-question-genera... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-question-generation\nThe MTL-question-generation model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi ... |
text2text-generation | transformers |
# MTL-summarization
The MTL-summarization model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation", "summarization"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", "example_title": "Example1"},... | RUCAIBox/mtl-summarization | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"summarization",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:01:19+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #summarization #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-summarization
The MTL-summarization model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-summarization is supervised pre-trained... | [
"# MTL-summarization\nThe MTL-summarization model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-summarization is supervised... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #summarization #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-summarization\nThe MTL-summarization model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tian... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **ReacherBulletEnv-v0**
This is a trained model of a **PPO** agent playing **ReacherBulletEnv-v0**
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 Bas... | {"library_name": "stable-baselines3", "tags": ["ReacherBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "ReacherBulletEnv-v0", "type... | sb3/ppo-ReacherBulletEnv-v0 | null | [
"stable-baselines3",
"ReacherBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:01:19+00:00 | [] | [] | TAGS
#stable-baselines3 #ReacherBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing ReacherBulletEnv-v0
This is a trained model of a PPO agent playing ReacherBulletEnv-v0
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.
## U... | [
"# PPO Agent playing ReacherBulletEnv-v0\nThis is a trained model of a PPO agent playing ReacherBulletEnv-v0\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 incl... | [
"TAGS\n#stable-baselines3 #ReacherBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing ReacherBulletEnv-v0\nThis is a trained model of a PPO agent playing ReacherBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a tra... |
text2text-generation | transformers |
# MTL-data-to-text
The MTL-data-to-text model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MV... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Describe the following data: Iron Man | instance of | Superhero [SEP] Stan Lee | creator | Iron Man", "example_title": "Example1"}, {"text": "Describe the follo... | RUCAIBox/mtl-data-to-text | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:01:55+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-data-to-text
The MTL-data-to-text model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-data-to-text is supervised pre-trained us... | [
"# MTL-data-to-text\nThe MTL-data-to-text model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-data-to-text is supervised pr... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-data-to-text\nThe MTL-data-to-text model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** 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": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | sb3/ppo-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:02:09+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a PPO 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... | [
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a PPO 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",
"# PPO Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a PPO agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text2text-generation | transformers |
# MTL-open-dialog
The MTL-open-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP]... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation", "conversational"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Given the dialog: do you like dance? [SEP] Yes I do. Did you know Bruce Lee was a cha cha dancer?", "example_title": "Example1"}, {"text": "Gi... | RUCAIBox/mtl-open-dialog | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"conversational",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:02:35+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #conversational #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-open-dialog
The MTL-open-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-open-dialog is supervised pre-trained using... | [
"# MTL-open-dialog\nThe MTL-open-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-open-dialog is supervised pre-t... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #conversational #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-open-dialog\nThe MTL-open-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **HopperBulletEnv-v0**
This is a trained model of a **PPO** agent playing **HopperBulletEnv-v0**
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 Basel... | {"library_name": "stable-baselines3", "tags": ["HopperBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HopperBulletEnv-v0", "type":... | sb3/ppo-HopperBulletEnv-v0 | null | [
"stable-baselines3",
"HopperBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:03:04+00:00 | [] | [] | TAGS
#stable-baselines3 #HopperBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing HopperBulletEnv-v0
This is a trained model of a PPO agent playing HopperBulletEnv-v0
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.
## Usa... | [
"# PPO Agent playing HopperBulletEnv-v0\nThis is a trained model of a PPO agent playing HopperBulletEnv-v0\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 includ... | [
"TAGS\n#stable-baselines3 #HopperBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing HopperBulletEnv-v0\nThis is a trained model of a PPO agent playing HopperBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a traini... |
text2text-generation | transformers |
# MTL-task-dialog
The MTL-task-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP]... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation", "text2text-generation"], "pipeline_tag": "text2text-generation", "widget": [{"text": "Given the task dialog: Belief state [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest.", "example_title": "Example1"}, {"... | RUCAIBox/mtl-task-dialog | null | [
"transformers",
"pytorch",
"mvp",
"text-generation",
"text2text-generation",
"en",
"arxiv:2206.12131",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T11:03:58+00:00 | [
"2206.12131"
] | [
"en"
] | TAGS
#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-task-dialog
The MTL-task-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found URL
## Model Description
MTL-task-dialog is supervised pre-trained using... | [
"# MTL-task-dialog\nThe MTL-task-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.\n\nThe detailed information and instructions can be found URL",
"## Model Description\nMTL-task-dialog is supervised pre-t... | [
"TAGS\n#transformers #pytorch #mvp #text-generation #text2text-generation #en #arxiv-2206.12131 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-task-dialog\nThe MTL-task-dialog model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **PPO** agent playing **HalfCheetahBulletEnv-v0**
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 St... | {"library_name": "stable-baselines3", "tags": ["HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "HalfCheetahBulletEnv-v0... | sb3/ppo-HalfCheetahBulletEnv-v0 | null | [
"stable-baselines3",
"HalfCheetahBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:04:03+00:00 | [] | [] | TAGS
#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing HalfCheetahBulletEnv-v0
This is a trained model of a PPO agent playing HalfCheetahBulletEnv-v0
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 include... | [
"# PPO Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a PPO agent playing HalfCheetahBulletEnv-v0\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 age... | [
"TAGS\n#stable-baselines3 #HalfCheetahBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing HalfCheetahBulletEnv-v0\nThis is a trained model of a PPO agent playing HalfCheetahBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **BreakoutNoFrameskip-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 Stab... | {"library_name": "stable-baselines3", "tags": ["BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BreakoutNoFrameskip-v4",... | sb3/ppo-BreakoutNoFrameskip-v4 | null | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:05:04+00:00 | [] | [] | TAGS
#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BreakoutNoFrameskip-v4
This is a trained model of a PPO agent playing BreakoutNoFrameskip-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.... | [
"# PPO Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a PPO agent playing BreakoutNoFrameskip-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 agent... | [
"TAGS\n#stable-baselines3 #BreakoutNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BreakoutNoFrameskip-v4\nThis is a trained model of a PPO agent playing BreakoutNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **SeaquestNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SeaquestNoFrameskip-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 Stab... | {"library_name": "stable-baselines3", "tags": ["SeaquestNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SeaquestNoFrameskip-v4",... | sb3/ppo-SeaquestNoFrameskip-v4 | null | [
"stable-baselines3",
"SeaquestNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:06:24+00:00 | [] | [] | TAGS
#stable-baselines3 #SeaquestNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing SeaquestNoFrameskip-v4
This is a trained model of a PPO agent playing SeaquestNoFrameskip-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.... | [
"# PPO Agent playing SeaquestNoFrameskip-v4\nThis is a trained model of a PPO agent playing SeaquestNoFrameskip-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 agent... | [
"TAGS\n#stable-baselines3 #SeaquestNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing SeaquestNoFrameskip-v4\nThis is a trained model of a PPO agent playing SeaquestNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **AsteroidsNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **AsteroidsNoFrameskip-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 St... | {"library_name": "stable-baselines3", "tags": ["AsteroidsNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AsteroidsNoFrameskip-v4... | sb3/ppo-AsteroidsNoFrameskip-v4 | null | [
"stable-baselines3",
"AsteroidsNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:07:42+00:00 | [] | [] | TAGS
#stable-baselines3 #AsteroidsNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing AsteroidsNoFrameskip-v4
This is a trained model of a PPO agent playing AsteroidsNoFrameskip-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 include... | [
"# PPO Agent playing AsteroidsNoFrameskip-v4\nThis is a trained model of a PPO agent playing AsteroidsNoFrameskip-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 age... | [
"TAGS\n#stable-baselines3 #AsteroidsNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing AsteroidsNoFrameskip-v4\nThis is a trained model of a PPO agent playing AsteroidsNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLanderContinuous-v2**
This is a trained model of a **PPO** agent playing **LunarLanderContinuous-v2**
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 ... | {"library_name": "stable-baselines3", "tags": ["LunarLanderContinuous-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLanderContinuous-... | sb3/ppo-LunarLanderContinuous-v2 | null | [
"stable-baselines3",
"LunarLanderContinuous-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:08:37+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLanderContinuous-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLanderContinuous-v2
This is a trained model of a PPO agent playing LunarLanderContinuous-v2
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 inclu... | [
"# PPO Agent playing LunarLanderContinuous-v2\nThis is a trained model of a PPO agent playing LunarLanderContinuous-v2\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 a... | [
"TAGS\n#stable-baselines3 #LunarLanderContinuous-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLanderContinuous-v2\nThis is a trained model of a PPO agent playing LunarLanderContinuous-v2\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe ... |
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/1531770686309646338/i1LU... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/eurovision/1654172290217/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/eurovision | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T11:13:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Eurovision Song Contest
@eurovision
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training da... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines... | {"library_name": "stable-baselines3", "tags": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "Bi... | sb3/ppo-BipedalWalker-v3 | null | [
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:25:47+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3
using the stable-baselines3 library
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.
## Usage (... | [
"# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO 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 agents included."... | [
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training fra... |
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/773905129129046016/EZcRP... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/esfinn/1654173312571/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/esfinn | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T11:34:00+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Emily Finn
@esfinn
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1531896348416483329/bPsi... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gaytimes-grindr/1654174210818/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gaytimes-grindr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T11:34:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Grindr & GAY TIMES
@gaytimes-grindr
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-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 Bas... | {"library_name": "stable-baselines3", "tags": ["QbertNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "QbertNoFrameskip-v4", "type... | sb3/ppo-QbertNoFrameskip-v4 | null | [
"stable-baselines3",
"QbertNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"has_space",
"region:us"
] | null | 2022-06-02T11:43:00+00:00 | [] | [] | TAGS
#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #has_space #region-us
|
# PPO Agent playing QbertNoFrameskip-v4
This is a trained model of a PPO agent playing QbertNoFrameskip-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.
## U... | [
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-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 incl... | [
"TAGS\n#stable-baselines3 #QbertNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #has_space #region-us \n",
"# PPO Agent playing QbertNoFrameskip-v4\nThis is a trained model of a PPO agent playing QbertNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Z... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **BipedalWalkerHardcore-v3**
This is a trained model of a **PPO** agent playing **BipedalWalkerHardcore-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 for ... | {"library_name": "stable-baselines3", "tags": ["BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalkerHardcore-... | sb3/ppo-BipedalWalkerHardcore-v3 | null | [
"stable-baselines3",
"BipedalWalkerHardcore-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:44:08+00:00 | [] | [] | TAGS
#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing BipedalWalkerHardcore-v3
This is a trained model of a PPO agent playing BipedalWalkerHardcore-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 inclu... | [
"# PPO Agent playing BipedalWalkerHardcore-v3\nThis is a trained model of a PPO agent playing BipedalWalkerHardcore-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 a... | [
"TAGS\n#stable-baselines3 #BipedalWalkerHardcore-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing BipedalWalkerHardcore-v3\nThis is a trained model of a PPO agent playing BipedalWalkerHardcore-v3\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe ... |
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/1476203864063893505/j7Ep... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/eurunuela/1654174252782/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/eurunuela | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-06-02T11:49:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Eneko Uruñuela
@eurunuela
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
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": ["Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2DBulletEnv-v0", "ty... | sb3/ppo-Walker2DBulletEnv-v0 | null | [
"stable-baselines3",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-06-02T11:57:16+00:00 | [] | [] | TAGS
#stable-baselines3 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing Walker2DBulletEnv-v0
This is a trained model of a PPO agent playing Walker2DBulletEnv-v0
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.
##... | [
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\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 #Walker2DBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing Walker2DBulletEnv-v0\nThis is a trained model of a PPO agent playing Walker2DBulletEnv-v0\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a ... |
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