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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_500v4_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": ["article500v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:29:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v4\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2097
* Precision: 0.7284
* Recall: 0.7543
* F1: 0.7412
... | [
"### 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-article500v4_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_500v5_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": ["article500v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:35:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v5\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1848
* Precision: 0.7302
* Recall: 0.7657
* F1: 0.7476
... | [
"### 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-article500v5_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_500v6_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": ["article500v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:41:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v6\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2052
* Precision: 0.7276
* Recall: 0.7655
* F1: 0.7461
... | [
"### 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-article500v6_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. -->
# sentiment-10Epochs-2-work-please
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-bas... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "sentiment-10Epochs-2-work-please", "results": []}]} | sepidmnorozy/sentiment-10Epochs-2-work-please | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:46:19+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| sentiment-10Epochs-2-work-please
================================
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7450
* Accuracy: 0.8549
* F1: 0.8516
* Precision: 0.8714
* Recall: 0.8327
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* e... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | miguelwon/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:47:00+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1375
* F1: 0.8615
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
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_500v7_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": ["article500v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:47:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v7\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1961
* Precision: 0.7235
* Recall: 0.7613
* F1: 0.7419
... | [
"### 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-article500v7_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"]} | ClementRomac/TA_ALP-GMM_SAC_spider_s1 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"region:us"
] | null | 2022-08-11T11:48:21+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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.
*This policy was not part of TeachMyAgent's benchmark*
Results
-------
Percentage of ... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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_500v8_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": ["article500v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:53:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v8\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2113
* Precision: 0.7349
* Recall: 0.7560
* F1: 0.7453
... | [
"### 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-article500v8_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... |
feature-extraction | transformers |
# Model description
**XLMR-BERTovski** is a large pre-trained language model trained on Bulgarian and Macedonian texts. It was created by continuing training from the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main dev... | {"language": ["bg", "mk", "multilingual"], "license": "cc0-1.0", "tags": ["BERTovski", "MaCoCu"]} | MaCoCu/XLMR-BERTovski | null | [
"transformers",
"pytorch",
"tf",
"jax",
"xlm-roberta",
"feature-extraction",
"BERTovski",
"MaCoCu",
"bg",
"mk",
"multilingual",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:56:30+00:00 | [] | [
"bg",
"mk",
"multilingual"
] | TAGS
#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #BERTovski #MaCoCu #bg #mk #multilingual #license-cc0-1.0 #endpoints_compatible #region-us
| Model description
=================
XLMR-BERTovski is a large pre-trained language model trained on Bulgarian and Macedonian texts. It was created by continuing training from the XLM-RoBERTa-large model. It was developed as part of the MaCoCu project. The main developer is Rik van Noord from the University of Groning... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #BERTovski #MaCoCu #bg #mk #multilingual #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# Model description
**MaltBERTa** is a large pre-trained language model trained on Maltese texts. It was trained from scratch using the RoBERTa architecture. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main developer is [Rik van Noord](https://www.rikvannoord.nl/) from the University of ... | {"language": ["mt"], "license": "cc0-1.0", "tags": ["MaltBERTa", "MaCoCu"]} | MaCoCu/MaltBERTa | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"MaltBERTa",
"MaCoCu",
"mt",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:57:17+00:00 | [] | [
"mt"
] | TAGS
#transformers #pytorch #tf #jax #roberta #feature-extraction #MaltBERTa #MaCoCu #mt #license-cc0-1.0 #endpoints_compatible #region-us
| Model description
=================
MaltBERTa is a large pre-trained language model trained on Maltese texts. It was trained from scratch using the RoBERTa architecture. It was developed as part of the MaCoCu project. The main developer is Rik van Noord from the University of Groningen.
MaltBERTa was trained on 3.2... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #feature-extraction #MaltBERTa #MaCoCu #mt #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# Model description
**XLMR-MaltBERTa** is a large pre-trained language model trained on Maltese texts. It was created by continuing training from the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main developer is [Rik va... | {"language": ["mt"], "license": "cc0-1.0", "tags": ["MaltBERTa", "MaCoCu"]} | MaCoCu/XLMR-MaltBERTa | null | [
"transformers",
"pytorch",
"tf",
"jax",
"xlm-roberta",
"feature-extraction",
"MaltBERTa",
"MaCoCu",
"mt",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:57:46+00:00 | [] | [
"mt"
] | TAGS
#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaltBERTa #MaCoCu #mt #license-cc0-1.0 #endpoints_compatible #region-us
| Model description
=================
XLMR-MaltBERTa is a large pre-trained language model trained on Maltese texts. It was created by continuing training from the XLM-RoBERTa-large model. It was developed as part of the MaCoCu project. The main developer is Rik van Noord from the University of Groningen.
XLMR-MaltBE... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaltBERTa #MaCoCu #mt #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Article_500v9_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": ["article500v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Article_500v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "d... | DOOGLAK/Article_500v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T11:59:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-article500v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Article\_500v9\_NER\_Model\_3Epochs\_AUGMENTED
==============================================
This model is a fine-tuned version of bert-base-cased on the article500v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1931
* Precision: 0.7438
* Recall: 0.7618
* F1: 0.7527
... | [
"### 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-article500v9_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... |
feature-extraction | transformers |
# Model description
**XLMR-MaCoCu-is** is a large pre-trained language model trained on **Icelandic** texts. It was created by continuing training from the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project and only uses data that ... | {"language": ["is"], "license": "cc0-1.0", "tags": ["MaCoCu"]} | MaCoCu/XLMR-MaCoCu-is | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"xlm-roberta",
"feature-extraction",
"MaCoCu",
"is",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:01:03+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #xlm-roberta #feature-extraction #MaCoCu #is #license-cc0-1.0 #endpoints_compatible #region-us
| Model description
=================
XLMR-MaCoCu-is is a large pre-trained language model trained on Icelandic texts. It was created by continuing training from the XLM-RoBERTa-large model. It was developed as part of the MaCoCu project and only uses data that was crawled during the project. The main developer is Rik ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #xlm-roberta #feature-extraction #MaCoCu #is #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# Model description
**XLMR-MaCoCu-tr** is a large pre-trained language model trained on **Turkish** texts. It was created by continuing training from the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project and only uses data that wa... | {"language": ["tr"], "license": "cc0-1.0", "tags": ["MaCoCu"]} | MaCoCu/XLMR-MaCoCu-tr | null | [
"transformers",
"pytorch",
"tf",
"jax",
"xlm-roberta",
"feature-extraction",
"MaCoCu",
"tr",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:01:57+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaCoCu #tr #license-cc0-1.0 #endpoints_compatible #region-us
| Model description
=================
XLMR-MaCoCu-tr is a large pre-trained language model trained on Turkish texts. It was created by continuing training from the XLM-RoBERTa-large model. It was developed as part of the MaCoCu project and only uses data that was crawled during the project. The main developer is Rik va... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #xlm-roberta #feature-extraction #MaCoCu #tr #license-cc0-1.0 #endpoints_compatible #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"]} | ClementRomac/TA_ALP-GMM_SAC_spider_s4 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"region:us"
] | null | 2022-08-11T12:04:18+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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.
*This policy was not part of TeachMyAgent's benchmark*
Results
-------
Percentage of ... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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. -->
# Tagged_One_50v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:09:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v0\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6550
* Precision: 0.0233
* Recall: 0.0002
* F1... | [
"### 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-tagged_one50v0_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 train... |
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"]} | ClementRomac/TA_Random_SAC_chimpanzee_easy_parkour_s2 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"region:us"
] | null | 2022-08-11T12:10:36+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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.
*This policy was not part of TeachMyAgent's benchmark. It was trained on the easy task sp... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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. -->
# Tagged_One_50v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:15:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v1\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6207
* Precision: 0.1907
* Recall: 0.0271
* F1... | [
"### 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-tagged_one50v1_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 train... |
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"]} | ClementRomac/TA_Random_SAC_chimpanzee_easy_parkour_s15 | null | [
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"region:us"
] | null | 2022-08-11T12:15:54+00:00 | [
"2103.09815"
] | [] | TAGS
#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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.
*This policy was not part of TeachMyAgent's benchmark. It was trained on the easy task sp... | [] | [
"TAGS\n#sac #deep-reinforcement-learning #reinforcement-learning #teach-my-agent-parkour #arxiv-2103.09815 #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. -->
# Tagged_One_50v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:20:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v2\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6200
* Precision: 0.125
* Recall: 0.0007
* F1:... | [
"### 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-tagged_one50v2_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 train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# categorization-finetuned-20220721-164940-distilled-20220811-132317
This model is a fine-tuned version of [carted-nlp/categorizat... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "categorization-finetuned-20220721-164940-distilled-20220811-132317", "results": []}]} | carted-nlp/categorization-finetuned-20220721-164940-distilled-20220811-132317 | null | [
"transformers",
"pytorch",
"tensorboard",
"onnx",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:25:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #onnx #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| categorization-finetuned-20220721-164940-distilled-20220811-132317
==================================================================
This model is a fine-tuned version of carted-nlp/categorization-finetuned-20220721-164940 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1522... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 128\n* seed: 314\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #onnx #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 64\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. -->
# Tagged_One_50v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:26:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v3\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6197
* Precision: 0.1311
* Recall: 0.0066
* F1... | [
"### 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-tagged_one50v3_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 train... |
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. -->
# Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:31:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v4\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5788
* Precision: 0.3560
* Recall: 0.0421
* F1... | [
"### 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-tagged_one50v4_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 train... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutor... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm"]} | mrm8488/Worm_v2 | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] | null | 2022-08-11T12:35:19+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us
|
# ppo Agent playing Worm
This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
#... | [
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the training\... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Worm #region-us \n",
"# ppo Agent playing Worm\n This is a trained model of a ppo agent playing Worm using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\... |
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. -->
# Tagged_One_50v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:36:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v5\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6440
* Precision: 0.1164
* Recall: 0.0083
* F1... | [
"### 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-tagged_one50v5_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 train... |
null | null |
# OFA-Base-RefCOCO
This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on RefCOCO for visual grounding.
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-refcoco-fairseq-version | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-11T12:39:10+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# OFA-Base-RefCOCO
This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on RefCOCO for visual grounding.
For more information, please refer to the official github (URL
Temporarily, we only provide the finetuned checkpoints based on the official co... | [
"# OFA-Base-RefCOCO\nThis is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on RefCOCO for visual grounding. \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-RefCOCO\nThis is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on RefCOCO for visual grounding. \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. -->
# Tagged_One_50v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:41:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v6\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6728
* Precision: 0.0625
* Recall: 0.0005
* F1... | [
"### 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-tagged_one50v6_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 train... |
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. -->
# Tagged_One_50v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:46:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v7\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6441
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
... | [
"### 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-tagged_one50v7_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 train... |
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. -->
# Tagged_One_50v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:52:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v8\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5935
* Precision: 0.0917
* Recall: 0.0054
* F1... | [
"### 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-tagged_one50v8_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 train... |
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. -->
# Tagged_One_50v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one50v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_50v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_One_50v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T12:57:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one50v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_50v9\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one50v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6504
* Precision: 0.5
* Recall: 0.0002
* F1: 0... | [
"### 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-tagged_one50v9_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 train... |
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. -->
# Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:02:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v0\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4700
* Precision: 0.1690
* Recall: 0.0899
*... | [
"### 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-tagged_one100v0_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 trai... |
null | null |
# OFA-Base-VQA
This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on VQA 2.0.
For more information, please refer to the official github ([https://github.com/OFA-Sys/OFA](https://github.com/OFA-Sys/OFA))
Temporarily, we only provide the finetuned... | {"license": "apache-2.0"} | OFA-Sys/ofa-base-vqa-fairseq-version | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-11T13:08:01+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# OFA-Base-VQA
This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on VQA 2.0.
For more information, please refer to the official github (URL
Temporarily, we only provide the finetuned checkpoints based on the official code. | [
"# OFA-Base-VQA\nThis is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on VQA 2.0. \n\nFor more information, please refer to the official github (URL\n\nTemporarily, we only provide the finetuned checkpoints based on the official code."
] | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# OFA-Base-VQA\nThis is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on VQA 2.0. \n\nFor more information, please refer to the official github (URL\n\nTemporarily, we only provide the finetuned checkpo... |
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. -->
# Tagged_One_100v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:08:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v1\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4613
* Precision: 0.2325
* Recall: 0.1424
*... | [
"### 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-tagged_one100v1_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 trai... |
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. -->
# Tagged_One_100v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:13:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v2\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4407
* Precision: 0.2902
* Recall: 0.1286
*... | [
"### 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-tagged_one100v2_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 trai... |
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. -->
# Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:19:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v3\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4863
* Precision: 0.2056
* Recall: 0.0896
*... | [
"### 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-tagged_one100v3_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 trai... |
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. -->
# Tagged_One_100v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:25:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v4\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4506
* Precision: 0.1649
* Recall: 0.0818
*... | [
"### 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-tagged_one100v4_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 trai... |
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. -->
# Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:30:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v5\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4636
* Precision: 0.2791
* Recall: 0.2144
*... | [
"### 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-tagged_one100v5_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 trai... |
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. -->
# Tagged_One_100v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:35:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v6\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5346
* Precision: 0.2441
* Recall: 0.1391
*... | [
"### 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-tagged_one100v6_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 trai... |
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. -->
# Tagged_One_100v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:41:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v7\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5232
* Precision: 0.2402
* Recall: 0.1069
*... | [
"### 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-tagged_one100v7_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 trai... |
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. -->
# Tagged_One_100v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:47:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v8\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5649
* Precision: 0.1885
* Recall: 0.0498
*... | [
"### 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-tagged_one100v8_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 trai... |
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. -->
# Tagged_One_100v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one100v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_100v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_100v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:53:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one100v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_100v9\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one100v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4255
* Precision: 0.3040
* Recall: 0.2132
*... | [
"### 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-tagged_one100v9_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 trai... |
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. -->
# Tagged_One_250v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T13:59:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v0\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4287
* Precision: 0.5125
* Recall: 0.3694
*... | [
"### 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-tagged_one250v0_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 trai... |
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. -->
# Tagged_One_250v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:05:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v1\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3321
* Precision: 0.5896
* Recall: 0.5099
*... | [
"### 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-tagged_one250v1_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 trai... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args... | roscoyoon/distilbert-base-uncased-distilled | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:06:07+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-distilled
=================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2061
* Accuracy: 0.9448
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate:... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1161630886963683328/SgNq... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/henryfarrell/1660230533136/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/henryfarrell | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T14:08:06+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Henry Farrell
@henryfarrell
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/433603570/Pilgrim_Beart_... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pilgrimbeart/1660230691248/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pilgrimbeart | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-11T14:10:14+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Pilgrim Beart
@pilgrimbeart
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-----... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:10:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v2\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3573
* Precision: 0.5859
* Recall: 0.5074
*... | [
"### 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-tagged_one250v2_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 trai... |
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. -->
# Tagged_One_250v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:16:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v3\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3179
* Precision: 0.5783
* Recall: 0.4806
*... | [
"### 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-tagged_one250v3_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 trai... |
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. -->
# Tagged_One_250v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:22:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v4\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3389
* Precision: 0.5685
* Recall: 0.4847
*... | [
"### 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-tagged_one250v4_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 trai... |
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. -->
# Tagged_One_250v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:27:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v5\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3623
* Precision: 0.5500
* Recall: 0.4923
*... | [
"### 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-tagged_one250v5_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 trai... |
text-classification | transformers |
# Discord Sentiment Analysis - (Context: NFTs)
This is a model derived from Twitter-roBERTa-base model trained on ~10K Discord messages from NFT-based Discord servers and finetuned for sentiment analysis with manually labelled data.
The original Twitter-roBERTa-base model can be found [here](https://huggingface.co/... | {"widget": [{"text": "Excited for the mint"}, {"text": "lfg"}, {"text": "no wl"}]} | BVK97/Discord-NFT-Sentiment | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:33:38+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# Discord Sentiment Analysis - (Context: NFTs)
This is a model derived from Twitter-roBERTa-base model trained on ~10K Discord messages from NFT-based Discord servers and finetuned for sentiment analysis with manually labelled data.
The original Twitter-roBERTa-base model can be found here. This model is suitable f... | [
"# Discord Sentiment Analysis - (Context: NFTs) \n\nThis is a model derived from Twitter-roBERTa-base model trained on ~10K Discord messages from NFT-based Discord servers and finetuned for sentiment analysis with manually labelled data. \nThe original Twitter-roBERTa-base model can be found here. This model is sui... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Discord Sentiment Analysis - (Context: NFTs) \n\nThis is a model derived from Twitter-roBERTa-base model trained on ~10K Discord messages from NFT-based Discord servers and finetuned for sent... |
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. -->
# Tagged_One_250v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:33:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v6\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3273
* Precision: 0.5705
* Recall: 0.4716
*... | [
"### 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-tagged_one250v6_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 trai... |
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. -->
# Tagged_One_250v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:40:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v7\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3809
* Precision: 0.5509
* Recall: 0.4676
*... | [
"### 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-tagged_one250v7_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 trai... |
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. -->
# Tagged_One_250v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:45:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v8\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3389
* Precision: 0.5352
* Recall: 0.4795
*... | [
"### 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-tagged_one250v8_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 trai... |
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. -->
# Tagged_One_250v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one250v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_250v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_250v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:51:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one250v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_250v9\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one250v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3012
* Precision: 0.5795
* Recall: 0.5335
*... | [
"### 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-tagged_one250v9_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 trai... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TheJarmanitor/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additiona... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | TheJarmanitor/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-11T14:51:58+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
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. -->
# Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:57:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v0\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2679
* Precision: 0.6663
* Recall: 0.6838
*... | [
"### 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-tagged_one500v0_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 trai... |
text-classification | transformers | --alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 0.0 --alpha_clm 0.0 --mlm \ | {} | alishudi/distil_wo_act | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T14:59:35+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 0.0 --alpha_clm 0.0 --mlm \ | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_One_500v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:03:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v1\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2834
* Precision: 0.7132
* Recall: 0.6693
*... | [
"### 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-tagged_one500v1_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 trai... |
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. -->
# Tagged_One_500v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:09:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v2\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2644
* Precision: 0.6801
* Recall: 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-tagged_one500v2_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 trai... |
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. -->
# Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:16:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v3\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2659
* Precision: 0.6975
* Recall: 0.6782
*... | [
"### 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-tagged_one500v3_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 trai... |
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. -->
# Tagged_One_500v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:21:58+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v4\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2804
* Precision: 0.6656
* Recall: 0.6225
*... | [
"### 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-tagged_one500v4_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 trai... |
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... | QianMolloy/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-11T15:22:42+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. -->
# Tagged_One_500v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:27:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v5\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2523
* Precision: 0.6985
* Recall: 0.6818
*... | [
"### 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-tagged_one500v5_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 trai... |
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. -->
# Tagged_One_500v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:33:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v6\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2690
* Precision: 0.6867
* Recall: 0.6719
*... | [
"### 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-tagged_one500v6_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 trai... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-gc-art2e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-gc-art2e", "results": []}]} | waynedsouza/distilbert-base-uncased-gc-art2e | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:39:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-gc-art2e
================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0863
* Accuracy: 0.982
* F1: 0.9731
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# Tagged_One_500v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:40:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v7\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2679
* Precision: 0.6701
* Recall: 0.6767
*... | [
"### 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-tagged_one500v7_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 trai... |
null | null | # how to use
```python
# !pip install transformers
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin
class Net(nn.Module,PyTorchModelHubMixin):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool... | {} | Adapting/cifar10-image-classification | null | [
"pytorch",
"region:us"
] | null | 2022-08-11T15:43:20+00:00 | [] | [] | TAGS
#pytorch #region-us
| # how to use
example codes for testing the model: link | [
"# how to use\n\n\n\nexample codes for testing the model: link"
] | [
"TAGS\n#pytorch #region-us \n",
"# how to use\n\n\n\nexample codes for testing the model: link"
] |
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. -->
# Tagged_One_500v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:46:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v8\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2761
* Precision: 0.6785
* Recall: 0.6773
*... | [
"### 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-tagged_one500v8_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 trai... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-gc-art3e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-gc-art3e", "results": []}]} | waynedsouza/distilbert-base-uncased-gc-art3e | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:46:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-gc-art3e
================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0841
* Accuracy: 0.983
* F1: 0.9755
Model description
-----------------
More information neede... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# Tagged_One_500v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_one500v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_One_500v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_One_500v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:52:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_one500v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_One\_500v9\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_one500v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2469
* Precision: 0.7016
* Recall: 0.7011
*... | [
"### 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-tagged_one500v9_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 trai... |
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. -->
# Tagged_Uni_50v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:57:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v0\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6180
* Precision: 0.1063
* Recall: 0.0090
* F1... | [
"### 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-tagged_uni50v0_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 train... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-gc-art1e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-gc-art1e", "results": []}]} | waynedsouza/distilbert-base-uncased-gc-art1e | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T15:58:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-gc-art1e
================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0928
* Accuracy: 0.982
* F1: 0.9763
Model description
-----------------
More information neede... | [
"### 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: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "co... | Yao92/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:01:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8278
* Matthews Correlation: 0.5303
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:03:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v1\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5851
* Precision: 0.1466
* Recall: 0.0256
* F1... | [
"### 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-tagged_uni50v1_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 train... |
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. -->
# Tagged_Uni_50v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:08:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v2\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6159
* Precision: 0.08
* Recall: 0.0005
* F1: ... | [
"### 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-tagged_uni50v2_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 train... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TheJarmanitor/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | TheJarmanitor/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-11T16:12:02+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
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. -->
# Tagged_Uni_50v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:14:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v3\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5987
* Precision: 0.1477
* Recall: 0.0140
* F1... | [
"### 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-tagged_uni50v3_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 train... |
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. -->
# Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:20:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v4\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5415
* Precision: 0.2717
* Recall: 0.0754
* F1... | [
"### 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-tagged_uni50v4_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 train... |
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. -->
# Tagged_Uni_50v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:26:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v5\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6039
* Precision: 0.2311
* Recall: 0.0350
* F1... | [
"### 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-tagged_uni50v5_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 train... |
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. -->
# Tagged_Uni_50v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:31:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v6\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6142
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
... | [
"### 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-tagged_uni50v6_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 train... |
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. -->
# Tagged_Uni_50v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:37:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v7\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6772
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
... | [
"### 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-tagged_uni50v7_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 train... |
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. -->
# Tagged_Uni_50v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:41:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v8\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5527
* Precision: 0.1546
* Recall: 0.0230
* F1... | [
"### 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-tagged_uni50v8_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 train... |
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. -->
# Tagged_Uni_50v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni50v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_50v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}... | DOOGLAK/Tagged_Uni_50v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:47:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni50v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_50v9\_NER\_Model\_3Epochs\_AUGMENTED
=================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni50v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6233
* Precision: 0.5
* Recall: 0.0002
* F1: 0... | [
"### 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-tagged_uni50v9_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 train... |
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. -->
# Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:53:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v0\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4601
* Precision: 0.1802
* Recall: 0.0830
*... | [
"### 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-tagged_uni100v0_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 trai... |
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. -->
# Tagged_Uni_100v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T16:59:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v1\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4031
* Precision: 0.2364
* Recall: 0.1843
*... | [
"### 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-tagged_uni100v1_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 trai... |
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. -->
# Tagged_Uni_100v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v2_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v2_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v2_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:04:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v2_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v2\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v2\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4048
* Precision: 0.2783
* Recall: 0.1589
*... | [
"### 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-tagged_uni100v2_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 trai... |
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. -->
# Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v3_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:10:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v3_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v3\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v3\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4884
* Precision: 0.2764
* Recall: 0.1080
*... | [
"### 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-tagged_uni100v3_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 trai... |
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. -->
# Tagged_Uni_100v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v4_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v4_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v4_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:16:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v4_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v4\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v4\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3691
* Precision: 0.2528
* Recall: 0.1915
*... | [
"### 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-tagged_uni100v4_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 trai... |
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. -->
# Tagged_Uni_100v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v5_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v5_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v5_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:22:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v5_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v5\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v5\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4479
* Precision: 0.2748
* Recall: 0.2011
*... | [
"### 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-tagged_uni100v5_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 trai... |
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. -->
# Tagged_Uni_100v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v6_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v6_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v6_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:27:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v6_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v6\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v6\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4381
* Precision: 0.2402
* Recall: 0.1964
*... | [
"### 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-tagged_uni100v6_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 trai... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | athairus/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:28:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1339
* F1: 0.8663
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
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. -->
# Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v7_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:33:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v7_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v7\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v7\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5083
* Precision: 0.2364
* Recall: 0.1162
*... | [
"### 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-tagged_uni100v7_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 trai... |
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. -->
# Tagged_Uni_100v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v8_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v8_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v8_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:38:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v8_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v8\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v8\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5374
* Precision: 0.2341
* Recall: 0.0822
*... | [
"### 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-tagged_uni100v8_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 trai... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-AndresV0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"ty... | andres-hsn/Reinforce-AndresV0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-08-11T17:42:39+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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. -->
# Tagged_Uni_100v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni100v9_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_100v9_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_100v9_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:44:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni100v9_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_100v9\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni100v9\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4080
* Precision: 0.3227
* Recall: 0.2305
*... | [
"### 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-tagged_uni100v9_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 trai... |
null | null |
# SpecLab Model Card
This model card focuses on the model associated with the SpecLab space on Hugging Face. Temporarily, please [contact me](https://haoliyin.me) for the demo.
## Model Details
* **Developed by:** Haoli Yin
* **Model type:** Atrous Spatial Pyramid Pooling (ASPP) model for Specular Reflection Segmen... | {"language": "en", "license": "gpl-3.0", "tags": ["segmentation"], "inference": false, "co2_eq_emissions": {"emissions": 7540, "source": "MLCo2 Machine Learning Impact calculator", "geographical_location": "East USA", "hardware_used": "Tesla V100-SXM2 GPU"}, "model-index": [{"name": "SpecLab", "results": []}]} | Nano1337/SpecLab | null | [
"segmentation",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
"co2_eq_emissions",
"has_space",
"region:us"
] | null | 2022-08-11T17:44:40+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#segmentation #en #arxiv-1910.09700 #license-gpl-3.0 #co2_eq_emissions #has_space #region-us
|
# SpecLab Model Card
This model card focuses on the model associated with the SpecLab space on Hugging Face. Temporarily, please contact me for the demo.
## Model Details
* Developed by: Haoli Yin
* Model type: Atrous Spatial Pyramid Pooling (ASPP) model for Specular Reflection Segmentation in Endoscopic Images
* L... | [
"# SpecLab Model Card\n\nThis model card focuses on the model associated with the SpecLab space on Hugging Face. Temporarily, please contact me for the demo.",
"## Model Details\n\n* Developed by: Haoli Yin\n* Model type: Atrous Spatial Pyramid Pooling (ASPP) model for Specular Reflection Segmentation in Endoscop... | [
"TAGS\n#segmentation #en #arxiv-1910.09700 #license-gpl-3.0 #co2_eq_emissions #has_space #region-us \n",
"# SpecLab Model Card\n\nThis model card focuses on the model associated with the SpecLab space on Hugging Face. Temporarily, please contact me for the demo.",
"## Model Details\n\n* Developed by: Haoli Yin\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730
## Validation Metrics
- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision... | {"language": ["zh"], "tags": ["autotrain", "text-classification"], "datasets": ["yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 151.97297148175758}} | 0x-YuAN/CL_1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"zh",
"dataset:yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:47:58+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1241547318
- CO2 Emissions (in grams): 151.9730
## Validation Metrics
- Loss: 0.512
- Accuracy: 0.862
- Macro F1: 0.862
- Micro F1: 0.862
- Weighted F1: 0.862
- Macro Precision: 0.863
- Micro Precision: 0.862
- Weighted Precision... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1241547318\n- CO2 Emissions (in grams): 151.9730",
"## Validation Metrics\n\n- Loss: 0.512\n- Accuracy: 0.862\n- Macro F1: 0.862\n- Micro F1: 0.862\n- Weighted F1: 0.862\n- Macro Precision: 0.863\n- Micro Precision: 0.862\... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #zh #dataset-yuan1729/autotrain-data-YuAN-lawthone-CL_facts_backTrans #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 124154... |
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. -->
# Tagged_Uni_250v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v0_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v0_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v0_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:49:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v0_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v0\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v0\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3679
* Precision: 0.4748
* Recall: 0.3732
*... | [
"### 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-tagged_uni250v0_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 trai... |
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. -->
# Tagged_Uni_250v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tagged_uni250v1_wikigold_split"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Tagged_Uni_250v1_NER_Model_3Epochs_AUGMENTED", "results": [{"task": {"type": "token-classification", "name": "Token Classification... | DOOGLAK/Tagged_Uni_250v1_NER_Model_3Epochs_AUGMENTED | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-11T17:55:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-tagged_uni250v1_wikigold_split #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Tagged\_Uni\_250v1\_NER\_Model\_3Epochs\_AUGMENTED
==================================================
This model is a fine-tuned version of bert-base-cased on the tagged\_uni250v1\_wikigold\_split dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3057
* Precision: 0.5972
* Recall: 0.5291
*... | [
"### 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-tagged_uni250v1_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 trai... |
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