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# Adapter `AdapterHub/roberta-base-pf-conll2003_pos` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [pos/conll2003](https://adapterhub.ml/explore/pos/conll2003/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the ... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:pos/conll2003", "adapter-transformers", "token-classification"], "datasets": ["conll2003"]} | token-classification | AdapterHub/roberta-base-pf-conll2003_pos | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:pos/conll2003",
"en",
"dataset:conll2003",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-pos/conll2003 #en #dataset-conll2003 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-conll2003_pos' for roberta-base
An adapter for the 'roberta-base' model that was trained on the pos/conll2003 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transfor... | [
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"## Usage\n\nFirst, install 'ad... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-copa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/copa](https://adapterhub.ml/explore/comsense/copa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the *... | {"language": ["en"], "tags": ["roberta", "adapterhub:comsense/copa", "adapter-transformers"]} | null | AdapterHub/roberta-base-pf-copa | [
"adapter-transformers",
"roberta",
"adapterhub:comsense/copa",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-comsense/copa #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-copa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the comsense/copa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transform... | [
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# Adapter `AdapterHub/roberta-base-pf-cosmos_qa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/cosmosqa](https://adapterhub.ml/explore/comsense/cosmosqa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usa... | {"language": ["en"], "tags": ["roberta", "adapterhub:comsense/cosmosqa", "adapter-transformers"], "datasets": ["cosmos_qa"]} | null | AdapterHub/roberta-base-pf-cosmos_qa | [
"adapter-transformers",
"roberta",
"adapterhub:comsense/cosmosqa",
"en",
"dataset:cosmos_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-comsense/cosmosqa #en #dataset-cosmos_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-cosmos_qa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the comsense/cosmosqa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-... | [
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# Adapter `AdapterHub/roberta-base-pf-cq` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/cq](https://adapterhub.ml/explore/qa/cq/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-trans... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/cq", "adapter-transformers"]} | question-answering | AdapterHub/roberta-base-pf-cq | [
"adapter-transformers",
"roberta",
"question-answering",
"adapterhub:qa/cq",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #adapterhub-qa/cq #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-cq' for roberta-base
An adapter for the 'roberta-base' model that was trained on the qa/cq dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
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# Adapter `AdapterHub/roberta-base-pf-drop` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [drop](https://huggingface.co/datasets/drop/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-tra... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["drop"]} | question-answering | AdapterHub/roberta-base-pf-drop | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:drop",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-drop #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-drop' for roberta-base
An adapter for the 'roberta-base' model that was trained on the drop dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-drop' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the drop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
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# Adapter `AdapterHub/roberta-base-pf-duorc_p` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapte... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["duorc"]} | question-answering | AdapterHub/roberta-base-pf-duorc_p | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:duorc",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-duorc_p' for roberta-base
An adapter for the 'roberta-base' model that was trained on the duorc dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformer... | [
"# Adapter 'AdapterHub/roberta-base-pf-duorc_p' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
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# Adapter `AdapterHub/roberta-base-pf-duorc_s` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [duorc](https://huggingface.co/datasets/duorc/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapte... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["duorc"]} | question-answering | AdapterHub/roberta-base-pf-duorc_s | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:duorc",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-duorc #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-duorc_s' for roberta-base
An adapter for the 'roberta-base' model that was trained on the duorc dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformer... | [
"# Adapter 'AdapterHub/roberta-base-pf-duorc_s' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the duorc dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
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# Adapter `AdapterHub/roberta-base-pf-emo` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [emo](https://huggingface.co/datasets/emo/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transforme... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["emo"]} | text-classification | AdapterHub/roberta-base-pf-emo | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:emo",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-emo #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-emo' for roberta-base
An adapter for the 'roberta-base' model that was trained on the emo dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_Not... | [
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# Adapter `AdapterHub/roberta-base-pf-emotion` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [emotion](https://huggingface.co/datasets/emotion/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapte... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["emotion"]} | text-classification | AdapterHub/roberta-base-pf-emotion | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:emotion",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-emotion' for roberta-base
An adapter for the 'roberta-base' model that was trained on the emotion dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers'... | [
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"passage: TAGS\n#adapter-transformers #roberta #text-classification #en #dataset-emotion #arxiv-2104.08247 #region-us \n# Adapter 'AdapterHub/roberta-base-pf-emotion' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the emotion dataset and includes a prediction head for classification... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-fce_error_detection` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [ged/fce](https://adapterhub.ml/explore/ged/fce/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[ada... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:ged/fce", "adapter-transformers"], "datasets": ["fce_error_detection"]} | token-classification | AdapterHub/roberta-base-pf-fce_error_detection | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:ged/fce",
"en",
"dataset:fce_error_detection",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-ged/fce #en #dataset-fce_error_detection #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-fce_error_detection' for roberta-base
An adapter for the 'roberta-base' model that was trained on the ged/fce dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transfor... | [
"# Adapter 'AdapterHub/roberta-base-pf-fce_error_detection' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the ged/fce dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'ad... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-hellaswag` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/hellaswag](https://adapterhub.ml/explore/comsense/hellaswag/) dataset and includes a prediction head for multiple choice.
This adapter was created for u... | {"language": ["en"], "tags": ["roberta", "adapterhub:comsense/hellaswag", "adapter-transformers"], "datasets": ["hellaswag"]} | null | AdapterHub/roberta-base-pf-hellaswag | [
"adapter-transformers",
"roberta",
"adapterhub:comsense/hellaswag",
"en",
"dataset:hellaswag",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-comsense/hellaswag #en #dataset-hellaswag #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-hellaswag' for roberta-base
An adapter for the 'roberta-base' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter... | [
"# Adapter 'AdapterHub/roberta-base-pf-hellaswag' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the comsense/hellaswag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, in... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-hotpotqa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [hotpot_qa](https://huggingface.co/datasets/hotpot_qa/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the ... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["hotpot_qa"]} | question-answering | AdapterHub/roberta-base-pf-hotpotqa | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:hotpot_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-hotpot_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-hotpotqa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transf... | [
"# Adapter 'AdapterHub/roberta-base-pf-hotpotqa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the hotpot_qa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install '... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-imdb` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/imdb](https://adapterhub.ml/explore/sentiment/imdb/) dataset and includes a prediction head for classification.
This adapter was created for usage with the ... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sentiment/imdb", "adapter-transformers"], "datasets": ["imdb"]} | text-classification | AdapterHub/roberta-base-pf-imdb | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:sentiment/imdb",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-sentiment/imdb #en #dataset-imdb #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-imdb' for roberta-base
An adapter for the 'roberta-base' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transform... | [
"# Adapter 'AdapterHub/roberta-base-pf-imdb' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the sentiment/imdb dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'ada... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-mit_movie_trivia` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) dataset and includes a prediction head for tagging.
This adapter was created fo... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:ner/mit_movie_trivia", "adapter-transformers"]} | token-classification | AdapterHub/roberta-base-pf-mit_movie_trivia | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:ner/mit_movie_trivia",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-ner/mit_movie_trivia #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-mit_movie_trivia' for roberta-base
An adapter for the 'roberta-base' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapte... | [
"# Adapter 'AdapterHub/roberta-base-pf-mit_movie_trivia' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the ner/mit_movie_trivia dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, i... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-mnli` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[a... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/multinli", "adapter-transformers"], "datasets": ["multi_nli"]} | text-classification | AdapterHub/roberta-base-pf-mnli | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:nli/multinli",
"en",
"dataset:multi_nli",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-nli/multinli #en #dataset-multi_nli #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-mnli' for roberta-base
An adapter for the 'roberta-base' model that was trained on the nli/multinli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformer... | [
"# Adapter 'AdapterHub/roberta-base-pf-mnli' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the nli/multinli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapt... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-mrpc` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/mrpc](https://adapterhub.ml/explore/sts/mrpc/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-t... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sts/mrpc", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-mrpc | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:sts/mrpc",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-sts/mrpc #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-mrpc' for roberta-base
An adapter for the 'roberta-base' model that was trained on the sts/mrpc dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-mrpc' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the sts/mrpc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-t... | [
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0.020553... |
null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-multirc` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/multirc](https://adapterhub.ml/explore/rc/multirc/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[ad... | {"language": ["en"], "tags": ["text-classification", "adapterhub:rc/multirc", "roberta", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-multirc | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:rc/multirc",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-rc/multirc #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-multirc' for roberta-base
An adapter for the 'roberta-base' model that was trained on the rc/multirc dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transforme... | [
"# Adapter 'AdapterHub/roberta-base-pf-multirc' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the rc/multirc dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adap... | [
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"# Adapter 'AdapterHub/roberta-base-pf-multirc' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the rc/multirc dataset and includes a prediction head for classifi... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-newsqa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [newsqa](https://huggingface.co/datasets/newsqa/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapt... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["newsqa"]} | question-answering | AdapterHub/roberta-base-pf-newsqa | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:newsqa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-newsqa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-newsqa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the newsqa dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformer... | [
"# Adapter 'AdapterHub/roberta-base-pf-newsqa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the newsqa dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapt... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-pmb_sem_tagging` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [semtag/pmb](https://adapterhub.ml/explore/semtag/pmb/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[a... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:semtag/pmb", "adapter-transformers"]} | token-classification | AdapterHub/roberta-base-pf-pmb_sem_tagging | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:semtag/pmb",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-semtag/pmb #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-pmb_sem_tagging' for roberta-base
An adapter for the 'roberta-base' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transform... | [
"# Adapter 'AdapterHub/roberta-base-pf-pmb_sem_tagging' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the semtag/pmb dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'ada... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-qnli` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/qnli](https://adapterhub.ml/explore/nli/qnli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-t... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/qnli", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-qnli | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:nli/qnli",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-nli/qnli #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-qnli' for roberta-base
An adapter for the 'roberta-base' model that was trained on the nli/qnli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-qnli' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the nli/qnli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-t... | [
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0.031984496... |
null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-qqp` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/qqp](https://adapterhub.ml/explore/sts/qqp/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-tran... | {"language": ["en"], "tags": ["text-classification", "adapter-transformers", "adapterhub:sts/qqp", "roberta"]} | text-classification | AdapterHub/roberta-base-pf-qqp | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:sts/qqp",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-sts/qqp #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-qqp' for roberta-base
An adapter for the 'roberta-base' model that was trained on the sts/qqp dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-qqp' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the sts/qqp dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-tra... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-quail` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [quail](https://huggingface.co/datasets/quail/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-tra... | {"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["quail"]} | null | AdapterHub/roberta-base-pf-quail | [
"adapter-transformers",
"roberta",
"en",
"dataset:quail",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #en #dataset-quail #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-quail' for roberta-base
An adapter for the 'roberta-base' model that was trained on the quail dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-quail' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the quail dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-tr... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-quartz` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [quartz](https://huggingface.co/datasets/quartz/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-... | {"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["quartz"]} | null | AdapterHub/roberta-base-pf-quartz | [
"adapter-transformers",
"roberta",
"en",
"dataset:quartz",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #en #dataset-quartz #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-quartz' for roberta-base
An adapter for the 'roberta-base' model that was trained on the quartz dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':... | [
"# Adapter 'AdapterHub/roberta-base-pf-quartz' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the quartz dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-quoref` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [quoref](https://huggingface.co/datasets/quoref/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapt... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapter-transformers"], "datasets": ["quoref"]} | question-answering | AdapterHub/roberta-base-pf-quoref | [
"adapter-transformers",
"roberta",
"question-answering",
"en",
"dataset:quoref",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #en #dataset-quoref #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-quoref' for roberta-base
An adapter for the 'roberta-base' model that was trained on the quoref dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformer... | [
"# Adapter 'AdapterHub/roberta-base-pf-quoref' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the quoref dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapt... | [
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0.026445... |
null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-race` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/race](https://adapterhub.ml/explore/rc/race/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-tr... | {"language": ["en"], "tags": ["adapterhub:rc/race", "roberta", "adapter-transformers"], "datasets": ["race"]} | null | AdapterHub/roberta-base-pf-race | [
"adapter-transformers",
"roberta",
"adapterhub:rc/race",
"en",
"dataset:race",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-rc/race #en #dataset-race #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-race' for roberta-base
An adapter for the 'roberta-base' model that was trained on the rc/race dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-race' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the rc/race dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-t... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-record` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [rc/record](https://adapterhub.ml/explore/rc/record/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapt... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:rc/record", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-record | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:rc/record",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-rc/record #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-record' for roberta-base
An adapter for the 'roberta-base' model that was trained on the rc/record dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers... | [
"# Adapter 'AdapterHub/roberta-base-pf-record' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the rc/record dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapte... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-rotten_tomatoes` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/rotten_tomatoes](https://adapterhub.ml/explore/sentiment/rotten_tomatoes/) dataset and includes a prediction head for classification.
This adapte... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sentiment/rotten_tomatoes", "adapter-transformers"], "datasets": ["rotten_tomatoes"]} | text-classification | AdapterHub/roberta-base-pf-rotten_tomatoes | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:sentiment/rotten_tomatoes",
"en",
"dataset:rotten_tomatoes",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-sentiment/rotten_tomatoes #en #dataset-rotten_tomatoes #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-rotten_tomatoes' for roberta-base
An adapter for the 'roberta-base' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, inst... | [
"# Adapter 'AdapterHub/roberta-base-pf-rotten_tomatoes' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the sentiment/rotten_tomatoes dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-rte` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/rte](https://adapterhub.ml/explore/nli/rte/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-tran... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/rte", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-rte | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:nli/rte",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-nli/rte #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-rte' for roberta-base
An adapter for the 'roberta-base' model that was trained on the nli/rte dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-rte' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the nli/rte dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-tra... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-scicite` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [scicite](https://huggingface.co/datasets/scicite/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapte... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["scicite"]} | text-classification | AdapterHub/roberta-base-pf-scicite | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:scicite",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-scicite #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-scicite' for roberta-base
An adapter for the 'roberta-base' model that was trained on the scicite dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers'... | [
"# Adapter 'AdapterHub/roberta-base-pf-scicite' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the scicite dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-scitail` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/scitail](https://adapterhub.ml/explore/nli/scitail/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:nli/scitail", "adapter-transformers"], "datasets": ["scitail"]} | text-classification | AdapterHub/roberta-base-pf-scitail | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:nli/scitail",
"en",
"dataset:scitail",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-nli/scitail #en #dataset-scitail #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-scitail' for roberta-base
An adapter for the 'roberta-base' model that was trained on the nli/scitail dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transform... | [
"# Adapter 'AdapterHub/roberta-base-pf-scitail' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the nli/scitail dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'ada... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-sick` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [nli/sick](https://adapterhub.ml/explore/nli/sick/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-t... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers", "adapterhub:nli/sick", "text-classification"], "datasets": ["sick"]} | text-classification | AdapterHub/roberta-base-pf-sick | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:nli/sick",
"en",
"dataset:sick",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-nli/sick #en #dataset-sick #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-sick' for roberta-base
An adapter for the 'roberta-base' model that was trained on the nli/sick dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
... | [
"# Adapter 'AdapterHub/roberta-base-pf-sick' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the nli/sick dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-t... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-snli` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [snli](https://huggingface.co/datasets/snli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transfo... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["snli"]} | text-classification | AdapterHub/roberta-base-pf-snli | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:snli",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-snli' for roberta-base
An adapter for the 'roberta-base' model that was trained on the snli dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_N... | [
"# Adapter 'AdapterHub/roberta-base-pf-snli' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the snli dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-trans... | [
"TAGS\n#adapter-transformers #roberta #text-classification #en #dataset-snli #arxiv-2104.08247 #region-us \n",
"# Adapter 'AdapterHub/roberta-base-pf-snli' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the snli dataset and includes a prediction head for classification.\n\nThis ad... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-social_i_qa` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [social_i_qa](https://huggingface.co/datasets/social_i_qa/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with ... | {"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["social_i_qa"]} | null | AdapterHub/roberta-base-pf-social_i_qa | [
"adapter-transformers",
"roberta",
"en",
"dataset:social_i_qa",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #en #dataset-social_i_qa #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-social_i_qa' for roberta-base
An adapter for the 'roberta-base' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-tran... | [
"# Adapter 'AdapterHub/roberta-base-pf-social_i_qa' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the social_i_qa dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-squad` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[ad... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/squad1", "adapter-transformers"], "datasets": ["squad"]} | question-answering | AdapterHub/roberta-base-pf-squad | [
"adapter-transformers",
"roberta",
"question-answering",
"adapterhub:qa/squad1",
"en",
"dataset:squad",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #adapterhub-qa/squad1 #en #dataset-squad #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-squad' for roberta-base
An adapter for the 'roberta-base' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transform... | [
"# Adapter 'AdapterHub/roberta-base-pf-squad' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the qa/squad1 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'ada... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-squad_v2` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/squad2](https://adapterhub.ml/explore/qa/squad2/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/squad2", "adapter-transformers"], "datasets": ["squad_v2"]} | question-answering | AdapterHub/roberta-base-pf-squad_v2 | [
"adapter-transformers",
"roberta",
"question-answering",
"adapterhub:qa/squad2",
"en",
"dataset:squad_v2",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #adapterhub-qa/squad2 #en #dataset-squad_v2 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-squad_v2' for roberta-base
An adapter for the 'roberta-base' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transf... | [
"# Adapter 'AdapterHub/roberta-base-pf-squad_v2' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the qa/squad2 dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install '... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-sst2` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sentiment/sst-2](https://adapterhub.ml/explore/sentiment/sst-2/) dataset and includes a prediction head for classification.
This adapter was created for usage with th... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sentiment/sst-2", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-sst2 | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:sentiment/sst-2",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-sentiment/sst-2 #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-sst2' for roberta-base
An adapter for the 'roberta-base' model that was trained on the sentiment/sst-2 dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transfor... | [
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"## Usage\n\nFirst, install 'ad... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-stsb` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [sts/sts-b](https://adapterhub.ml/explore/sts/sts-b/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:sts/sts-b", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-stsb | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:sts/sts-b",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-sts/sts-b #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-stsb' for roberta-base
An adapter for the 'roberta-base' model that was trained on the sts/sts-b dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':... | [
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"## Usage\n\nFirst, install 'adapter-... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-swag` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [swag](https://huggingface.co/datasets/swag/) dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the **[adapter-transf... | {"language": ["en"], "tags": ["roberta", "adapter-transformers"], "datasets": ["swag"]} | null | AdapterHub/roberta-base-pf-swag | [
"adapter-transformers",
"roberta",
"en",
"dataset:swag",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #en #dataset-swag #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-swag' for roberta-base
An adapter for the 'roberta-base' model that was trained on the swag dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_... | [
"# Adapter 'AdapterHub/roberta-base-pf-swag' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the swag dataset and includes a prediction head for multiple choice.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-tran... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-trec` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [trec](https://huggingface.co/datasets/trec/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transfo... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["trec"]} | text-classification | AdapterHub/roberta-base-pf-trec | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:trec",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-trec #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-trec' for roberta-base
An adapter for the 'roberta-base' model that was trained on the trec dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_N... | [
"# Adapter 'AdapterHub/roberta-base-pf-trec' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the trec dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-trans... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-ud_deprel` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [deprel/ud_ewt](https://adapterhub.ml/explore/deprel/ud_ewt/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[a... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:deprel/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]} | token-classification | AdapterHub/roberta-base-pf-ud_deprel | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:deprel/ud_ewt",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-deprel/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-ud_deprel' for roberta-base
An adapter for the 'roberta-base' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers... | [
"# Adapter 'AdapterHub/roberta-base-pf-ud_deprel' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the deprel/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapte... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-ud_en_ewt` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [dp/ud_ewt](https://adapterhub.ml/explore/dp/ud_ewt/) dataset and includes a prediction head for dependency parsing.
This adapter was created for usage with the *... | {"language": ["en"], "tags": ["roberta", "adapterhub:dp/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]} | null | AdapterHub/roberta-base-pf-ud_en_ewt | [
"adapter-transformers",
"roberta",
"adapterhub:dp/ud_ewt",
"en",
"dataset:universal_dependencies",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us
| Adapter 'AdapterHub/roberta-base-pf-ud\_en\_ewt' for roberta-base
=================================================================
An adapter for the 'roberta-base' model that was trained on the dp/ud\_ewt dataset and includes a prediction head for dependency parsing.
This adapter was created for usage with the ad... | [] | [
"TAGS\n#adapter-transformers #roberta #adapterhub-dp/ud_ewt #en #dataset-universal_dependencies #region-us \n"
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-ud_pos` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [pos/ud_ewt](https://adapterhub.ml/explore/pos/ud_ewt/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-tr... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapterhub:pos/ud_ewt", "adapter-transformers"], "datasets": ["universal_dependencies"]} | token-classification | AdapterHub/roberta-base-pf-ud_pos | [
"adapter-transformers",
"roberta",
"token-classification",
"adapterhub:pos/ud_ewt",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #adapterhub-pos/ud_ewt #en #dataset-universal_dependencies #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-ud_pos' for roberta-base
An adapter for the 'roberta-base' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_... | [
"# Adapter 'AdapterHub/roberta-base-pf-ud_pos' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the pos/ud_ewt dataset and includes a prediction head for tagging.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapter-tran... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-wic` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [wordsence/wic](https://adapterhub.ml/explore/wordsence/wic/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapterhub:wordsence/wic", "adapter-transformers"]} | text-classification | AdapterHub/roberta-base-pf-wic | [
"adapter-transformers",
"roberta",
"text-classification",
"adapterhub:wordsence/wic",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #adapterhub-wordsence/wic #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-wic' for roberta-base
An adapter for the 'roberta-base' model that was trained on the wordsence/wic dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformer... | [
"# Adapter 'AdapterHub/roberta-base-pf-wic' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the wordsence/wic dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install 'adapt... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-wikihop` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [qa/wikihop](https://adapterhub.ml/explore/qa/wikihop/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the *... | {"language": ["en"], "tags": ["question-answering", "roberta", "adapterhub:qa/wikihop", "adapter-transformers"]} | question-answering | AdapterHub/roberta-base-pf-wikihop | [
"adapter-transformers",
"roberta",
"question-answering",
"adapterhub:qa/wikihop",
"en",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #question-answering #adapterhub-qa/wikihop #en #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-wikihop' for roberta-base
An adapter for the 'roberta-base' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transf... | [
"# Adapter 'AdapterHub/roberta-base-pf-wikihop' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the qa/wikihop dataset and includes a prediction head for question answering.\n\nThis adapter was created for usage with the adapter-transformers library.",
"## Usage\n\nFirst, install '... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-winogrande` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [comsense/winogrande](https://adapterhub.ml/explore/comsense/winogrande/) dataset and includes a prediction head for multiple choice.
This adapter was created fo... | {"language": ["en"], "tags": ["roberta", "adapterhub:comsense/winogrande", "adapter-transformers"], "datasets": ["winogrande"]} | null | AdapterHub/roberta-base-pf-winogrande | [
"adapter-transformers",
"roberta",
"adapterhub:comsense/winogrande",
"en",
"dataset:winogrande",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #adapterhub-comsense/winogrande #en #dataset-winogrande #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-winogrande' for roberta-base
An adapter for the 'roberta-base' model that was trained on the comsense/winogrande dataset and includes a prediction head for multiple choice.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapt... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-wnut_17` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [wnut_17](https://huggingface.co/datasets/wnut_17/) dataset and includes a prediction head for tagging.
This adapter was created for usage with the **[adapter-trans... | {"language": ["en"], "tags": ["token-classification", "roberta", "adapter-transformers"], "datasets": ["wnut_17"]} | token-classification | AdapterHub/roberta-base-pf-wnut_17 | [
"adapter-transformers",
"roberta",
"token-classification",
"en",
"dataset:wnut_17",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #token-classification #en #dataset-wnut_17 #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-wnut_17' for roberta-base
An adapter for the 'roberta-base' model that was trained on the wnut_17 dataset and includes a prediction head for tagging.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-transformers':
_No... | [
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null | null | adapter-transformers |
# Adapter `AdapterHub/roberta-base-pf-yelp_polarity` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [yelp_polarity](https://huggingface.co/datasets/yelp_polarity/) dataset and includes a prediction head for classification.
This adapter was created for usage ... | {"language": ["en"], "tags": ["text-classification", "roberta", "adapter-transformers"], "datasets": ["yelp_polarity"]} | text-classification | AdapterHub/roberta-base-pf-yelp_polarity | [
"adapter-transformers",
"roberta",
"text-classification",
"en",
"dataset:yelp_polarity",
"arxiv:2104.08247",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.08247"
] | [
"en"
] | TAGS
#adapter-transformers #roberta #text-classification #en #dataset-yelp_polarity #arxiv-2104.08247 #region-us
|
# Adapter 'AdapterHub/roberta-base-pf-yelp_polarity' for roberta-base
An adapter for the 'roberta-base' model that was trained on the yelp_polarity dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
## Usage
First, install 'adapter-t... | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | AdharshJolly/HarryPotterBot-Model | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# Model
- Problem type: Binary Classification
- Model ID: 12592372
## Validation Metrics
- Loss: 0.23033875226974487
- Accuracy: 0.9138655462184874
- Precision: 0.9087136929460581
- Recall: 0.9201680672268907
- AUC: 0.9690346726926065
- F1: 0.9144050104384133
## Usage
You can use cURL to access this model:
```
$... | {"language": "eng", "datasets": ["Adi2K/autonlp-data-Priv-Consent"], "widget": [{"text": "You can control cookies and tracking tools. To learn how to manage how we - and our vendors - use cookies and other tracking tools, please click here."}]} | text-classification | Adi2K/Priv-Consent | [
"transformers",
"pytorch",
"bert",
"text-classification",
"eng",
"dataset:Adi2K/autonlp-data-Priv-Consent",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"eng"
] | TAGS
#transformers #pytorch #bert #text-classification #eng #dataset-Adi2K/autonlp-data-Priv-Consent #autotrain_compatible #endpoints_compatible #region-us
|
# Model
- Problem type: Binary Classification
- Model ID: 12592372
## Validation Metrics
- Loss: 0.23033875226974487
- Accuracy: 0.9138655462184874
- Precision: 0.9087136929460581
- Recall: 0.9201680672268907
- AUC: 0.9690346726926065
- F1: 0.9144050104384133
## Usage
You can use cURL to access this model:
Or ... | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | automatic-speech-recognition | Adil617/wav2vec2-base-timit-demo-colab | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9314
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitat... | [
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null | null | transformers |
# Harry Potter DialoGPT model | {"tags": ["conversational"]} | text-generation | AdrianGzz/DialoGPT-small-harrypotter | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers | # DialoGPT Trained on the Speech of a Game Character
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
# Let's chat for 4 lines
f... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | text-generation | Aero/Tsubomi-Haruno | [
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"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT Trained on the Speech of a Game Character
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null | null | null |
#HAL | {"tags": ["conversational"]} | text-generation | AetherIT/DialoGPT-small-Hal | [
"conversational",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# Tomato_Leaf_Classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nater... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Aftabhussain/Tomato_Leaf_Classifier | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Tomato_Leaf_Classifier
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### Bacterial_spot
!Bacterial_spot
#### Healthy
!Healthy | [
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null | null | transformers | A monolingual T5 model for Persian trained on OSCAR 21.09 (https://oscar-corpus.com/) corpus with self-supervised method. 35 Gig deduplicated version of Persian data was used for pre-training the model.
It's similar to the English T5 model but just for Persian. You may need to fine-tune it on your specific task.
Exa... | {} | text2text-generation | Ahmad/parsT5-base | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A monolingual T5 model for Persian trained on OSCAR 21.09 (URL corpus with self-supervised method. 35 Gig deduplicated version of Persian data was used for pre-training the model.
It's similar to the English T5 model but just for Persian. You may need to fine-tune it on your specific task.
Example code:
Steps:... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
48
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null | null | transformers | A checkpoint for training Persian T5 model. This repository can be cloned and pre-training can be resumed. This model uses flax and is for training.
For more information and getting the training code please refer to:
https://github.com/puraminy/parsT5
| {} | text2text-generation | Ahmad/parsT5 | [
"transformers",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A checkpoint for training Persian T5 model. This repository can be cloned and pre-training can be resumed. This model uses flax and is for training.
For more information and getting the training code please refer to:
URL
| [] | [
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0.096532... |
null | null | transformers |
This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks.
LABEL_1: Positive
LABEL_2: Negative
LABEL_0: Neutral
This work is an integral component of my Master's degree thesis and represents the culmination of exte... | {"language": ["ar"], "license": "apache-2.0", "tags": ["sentiment analysis", "classification", "arabic dialect", "tunisian dialect"]} | text-classification | AhmedBou/TuniBert | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentiment analysis",
"classification",
"arabic dialect",
"tunisian dialect",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #sentiment analysis #classification #arabic dialect #tunisian dialect #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks.
LABEL_1: Positive
LABEL_2: Negative
LABEL_0: Neutral
This work is an integral component of my Master's degree thesis and represents the culmination of exte... | [] | [
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null | null | transformers |
```
```
[](https://paperswithcode.com/sota/code-generation-on-conala?p=mariancg-a-code-generation-transformer-model)
```
```
# MarianCG: a code generation transformer... | {"widget": [{"text": "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]"}, {"text": "check if all elements in list `mylist` are identical"}, {"text": "enable debug mode on flask application `app`"}, {"text": "getting the length of `my_tuple`"}, {"text": "find all... | text2text-generation | AhmedSSoliman/MarianCG-CoNaLa | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
![PWC](URL
# MarianCG: a code generation transformer model inspired by machine translation
This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that c... | [
"# MarianCG: a code generation transformer model inspired by machine translation\nThis model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be abl... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
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43,
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0.1284584999... |
null | null | transformers |
# Back to the Future DialoGPT Model | {"tags": ["conversational"]} | text-generation | AiPorter/DialoGPT-small-Back_to_the_future | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Back to the Future DialoGPT Model | [
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0.096307... |
null | null | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | text-generation | Aibox/DialoGPT-small-rick | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
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0.06... |
null | null | null | Trained on Stephen King's top 50 books as .txt files. | {} | null | Aidan8756/stephenKingModel | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Trained on Stephen King's top 50 books as .txt files. | [] | [
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0.03... |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bashkir-cv7_opt
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfa... | {"language": ["ba"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bashkir-cv7_opt",... | automatic-speech-recognition | AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"end... | 2022-03-02T23:29:04+00:00 | [] | [
"ba"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ba #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# wav2vec2-large-xls-r-300m-bashkir-cv7_opt
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Training Loss: 0.268400
- Validation Loss: 0.088252
- WER without LM: 0.085588
- WER with... | [
"# wav2vec2-large-xls-r-300m-bashkir-cv7_opt\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.\nIt achieves the following results on the evaluation set:\n- Training Loss: 0.268400\n- Validation Loss: 0.088252\n- WER without LM: 0.085588\n-... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ba #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
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null | null | transformers | you can use this model with simpletransfomers.
```
!pip install simpletransformers
from simpletransformers.t5 import T5Model
model = T5Model("mt5", "AimB/mT5-en-kr-natural")
print(model.predict(["I feel good today"]))
print(model.predict(["우리집 고양이는 세상에서 제일 귀엽습니다"]))
``` | {} | text2text-generation | AimB/mT5-en-kr-natural | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| you can use this model with simpletransfomers.
| [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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0.15311330556869507,
0.2192111611366272,
-0.01395078282803297,
0.091042... |
null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35248482
- CO2 Emissions (in grams): 7.989144645413398
## Validation Metrics
- Loss: 0.13783401250839233
- Accuracy: 0.9728654124457308
- Macro F1: 0.949537871674076
- Micro F1: 0.9728654124457308
- Weighted F1: 0.9732422812610365
... | {"language": "en", "tags": "autonlp", "datasets": ["Aimendo/autonlp-data-triage"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 7.989144645413398} | text-classification | Aimendo/autonlp-triage-35248482 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:Aimendo/autonlp-data-triage",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Aimendo/autonlp-data-triage #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 35248482
- CO2 Emissions (in grams): 7.989144645413398
## Validation Metrics
- Loss: 0.13783401250839233
- Accuracy: 0.9728654124457308
- Macro F1: 0.949537871674076
- Micro F1: 0.9728654124457308
- Weighted F1: 0.9732422812610365
... | [
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"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Aimendo/autonlp-data-triage #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 35248482\n- CO2 Emissions (in grams): 7... | [
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null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 530014983
- CO2 Emissions (in grams): 55.10196329868386
## Validation Metrics
- Loss: 0.23171618580818176
- Accuracy: 0.9298837645294338
- Precision: 0.9314414866901055
- Recall: 0.9279459594696022
- AUC: 0.979447403984557
- F1: 0.92969... | {"language": "en", "tags": "autonlp", "datasets": ["Ajay191191/autonlp-data-Test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 55.10196329868386} | text-classification | Ajay191191/autonlp-Test-530014983 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:Ajay191191/autonlp-data-Test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Ajay191191/autonlp-data-Test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 530014983
- CO2 Emissions (in grams): 55.10196329868386
## Validation Metrics
- Loss: 0.23171618580818176
- Accuracy: 0.9298837645294338
- Precision: 0.9314414866901055
- Recall: 0.9279459594696022
- AUC: 0.979447403984557
- F1: 0.92969... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 530014983\n- CO2 Emissions (in grams): 55.10196329868386",
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"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Ajay191191/autonlp-data-Test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
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0.19051752984523773,
0.015336073003709316,
0.135453268885... |
null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 16122692
## Validation Metrics
- Loss: 1.1877621412277222
- Rouge1: 42.0713
- Rouge2: 23.3043
- RougeL: 37.3755
- RougeLsum: 37.8961
- Gen Len: 60.7117
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bea... | {"language": "unk", "tags": "autonlp", "datasets": ["Ajaykannan6/autonlp-data-manthan"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | text2text-generation | Ajaykannan6/autonlp-manthan-16122692 | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autonlp",
"unk",
"dataset:Ajaykannan6/autonlp-data-manthan",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autonlp #unk #dataset-Ajaykannan6/autonlp-data-manthan #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 16122692
## Validation Metrics
- Loss: 1.1877621412277222
- Rouge1: 42.0713
- Rouge2: 23.3043
- RougeL: 37.3755
- RougeLsum: 37.8961
- Gen Len: 60.7117
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 16122692",
"## Validation Metrics\n\n- Loss: 1.1877621412277222\n- Rouge1: 42.0713\n- Rouge2: 23.3043\n- RougeL: 37.3755\n- RougeLsum: 37.8961\n- Gen Len: 60.7117",
"## Usage\n\nYou can use cURL to access this model:"
] | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 16122692",
"## Validation Metrics\n\n- Loss: 1.18776214122772... | [
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0.1722399145364... |
null | null | 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-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | question-answering | Akari/albert-base-v2-finetuned-squad | [
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| albert-base-v2-finetuned-squad
==============================
This model is a fine-tuned version of albert-base-v2 on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9492
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #albert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... | [
-0.10188356041908264,
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0.0724877044... |
null | null | 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-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-wikitext2", "results": []}]} | fill-mask | Akash7897/bert-base-cased-wikitext2 | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-cased-wikitext2
=========================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.8544
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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0.17348450422286987,
0.009888887405395508,
0.1298822164... |
null | null | 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... | text-classification | Akash7897/distilbert-base-uncased-finetuned-cola | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-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: 1.0789
* Matthews Correlation: 0.5222
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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... | [
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0.1208245605... |
null | null | 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"}... | text-classification | Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3010
* Accuracy: 0.9037
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: 1",
"### 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",
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null | null | 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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the fo... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-wikitext2", "results": []}]} | text-generation | Akash7897/gpt2-wikitext2 | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-wikitext2
==============
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.1079
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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null | null | transformers |
# Akashpb13/Central_kurdish_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with inv... | {"language": ["ckb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ckb", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Central... | automatic-speech-recognition | Akashpb13/Central_kurdish_xlsr | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ckb",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
... | 2022-03-02T23:29:04+00:00 | [] | [
"ckb"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ckb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #regi... | Akashpb13/Central\_kurdish\_xlsr
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, repo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000095637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_... | [
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null | null | transformers |
# Akashpb13/Galician_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invali... | {"language": ["gl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "gl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Galician_... | automatic-speech-recognition | Akashpb13/Galician_xlsr | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
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"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | 2022-03-02T23:29:04+00:00 | [] | [
"gl"
] | TAGS
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| Akashpb13/Galician\_xlsr
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other,... | [
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null | null | transformers |
# Akashpb13/Hausa_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
- Loss: 0.2... | {"language": ["ha"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "ha", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Hausa_xls... | automatic-speech-recognition | Akashpb13/Hausa_xlsr | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
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"hf-asr-leaderboard",
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"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | 2022-03-02T23:29:04+00:00 | [] | [
"ha"
] | TAGS
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| Akashpb13/Hausa\_xlsr
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):
* Loss: 0.275118
* Wer: 0.329955
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000096\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps:... | [
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null | null | transformers |
# Akashpb13/Kabyle_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev data... | {"language": ["kab"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sw", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Kabyle_x... | automatic-speech-recognition | Akashpb13/Kabyle_xlsr | [
"transformers",
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"automatic-speech-recognition",
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"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"kab",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-... | 2022-03-02T23:29:04+00:00 | [] | [
"kab"
] | TAGS
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| Akashpb13/Kabyle\_xlsr
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
* Loss: 0.159032
* We... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000096\n* train\\_batch\\_size: 8\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* ... | [
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null | null | transformers |
# Akashpb13/Swahili_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev dat... | {"language": ["sw"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "sw"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/Swahili_x... | automatic-speech-recognition | Akashpb13/Swahili_xlsr | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
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"sw",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"... | 2022-03-02T23:29:04+00:00 | [] | [
"sw"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #sw #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #regio... | Akashpb13/Swahili\_xlsr
=======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
* Loss: 0.159032
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000096\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps:... | [
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null | null | transformers |
# Akashpb13/xlsr_hungarian_new
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with inval... | {"language": ["hu"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hu", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/xlsr_hung... | automatic-speech-recognition | Akashpb13/xlsr_hungarian_new | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
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"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hu #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Akashpb13/xlsr\_hungarian\_new
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000095637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 16\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\... | [
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null | null | transformers |
# Akashpb13/xlsr_kurmanji_kurdish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged wit... | {"language": ["kmr", "ku"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "kmr", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "Akashpb13/x... | automatic-speech-recognition | Akashpb13/xlsr_kurmanji_kurdish | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
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"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-... | 2022-03-02T23:29:04+00:00 | [] | [
"kmr",
"ku"
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#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #kmr #robust-speech-event #model_for_talk #hf-asr-leaderboard #ku #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #... | Akashpb13/xlsr\_kurmanji\_kurdish
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - hu dataset.
It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000096\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 16\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps... | [
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null | null | transformers | # Wav2Vec2-Large-XLSR-53-Maltese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Maltese 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 be u... | {"language": "mt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Maltese by Akash PB", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "data... | automatic-speech-recognition | Akashpb13/xlsr_maltese_wav2vec2 | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"mt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Maltese
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Maltese 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:
Test Result: 29.42 %
| [
"# Wav2Vec2-Large-XLSR-53-Maltese\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Maltese using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\nTest Result: 29.42 %"
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Akjder/DialoGPT-small-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
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null | null | transformers |
# BEiT for Face Mask Detection
BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei.
## Model description
The BEiT mo... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]} | image-classification | AkshatSurolia/BEiT-FaceMask-Finetuned | [
"transformers",
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BEiT for Face Mask Detection
BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei.
## Model description
The BEiT mo... | [
"# BEiT for Face Mask Detection\r\n\r\nBEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei.",
"## Model description\r\n\r\n... | [
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null | null | transformers |
# ConvNeXt for Face Mask Detection
ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al.
## Training Metrics
epoch = ... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]} | image-classification | AkshatSurolia/ConvNeXt-FaceMask-Finetuned | [
"transformers",
"pytorch",
"safetensors",
"convnext",
"image-classification",
"dataset:Face-Mask18K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #convnext #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ConvNeXt for Face Mask Detection
ConvNeXt model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao et al.
## Training Metrics
epoch = ... | [
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null | null | transformers |
# Distilled Data-efficient Image Transformer for Face Mask Detection
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image tran... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]} | image-classification | AkshatSurolia/DeiT-FaceMask-Finetuned | [
"transformers",
"pytorch",
"deit",
"image-classification",
"dataset:Face-Mask18K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #deit #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Distilled Data-efficient Image Transformer for Face Mask Detection
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image tran... | [
"# Distilled Data-efficient Image Transformer for Face Mask Detection\r\n\r\nDistilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient ima... | [
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null | null | transformers |
# Clinical BERT for ICD-10 Prediction
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries.
---
## ... | {"license": "apache-2.0", "tags": ["text-classification"]} | text-classification | AkshatSurolia/ICD-10-Code-Prediction | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Clinical BERT for ICD-10 Prediction
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries.
---
## ... | [
"# Clinical BERT for ICD-10 Prediction\n\nThe Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. \n \n... | [
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null | null | transformers |
# Vision Transformer (ViT) for Face Mask Detection
Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention b... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["Face-Mask18K"]} | image-classification | AkshatSurolia/ViT-FaceMask-Finetuned | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #vit #image-classification #dataset-Face-Mask18K #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Vision Transformer (ViT) for Face Mask Detection
Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention b... | [
"# Vision Transformer (ViT) for Face Mask Detection\r\n\r\nVision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through atte... | [
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null | null | null |
# Spoken Language Identification Model
## Model description
The model can classify a speech utterance according to the language spoken.
It covers following different languages (
English,
Indonesian,
Japanese,
Korean,
Thai,
Vietnamese,
Mandarin Chinese).
| {"language": "multilingual", "license": "apache-2.0", "tags": ["LID", "spoken language recognition"], "datasets": ["VoxLingua107"], "metrics": ["ER"], "inference": false} | null | AkshaySg/LanguageIdentification | [
"LID",
"spoken language recognition",
"multilingual",
"dataset:VoxLingua107",
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"multilingual"
] | TAGS
#LID #spoken language recognition #multilingual #dataset-VoxLingua107 #license-apache-2.0 #region-us
|
# Spoken Language Identification Model
## Model description
The model can classify a speech utterance according to the language spoken.
It covers following different languages (
English,
Indonesian,
Japanese,
Korean,
Thai,
Vietnamese,
Mandarin Chinese).
| [
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null | null | speechbrain |
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.
The model can classify a speech utt... | {"language": "multilingual", "license": "apache-2.0", "tags": ["audio-classification", "speechbrain", "embeddings", "Language", "Identification", "pytorch", "ECAPA-TDNN", "TDNN", "VoxLingua107"], "datasets": ["VoxLingua107"], "metrics": ["Accuracy"], "widget": [{"example_title": "English Sample", "src": "https://cdn-me... | audio-classification | AkshaySg/langid | [
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"multilingual",
"dataset:VoxLingua107",
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"multilingual"
] | TAGS
#speechbrain #audio-classification #embeddings #Language #Identification #pytorch #ECAPA-TDNN #TDNN #VoxLingua107 #multilingual #dataset-VoxLingua107 #license-apache-2.0 #region-us
|
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.
The model can classify a speech utt... | [
"# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model",
"## Model description\n\nThis is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.\nThe model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.\n\nThe model can classify... | [
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null | null | 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-srb-base-cased-oscar
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model descr... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "bert-srb-base-cased-oscar", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | fill-mask | Aleksandar/bert-srb-base-cased-oscar | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bert-srb-base-cased-oscar
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# bert-srb-base-cased-oscar\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-srb-base-cased-oscar\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitati... | [
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0.180604979... |
null | null | 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-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluatio... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-srb-ner-setimes", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9645112274185379}}]}]} | token-classification | Aleksandar/bert-srb-ner-setimes | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-srb-ner-setimes
====================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1955
* Precision: 0.8229
* Recall: 0.8465
* F1: 0.8345
* Accuracy: 0.9645
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Traini... | [
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0.10982727259397507,
0.1829705834388733,
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0.099380023... |
null | null | 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-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-srb-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "sr"}, "metric": {... | token-classification | Aleksandar/bert-srb-ner | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #dataset-wikiann #autotrain_compatible #endpoints_compatible #region-us
| bert-srb-ner
============
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3561
* Precision: 0.8909
* Recall: 0.9082
* F1: 0.8995
* Accuracy: 0.9547
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Traini... | [
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null | null | 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-srb-base-cased-oscar
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "distilbert-srb-base-cased-oscar", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | fill-mask | Aleksandar/distilbert-srb-base-cased-oscar | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-srb-base-cased-oscar
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
... | [
"# distilbert-srb-base-cased-oscar\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"... | [
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"## Model description\n\nMore information needed",
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null | null | 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-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the eva... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-srb-ner-setimes", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9665376552169005}}]}]} | token-classification | Aleksandar/distilbert-srb-ner-setimes | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| distilbert-srb-ner-setimes
==========================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1838
* Precision: 0.8370
* Recall: 0.8617
* F1: 0.8492
* Accuracy: 0.9665
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Traini... | [
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0.13141031... |
null | null | 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-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluati... | {"language": ["sr"], "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-srb-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", ... | token-classification | Aleksandar/distilbert-srb-ner | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"sr",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"sr"
] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #sr #dataset-wikiann #autotrain_compatible #endpoints_compatible #region-us
| distilbert-srb-ner
==================
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2972
* Precision: 0.8871
* Recall: 0.9100
* F1: 0.8984
* Accuracy: 0.9577
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Traini... | [
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-0.10514558851718903,
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0.19520176947116852,
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0.1150881... |
null | null | 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-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evalua... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "electra-srb-ner-setimes", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9546789604788638}}]}]} | token-classification | Aleksandar/electra-srb-ner-setimes | [
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #electra #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electra-srb-ner-setimes
=======================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2804
* Precision: 0.8286
* Recall: 0.8081
* F1: 0.8182
* Accuracy: 0.9547
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: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
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0.135427683... |
null | null | 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-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation ... | {"tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "electra-srb-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "sr"}, "metric"... | token-classification | Aleksandar/electra-srb-ner | [
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #electra #token-classification #generated_from_trainer #dataset-wikiann #autotrain_compatible #endpoints_compatible #region-us
| electra-srb-ner
===============
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3406
* Precision: 0.8934
* Recall: 0.9087
* F1: 0.9010
* Accuracy: 0.9568
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Traini... | [
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null | null | 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-srb-oscar
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "electra-srb-oscar", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | fill-mask | Aleksandar/electra-srb-oscar | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# electra-srb-oscar
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following ... | [
"# electra-srb-oscar\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training h... | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
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null | null | 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. -->
# herbert-base-cased-finetuned-squad
This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/all... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "herbert-base-cased-finetuned-squad", "results": []}]} | question-answering | Aleksandra/herbert-base-cased-finetuned-squad | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us
| herbert-base-cased-finetuned-squad
==================================
This model is a fine-tuned version of allegro/herbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2071
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: 3",
"### Traini... | [
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null | null | transformers |
# xlm-roberta-en-ru-emoji
- Problem type: Multi-class Classification | {"language": ["en", "ru"], "datasets": ["tweet_eval"], "model_index": [{"name": "xlm-roberta-en-ru-emoji", "results": [{"task": {"name": "Sentiment Analysis", "type": "sentiment-analysis"}, "dataset": {"name": "Tweet Eval", "type": "tweet_eval", "args": "emoji"}}]}], "widget": [{"text": "\u041e\u0442\u043b\u0438\u0447\... | text-classification | adorkin/xlm-roberta-en-ru-emoji | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:tweet_eval",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en",
"ru"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #en #ru #dataset-tweet_eval #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-en-ru-emoji
- Problem type: Multi-class Classification | [
"# xlm-roberta-en-ru-emoji \n- Problem type: Multi-class Classification"
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #en #ru #dataset-tweet_eval #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-en-ru-emoji \n- Problem type: Multi-class Classification"
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57,
22
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"passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #en #ru #dataset-tweet_eval #autotrain_compatible #endpoints_compatible #region-us \n# xlm-roberta-en-ru-emoji \n- Problem type: Multi-class Classification"
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null | null | 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
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert", "results": []}]} | text-classification | AlekseyKorshuk/bert | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert
====
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5316
* Accuracy: 0.2936
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More i... | [
"### 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* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\n* ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... | [
53,
147,
4,
33
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"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_siz... | [
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0.10187046974897385,
0.11519768089056015,
0.049953874200582504,
0.162658289... |
null | null | transformers | **Usage HuggingFace Transformers for header generation task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-HeaderGeneration")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
input_ ... | {} | text2text-generation | AlekseyKulnevich/Pegasus-HeaderGeneration | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Usage HuggingFace Transformers for header generation task
Decoder configuration examples:
Input text you can see here
output:
1. *the impact of climate change on tropical cyclones*
2. *the impact of human induced climate change on tropical cyclones*
3. *the impact of climate change on tropical cyclone formation ... | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
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-0.03792005777359009,
0.13300150632858276,
0.18938398361206055,
-0.010199892334640026,
0.117705... |
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