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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. -->
# Article_100v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:09:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v2\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3105
* Precision: 0.4554
* Recall: 0.4162
* F1: 0.4350
... | [
"### 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-article100v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.05-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.05-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "r... | Lvxue/distilled-mt5-small-0.05-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T08:14:20+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.05-0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8399
- Bleu: 7.0815
- Gen Len: 43.6583
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# distilled-mt5-small-0.05-0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8399\n- Bleu: 7.0815\n- Gen Len: 43.6583",
"## Model description\n\nMore information needed",
"## Intended uses & limitations... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.05-0.5\n\nThis model is a fine-tuned version of google/mt5-small ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.07-0.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.07-0.25", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "... | Lvxue/distilled-mt5-small-0.07-0.25 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T08:14:58+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.07-0.25
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8593
- Bleu: 7.0665
- Gen Len: 43.5793
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# distilled-mt5-small-0.07-0.25\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8593\n- Bleu: 7.0665\n- Gen Len: 43.5793",
"## Model description\n\nMore information needed",
"## Intended uses & limitation... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.07-0.25\n\nThis model is a fine-tuned version of google/mt5-small... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.07-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.07-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "r... | Lvxue/distilled-mt5-small-0.07-0.5 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T08:16:11+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.07-0.5
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8729
- Bleu: 6.6788
- Gen Len: 43.8899
## Model description
More information needed
## Intended uses & limitations
More information... | [
"# distilled-mt5-small-0.07-0.5\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8729\n- Bleu: 6.6788\n- Gen Len: 43.8899",
"## Model description\n\nMore information needed",
"## Intended uses & limitations... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.07-0.5\n\nThis model is a fine-tuned version of google/mt5-small ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-0.05-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"language": ["en", "ro"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "distilled-mt5-small-0.05-1", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-... | Lvxue/distilled-mt5-small-0.05-1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T08:16:17+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilled-mt5-small-0.05-1
This model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8106
- Bleu: 6.997
- Gen Len: 46.2551
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
"# distilled-mt5-small-0.05-1\n\nThis model is a fine-tuned version of google/mt5-small on the wmt16 ro-en dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.8106\n- Bleu: 6.997\n- Gen Len: 46.2551",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #en #ro #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilled-mt5-small-0.05-1\n\nThis model is a fine-tuned version of google/mt5-small on... |
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. -->
# Article_100v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:16:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v3\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3754
* Precision: 0.3300
* Recall: 0.3119
* F1: 0.3207
... | [
"### 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-article100v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tuto-distilbert-base-uncased-mlm
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "tuto-distilbert-base-uncased-mlm", "results": []}]} | bthomas/tuto-distilbert-base-uncased-mlm | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:22:31+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| tuto-distilbert-base-uncased-mlm
================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6807
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
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. -->
# Article_100v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:24:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v4\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3216
* Precision: 0.4062
* Recall: 0.3791
* F1: 0.3922
... | [
"### 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-article100v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_100v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:31:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v5\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3560
* Precision: 0.5067
* Recall: 0.4801
* F1: 0.4931
... | [
"### 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-article100v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | DevashishSiwatch/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:33:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-google-colab
=====================================
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: 0.5108
* Wer: 0.3342
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
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. -->
# Article_100v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:37:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v6\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2900
* Precision: 0.5109
* Recall: 0.5018
* F1: 0.5063
... | [
"### 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-article100v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-50-finetuned-v1
This model was trained from scratch on the None dataset.
## Model description
More information nee... | {"tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "mbart-large-50-finetuned-v1", "results": []}]} | z-rahimi-r/mbart-large-50-finetuned-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-11T08:40:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mbart-large-50-finetuned-v1
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following... | [
"# mbart-large-50-finetuned-v1\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# mbart-large-50-finetuned-v1\n\nThis model was trained from scratch on the None dataset.",
"## Model description\n\nMore informa... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_100v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:45:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v7\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4325
* Precision: 0.4198
* Recall: 0.3217
* F1: 0.3643
... | [
"### 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-article100v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_100v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T08:52:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v8\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3906
* Precision: 0.3867
* Recall: 0.2513
* F1: 0.3046
... | [
"### 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-article100v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_100v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article100v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_100v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_100v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:00:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article100v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_100v9\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article100v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3011
* Precision: 0.4913
* Recall: 0.5293
* F1: 0.5096
... | [
"### 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-article100v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
null | null |
# OFA-Base-SNLIVE
This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on SNLI-VE for visual entailment.
For more information, please refer to the official github ([https://github.com/OFA-Sys/OFA](https://github.com/OFA-Sys/OFA))
Temporarily, we o... | {"license": "apache-2.0"} | OFA-Sys/ofa-base-snlive-fairseq-version | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-11T09:05:56+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# OFA-Base-SNLIVE
This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on SNLI-VE for visual entailment.
For more information, please refer to the official github (URL
Temporarily, we only provide the finetuned checkpoints based on the official co... | [
"# OFA-Base-SNLIVE\nThis is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on SNLI-VE for visual entailment. \n\nFor more information, please refer to the official github (URL\n\nTemporarily, we only provide the finetuned checkpoints based on the of... | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# OFA-Base-SNLIVE\nThis is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on SNLI-VE for visual entailment. \n\nFor more information, please refer to the official github (URL\n\nTemporarily, we only prov... |
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. -->
# Article_250v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:08:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v0\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2589
* Precision: 0.6609
* Recall: 0.6239
* F1: 0.6419
... | [
"### 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-article250v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_chimpanzee_s26", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-par... | flowers-team/TA_ALP-GMM_SAC_chimpanzee_s26 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:08:36+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_chimpanzee_s28", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-par... | flowers-team/TA_ALP-GMM_SAC_chimpanzee_s28 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:12:39+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_chimpanzee_s18", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-par... | flowers-team/TA_ALP-GMM_SAC_chimpanzee_s18 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:12:51+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_bipedal_s4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_ALP-GMM_SAC_bipedal_s4 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:13:06+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_bipedal_s12", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkou... | flowers-team/TA_ALP-GMM_SAC_bipedal_s12 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:13:19+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_bipedal_s2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_ALP-GMM_SAC_bipedal_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:13:32+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_fish_s44", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}... | flowers-team/TA_ALP-GMM_SAC_fish_s44 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:13:44+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_fish_s37", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}... | flowers-team/TA_ALP-GMM_SAC_fish_s37 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:13:56+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ALP-GMM_SAC_fish_s45", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}... | flowers-team/TA_ALP-GMM_SAC_fish_s45 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:14:09+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #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. -->
# Article_250v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:16:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v1\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2324
* Precision: 0.6699
* Recall: 0.6657
* F1: 0.6678
... | [
"### 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-article250v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_250v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:21:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v2\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2484
* Precision: 0.6846
* Recall: 0.6809
* F1: 0.6827
... | [
"### 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-article250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_250v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:27:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v3\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2531
* Precision: 0.6347
* Recall: 0.6342
* F1: 0.6345
... | [
"### 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-article250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tn-En_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tn-en](https://huggingface.co/Helsinki-NLP/opus-mt-tn-e... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Tn-En_update", "results": []}]} | kabelomalapane/Tn-En_update | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:29:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Tn-En\_update
=============
This model is a fine-tuned version of Helsinki-NLP/opus-mt-tn-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2371
* Bleu: 41.6029
Model description
-----------------
More information needed
Intended uses & limitations
--------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_chimpanzee_s12", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-age... | flowers-team/TA_Setter-Solver_SAC_chimpanzee_s12 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:31:19+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_chimpanzee_s3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agen... | flowers-team/TA_Setter-Solver_SAC_chimpanzee_s3 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:31:33+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_chimpanzee_s10", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-age... | flowers-team/TA_Setter-Solver_SAC_chimpanzee_s10 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T09:31:53+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
text-generation | transformers | #Blaine Anderson DialoGPT Model | {"tags": ["conversational"]} | Sophiejs/DialoGPT-small-BlaineBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T09:32:43+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #Blaine Anderson DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | Chrispfield/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:33:55+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3196
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size:... |
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. -->
# Article_250v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:35:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v4\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2337
* Precision: 0.6301
* Recall: 0.6385
* F1: 0.6342
... | [
"### 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-article250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_250v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T09:43:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v5\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2378
* Precision: 0.6724
* Recall: 0.6475
* F1: 0.6597
... | [
"### 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-article250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# nb-bert-large-user-needs
This model is a fine-tuned version of [NbAiLab/nb-bert-large](https://huggingface.co/NbAiLab/nb-bert-la... | {"language": ["no", "nb", "nn"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Fl\u00f8yfjelltunnelen p\u00e5 E39 retning sentrum er \u00e5pen for fri ferdsel."}, {"text": "Slik kan du redusere str\u00f8mregningen din"}], "pipeline... | thusken/nb-bert-large-user-needs | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"no",
"nb",
"nn",
"base_model:NbAiLab/nb-bert-large",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-11T10:15:43+00:00 | [] | [
"no",
"nb",
"nn"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #no #nb #nn #base_model-NbAiLab/nb-bert-large #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| nb-bert-large-user-needs
========================
This model is a fine-tuned version of NbAiLab/nb-bert-large on a dataset of 2000 articles from Bergens Tidende, published between 06/01/2020 and 02/02/2020. These articles are labelled as one of six classes / user needs, as introduced by the BBC in 2017. It achieves t... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #no #nb #nn #base_model-NbAiLab/nb-bert-large #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | Eylul/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-11T10:22:27+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
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. -->
# Article_250v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:28:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v6\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2146
* Precision: 0.6597
* Recall: 0.6778
* F1: 0.6686
... | [
"### 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-article250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_fish_s0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Setter-Solver_SAC_fish_s0 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:32:47+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_fish_s11", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-par... | flowers-team/TA_Setter-Solver_SAC_fish_s11 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:33:04+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_fish_s4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Setter-Solver_SAC_fish_s4 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:33:16+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #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. -->
# Article_250v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:34:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v7\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2210
* Precision: 0.6857
* Recall: 0.7036
* F1: 0.6946
... | [
"### 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-article250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_bipedal_s2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Self-Paced_SAC_bipedal_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:40:15+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #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. -->
# Article_250v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:40:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v8\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2522
* Precision: 0.6710
* Recall: 0.6662
* F1: 0.6686
... | [
"### 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-article250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_bipedal_s8", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Self-Paced_SAC_bipedal_s8 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:40:52+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_bipedal_s3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Self-Paced_SAC_bipedal_s3 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:41:10+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_bipedal_s3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_RIAC_SAC_bipedal_s3 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:41:31+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_bipedal_s4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_RIAC_SAC_bipedal_s4 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:41:50+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_bipedal_s13", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}... | flowers-team/TA_RIAC_SAC_bipedal_s13 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:42:08+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_chimpanzee_s1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-p... | flowers-team/TA_Self-Paced_SAC_chimpanzee_s1 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:42:20+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_chimpanzee_s15", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-... | flowers-team/TA_Self-Paced_SAC_chimpanzee_s15 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:42:35+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_chimpanzee_s10", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-... | flowers-team/TA_Self-Paced_SAC_chimpanzee_s10 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:42:48+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_fish_s13", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkou... | flowers-team/TA_Self-Paced_SAC_fish_s13 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:43:23+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/fa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-timit-demo-google-colab", "results": []}]} | DevashishSiwatch/wav2vec2-large-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:43:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-timit-demo-google-colab
======================================
This model is a fine-tuned version of facebook/wav2vec2-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4603
* Wer: 0.3096
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_fish_s5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_Self-Paced_SAC_fish_s5 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:43:45+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Self-Paced_SAC_fish_s11", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkou... | flowers-team/TA_Self-Paced_SAC_fish_s11 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:44:17+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_chimpanzee_s20", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_ADR_SAC_chimpanzee_s20 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:44:51+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_chimpanzee_s24", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_ADR_SAC_chimpanzee_s24 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:45:16+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_chimpanzee_s26", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_ADR_SAC_chimpanzee_s26 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:45:29+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #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. -->
# Article_250v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article250v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_250v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_250v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:46:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article250v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_250v9\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article250v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2025
* Precision: 0.6809
* Recall: 0.6954
* F1: 0.6881
... | [
"### 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-article250v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_fish_s35", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_Random_SAC_fish_s35 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:46:56+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_fish_s36", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_Random_SAC_fish_s36 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:47:10+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_fish_s46", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_Random_SAC_fish_s46 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:47:23+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_bipedal_s1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_GoalGAN_SAC_bipedal_s1 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:47:36+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_bipedal_s8", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_GoalGAN_SAC_bipedal_s8 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:47:48+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_bipedal_s2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_GoalGAN_SAC_bipedal_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:48:01+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_bipedal_s10", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-... | flowers-team/TA_Setter-Solver_SAC_bipedal_s10 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:48:14+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_bipedal_s7", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-p... | flowers-team/TA_Setter-Solver_SAC_bipedal_s7 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:48:31+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Setter-Solver_SAC_bipedal_s4", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-p... | flowers-team/TA_Setter-Solver_SAC_bipedal_s4 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:48:51+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_fish_s5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_GoalGAN_SAC_fish_s5 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:50:26+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_fish_s0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_GoalGAN_SAC_fish_s0 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:50:39+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_fish_s10", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}... | flowers-team/TA_GoalGAN_SAC_fish_s10 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:50:51+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_chimpanzee_s3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_RIAC_SAC_chimpanzee_s3 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:51:04+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_chimpanzee_s7", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_RIAC_SAC_chimpanzee_s7 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:51:17+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_chimpanzee_s10", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkou... | flowers-team/TA_RIAC_SAC_chimpanzee_s10 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:51:44+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_fish_s32", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, "m... | flowers-team/TA_ADR_SAC_fish_s32 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:52:09+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_fish_s40", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, "m... | flowers-team/TA_ADR_SAC_fish_s40 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:52:22+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_fish_s46", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, "m... | flowers-team/TA_ADR_SAC_fish_s46 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:52:39+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_fish_s3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, "m... | flowers-team/TA_RIAC_SAC_fish_s3 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:53:03+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #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. -->
# Article_500v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:53:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v0\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2180
* Precision: 0.7005
* Recall: 0.7454
* F1: 0.7222
... | [
"### 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-article500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_fish_s2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, "m... | flowers-team/TA_RIAC_SAC_fish_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:53:27+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "RIAC_SAC_fish_s5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, "m... | flowers-team/TA_RIAC_SAC_fish_s5 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:53:56+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_chimpanzee_s24", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Random_SAC_chimpanzee_s24 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:54:11+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_chimpanzee_s28", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Random_SAC_chimpanzee_s28 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:54:29+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_chimpanzee_s19", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_Random_SAC_chimpanzee_s19 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:54:51+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_chimpanzee_s11", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-par... | flowers-team/TA_GoalGAN_SAC_chimpanzee_s11 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:55:07+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_chimpanzee_s2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-park... | flowers-team/TA_GoalGAN_SAC_chimpanzee_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:55:40+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "GoalGAN_SAC_chimpanzee_s15", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-par... | flowers-team/TA_GoalGAN_SAC_chimpanzee_s15 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:55:52+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_bipedal_s5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"... | flowers-team/TA_Random_SAC_bipedal_s5 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:56:35+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_bipedal_s15", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour... | flowers-team/TA_Random_SAC_bipedal_s15 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:56:47+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "Random_SAC_bipedal_s1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"... | flowers-team/TA_Random_SAC_bipedal_s1 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:57:00+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_bipedal_s15", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"},... | flowers-team/TA_ADR_SAC_bipedal_s15 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:57:56+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_bipedal_s1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, ... | flowers-team/TA_ADR_SAC_bipedal_s1 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:58:26+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us \n"
] |
reinforcement-learning | null |
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowe... | {"tags": ["sac", "deep-reinforcement-learning", "reinforcement-learning", "teach-my-agent-parkour"], "model-index": [{"name": "ADR_SAC_bipedal_s2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "teach-my-agent-parkour", "type": "teach-my-agent-parkour"}, ... | flowers-team/TA_ADR_SAC_bipedal_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] | null | 2022-08-11T10:58:38+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #region-us
| Deep RL Agent Playing TeachMyAgent's parkour.
=============================================
You can find more info about TeachMyAgent here.
Results of our benchmark can be found in our paper.
You can test this policy here
Results
-------
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions step... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #model-index #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. -->
# Article_500v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T10:59:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v1\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2039
* Precision: 0.7456
* Recall: 0.7715
* F1: 0.7583
... | [
"### 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-article500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.ev... | {"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... | yogeshkulkarni/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-11T11:04:45+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)"
] |
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. -->
# Article_500v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:05:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v2\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2086
* Precision: 0.7113
* Recall: 0.7526
* F1: 0.7314
... | [
"### 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-article500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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. -->
# Article_500v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["article500v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:23:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v3\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2187
* Precision: 0.7293
* Recall: 0.7575
* F1: 0.7431
... | [
"### 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-article500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trainin... |
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