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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | anasaqsme/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
question-answering | transformers |
# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)
## Overview
- Language model: xlm-roberta-large
- Fine-tune: [deepset/xlm-roberta-large-squad2](https://huggingface.co/deepset/xlm-roberta-large-squad2)
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: [mailong25/b... | {"language": "vi", "license": "mit", "tags": ["vi", "xlm-roberta"], "metrics": ["f1", "em"], "widget": [{"text": "To\u00e0 nh\u00e0 n\u00e0o cao nh\u1ea5t Vi\u1ec7t Nam?", "context": "Landmark 81 l\u00e0 m\u1ed9t to\u00e0 nh\u00e0 ch\u1ecdc tr\u1eddi trong t\u1ed5 h\u1ee3p d\u1ef1 \u00e1n Vinhomes T\u00e2n C\u1ea3ng, m... | ancs21/xlm-roberta-large-vi-qa | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"vi",
"license:mit",
"endpoints_compatible",
"region:us"
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"vi"
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#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us
|
# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)
## Overview
- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.... | [
"# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)",
"## Overview\n\n- Language model: xlm-roberta-large\n- Fine-tune: deepset/xlm-roberta-large-squad2\n- Language: Vietnamese\n- Downstream-task: Extractive QA\n- Dataset: mailong25/bert-vietnamese-question-answering\n- Training d... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us \n",
"# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)",
"## Overview\n\n- Language model: xlm-roberta-large\n- Fine-tune: deepset/xlm-roberta-large-squad2\n- Langu... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-cased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "... | andi611/bert-base-cased-ner-conll2003 | null | [
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"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| bert-base-cased-ner
===================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0620
* Precision: 0.9406
* Recall: 0.9463
* F1: 0.9434
* Accuracy: 0.9861
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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-0... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-uncased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type":... | andi611/bert-base-uncased-ner-conll2003 | null | [
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"pytorch",
"tensorboard",
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"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-ner
=====================
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1258
* Precision: 0.0269
* Recall: 0.1379
* F1: 0.0451
* Accuracy: 0.1988
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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-0... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-ner
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-large-uncased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type"... | andi611/bert-large-uncased-ner-conll2003 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-ner
======================
This model is a fine-tuned version of bert-large-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0591
* Precision: 0.9465
* Recall: 0.9568
* F1: 0.9517
* Accuracy: 0.9877
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-0... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-ner-conll2003
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-ner-conll2003", "results": [{"task": {"name": "Token Classification", "type": "token-classifi... | andi611/bert-large-uncased-whole-word-masking-ner-conll2003 | null | [
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"bert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
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#transformers #pytorch #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-large-uncased-whole-word-masking-ner-conll2003
===================================================
This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0592
* Precision: 0.9527
* Recall: 0.9569
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* trai... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat
This model is a fine-tuned versio... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classifi... | andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:conll2003",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More... | [
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
This model is a fine-tuned versi... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classif... | andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:conll2003",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
Mor... | [
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
This model is a fine-tuned version of [deep... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, ... | andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:conll2003",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat
This model is a fine-tuned version of [deep... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "mit_movie"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, ... | andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:mit_movie",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_movie #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_movie datasets.
## Model description
More information needed
## Intended uses & limitations
More informati... | [
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_movie datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_movie #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of ... | {"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "mit_restaurant"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classifi... | andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat | null | [
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"pytorch",
"bert",
"question-answering",
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"dataset:squad_v2",
"dataset:mit_restaurant",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_restaurant datasets.
## Model description
More information needed
## Intended uses & limitations
More... | [
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_restaurant datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version ... |
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-agnews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-agnews", "results": [{"dataset": {"name": "ag_news", "type": "ag_news", "args": "default"}, "metric": {"name": "Accuracy", "type": "accura... | andi611/distilbert-base-uncased-ner-agnews | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:ag_news",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-agnews
==============================
This model is a fine-tuned version of distilbert-base-uncased on the ag\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1652
* Accuracy: 0.9474
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-ag_news #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: 3e-05\n* t... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "... | andi611/distilbert-base-uncased-ner-conll2003 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-ner
===========================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0664
* Precision: 0.9332
* Recall: 0.9423
* F1: 0.9377
* Accuracy: 0.9852
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-ner-mit-restaurant
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mit_restaurant"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-ner-mit-restaurant", "results": [{"task": {"name": "Token Classification", "type": "token-classificati... | andi611/distilbert-base-uncased-ner-mit-restaurant | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:mit_restaurant",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-ner-mit-restaurant
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the mit\_restaurant dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3097
* Precision: 0.7874
* Recall: 0.8104
* F1: 0.7988
* Accuracy: 0.... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #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... |
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-boolq
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["boolq"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-boolq", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "boolq", "type": "boolq", "ar... | andi611/distilbert-base-uncased-qa-boolq | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:boolq",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-boolq
=============================
This model is a fine-tuned version of distilbert-base-uncased on the boolq dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2071
* Accuracy: 0.7315
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* tra... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-qa-with-ner
This model is a fine-tuned version of [andi611/distilbert-base-uncased-qa](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-qa-with-ner", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]} | andi611/distilbert-base-uncased-qa-with-ner | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-qa-with-ner
This model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# distilbert-base-uncased-qa-with-ner\n\nThis model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-qa-with-ner\n\nThis model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 dataset.",
"## Mo... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-qa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-bas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "distilbert-base-uncased-qa", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]} | andi611/distilbert-base-uncased-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-qa
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation... | [
"# distilbert-base-uncased-qa\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1925",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-qa\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of [twmkn9/distil... | {"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "mit_restaurant"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v... | andi611/distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:mit_restaurant",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the squad_v2 and the mit_restaurant datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the squad_v2 and the mit_restaurant datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information ... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-u... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat
This model is a fine-tuned version of [twmkn9/distilbert... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"... | andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Mo... | [
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
This model is a fine-tuned version of [twmkn9/distilbert-base-uncase... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg-with-multi", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]} | andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-multi | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informati... | [
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation ... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat
This model is a fine-tuned version of [twmkn9/distilbert-base-uncas... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]} | andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More informat... | [
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner-with-neg
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](h... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]} | andi611/distilbert-base-uncased-squad2-with-ner-with-neg | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# distilbert-base-uncased-squad2-with-ner-with-neg\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMor... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad2-with-ner
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://hu... | {"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]} | andi611/distilbert-base-uncased-squad2-with-ner | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# distilbert-base-uncased-squad2-with-ner\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model descripti... |
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. -->
# roberta-base-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 data... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "roberta-base-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]} | andi611/roberta-base-ner-conll2003 | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-ner
This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315... | [
"# roberta-base-ner\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0814\n- eval_precision: 0.9101\n- eval_recall: 0.9336\n- eval_f1: 0.9217\n- eval_accuracy: 0.9799\n- eval_runtime: 10.2964\n- eval_samples_per... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-ner\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.\nIt achieves the following results on... |
text-generation | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | andikarachman/DialoGPT-small-sheldon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | anditya/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8885
* Mae: 0.4390
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-multilingual-uncased](htt... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola", "results": []}]} | andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola
===========================================================
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0423
* Train Accuracy: 0.9869
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 43750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'cla... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andreiliphdpr/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "andreiliphdpr/distilbert-base-uncased-finetuned-cola", "results": []}]} | andreiliphdpr/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| andreiliphdpr/distilbert-base-uncased-finetuned-cola
====================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0015
* Train Accuracy: 0.9995
* Validation Loss: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 43750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate':... |
feature-extraction | transformers |
# SimCLS
SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890).
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abst... | {"language": ["en"], "tags": ["simcls"], "datasets": ["billsum"]} | andrejmiscic/simcls-scorer-billsum | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"simcls",
"en",
"dataset:billsum",
"arxiv:2106.01890",
"arxiv:1910.00523",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.01890",
"1910.00523"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-billsum #arxiv-2106.01890 #arxiv-1910.00523 #endpoints_compatible #region-us
| SimCLS
======
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization.
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *gene... | [
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\nWe believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of t... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-billsum #arxiv-2106.01890 #arxiv-1910.00523 #endpoints_compatible #region-us \n",
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper ... |
feature-extraction | transformers |
# SimCLS
SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890).
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstr... | {"language": ["en"], "tags": ["simcls"], "datasets": ["cnn_dailymail"]} | andrejmiscic/simcls-scorer-cnndm | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"simcls",
"en",
"dataset:cnn_dailymail",
"arxiv:2106.01890",
"arxiv:1602.06023",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.01890",
"1602.06023"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-cnn_dailymail #arxiv-2106.01890 #arxiv-1602.06023 #endpoints_compatible #region-us
| SimCLS
======
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization.
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *gene... | [
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\n\n\n\nof the original work"
] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-cnn_dailymail #arxiv-2106.01890 #arxiv-1602.06023 #endpoints_compatible #region-us \n",
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS ... |
feature-extraction | transformers |
# SimCLS
SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890).
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstra... | {"language": ["en"], "tags": ["simcls"], "datasets": ["xsum"]} | andrejmiscic/simcls-scorer-xsum | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"simcls",
"en",
"dataset:xsum",
"arxiv:2106.01890",
"arxiv:1808.08745",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.01890",
"1808.08745"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-xsum #arxiv-2106.01890 #arxiv-1808.08745 #endpoints_compatible #region-us
| SimCLS
======
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization.
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *gene... | [
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\n\n\n\nof the original work"
] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-xsum #arxiv-2106.01890 #arxiv-1808.08745 #endpoints_compatible #region-us \n",
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-cased-finetuned-squad", "results": []}]} | andresestevez/bert-base-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpa... | [
"# bert-base-cased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proce... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-cased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information n... |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | anduush/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
text-generation | transformers | # Medical History Model based on ruGPT2 by @sberbank-ai
A simple model for helping medical staff to complete patient's medical histories.
Model used pretrained [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2)
| {"language": ["ru"], "license": "mit", "tags": ["PyTorch", "Transformers"]} | anechaev/ru_med_gpt3sm_based_on_gpt2 | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Medical History Model based on ruGPT2 by @sberbank-ai
A simple model for helping medical staff to complete patient's medical histories.
Model used pretrained sberbank-ai/rugpt3small_based_on_gpt2
| [
"# Medical History Model based on ruGPT2 by @sberbank-ai\n\nA simple model for helping medical staff to complete patient's medical histories.\nModel used pretrained sberbank-ai/rugpt3small_based_on_gpt2"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Medical History Model based on ruGPT2 by @sberbank-ai\n\nA simple model for helping medical staff to complete patient'... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 583416409
- CO2 Emissions (in grams): 72.26141764997115
## Validation Metrics
- Loss: 1.4701834917068481
- Rouge1: 47.7785
- Rouge2: 24.8518
- RougeL: 40.2231
- RougeLsum: 43.9487
- Gen Len: 18.8029
## Usage
You can use cURL to access this mo... | {"language": "en", "tags": "autonlp", "datasets": ["anegi/autonlp-data-dialogue-summariztion"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 72.26141764997115} | anegi/autonlp-dialogue-summariztion-583416409 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autonlp",
"en",
"dataset:anegi/autonlp-data-dialogue-summariztion",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autonlp #en #dataset-anegi/autonlp-data-dialogue-summariztion #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 583416409
- CO2 Emissions (in grams): 72.26141764997115
## Validation Metrics
- Loss: 1.4701834917068481
- Rouge1: 47.7785
- Rouge2: 24.8518
- RougeL: 40.2231
- RougeLsum: 43.9487
- Gen Len: 18.8029
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 583416409\n- CO2 Emissions (in grams): 72.26141764997115",
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"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autonlp #en #dataset-anegi/autonlp-data-dialogue-summariztion #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 583416409\n- CO2 Emissions (in grams):... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 412010597
- CO2 Emissions (in grams): 10.411685187181709
## Validation Metrics
- Loss: 0.12585781514644623
- Accuracy: 0.9475446428571429
- Precision: 0.9454660748256183
- Recall: 0.964424320827943
- AUC: 0.990229573862156
- F1: 0.95485... | {"language": "en", "tags": "autonlp", "datasets": ["anel/autonlp-data-cml"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.411685187181709} | anel/autonlp-cml-412010597 | null | [
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"en",
"dataset:anel/autonlp-data-cml",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-anel/autonlp-data-cml #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 412010597
- CO2 Emissions (in grams): 10.411685187181709
## Validation Metrics
- Loss: 0.12585781514644623
- Accuracy: 0.9475446428571429
- Precision: 0.9454660748256183
- Recall: 0.964424320827943
- AUC: 0.990229573862156
- F1: 0.95485... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 412010597\n- CO2 Emissions (in grams): 10.41168... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 432211280
- CO2 Emissions (in grams): 8.898145050355591
## Validation Metrics
- Loss: 0.12489336729049683
- Accuracy: 0.9520089285714286
- Precision: 0.9436443331246086
- Recall: 0.9747736093143596
- AUC: 0.9910066767410616
- F1: 0.9589... | {"language": "en", "tags": "autonlp", "datasets": ["anelnurkayeva/autonlp-data-covid"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 8.898145050355591} | anelnurkayeva/autonlp-covid-432211280 | null | [
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"roberta",
"text-classification",
"autonlp",
"en",
"dataset:anelnurkayeva/autonlp-data-covid",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-anelnurkayeva/autonlp-data-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 432211280
- CO2 Emissions (in grams): 8.898145050355591
## Validation Metrics
- Loss: 0.12489336729049683
- Accuracy: 0.9520089285714286
- Precision: 0.9436443331246086
- Recall: 0.9747736093143596
- AUC: 0.9910066767410616
- F1: 0.9589... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 432211280\n- CO2 Emissions (in grams... |
fill-mask | transformers |
# BERT for Patents
BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.
If you want to learn more about the model, check out the [blog post](https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis), [white paper]... | {"language": ["en"], "license": "apache-2.0", "tags": ["masked-lm", "pytorch"], "metrics": ["perplexity"], "pipeline-tag": "fill-mask", "mask-token": "[MASK]", "widget": [{"text": "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible."}, {"text"... | anferico/bert-for-patents | null | [
"transformers",
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"tf",
"safetensors",
"fill-mask",
"masked-lm",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #fill-mask #masked-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT for Patents
BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.
If you want to learn more about the model, check out the blog post, white paper and GitHub page containing the original TensorFlow checkpoint.
---
### Projects using this mo... | [
"# BERT for Patents\n\nBERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.\n\nIf you want to learn more about the model, check out the blog post, white paper and GitHub page containing the original TensorFlow checkpoint.\n\n---",
"### Projects... | [
"TAGS\n#transformers #pytorch #tf #safetensors #fill-mask #masked-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT for Patents\n\nBERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.\n\nI... |
text-generation | transformers |
#Monke Messenger DialoGPT Model | {"tags": ["conversational"]} | ange/DialoGPT-medium-Monke | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Monke Messenger DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "Common Voice tr", "type": ... | aniltrkkn/wav2vec2-large-xlsr-53-turkish | null | [
"transformers",
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"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as ... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.\nW... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-BioclinicalBERT-ADR
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emi... | {"tags": ["generated_from_trainer"], "datasets": ["ade_corpus_v2"], "model-index": [{"name": "sagemaker-BioclinicalBERT-ADR", "results": []}]} | anindabitm/sagemaker-BioclinicalBERT-ADR | null | [
"transformers",
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"bert",
"question-answering",
"generated_from_trainer",
"dataset:ade_corpus_v2",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-ade_corpus_v2 #endpoints_compatible #has_space #region-us
| sagemaker-BioclinicalBERT-ADR
=============================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the ade\_corpus\_v2 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Train... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-ade_corpus_v2 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_ba... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default... | anindabitm/sagemaker-distilbert-emotion | null | [
"transformers",
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"distilbert",
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"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-emotion
============================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2434
* Accuracy: 0.9165
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 3... |
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. -->
# albert-base-v2-finetuned-qnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "qnli"}, "metric... | anirudh21/albert-base-v2-finetuned-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-finetuned-qnli
=============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3194
* Accuracy: 0.9112
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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 #albert #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\\_r... |
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. -->
# albert-base-v2-finetuned-rte
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics"... | anirudh21/albert-base-v2-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-finetuned-rte
============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2496
* Accuracy: 0.7581
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\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 #albert #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\\_r... |
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. -->
# albert-base-v2-finetuned-wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metric... | anirudh21/albert-base-v2-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-base-v2-finetuned-wnli
=============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6878
* Accuracy: 0.5634
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### 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 #albert #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\\_r... |
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. -->
# albert-large-v2-finetuned-rte
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-large-v2-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics... | anirudh21/albert-large-v2-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-large-v2-finetuned-rte
=============================
This model is a fine-tuned version of albert-large-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6827
* Accuracy: 0.5487
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### 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 #albert #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\\_r... |
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. -->
# albert-large-v2-finetuned-wnli
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-large-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metri... | anirudh21/albert-large-v2-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-large-v2-finetuned-wnli
==============================
This model is a fine-tuned version of albert-large-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6919
* Accuracy: 0.5352
Model description
-----------------
More information needed
Intended uses & limita... | [
"### 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 #albert #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\\_r... |
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. -->
# albert-xlarge-v2-finetuned-mrpc
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "albert-xlarge-v2-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"},... | anirudh21/albert-xlarge-v2-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-xlarge-v2-finetuned-mrpc
===============================
This model is a fine-tuned version of albert-xlarge-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5563
* Accuracy: 0.7132
* F1: 0.8146
Model description
-----------------
More information needed
Intend... | [
"### 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 #albert #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\\_r... |
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. -->
# albert-xlarge-v2-finetuned-wnli
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-xlarge-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metr... | anirudh21/albert-xlarge-v2-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-xlarge-v2-finetuned-wnli
===============================
This model is a fine-tuned version of albert-xlarge-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6869
* Accuracy: 0.5634
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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 #albert #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\\_r... |
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. -->
# albert-xxlarge-v2-finetuned-wnli
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-xxlarge-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "met... | anirudh21/albert-xxlarge-v2-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| albert-xxlarge-v2-finetuned-wnli
================================
This model is a fine-tuned version of albert-xxlarge-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6970
* Accuracy: 0.5070
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #albert #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\\_r... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | anirudh21/bert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-cola
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9664
* Matthews Correlation: 0.5797
Model description
-----------------
More information needed
Inte... | [
"### 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 #bert #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\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}... | anirudh21/bert-base-uncased-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-mrpc
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6645
* Accuracy: 0.7917
* F1: 0.8590
Model description
-----------------
More information needed
Int... | [
"### 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 #bert #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\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-qnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "qnli"}, "met... | anirudh21/bert-base-uncased-finetuned-qnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-qnli
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6268
* Accuracy: 0.7917
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-rte
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metri... | anirudh21/bert-base-uncased-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-rte
===============================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8075
* Accuracy: 0.6643
Model description
-----------------
More information needed
Intended uses & li... | [
"### 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 #bert #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\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "met... | anirudh21/bert-base-uncased-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-wnli
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6854
* Accuracy: 0.5634
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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\\_rat... |
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", "ar... | anirudh21/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 | 2022-03-02T23:29:05+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: 0.8623
* Matthews Correlation: 0.5224
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... |
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-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | anirudh21/distilbert-base-uncased-finetuned-mrpc | 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 | 2022-03-02T23:29:05+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-mrpc
======================================
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: 0.3830
* Accuracy: 0.8456
* F1: 0.8959
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- 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-qnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"},... | anirudh21/distilbert-base-uncased-finetuned-qnli | 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 | 2022-03-02T23:29:05+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-qnli
======================================
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: 0.8121
* Accuracy: 0.6065
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- 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-rte
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, ... | anirudh21/distilbert-base-uncased-finetuned-rte | 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 | 2022-03-02T23:29:05+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-rte
=====================================
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: 0.6661
* Accuracy: 0.6173
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}... | anirudh21/distilbert-base-uncased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4028
* Accuracy: 0.9083
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- 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-wnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}... | anirudh21/distilbert-base-uncased-finetuned-wnli | 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 | 2022-03-02T23:29:05+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-wnli
======================================
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: 0.6883
* Accuracy: 0.5634
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-discriminator-finetuned-rte
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "electra-base-discriminator-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"... | anirudh21/electra-base-discriminator-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-base-discriminator-finetuned-rte
========================================
This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4793
* Accuracy: 0.8231
Model description
-----------------
More infor... | [
"### 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 #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-discriminator-finetuned-wnli
This model is a fine-tuned version of [google/electra-base-discriminator](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "electra-base-discriminator-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnl... | anirudh21/electra-base-discriminator-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-base-discriminator-finetuned-wnli
=========================================
This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6893
* Accuracy: 0.5634
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- 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. -->
# xlnet-base-cased-finetuned-rte
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased)... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-cased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"... | anirudh21/xlnet-base-cased-finetuned-rte | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased-finetuned-rte
==============================
This model is a fine-tuned version of xlnet-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0656
* Accuracy: 0.6895
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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 #xlnet #text-classification #generated_from_trainer #dataset-glue #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: 2e-... |
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. -->
# xlnet-base-cased-finetuned-wnli
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-cased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [... | anirudh21/xlnet-base-cased-finetuned-wnli | null | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlnet-base-cased-finetuned-wnli
===============================
This model is a fine-tuned version of xlnet-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6874
* Accuracy: 0.5634
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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 #xlnet #text-classification #generated_from_trainer #dataset-glue #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: 2e-... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-base-english
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on ... | {"tags": ["automatic-speech-recognition", "english_asr", "generated_from_trainer"], "model-index": [{"name": "wavlm-base-english", "results": []}]} | anjulRajendraSharma/WavLm-base-en | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"english_asr",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #english_asr #generated_from_trainer #endpoints_compatible #region-us
| wavlm-base-english
==================
This model is a fine-tuned version of microsoft/wavlm-base on the english\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0955
* Wer: 0.0773
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 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* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #english_asr #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-libri-clean-100h-base
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-... | {"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "wavlm-libri-clean-100h-base", "results": []}]} | anjulRajendraSharma/wavlm-base-libri-clean-100 | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #endpoints_compatible #region-us
| wavlm-libri-clean-100h-base
===========================
This model is a fine-tuned version of microsoft/wavlm-base on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0955
* Wer: 0.0773
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* e... |
text2text-generation | transformers | Model to summarize the meeting transcripts. | {} | ankitkhowal/minutes-of-meeting | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Model to summarize the meeting transcripts. | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# Open Domain Question Answering
A core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is a ben... | {"tags": ["small answer"], "datasets": ["natural_questions"]} | ankur310794/bert-large-uncased-nq-small-answer | null | [
"transformers",
"tf",
"bert",
"question-answering",
"small answer",
"dataset:natural_questions",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #small answer #dataset-natural_questions #endpoints_compatible #region-us
|
# Open Domain Question Answering
A core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is a ben... | [
"# Open Domain Question Answering\nA core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is ... | [
"TAGS\n#transformers #tf #bert #question-answering #small answer #dataset-natural_questions #endpoints_compatible #region-us \n",
"# Open Domain Question Answering\nA core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-a... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | ann101020/le2sbot-hp | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
token-classification | transformers | A POS-tagger for Old Church Slavonic trained on the Old Church Slavonic UD treebank (https://github.com/UniversalDependencies/UD_Old_Church_Slavonic-PROIEL). GitHub with api: https://github.com/annadmitrieva/chu-api | {"language": ["chu"], "license": "mit", "tags": ["Old Church Slavonic", "POS-tagging"], "widget": [{"text": "\u041d\u0435 \u043e\u0441\u046b\u0436\u0434\u0430\u0438\u0442\u0435 \u0434\u0430 \u043d\u0435 \u043e\u0441\u046b\u0436\u0434\u0435\u043d\u0438 \u0431\u046b\u0434\u0435\u0442\u0435"}]} | annadmitrieva/old-church-slavonic-pos | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"Old Church Slavonic",
"POS-tagging",
"chu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"chu"
] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #Old Church Slavonic #POS-tagging #chu #license-mit #autotrain_compatible #endpoints_compatible #region-us
| A POS-tagger for Old Church Slavonic trained on the Old Church Slavonic UD treebank (URL GitHub with api: URL | [] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #Old Church Slavonic #POS-tagging #chu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-addresso
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-addresso", "results": []}]} | annafavaro/bert-base-uncased-finetuned-addresso | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-addresso
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training... | [
"# bert-base-uncased-finetuned-addresso\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-addresso\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model desc... |
null | null | ktrain predictor for NER of ADR in patient forum discussions. Created in ktrain 0.29 with transformers 4.10. See requirements.txt to run model. | {} | annedirkson/ADR_extraction_patient_forum | null | [
"tf",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tf #region-us
| ktrain predictor for NER of ADR in patient forum discussions. Created in ktrain 0.29 with transformers 4.10. See URL to run model. | [] | [
"TAGS\n#tf #region-us \n"
] |
text-generation | transformers |
# German GPT-2 model
**Note**: This model was de-anonymized and now lives at:
https://huggingface.co/dbmdz/german-gpt2
Please use the new model name instead! | {"language": "de", "license": "mit", "widget": [{"text": "Heute ist sehr sch\u00f6nes Wetter in"}]} | anonymous-german-nlp/german-gpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# German GPT-2 model
Note: This model was de-anonymized and now lives at:
URL
Please use the new model name instead! | [
"# German GPT-2 model\n\nNote: This model was de-anonymized and now lives at:\n\nURL\n\nPlease use the new model name instead!"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# German GPT-2 model\n\nNote: This model was de-anonymized and now lives at:\n\nURL\n\nPlease use the new model name instead!"
] |
text-classification | transformers |
# Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
# Vaccinating COVID tweets
A fine-tuned model for fact-classification task on English tweets about COVID-19/vaccine.
## Intended uses & limitations
You can classify if the input tweet (or any o... | {"language": "en", "license": "apache-2.0", "datasets": ["tweets"], "widget": [{"text": "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."}]} | ans/vaccinating-covid-tweets | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:tweets",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-tweets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
=========================================================================================================================
Vaccinating COVID tweets
========================
A fine-tuned model for... | [
"#### How to use\n\n\nYou can use this model directly on this page or using 'transformers' in python.\n\n\n* Load pipeline and implement with input sequence\n* Expected output\n* 'true' examples\n* 'false' examples",
"#### Limitations and bias\n\n\nTo conservatively classify whether an input sequence is true or n... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #dataset-tweets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly on this page or using 'transformers' in python.\n\n\n* Load pipeline and implement with input sequen... |
null | null | This repository doesn't contain a model, but only a tokenizer that can be used with the
`tokenizers` library.
This tokenizer is just a copy of `bert-base-uncased`.
```python
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_pretrained("anthony/tokenizers-test")
```
| {} | anthony/tokenizers-test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This repository doesn't contain a model, but only a tokenizer that can be used with the
'tokenizers' library.
This tokenizer is just a copy of 'bert-base-uncased'.
| [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Belgian GPT-2 🇧🇪
**A GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).**
## Usage
You can use BelGPT-2 with [🤗 transformers](https://github.com/huggingface/transformers):
```python
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load pretrained model and ... | {"language": ["fr"], "license": ["mit"], "widget": [{"text": "Hier, Elon Musk a"}, {"text": "Pourquoi a-t-il"}, {"text": "Tout \u00e0 coup, elle"}]} | antoinelouis/belgpt2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #fr #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Belgian GPT-2 🇧🇪
================
A GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).
Usage
-----
You can use BelGPT-2 with transformers:
Data
----
Below is the list of all French copora used to pre-trained the model:
Documentation
-------------
Detailed documentation on ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #fr #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# NetBERT 📶
<img align="left" src="illustration.jpg" width="150"/>
<br><br><br>
NetBERT is a [BERT-base](https://huggingface.co/bert-base-cased) model further pre-trained on a huge corpus of computer networking text (~23Gb).
<br><br>
## Usage
You can use the raw model for masked language modeli... | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "widget": [{"text": "The nodes of a computer network may include [MASK]."}]} | antoinelouis/netbert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NetBERT
<img align="left" src="URL" width="150"/>
<br><br><br>
NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).
<br><br>
## Usage
You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a ... | [
"# NetBERT \n\n<img align=\"left\" src=\"URL\" width=\"150\"/>\n<br><br><br>\n\n NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).\n\n<br><br>",
"## Usage\n\nYou can use the raw model for masked language modeling (MLM), but it's mostly intended... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# NetBERT \n\n<img align=\"left\" src=\"URL\" width=\"150\"/>\n<br><br><br>\n\n NetBERT is a BERT-base model further pre-trained on a huge corpus of compute... |
audio-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. -->
# distilhubert-ft-common-language
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/di... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "distilhubert-ft-common-language", "results": []}]} | anton-l/distilhubert-ft-common-language | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us
| distilhubert-ft-common-language
===============================
This model is a fine-tuned version of ntu-spml/distilhubert on the common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7214
* Accuracy: 0.2797
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_b... |
audio-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. -->
# distilhubert-ft-keyword-spotting
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/d... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "distilhubert-ft-keyword-spotting", "results": []}]} | anton-l/distilhubert-ft-keyword-spotting | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| distilhubert-ft-keyword-spotting
================================
This model is a fine-tuned version of ntu-spml/distilhubert on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1163
* Accuracy: 0.9706
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 32\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_si... |
audio-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. -->
# hubert-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "hubert-base-ft-keyword-spotting", "results": []}]} | anton-l/hubert-base-ft-keyword-spotting | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #has_space #region-us
| hubert-base-ft-keyword-spotting
===============================
This model is a fine-tuned version of facebook/hubert-base-ls960 on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0774
* Accuracy: 0.9819
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\... |
audio-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. -->
# sew-mid-100k-ft-common-language
This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "sew-mid-100k-ft-common-language", "results": []}]} | anton-l/sew-mid-100k-ft-common-language | null | [
"transformers",
"pytorch",
"tensorboard",
"sew",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us
| sew-mid-100k-ft-common-language
===============================
This model is a fine-tuned version of asapp/sew-mid-100k on the common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1189
* Accuracy: 0.3842
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batc... |
audio-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. -->
# sew-mid-100k-ft-keyword-spotting
This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "sew-mid-100k-ft-keyword-spotting", "results": []}]} | anton-l/sew-mid-100k-ft-keyword-spotting | null | [
"transformers",
"pytorch",
"tensorboard",
"sew",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| sew-mid-100k-ft-keyword-spotting
================================
This model is a fine-tuned version of asapp/sew-mid-100k on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0975
* Accuracy: 0.9757
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size:... |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | anton-l/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0952
* Accuracy: 0.9823
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_... |
audio-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. -->
# wav2vec2-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ft-keyword-spotting", "results": []}]} | anton-l/wav2vec2-base-ft-keyword-spotting | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-ft-keyword-spotting
=================================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0824
* Accuracy: 0.9826
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_... |
audio-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. -->
# wav2vec2-base-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-keyword-spotting", "results": []}]} | anton-l/wav2vec2-base-keyword-spotting | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-keyword-spotting
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0746
* Accuracy: 0.9843
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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audio-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. -->
# wav2vec2-base-lang-id
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-ba... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-lang-id", "results": []}]} | anton-l/wav2vec2-base-lang-id | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| wav2vec2-base-lang-id
=====================
This model is a fine-tuned version of facebook/wav2vec2-base on the anton-l/common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9836
* Accuracy: 0.7945
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\... |
audio-classification | transformers | # Model Card for wav2vec2-base-superb-sv
# Model Details
## Model Description
- **Developed by:** Shu-wen Yang et al.
- **Shared by:** Anton Lozhkov
- **Model type:** Wav2Vec2 with an XVector head
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:**
- **Parent Model:** wav2vec2-l... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"]} | anton-l/wav2vec2-base-superb-sv | null | [
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| # Model Card for wav2vec2-base-superb-sv
# Model Details
## Model Description
- Developed by: Shu-wen Yang et al.
- Shared by: Anton Lozhkov
- Model type: Wav2Vec2 with an XVector head
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models:
- Parent Model: wav2vec2-large-lv60
- Resources for mo... | [
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"# Model Details",
"## Model De... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chuvash using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "cv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Chuvash XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "n... | anton-l/wav2vec2-large-xlsr-53-chuvash | null | [
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|
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-53-Chuvash\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mo... | [
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"# Wav2Vec2-Large-XLSR-53-Chuvash\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Com... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The... | {"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Estonian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "... | anton-l/wav2vec2-large-xlsr-53-estonian | null | [
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|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be eva... | [
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe ... | [
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"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the C... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
T... | {"language": "hu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Hungarian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", ... | anton-l/wav2vec2-large-xlsr-53-hungarian | null | [
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|
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be e... | [
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nTh... | [
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"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kyrgyz using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mod... | {"language": "ky", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Kyrgyz XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "na... | anton-l/wav2vec2-large-xlsr-53-kyrgyz | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluat... | [
"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mode... | [
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"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Commo... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Latvian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Latvian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "lv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Latvian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "n... | anton-l/wav2vec2-large-xlsr-53-latvian | null | [
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|
# Wav2Vec2-Large-XLSR-53-Latvian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Latvian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-53-Latvian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Latvian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mo... | [
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"# Wav2Vec2-Large-XLSR-53-Latvian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Latvian using the Com... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Lithuanian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
... | {"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Lithuanian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition",... | anton-l/wav2vec2-large-xlsr-53-lithuanian | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be... | [
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\n... | [
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"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using t... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
T... | {"language": "mn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Mongolian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", ... | anton-l/wav2vec2-large-xlsr-53-mongolian | null | [
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|
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be e... | [
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nTh... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Romanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romanian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The... | {"language": "ro", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Romanian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "... | anton-l/wav2vec2-large-xlsr-53-romanian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ro",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ro #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Romanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Romanian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be eva... | [
"# Wav2Vec2-Large-XLSR-53-Romanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romanian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ro #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Romanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romanian using the C... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Russian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The m... | {"language": "ru", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Russian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "n... | anton-l/wav2vec2-large-xlsr-53-russian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ru",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Russian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evalu... | [
"# Wav2Vec2-Large-XLSR-53-Russian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mo... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Russian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Russian us... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Sakha
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sakha using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": "sah", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Sakha XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "na... | anton-l/wav2vec2-large-xlsr-53-sakha | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sah",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sah"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Sakha
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated... | [
"# Wav2Vec2-Large-XLSR-53-Sakha\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Sakha\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Slovenian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Slovenian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
T... | {"language": "sl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Slovenian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", ... | anton-l/wav2vec2-large-xlsr-53-slovenian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sl"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Slovenian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be e... | [
"# Wav2Vec2-Large-XLSR-53-Slovenian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nTh... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Slovenian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tatar using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": "tt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Tatar XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "nam... | anton-l/wav2vec2-large-xlsr-53-tatar | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated... | [
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common ... |
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