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automatic-speech-recognition | transformers |
# bp500-base100k_voxpopuli: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"]} | lgris/bp500-base100k_voxpopuli | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"audio",
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"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"arxiv:2012.03411",
"l... | null | 2022-03-02T23:29:05+00:00 | [
"2012.03411"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #arxiv-2012.03411 #license-apache-2.0 #endpoints_compatible #region-us
| bp500-base100k\_voxpopuli: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
=============================================================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
* CETUC: contains approximately 145 hours of Br... | [
"#### Summary",
"#### Transcription examples\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets\n\n\n\n```\n/content/bp_dataset\n\n```",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.1419179499917191\n\n```",
"#### Common Voic... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #arxiv-2012.03411 #license-apache-2.0 #endpoints_compatible #region-us \n",
"##... |
automatic-speech-recognition | transformers |
# bp500-base10k_voxpopuli: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese ... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch", "hf-asr-leaderboard"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"], "model-index": [{"name"... | lgris/bp500-base10k_voxpopuli | null | [
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"dataset:common_voice",
"dataset:mls",
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"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
... | null | 2022-03-02T23:29:05+00:00 | [
"2012.03411"
] | [
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] | TAGS
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============================================================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
* CETUC: contains approximately 145 hours of Braz... | [
"#### Summary",
"#### Transcription examples\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets\n\n\n\n```\n/content/bp_dataset\n\n```",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.12096759949218888\n\n```",
"#### Common Voi... | [
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automatic-speech-recognition | transformers |
# bp500-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distri... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch", "hf-asr-leaderboard"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge", "tedx", "sid"], "metrics": ["wer"], "model-index": [{"name"... | lgris/bp500-xlsr | null | [
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"wav2vec2",
"automatic-speech-recognition",
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"dataset:mls",
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... | null | 2022-03-02T23:29:05+00:00 | [
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==============================================================
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
* CETUC: contains approximately 145 hours of Brazilian Portuguese speech dist... | [
"#### Summary",
"#### Transcription examples\n\n\n\nDemonstration\n-------------",
"### Imports and dependencies",
"### Helpers",
"### Model",
"### Download datasets\n\n\n\n```\n/content/bp_dataset\n\n```",
"### Tests",
"#### CETUC\n\n\n\n```\nCETUC WER: 0.05159097808687998\n\n```",
"#### Common Voi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #dataset-tedx #dataset-sid #arxiv-2012.03411 #license-apache-2.0 #model-index #endpoints... |
automatic-speech-recognition | transformers |
# bp_400h_xlsr2_300M | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "pt", "hf-asr-leaderboard"], "model-index": [{"name": "bp_400h_xlsr2_300M", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"na... | lgris/bp_400h_xlsr2_300M | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"pt",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
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|
# bp_400h_xlsr2_300M | [
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] | [
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"# bp_400h_xlsr2_300M"
] |
feature-extraction | transformers |
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
... | {"language": "pt", "license": "apache-2.0", "tags": ["speech"]} | lgris/distilxlsr_bp_12-16 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"speech",
"pt",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01900"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us
|
# DistilXLSR-53 for BP
DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on aud... | [
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrain... | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech a... |
feature-extraction | transformers |
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
... | {"language": "pt", "license": "apache-2.0", "tags": ["speech"]} | lgris/distilxlsr_bp_16-24 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"speech",
"pt",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01900"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us
|
# DistilXLSR-53 for BP
DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on aud... | [
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrain... | [
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"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech a... |
feature-extraction | transformers |
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
... | {"language": "pt", "license": "apache-2.0", "tags": ["speech"]} | lgris/distilxlsr_bp_4-12 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"speech",
"pt",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01900"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us
|
# DistilXLSR-53 for BP
DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on aud... | [
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrain... | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech a... |
feature-extraction | transformers |
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
... | {"language": "pt", "license": "apache-2.0", "tags": ["speech"]} | lgris/distilxlsr_bp_8-12-24 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"speech",
"pt",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01900"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us
|
# DistilXLSR-53 for BP
DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on aud... | [
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrain... | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech a... |
feature-extraction | transformers |
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
... | {"language": "pt", "license": "apache-2.0", "tags": ["speech"]} | lgris/distilxlsr_bp_8-12 | null | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"speech",
"pt",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.01900"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us
|
# DistilXLSR-53 for BP
DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pretrained on aud... | [
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it was pretrain... | [
"TAGS\n#transformers #pytorch #wav2vec2 #feature-extraction #speech #pt #arxiv-2110.01900 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DistilXLSR-53 for BP\nDistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese\n\nThe base model pretrained on 16kHz sampled speech a... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sew-tiny-portuguese-cv
This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on th... | {"language": ["pt"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "pt", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "sew-tiny-portuguese-cv", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "da... | lgris/sew-tiny-portuguese-cv | null | [
"transformers",
"pytorch",
"sew",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"pt",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #sew #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #pt #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sew-tiny-portuguese-cv
======================
This model is a fine-tuned version of lgris/sew-tiny-pt on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5110
* Wer: 0.2842
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #sew #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #pt #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sew-tiny-portuguese-cv7
This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on t... | {"language": ["pt"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "sew-tiny-portuguese-cv7", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic S... | lgris/sew-tiny-portuguese-cv7 | null | [
"transformers",
"pytorch",
"tensorboard",
"sew",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sew-tiny-portuguese-cv7
=======================
This model is a fine-tuned version of lgris/sew-tiny-pt on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4232
* Wer: 0.2745
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hy... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sew-tiny-portuguese-cv8
This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on t... | {"language": ["pt"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sew-tiny-portuguese-cv8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic S... | lgris/sew-tiny-portuguese-cv8 | null | [
"transformers",
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"tensorboard",
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"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sew-tiny-portuguese-cv8
=======================
This model is a fine-tuned version of lgris/sew-tiny-pt on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4082
* Wer: 0.3053
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #sew #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hy... |
feature-extraction | transformers |
# SEW-tiny-pt
This is a pretrained version of [SEW tiny by ASAPP Research](https://github.com/asappresearch/sew) trained over Brazilian Portuguese audio.
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model shoul... | {"language": "pt", "license": "apache-2.0", "tags": ["speech"]} | lgris/sew-tiny-pt | null | [
"transformers",
"pytorch",
"sew",
"feature-extraction",
"speech",
"pt",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"pt"
] | TAGS
#transformers #pytorch #sew #feature-extraction #speech #pt #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-tiny-pt
This is a pretrained version of SEW tiny by ASAPP Research trained over Brazilian Portuguese audio.
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, li... | [
"# SEW-tiny-pt\n\nThis is a pretrained version of SEW tiny by ASAPP Research trained over Brazilian Portuguese audio.\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream ... | [
"TAGS\n#transformers #pytorch #sew #feature-extraction #speech #pt #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-tiny-pt\n\nThis is a pretrained version of SEW tiny by ASAPP Research trained over Brazilian Portuguese audio.\n\nThe base model pretrained on 16kHz sampled speech ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-pt-cv
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "robust-speech-event", "pt", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pt-cv", "results": [{"task": {"type": "automatic-speech-recognition", "na... | lgris/wav2vec2-large-xls-r-300m-pt-cv | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"pt",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #pt #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-pt-cv
===============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3418
* Wer: 0.3581
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #pt #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-coraa-portuguese-cv7
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xlsr-coraa-portuguese-cv7", "results": []}]} | lgris/wav2vec2-large-xlsr-coraa-portuguese-cv7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-coraa-portuguese-cv7
========================================
This model is a fine-tuned version of Edresson/wav2vec2-large-xlsr-coraa-portuguese on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1777
* Wer: 0.1339
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used duri... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-coraa-portuguese-cv8
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xlsr-coraa-portuguese-cv8", "results": []}]} | lgris/wav2vec2-large-xlsr-coraa-portuguese-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-coraa-portuguese-cv8
========================================
This model is a fine-tuned version of Edresson/wav2vec2-large-xlsr-coraa-portuguese on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1626
* Wer: 0.1365
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
automatic-speech-recognition | transformers |
# Wav2vec 2.0 With Open Brazilian Portuguese Datasets v2
This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch", "hf-asr-leaderboard"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-lar... | lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2 | null | [
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"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
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"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"arxiv:2012.03411",
"license:apac... | null | 2022-03-02T23:29:05+00:00 | [
"2012.03411"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #arxiv-2012.03411 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2vec 2.0 With Open Brazilian Portuguese Datasets v2
This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- CETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximat... | [
"# Wav2vec 2.0 With Open Brazilian Portuguese Datasets v2\n\nThis a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:\n\n- CETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing ap... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #arxiv-2012.03411 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",... |
automatic-speech-recognition | transformers |
# Wav2vec 2.0 With Open Brazilian Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50... | {"language": "pt", "license": "apache-2.0", "tags": ["audio", "speech", "wav2vec2", "pt", "portuguese-speech-corpus", "automatic-speech-recognition", "speech", "PyTorch", "hf-asr-leaderboard"], "datasets": ["common_voice", "mls", "cetuc", "lapsbm", "voxforge"], "metrics": ["wer"]} | lgris/wav2vec2-large-xlsr-open-brazilian-portuguese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"arxiv:2012.03411",
"license:apac... | null | 2022-03-02T23:29:05+00:00 | [
"2012.03411"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #arxiv-2012.03411 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us... | Wav2vec 2.0 With Open Brazilian Portuguese Datasets
===================================================
This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
* CETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and... | [
"#### Datasets in number of instances and number of frames\n\n\nThe following image shows the overall distribution of the dataset:\n\n\n!datasets",
"#### Transcription examples\n\n\n\nImports and dependencies\n------------------------\n\n\nPreparation\n-----------\n\n\nTests\n-----",
"### Test against Common Vo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #pt #portuguese-speech-corpus #PyTorch #hf-asr-leaderboard #dataset-common_voice #dataset-mls #dataset-cetuc #dataset-lapsbm #dataset-voxforge #arxiv-2012.03411 #license-apache-2.0 #model-index #endpoints_compatible #has_space #reg... |
automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-cv8", "results": [{... | lgris/wav2vec2-xls-r-1b-cv8 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-cv8
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2007
* Wer: 0.1838
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-portuguese-CORAA-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/f... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "pt", "robust-speech-event", "hf-asr-leaderboard"], "model-index": [{"name": "wav2vec2-xls-r-1b-portuguese-CORAA-3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Re... | lgris/wav2vec2-xls-r-1b-portuguese-CORAA-3 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"pt",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-portuguese-CORAA-3
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on CORAA dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0029
* Wer: 0.6020
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-gn-cv8-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["gn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "gn", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-gn-cv8-3", "results": [{"task": {"type": "automatic-spee... | lgris/wav2vec2-xls-r-300m-gn-cv8-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gn",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-gn-cv8-3
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9517
* Wer: 0.8542
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-gn-cv8-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["gn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "gn", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-gn-cv8-4", "results": [{"task": {"type": "automatic-spee... | lgris/wav2vec2-xls-r-300m-gn-cv8-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gn",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-gn-cv8-4
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5805
* Wer: 0.7545
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-gn-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"language": ["gn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "gn", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-gn-cv8", "results": [{"task": {"type": "automatic-speech... | lgris/wav2vec2-xls-r-300m-gn-cv8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gn",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-gn-cv8
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9392
* Wer: 0.7033
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-gn-cv7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"language": ["gn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "gn", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-gn-cv7", "results": [{"task": {"type": "automatic-speech-reco... | lgris/wav2vec2-xls-r-gn-cv7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gn",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-gn-cv7
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7197
* Wer: 0.7434
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followi... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-pt-cv7-from-bp400h
This model is a fine-tuned version of [lgris/bp_400h_xlsr2_300M](https://huggingface.co/lgris/... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-pt-cv7-from-bp400h", "... | lgris/wav2vec2-xls-r-pt-cv7-from-bp400h | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-pt-cv7-from-bp400h
=================================
This model is a fine-tuned version of lgris/bp\_400h\_xlsr2\_300M on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1535
* Wer: 0.1254
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_base_10k_8khz_pt_cv7_2
This model is a fine-tuned version of [lgris/seasr_2022_base_10k_8khz_pt](https://huggingface.co... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2_base_10k_8khz_pt_cv7_2", "re... | lgris/wav2vec2_base_10k_8khz_pt_cv7_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"pt",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2\_base\_10k\_8khz\_pt\_cv7\_2
=====================================
This model is a fine-tuned version of lgris/seasr\_2022\_base\_10k\_8khz\_pt on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 76.3426
* Wer: 0.1979
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-large-CORAA-pt-cv7
This model is a fine-tuned version of [lgris/WavLM-large-CORAA-pt](https://huggingface.co/lgris/WavLM-l... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "pt"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wavlm-large-CORAA-pt-cv7", "results": []}]} | lgris/wavlm-large-CORAA-pt-cv7 | null | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #pt #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| wavlm-large-CORAA-pt-cv7
========================
This model is a fine-tuned version of lgris/WavLM-large-CORAA-pt on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2546
* Wer: 0.2261
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #pt #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
token-classification | transformers |
ELECTRA-minuscule-discriminator
===============================
A ridiculously small ELECTRA discriminator model for testing purposes.
**THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT.**
| {"language": "multilingual", "license": "cc0-1.0", "tags": ["electra", "testing", "minuscule"], "thumbnail": "url to a thumbnail used in social sharing"} | lgrobol/electra-minuscule-discriminator | null | [
"transformers",
"pytorch",
"electra",
"token-classification",
"testing",
"minuscule",
"multilingual",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #electra #token-classification #testing #minuscule #multilingual #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
|
ELECTRA-minuscule-discriminator
===============================
A ridiculously small ELECTRA discriminator model for testing purposes.
THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT.
| [] | [
"TAGS\n#transformers #pytorch #electra #token-classification #testing #minuscule #multilingual #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
ELECTRA-minuscule-generator
===============================
A ridiculously small ELECTRA generator model for testing purposes.
**THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT.**
| {"language": "multilingual", "license": "cc0-1.0", "tags": ["electra", "testing", "minuscule"]} | lgrobol/electra-minuscule-generator | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"fill-mask",
"testing",
"minuscule",
"multilingual",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #electra #fill-mask #testing #minuscule #multilingual #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
|
ELECTRA-minuscule-generator
===============================
A ridiculously small ELECTRA generator model for testing purposes.
THIS MODEL HAS NOT BEEN TRAINED, DO NOT EXPECT ANYThING OF IT.
| [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #fill-mask #testing #minuscule #multilingual #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | FlauBERT-minuscule
==================
A ridiculously small model for testing purposes. | {} | lgrobol/flaubert-minuscule | null | [
"transformers",
"pytorch",
"safetensors",
"flaubert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #flaubert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| FlauBERT-minuscule
==================
A ridiculously small model for testing purposes. | [] | [
"TAGS\n#transformers #pytorch #safetensors #flaubert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | RoBERTa-minuscule
==================
A ridiculously small model for testing purposes. | {} | lgrobol/roberta-minuscule | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa-minuscule
==================
A ridiculously small model for testing purposes. | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | lhbit20010120/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6423
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... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-classification | transformers | Distilbert finetuned for Aspect-Based Sentiment Analysis (ABSA) with auxiliary sentence.
Fine-tuned using a dataset provided by NAVER for the CentraleSupélec NLP course.
```bibtex
@inproceedings{sun-etal-2019-utilizing,
title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sente... | {} | lhoestq/distilbert-base-uncased-finetuned-absa-as | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Distilbert finetuned for Aspect-Based Sentiment Analysis (ABSA) with auxiliary sentence.
Fine-tuned using a dataset provided by NAVER for the CentraleSupélec NLP course.
| [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# char-bert-base-uncased
This model is a fine-tuned version of [char-bert-base-uncased/checkpoint-1840240](https://huggingface.co/... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "char-bert-base-uncased", "results": []}]} | lhy/char-bert-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| char-bert-base-uncased
======================
This model is a fine-tuned version of char-bert-base-uncased/checkpoint-1840240 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1760
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 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: 25",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 16\n* see... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-zh-CN-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-zh-CN-colab", "results": []}]} | li666/wav2vec2-large-xls-r-300m-zh-CN-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-zh-CN-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# wav2vec2-large-xls-r-300m-zh-CN-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-zh-CN-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_vo... |
feature-extraction | transformers |
# mBERT fine-tuned on English semantic role labeling
## Model description
This model is the [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the fol... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["bert-base-multilingual-cased", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]} | liaad/srl-en_mbert-base | null | [
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"bert-base-multilingual-cased",
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"dataset:CoNLL-2012",
"arxiv:2101.01213",
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"r... | null | 2022-03-02T23:29:05+00:00 | [
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| mBERT fine-tuned on English semantic role labeling
==================================================
Model description
-----------------
This model is the 'bert-base-multilingual-cased' fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which result... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* The models were trained only for 5 epochs.\n* The English data was preprocessed to match the Portuguese ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #bert-base-multilingual-cased #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers... |
feature-extraction | transformers |
# XLM-R base fine-tuned on English semantic role labeling
## Model description
This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-base", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]} | liaad/srl-en_xlmr-base | null | [
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"dataset:CoNLL-2012",
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| XLM-R base fine-tuned on English semantic role labeling
=======================================================
Model description
-----------------
This model is the 'xlm-roberta-base' fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-base #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of th... |
feature-extraction | transformers |
# XLM-R large fine-tuned on English semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following mod... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]} | liaad/srl-en_xlmr-large | null | [
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| XLM-R large fine-tuned on English semantic role labeling
========================================================
Model description
-----------------
This model is the 'xlm-roberta-large' fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resul... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of t... |
feature-extraction | transformers |
# mBERT base fine-tune in English and Portuguese semantic role labeling
## Model description
This model is the [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the Pr... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["bert-base-multilingual-cased", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]} | liaad/srl-enpt_mbert-base | null | [
"transformers",
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"dataset:CoNLL-2012",
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"r... | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
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| mBERT base fine-tune in English and Portuguese semantic role labeling
=====================================================================
Model description
-----------------
This model is the 'bert-base-multilingual-cased' fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data ... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* The English data was preprocessed to match the Portuguese data, so there are some differences in role at... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #bert-base-multilingual-cased #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers... |
feature-extraction | transformers |
# XLM-R base fine-tune in English and Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. Th... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-base", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]} | liaad/srl-enpt_xlmr-base | null | [
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"dataset:PropBank.Br",
"dataset:CoNLL-2012",
"arxiv:2101.01213",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
"multilingual",
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] | TAGS
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| XLM-R base fine-tune in English and Portuguese semantic role labeling
=====================================================================
Model description
-----------------
This model is the 'xlm-roberta-base' fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fin... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-base #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of th... |
feature-extraction | transformers |
# XLM-R large fine-tuned in English and Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]} | liaad/srl-enpt_xlmr-large | null | [
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"feature-extraction",
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"semantic role labeling",
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"dataset:PropBank.Br",
"dataset:CoNLL-2012",
"arxiv:2101.01213",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
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| XLM-R large fine-tuned in English and Portuguese semantic role labeling
=======================================================================
Model description
-----------------
This model is the 'xlm-roberta-large' fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and the... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of t... |
feature-extraction | transformers |
# BERTimbau base fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`neuralmind/bert-base-portuguese-cased`](https://huggingface.co/neuralmind/bert-base-portuguese-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the follo... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["bert-base-portuguese-cased", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br"], "metrics": ["F1 Measure"]} | liaad/srl-pt_bertimbau-base | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
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] | [
"multilingual",
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| BERTimbau base fine-tuned on Portuguese semantic role labeling
==============================================================
Model description
-----------------
This model is the 'neuralmind/bert-base-portuguese-cased' fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resul... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.\n\n\nTraining procedure\n------------------\n\n\nThe model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 res... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #bert-base-portuguese-cased #semantic role labeling #finetuned #multilingual #pt #dataset-PropBank.Br #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\n\... |
feature-extraction | transformers |
# BERTimbau large fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`neuralmind/bert-large-portuguese-cased`](https://huggingface.co/neuralmind/bert-large-portuguese-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the fo... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["bert-large-portuguese-cased", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br"], "metrics": ["F1 Measure"]} | liaad/srl-pt_bertimbau-large | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
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] | [
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| BERTimbau large fine-tuned on Portuguese semantic role labeling
===============================================================
Model description
-----------------
This model is the 'neuralmind/bert-large-portuguese-cased' fine-tuned on Portuguese semantic role labeling data. This is part of a project from which re... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.\n\n\nTraining procedure\n------------------\n\n\nThe model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 res... | [
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"#### How to use\n\n\nTo use the transformers portion of this model:\n... |
feature-extraction | transformers |
# mBERT fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["bert-base-multilingual-cased", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br"], "metrics": ["F1 Measure"]} | liaad/srl-pt_mbert-base | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
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| mBERT fine-tuned on Portuguese semantic role labeling
=====================================================
Model description
-----------------
This model is the 'bert-base-multilingual-cased' fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.\n\n\nTraining procedure\n------------------\n\n\nThe model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 res... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #bert-base-multilingual-cased #semantic role labeling #finetuned #multilingual #pt #dataset-PropBank.Br #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\... |
feature-extraction | transformers |
# XLM-R base fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimba... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["xlm-roberta-base", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br"], "metrics": ["F1 Measure"]} | liaad/srl-pt_xlmr-base | null | [
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"arxiv:2101.01213",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
"multilingual",
"pt"
] | TAGS
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| XLM-R base fine-tuned on Portuguese semantic role labeling
==========================================================
Model description
-----------------
This model is the 'xlm-roberta-base' fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
*... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-base #semantic role labeling #finetuned #multilingual #pt #dataset-PropBank.Br #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use th... |
feature-extraction | transformers |
# XLM-R large fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-bas... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["xlm-roberta-large", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br"], "metrics": ["F1 Measure"]} | liaad/srl-pt_xlmr-large | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
"multilingual",
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #dataset-PropBank.Br #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us
| XLM-R large fine-tuned on Portuguese semantic role labeling
===========================================================
Model description
-----------------
This model is the 'xlm-roberta-large' fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #dataset-PropBank.Br #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use t... |
feature-extraction | transformers |
# XLM-R large fine-tuned in Portuguese Universal Dependencies and English and Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the Universal Dependencies Portuguese dataset, then fine-tuned on the CoNLL ... | {"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large", "semantic role labeling", "finetuned", "dependency parsing"], "datasets": ["PropBank.Br", "CoNLL-2012", "Universal Dependencies"], "metrics": "f1"} | liaad/ud_srl-enpt_xlmr-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"xlm-roberta-large",
"semantic role labeling",
"finetuned",
"dependency parsing",
"multilingual",
"pt",
"en",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
"multilingual",
"pt",
"en"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #dependency parsing #multilingual #pt #en #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us
| XLM-R large fine-tuned in Portuguese Universal Dependencies and English and Portuguese semantic role labeling
=============================================================================================================
Model description
-----------------
This model is the 'xlm-roberta-large' fine-tuned first on th... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #dependency parsing #multilingual #pt #en #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo us... |
feature-extraction | transformers |
# BERTimbau large fine-tune in Portuguese Universal Dependencies and semantic role labeling
## Model description
This model is the [`neuralmind/bert-large-portuguese-cased`](https://huggingface.co/neuralmind/bert-large-portuguese-cased) fine-tuned first on the Universal Dependencies Portuguese dataset and then fine-... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["bert-large-portuguese-cased", "semantic role labeling", "finetuned", "dependency parsing"], "datasets": ["PropBank.Br", "CoNLL-2012", "Universal Dependencies"], "metrics": ["F1 Measure"]} | liaad/ud_srl-pt_bertimbau-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"bert-large-portuguese-cased",
"semantic role labeling",
"finetuned",
"dependency parsing",
"multilingual",
"pt",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
"multilingual",
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #bert-large-portuguese-cased #semantic role labeling #finetuned #dependency parsing #multilingual #pt #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us
| BERTimbau large fine-tune in Portuguese Universal Dependencies and semantic role labeling
=========================================================================================
Model description
-----------------
This model is the 'neuralmind/bert-large-portuguese-cased' fine-tuned first on the Universal Depende... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* The model was trained only for 10 epochs in the Universal Dependencies dataset.\n\n\nTraining procedure\... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #bert-large-portuguese-cased #semantic role labeling #finetuned #dependency parsing #multilingual #pt #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\n\... |
feature-extraction | transformers |
# XLM-R large fine-tune in Portuguese Universal Dependencies and semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the Universal Dependencies Portuguese dataset and then fine-tuned on the PropBank.Br data. This is p... | {"language": ["multilingual", "pt"], "license": "apache-2.0", "tags": ["xlm-roberta-large", "semantic role labeling", "finetuned", "dependency parsing"], "datasets": ["PropBank.Br", "CoNLL-2012", "Universal Dependencies"], "metrics": ["F1 Measure"]} | liaad/ud_srl-pt_xlmr-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"xlm-roberta-large",
"semantic role labeling",
"finetuned",
"dependency parsing",
"multilingual",
"pt",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.01213"
] | [
"multilingual",
"pt"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #dependency parsing #multilingual #pt #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us
| XLM-R large fine-tune in Portuguese Universal Dependencies and semantic role labeling
=====================================================================================
Model description
-----------------
This model is the 'xlm-roberta-large' fine-tuned first on the Universal Dependencies Portuguese dataset and ... | [
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use the full SRL model (transformers portion + a decoding layer), refer to the project's github.",
"#### Limitations and bias\n\n\n* This model does not include a Tensorflow version. This is because the \"type\\_vocab\\_size\" in this mo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #dependency parsing #multilingual #pt #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTo use the transformers portion of this model:\n\n\nTo use th... |
text-classification | transformers |
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- **Language model**: BertForSequenceClassification
- **Model size**: 410M
- **Language**: Chinese
## Example
```python
>>> from transformers import AutoModelForSequenceClassification , AutoTokenizer, ... | {"language": "zh", "widget": [{"text": "\u6211\u559c\u6b22\u4e0b\u96e8\u3002"}, {"text": "\u6211\u8ba8\u538c\u4ed6\u3002"}]} | liam168/c2-roberta-base-finetuned-dianping-chinese | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #zh #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- Language model: BertForSequenceClassification
- Model size: 410M
- Language: Chinese
## Example
| [
"# liam168/c2-roberta-base-finetuned-dianping-chinese",
"## Model description\n\n用中文对话情绪语料训练的模型，2分类：乐观和悲观。",
"## Overview\n\n- Language model: BertForSequenceClassification\n- Model size: 410M\n- Language: Chinese",
"## Example"
] | [
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"# liam168/c2-roberta-base-finetuned-dianping-chinese",
"## Model description\n\n用中文对话情绪语料训练的模型，2分类：乐观和悲观。",
"## Overview\n\n- Language model: BertForSequenceClassification\n- Mo... |
text-classification | transformers |
# liam168/c4-zh-distilbert-base-uncased
## Model description
用 ["女性","体育","文学","校园"]4类数据训练的分类模型。
## Overview
- **Language model**: DistilBERT
- **Model size**: 280M
- **Language**: Chinese
## Example
```python
>>> from transformers import DistilBertForSequenceClassification , AutoTokenizer, pipeline
>>> model_... | {"language": "zh", "license": "apache-2.0", "tags": ["exbert"], "widget": [{"text": "\u5973\u4eba\u505a\u5f97\u8d8a\u7eaf\u7cb9\uff0c\u76ae\u80a4\u548c\u8eab\u6750\u5c31\u8d8a\u597d"}, {"text": "\u6211\u559c\u6b22\u7bee\u7403"}]} | liam168/c4-zh-distilbert-base-uncased | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"exbert",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #distilbert #text-classification #exbert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# liam168/c4-zh-distilbert-base-uncased
## Model description
用 ["女性","体育","文学","校园"]4类数据训练的分类模型。
## Overview
- Language model: DistilBERT
- Model size: 280M
- Language: Chinese
## Example
| [
"# liam168/c4-zh-distilbert-base-uncased",
"## Model description\n\n用 [\"女性\",\"体育\",\"文学\",\"校园\"]4类数据训练的分类模型。",
"## Overview\n\n- Language model: DistilBERT\n- Model size: 280M \n- Language: Chinese",
"## Example"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #exbert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# liam168/c4-zh-distilbert-base-uncased",
"## Model description\n\n用 [\"女性\",\"体育\",\"文学\",\"校园\"]4类数据训练的分类模型。",
"## Overview\n\n- Language model: DistilB... |
text-generation | transformers |
# liam168/chat-DialoGPT-small-en
## Model description
用英文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
mode_name = 'liam168/chat-DialoGPT-small-en'
tokenizer = AutoTokenizer.fro... | {"language": "en", "license": "apache-2.0", "widget": [{"text": "I got a surprise for you, Morty."}]} | liam168/chat-DialoGPT-small-en | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# liam168/chat-DialoGPT-small-en
## Model description
用英文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
| [
"# liam168/chat-DialoGPT-small-en",
"## Model description\n\n用英文聊天数据训练的模型；",
"### How to use\n\nNow we are ready to try out how the model works as a chatting partner!"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# liam168/chat-DialoGPT-small-en",
"## Model description\n\n用英文聊天数据训练的模型；",
"### How to use\n\nNow we are ready to try out how the model works as... |
text-generation | transformers |
# liam168/chat-DialoGPT-small-zh
## Model description
用中文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
mode_name = 'liam168/chat-DialoGPT-small-zh'
tokenizer = AutoTokenizer.from... | {"language": "zh", "license": "apache-2.0", "widget": [{"text": "\u4f60\u4eec\u5bbf\u820d\u90fd\u662f\u8fd9\u4e48\u5389\u5bb3\u7684\u4eba\u5417"}]} | liam168/chat-DialoGPT-small-zh | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# liam168/chat-DialoGPT-small-zh
## Model description
用中文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
| [
"# liam168/chat-DialoGPT-small-zh",
"## Model description\n\n用中文聊天数据训练的模型；",
"### How to use\n\nNow we are ready to try out how the model works as a chatting partner!"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# liam168/chat-DialoGPT-small-zh",
"## Model description\n\n用中文聊天数据训练的模型；",
"### How to use\n\nNow we are ready to try out how the mod... |
text-generation | transformers |
# gen-gpt2-medium-chinese
# Overview
- **Language model**: GPT2-Medium
- **Model size**: 68M
- **Language**: Chinese
# Example
```python
from transformers import TFGPT2LMHeadModel,AutoTokenizer
from transformers import TextGenerationPipeline
mode_name = 'liam168/gen-gpt2-medium-chinese'
tokenizer = AutoTokeniz... | {"language": "zh", "widget": [{"text": "\u6653\u65e5\u5343\u7ea2"}, {"text": "\u957f\u8857\u8e9e\u8e40"}]} | liam168/gen-gpt2-medium-chinese | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gen-gpt2-medium-chinese
# Overview
- Language model: GPT2-Medium
- Model size: 68M
- Language: Chinese
# Example
输出
| [
"# gen-gpt2-medium-chinese",
"# Overview\n\n- Language model: GPT2-Medium\n- Model size: 68M \n- Language: Chinese",
"# Example\n\n\n输出"
] | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #zh #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gen-gpt2-medium-chinese",
"# Overview\n\n- Language model: GPT2-Medium\n- Model size: 68M \n- Language: Chinese",
"# Example\n\n\n输出"
] |
question-answering | transformers | # Chinese RoBERTa-Base Model for QA
## Model description
用中文预料微调的QA模型.
## Overview
- **Language model**: RoBERTa-Base
- **Model size**: 400M
- **Language**: Chinese
## How to use
You can use the model directly with a pipeline for extractive question answering:
```python
>>> from transformers import AutoModelFo... | {"language": "zh", "widget": [{"text": "\u8457\u540d\u8bd7\u6b4c\u300a\u5047\u5982\u751f\u6d3b\u6b3a\u9a97\u4e86\u4f60\u300b\u7684\u4f5c\u8005\u662f", "context": "\u666e\u5e0c\u91d1\u4ece\u90a3\u91cc\u5b66\u4e60\u4eba\u6c11\u7684\u8bed\u8a00\uff0c\u5438\u53d6\u4e86\u8bb8\u591a\u6709\u76ca\u7684\u517b\u6599\uff0c\u8fd9\... | liam168/qa-roberta-base-chinese-extractive | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #question-answering #zh #endpoints_compatible #region-us
| # Chinese RoBERTa-Base Model for QA
## Model description
用中文预料微调的QA模型.
## Overview
- Language model: RoBERTa-Base
- Model size: 400M
- Language: Chinese
## How to use
You can use the model directly with a pipeline for extractive question answering:
## Contact
liam168520@URL
| [
"# Chinese RoBERTa-Base Model for QA",
"## Model description\n\n用中文预料微调的QA模型.",
"## Overview\n\n- Language model: RoBERTa-Base\n- Model size: 400M\n- Language: Chinese",
"## How to use\n\nYou can use the model directly with a pipeline for extractive question answering:",
"## Contact\n\nliam168520@URL"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #zh #endpoints_compatible #region-us \n",
"# Chinese RoBERTa-Base Model for QA",
"## Model description\n\n用中文预料微调的QA模型.",
"## Overview\n\n- Language model: RoBERTa-Base\n- Model size: 400M\n- Language: Chinese",
"## How to use\n\nYou can use the model ... |
translation | transformers |
# liam168/trans-opus-mt-en-zh
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
* target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
## How to use
```python
>>> from transformers import AutoMod... | {"language": ["en", "zh"], "tags": ["translation"], "widget": [{"text": "I like to study Data Science and Machine Learning."}]} | liam168/trans-opus-mt-en-zh | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"zh"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# liam168/trans-opus-mt-en-zh
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
* target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
## How to use
## Contact
liam168520@URL
| [
"# liam168/trans-opus-mt-en-zh",
"## Model description\n\n* source group: English\n* target group: Chinese\n* model: transformer\n* source language(s): eng\n* target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant",
"## How to use",
"## Contact\n\nliam168520... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# liam168/trans-opus-mt-en-zh",
"## Model description\n\n* source group: English\n* target group: Chinese\n* model: transformer\n* source language(s): eng\n* t... |
translation | transformers |
# liam168/trans-opus-mt-zh-en
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
## How to use
```python
>>> from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
>>> mode_name = 'liam168/trans-opus-mt-zh-en'
>>> model = AutoModelWith... | {"language": ["en", "zh"], "tags": ["translation"], "widget": [{"text": "\u6211\u559c\u6b22\u5b66\u4e60\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\u3002"}]} | liam168/trans-opus-mt-zh-en | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"zh"
] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# liam168/trans-opus-mt-zh-en
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
## How to use
## Contact
liam168520@URL
| [
"# liam168/trans-opus-mt-zh-en",
"## Model description\n\n* source group: English \n* target group: Chinese \n* model: transformer\n* source language(s): eng",
"## How to use",
"## Contact\n\nliam168520@URL"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #en #zh #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# liam168/trans-opus-mt-zh-en",
"## Model description\n\n* source group: English \n* target group: Chinese \n* model: transformer\n* source language(s): eng",
... |
null | null | ## Title Generator
References this [notebook](https://shivanandroy.com/transformers-generating-arxiv-papers-title-from-abstracts/)
Using `t5-small`, trained on a batch size of 16 for 4 epochs, utilising the ArXiV dataset through the `SimpleTransformers` library. Around 15k data was used for training and 3.7k data for ... | {} | lianaling/title-generator-t5 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ## Title Generator
References this notebook
Using 't5-small', trained on a batch size of 16 for 4 epochs, utilising the ArXiV dataset through the 'SimpleTransformers' library. Around 15k data was used for training and 3.7k data for evaluation.
This is a '.pkl' file.
### Prerequisites
Install 'simpletransformers' lib... | [
"## Title Generator\nReferences this notebook\n\nUsing 't5-small', trained on a batch size of 16 for 4 epochs, utilising the ArXiV dataset through the 'SimpleTransformers' library. Around 15k data was used for training and 3.7k data for evaluation.\n\nThis is a '.pkl' file.",
"### Prerequisites\nInstall 'simpletr... | [
"TAGS\n#region-us \n",
"## Title Generator\nReferences this notebook\n\nUsing 't5-small', trained on a batch size of 16 for 4 epochs, utilising the ArXiV dataset through the 'SimpleTransformers' library. Around 15k data was used for training and 3.7k data for evaluation.\n\nThis is a '.pkl' file.",
"### Prerequ... |
null | null | https://arthritisrelieftx.com/123movies-watch-space-jam-a-new-legacy-2021-full-online-free-hd/
https://www.mycentraloregon.com/2021/07/16/how-to-watch-space-jam-a-new-legacy-free-streaming-space-jam-2-on-hbo-max-available-online/ | {} | liano/aura | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | https://ulmerderm.com/blog/space-jam/official-watch-space-jam-2-2021-online-for-free-123movies/
https://ulmerderm.com/blog/space-jam/official-watch-space-jam-2-2021-online-for-free-123movies/
https://ulmerderm.com/blog/space-jam/official-watch-space-jam-2-2021-online-for-free-123movies/
https://ulmerderm.com/blog/space... | {} | liano/vioan | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
null | null | this is a model for ecom representation | {} | liatwilight/sbert-ecom | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| this is a model for ecom representation | [] | [
"TAGS\n#region-us \n"
] |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `lichenda/wsj0_2mix_skim_noncausal`
This model was trained by LiChenda using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout ac3c10cfe4faf82c0bb30f8b32d9e8692363e0a9
pip install -e .
cd egs2/wsj0_2mix/enh1
./r... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | lichenda/wsj0_2mix_skim_noncausal | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'lichenda/wsj0\_2mix\_skim\_noncausal'
This model was trained by LiChenda using wsj0\_2mix recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Feb 23 16:42:06 CST 2022'
* python version: '3.7.11 (default, Jul 27 202... | [
"### 'lichenda/wsj0\\_2mix\\_skim\\_noncausal'\n\n\nThis model was trained by LiChenda using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Feb 23 16:42:06 CST 2022'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'lichenda/wsj0\\_2mix\\_skim\\_noncausal'\n\n\nThis model was trained by LiChenda using wsj0\\_2mix recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951
## Validation Metrics
- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.81773... | {"language": "unk", "tags": "autonlp", "datasets": ["lidiia/autonlp-data-trans_class_arg"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.9756221672668951} | lidiia/autonlp-trans_class_arg-32957902 | null | [
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"unk",
"dataset:lidiia/autonlp-data-trans_class_arg",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-lidiia/autonlp-data-trans_class_arg #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951
## Validation Metrics
- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.81773... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 32957902\n- CO2 Emissions (in grams): 0.9756221672668951",
"## Validation Metrics\n\n- Loss: 0.2765039801597595\n- Accuracy: 0.8939828080229226\n- Precision: 0.7757009345794392\n- Recall: 0.8645833333333334\n- AUC: 0.955265974967... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-lidiia/autonlp-data-trans_class_arg #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 32957902\n- CO2 Emissions (in grams... |
summarization | transformers | ## `bart-base-samsum`
This model was obtained by fine-tuning `facebook/bart-base` on Samsum dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="lidiya/bart-base-samsum")
conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker?
Philipp: S... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Jeff: Can I train a \ud83e\udd17 Transformers model on Amazon SageMaker? \nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff: ok.\nJeff: and how can I get s... | lidiya/bart-base-samsum | null | [
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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| 'bart-base-samsum'
------------------
This model was obtained by fine-tuning 'facebook/bart-base' on Samsum dataset.
Usage
-----
Training procedure
------------------
* Colab notebook: URL
Results
-------
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
summarization | transformers | ## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="lidiya/bart-large-xsum-samsum")
conversation = '''Hannah: Hey, do... | {"language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry, can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at the park together\nHannah: I ... | lidiya/bart-large-xsum-samsum | null | [
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"pytorch",
"safetensors",
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"seq2seq",
"summarization",
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"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| 'bart-large-xsum-samsum'
------------------------
This model was obtained by fine-tuning 'facebook/bart-large-xsum' on Samsum dataset.
Usage
-----
Training procedure
------------------
* Colab notebook: URL
Results
-------
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #seq2seq #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
#Teia Moranta | {"tags": ["conversational"]} | life4free96/DialogGPT-med-TeiaMoranta | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Teia Moranta | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#rick sanchez | {"tags": ["conversational"]} | light/small-rickk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#rick sanchez | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# BiomedNLP-PubMedBERT finetuned on textual entailment (NLI)
The [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext?text=%5BMASK%5D+is+a+tumor+suppressor+gene) finetuned on the MNLI dataset. It should be useful in textua... | {"language": "en", "license": "mit", "tags": ["textual-entailment", "nli", "pytorch"], "datasets": ["mnli"], "widget": [{"text": "EpCAM is overexpressed in breast cancer. </s></s> EpCAM is downregulated in breast cancer."}]} | lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli | null | [
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"textual-entailment",
"nli",
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"dataset:mnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #textual-entailment #nli #en #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BiomedNLP-PubMedBERT finetuned on textual entailment (NLI)
==========================================================
The microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext finetuned on the MNLI dataset. It should be useful in textual entailment tasks involving biomedical corpora.
Usage
-----
Given two... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #textual-entailment #nli #en #dataset-mnli #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
translation | transformers |
## Greek to English NMT
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* source languages: el
* target languages: en
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + BPE segmentation
* metrics: bleu, chrf
### Model des... | {"language": ["en", "el"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["bleu"], "widget": [{"text": "\u039f \u03cc\u03c1\u03bf\u03c2 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03... | lighteternal/SSE-TUC-mt-el-en-cased | null | [
"transformers",
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"fsmt",
"text2text-generation",
"translation",
"en",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"el"
] | TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Greek to English NMT
--------------------
By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
------------------------------------------------------------------------------
* source languages: el
* target languages: en
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(f... | [
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (20k codes).\\\nMixed-case model.",
"### How to use\n\n\nTraining data\n-------------\n\n\nConsolidated corpus from Opus and CC-Matrix (~6.6GB in total)\n\n\nEval results\n------------\... | [
"TAGS\n#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (20k codes).\\\nMixed-case... |
translation | transformers |
## Greek to English NMT (lower-case output)
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* source languages: el
* target languages: en
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + BPE segmentation
* metrics: bleu,... | {"language": ["en", "el"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["bleu"], "widget": [{"text": "\u0397 \u03c4\u03cd\u03c7\u03b7 \u03b2\u03bf\u03b7\u03b8\u03ac\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03c4\u03bf\u03bb\u03bc\u03b7\u03c1\u03bf\u03cd\u03c2."}]} | lighteternal/SSE-TUC-mt-el-en-lowercase | null | [
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"fsmt",
"text2text-generation",
"translation",
"en",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"el"
] | TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Greek to English NMT (lower-case output)
----------------------------------------
By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
------------------------------------------------------------------------------
* source languages: el
* target languages: en
* licence: apache-2.0
* datase... | [
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (10k codes).\\\nLower-case model.",
"### How to use\n\n\nTraining data\n-------------\n\n\nConsolidated corpus from Opus and CC-Matrix (~6.6GB in total)\n\n\nEval results\n------------\... | [
"TAGS\n#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (10k codes).\\\nLower-case... |
translation | transformers |
## English to Greek NMT
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* source languages: en
* target languages: el
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + BPE segmentation
* metrics: bleu, chrf
### Model descr... | {"language": ["en", "el"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["bleu"], "widget": [{"text": "'Katerina', is the best name for a girl."}]} | lighteternal/SSE-TUC-mt-en-el-cased | null | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"en",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"el"
] | TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| English to Greek NMT
--------------------
By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
------------------------------------------------------------------------------
* source languages: en
* target languages: el
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(f... | [
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (20k codes).\\\nMixed-case model.",
"### How to use\n\n\nTraining data\n-------------\n\n\nConsolidated corpus from Opus and CC-Matrix (~6.6GB in total)\n\n\nEval results\n------------\... | [
"TAGS\n#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (20k codes).\\\nMixed-case... |
translation | transformers |
## English to Greek NMT (lower-case output)
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* source languages: en
* target languages: el
* licence: apache-2.0
* dataset: Opus, CCmatrix
* model: transformer(fairseq)
* pre-processing: tokenization + lower-casing + BPE segmentation
* m... | {"language": ["en", "el"], "license": "apache-2.0", "tags": ["translation"], "metrics": ["bleu"], "widget": [{"text": "Not all those who wander are lost."}]} | lighteternal/SSE-TUC-mt-en-el-lowercase | null | [
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"en",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"el"
] | TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #en #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| English to Greek NMT (lower-case output)
----------------------------------------
By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
------------------------------------------------------------------------------
* source languages: en
* target languages: el
* licence: apache-2.0
* datase... | [
"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (10k codes).\\\nLower-case model.",
"### How to use\n\n\nTraining data\n-------------\n\n\nConsolidated corpus from Opus and CC-Matrix (~6.6GB in total)\n\n\nEval results\n------------\... | [
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"### Model description\n\n\nTrained using the Fairseq framework, transformer\\_iwslt\\_de\\_en architecture.\\\nBPE segmentation (10k codes).\\\nLower-case... |
text-classification | transformers |
# Fact vs. opinion binary classifier, trained on a mixed EN-EL annotated corpus.
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This is an XLM-Roberta-base model with a binary classification head. Given a sentence, it can classify it either as a fact or an opinion based on its con... | {"language": ["en", "el", "multilingual"], "license": "apache-2.0", "tags": ["text-classification", "fact-or-opinion", "transformers"], "widget": [{"text": "\u039e\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03b7 \u03ba\u03b1\u03b8\u03b7\u03bb\u03c9\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03... | lighteternal/fact-or-opinion-xlmr-el | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"fact-or-opinion",
"en",
"el",
"multilingual",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"el",
"multilingual"
] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #fact-or-opinion #en #el #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Fact vs. opinion binary classifier, trained on a mixed EN-EL annotated corpus.
==============================================================================
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This is an XLM-Roberta-base model with a binary classification head. Given ... | [
"### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)\n\n\nThis is an XLM-Roberta-base model with a binary classification head. Given a sentence, it can classify it either as a fact or an opinion based on its content.\n\n\nYou can use this model in any of the XLM-R supported languages ... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #fact-or-opinion #en #el #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)\n\n\nThis is an XLM-Roberta-base m... |
text-generation | transformers | # Greek (el) GPT2 model - small
<img src="https://huggingface.co/lighteternal/gpt2-finetuned-greek-small/raw/main/GPT2el.png" width="600"/>
#### A new version (recommended) trained on 5x more data is available at: https://huggingface.co/lighteternal/gpt2-finetuned-greek
### By the Hellenic Army Academy (SSE) and ... | {"language": ["el"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "widget": [{"text": "\u03a4\u03bf \u03b1\u03b3\u03b1\u03c0\u03b7\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03c5 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9"}]} | lighteternal/gpt2-finetuned-greek-small | null | [
"transformers",
"pytorch",
"jax",
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"text-generation",
"causal-lm",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #causal-lm #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Greek (el) GPT2 model - small
<img src="URL width="600"/>
#### A new version (recommended) trained on 5x more data is available at: URL
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* language: el
* licence: apache-2.0
* dataset: ~5GB of Greek corpora
* model: GPT2 (12-lay... | [
"# Greek (el) GPT2 model - small\n\n\n<img src=\"URL width=\"600\"/>",
"#### A new version (recommended) trained on 5x more data is available at: URL",
"### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)\n\n* language: el\n* licence: apache-2.0\n* dataset: ~5GB of Greek corpora \... | [
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"# Greek (el) GPT2 model - small\n\n\n<img src=\"URL width=\"600\"/>",
"#### A new version (recommended) trained on 5x mo... |
text-generation | transformers |
# Greek (el) GPT2 model
<img src="https://huggingface.co/lighteternal/gpt2-finetuned-greek-small/raw/main/GPT2el.png" width="600"/>
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* language: el
* licence: apache-2.0
* dataset: ~23.4 GB of Greek corpora
* model: GPT2 (12-layer, 76... | {"language": ["el"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "widget": [{"text": "\u03a4\u03bf \u03b1\u03b3\u03b1\u03c0\u03b7\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03c5 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9"}]} | lighteternal/gpt2-finetuned-greek | null | [
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"pytorch",
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"text-generation",
"causal-lm",
"el",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #causal-lm #el #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Greek (el) GPT2 model
=====================
<img src="URL width="600"/>
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
* language: el
* licence: apache-2.0
* dataset: ~23.4 GB of Greek corpora
* model: GPT2 (12-layer, 768-hidden, 12-heads, 117M parameters. OpenAI GPT-2 English ... | [
"### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)\n\n\n* language: el\n* licence: apache-2.0\n* dataset: ~23.4 GB of Greek corpora\n* model: GPT2 (12-layer, 768-hidden, 12-heads, 117M parameters. OpenAI GPT-2 English model, finetuned for the Greek language)\n* pre-processing: token... | [
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"### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)\n\n\n* language: el\n* licence: apache-2.0\n* data... |
zero-shot-classification | transformers |
# Cross-Encoder for Greek Natural Language Inference (Textual Entailment) & Zero-Shot Classification
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications... | {"language": ["el", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-base"], "datasets": ["multi_nli", "snli", "allnli_greek"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u0397 Facebook \u03ba\u03c5\u03ba\u03bb\u03bf\u03c6\u03cc\u03c1\u03b7\u03c3\u03b5 \u03c4\u03b1 \... | lighteternal/nli-xlm-r-greek | null | [
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"xlm-roberta",
"text-classification",
"xlm-roberta-base",
"zero-shot-classification",
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"dataset:snli",
"dataset:allnli_greek",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"el",
"en"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-base #zero-shot-classification #el #en #dataset-multi_nli #dataset-snli #dataset-allnli_greek #arxiv-1908.10084 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Cross-Encoder for Greek Natural Language Inference (Textual Entailment) & Zero-Shot Classification
==================================================================================================
By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
-----------------------------------------... | [
"#### Usage with sentence\\_transformers\n\n\nPre-trained models can be used like this:",
"#### Usage with Transformers AutoModel\n\n\nYou can use the model also directly with Transformers library (without SentenceTransformers library):\n\n\nTo use the model for Zero-Shot Classification\n-------------------------... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-base #zero-shot-classification #el #en #dataset-multi_nli #dataset-snli #dataset-allnli_greek #arxiv-1908.10084 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### Usage with sentence\\_transformers\n\n\... |
automatic-speech-recognition | transformers |
# Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
### By the Hellenic Army Academy and the Technical University of Crete
* language: el
* licence: apache-2.0
* dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets + CSS10 (EL), 1.22GB: https://github.com/Kyuby... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "hf-asr-leaderboard", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Greek by Lighteternal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Spe... | lighteternal/wav2vec2-large-xlsr-53-greek | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
================================================================================
### By the Hellenic Army Academy and the Technical University of Crete
* language: el
* licence: apache-2.0
* dataset: CommonVoice (EL), 364MB: URL + CSS1... | [
"### By the Hellenic Army Academy and the Technical University of Crete\n\n\n* language: el\n* licence: apache-2.0\n* dataset: CommonVoice (EL), 364MB: URL + CSS10 (EL), 1.22GB: URL\n* model: XLSR-Wav2Vec2, trained for 50 epochs\n* metrics: Word Error Rate (WER)\n\n\nModel description\n-----------------\n\n\nUPDATE... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### By the Hellenic Army Academy and the Technical University of Crete\n\n\n* languag... |
feature-extraction | sentence-transformers | ## Testing Sentence Transformer
This Roberta model is trained from scratch using Masked Language Modelling task on a collection of medical reports | {"tags": ["sentence-transformers"], "pipeline_tag": "feature-extraction"} | ligolab/DxRoberta | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #endpoints_compatible #has_space #region-us
| ## Testing Sentence Transformer
This Roberta model is trained from scratch using Masked Language Modelling task on a collection of medical reports | [
"## Testing Sentence Transformer\nThis Roberta model is trained from scratch using Masked Language Modelling task on a collection of medical reports"
] | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #endpoints_compatible #has_space #region-us \n",
"## Testing Sentence Transformer\nThis Roberta model is trained from scratch using Masked Language Modelling task on a collection of medical reports"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It ac... | {"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]} | lijingxin/dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore inf... | [
"TAGS\n#transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-model\n\nThis model is a fine-tuned version of camembert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | lilitket/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-colab
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7126
* Wer: 0.8198
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-armenian-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-armenian-colab", "results": []}]} | lilitket/wav2vec2-large-xls-r-armenian-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-armenian-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedu... | [
"# wav2vec2-large-xls-r-armenian-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information neede... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-armenian-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
... |
text-generation | transformers |
#C3PO DialoGPT Model | {"tags": ["conversational"]} | limivan/DialoGPT-small-c3po | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#C3PO DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-hateful-memes-expanded
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-hateful-memes-expanded", "results": []}]} | limjiayi/bert-hateful-memes-expanded | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-hateful-memes-expanded
This model is a fine-tuned version of bert-base-uncased on texts from the following datasets:
- Hateful Memes, 'train', 'dev_seen' and 'dev_unseen'
- HarMeme, 'train', 'val' and 'test'
- MultiOFF, 'Training', 'Validation' and 'Testing'
It achieves the following results on the evaluati... | [
"# bert-hateful-memes-expanded\n\nThis model is a fine-tuned version of bert-base-uncased on texts from the following datasets:\n- Hateful Memes, 'train', 'dev_seen' and 'dev_unseen'\n- HarMeme, 'train', 'val' and 'test'\n- MultiOFF, 'Training', 'Validation' and 'Testing'\n\nIt achieves the following results on the... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-hateful-memes-expanded\n\nThis model is a fine-tuned version of bert-base-uncased on texts from the following datasets:\n- Hateful Memes, 'train', 'dev_see... |
sentence-similarity | sentence-transformers |
## Modèle de représentation d'un message Twitch à l'aide de ConvBERT
Modèle [sentence-transformers](https://www.SBERT.net): cela permet de mapper une séquence de texte en un vecteur numérique de dimension 256 et peut être utilisé pour des tâches de clustering ou de recherche sémantique.
L'expérimentation menée au s... | {"language": ["fr"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "twitch", "convbert"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "Bonsoir", "sentences": ["Salut !", "Hello", "Bonsoir!", "Bonsouar!", "Bonsouar !", "De rien"... | lincoln/2021twitchfr-conv-bert-small-mlm-simcse | null | [
"sentence-transformers",
"pytorch",
"convbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"twitch",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#sentence-transformers #pytorch #convbert #feature-extraction #sentence-similarity #transformers #twitch #fr #license-mit #endpoints_compatible #region-us
|
## Modèle de représentation d'un message Twitch à l'aide de ConvBERT
Modèle sentence-transformers: cela permet de mapper une séquence de texte en un vecteur numérique de dimension 256 et peut être utilisé pour des tâches de clustering ou de recherche sémantique.
L'expérimentation menée au sein de Lincoln avait pour... | [
"## Modèle de représentation d'un message Twitch à l'aide de ConvBERT\n\nModèle sentence-transformers: cela permet de mapper une séquence de texte en un vecteur numérique de dimension 256 et peut être utilisé pour des tâches de clustering ou de recherche sémantique.\n\nL'expérimentation menée au sein de Lincoln ava... | [
"TAGS\n#sentence-transformers #pytorch #convbert #feature-extraction #sentence-similarity #transformers #twitch #fr #license-mit #endpoints_compatible #region-us \n",
"## Modèle de représentation d'un message Twitch à l'aide de ConvBERT\n\nModèle sentence-transformers: cela permet de mapper une séquence de texte ... |
fill-mask | transformers |
## Modèle de Masking sur les données Twitch FR
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire inte... | {"language": ["fr"], "license": "mit", "tags": ["fill-mask", "convbert", "twitch"], "pipeline_tag": "fill-mask", "widget": [{"text": "<mask> tt le monde !"}, {"text": "cc<mask> va?"}, {"text": "<mask> la Fronce !"}]} | lincoln/2021twitchfr-conv-bert-small-mlm | null | [
"transformers",
"pytorch",
"tensorboard",
"convbert",
"fill-mask",
"twitch",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tensorboard #convbert #fill-mask #twitch #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Modèle de Masking sur les données Twitch FR
-------------------------------------------
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une pla... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #convbert #fill-mask #twitch #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
## Modèle de langue sur les données Twitch FR
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plateforme internet avec le vocabulaire inter... | {"language": ["fr"], "license": "mit", "tags": ["feature-extraction", "convbert", "twitch"], "pipeline_tag": "feature-extraction", "widget": [{"text": "LUL +1 xD La Fronce !"}]} | lincoln/2021twitchfr-conv-bert-small | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"convbert",
"feature-extraction",
"twitch",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #tensorboard #convbert #feature-extraction #twitch #fr #license-mit #endpoints_compatible #region-us
| Modèle de langue sur les données Twitch FR
------------------------------------------
L'expérimentation menée au sein de Lincoln avait pour principal objectif de mettre en œuvre des techniques NLP from scratch sur un corpus de messages issus d’un chat Twitch. Ces derniers sont exprimés en français, mais sur une plate... | [] | [
"TAGS\n#transformers #pytorch #tf #tensorboard #convbert #feature-extraction #twitch #fr #license-mit #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# Génération de question à partir d'un contexte
Le modèle est _fine tuné_ à partir du modèle [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) afin de générer des questions à partir d'un paragraphe et d'une suite de token. La suite de token représente la réponse sur laquelle la question est basée.
Input:... | {"language": ["fr"], "license": "mit", "tags": ["seq2seq", "barthez"], "datasets": ["squadFR", "fquad", "piaf"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "La science des donn\u00e9es est un domaine interdisciplinaire qui utilise des m\u00e9thodes, des processus, des algo... | lincoln/barthez-squadFR-fquad-piaf-question-generation | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"seq2seq",
"barthez",
"fr",
"dataset:squadFR",
"dataset:fquad",
"dataset:piaf",
"arxiv:2010.12321",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.12321"
] | [
"fr"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #seq2seq #barthez #fr #dataset-squadFR #dataset-fquad #dataset-piaf #arxiv-2010.12321 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Génération de question à partir d'un contexte
Le modèle est _fine tuné_ à partir du modèle moussaKam/barthez afin de générer des questions à partir d'un paragraphe et d'une suite de token. La suite de token représente la réponse sur laquelle la question est basée.
Input: _Les projecteurs peuvent être utilisées pou... | [
"# Génération de question à partir d'un contexte\n\nLe modèle est _fine tuné_ à partir du modèle moussaKam/barthez afin de générer des questions à partir d'un paragraphe et d'une suite de token. La suite de token représente la réponse sur laquelle la question est basée.\n\nInput: _Les projecteurs peuvent être utili... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #seq2seq #barthez #fr #dataset-squadFR #dataset-fquad #dataset-piaf #arxiv-2010.12321 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Génération de question à partir d'un contexte\n\nLe modèle est _fine tuné_ à partir du modèle... |
token-classification | transformers |
# Extraction de réponse
Ce modèle est _fine tuné_ à partir du modèle [camembert-base](https://huggingface.co/camembert-base) pour la tâche de classification de tokens.
L'objectif est d'identifier les suites de tokens probables qui pourrait être l'objet d'une question.
## Données d'apprentissage
La base d'entraine... | {"language": ["fr"], "license": "mit", "tags": ["camembert", "answer extraction"], "datasets": ["squadFR", "fquad", "piaf"]} | lincoln/camembert-squadFR-fquad-piaf-answer-extraction | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"answer extraction",
"fr",
"dataset:squadFR",
"dataset:fquad",
"dataset:piaf",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #token-classification #answer extraction #fr #dataset-squadFR #dataset-fquad #dataset-piaf #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Extraction de réponse
Ce modèle est _fine tuné_ à partir du modèle camembert-base pour la tâche de classification de tokens.
L'objectif est d'identifier les suites de tokens probables qui pourrait être l'objet d'une question.
## Données d'apprentissage
La base d'entrainement est la concatenation des bases Squad... | [
"# Extraction de réponse\n\nCe modèle est _fine tuné_ à partir du modèle camembert-base pour la tâche de classification de tokens. \nL'objectif est d'identifier les suites de tokens probables qui pourrait être l'objet d'une question.",
"## Données d'apprentissage\n\nLa base d'entrainement est la concatenation des... | [
"TAGS\n#transformers #pytorch #camembert #token-classification #answer extraction #fr #dataset-squadFR #dataset-fquad #dataset-piaf #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Extraction de réponse\n\nCe modèle est _fine tuné_ à partir du modèle camembert-base pour la tâche de clas... |
text-classification | transformers |
# Classification d'articles de presses avec Flaubert
Ce modèle se base sur le modèle [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased) et à été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM.
Dans leur papier, les équipes de reciTAL et de la Sorbo... | {"language": ["fr"], "license": "mit", "tags": ["text-classification", "flaubert"], "datasets": ["MLSUM"], "pipeline_tag": "text-classification", "widget": [{"text": "La bourse de paris en forte baisse apr\u00e8s que des canards ont envahit le parlement."}]} | lincoln/flaubert-mlsum-topic-classification | null | [
"transformers",
"pytorch",
"tf",
"flaubert",
"text-classification",
"fr",
"dataset:MLSUM",
"arxiv:2004.14900",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.14900"
] | [
"fr"
] | TAGS
#transformers #pytorch #tf #flaubert #text-classification #fr #dataset-MLSUM #arxiv-2004.14900 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Classification d'articles de presses avec Flaubert
Ce modèle se base sur le modèle 'flaubert/flaubert_base_cased' et à été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM.
Dans leur papier, les équipes de reciTAL et de la Sorbonne ont proposé comme ouverture de réaliser un modèle d... | [
"# Classification d'articles de presses avec Flaubert\n\nCe modèle se base sur le modèle 'flaubert/flaubert_base_cased' et à été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM. \nDans leur papier, les équipes de reciTAL et de la Sorbonne ont proposé comme ouverture de réaliser un m... | [
"TAGS\n#transformers #pytorch #tf #flaubert #text-classification #fr #dataset-MLSUM #arxiv-2004.14900 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Classification d'articles de presses avec Flaubert\n\nCe modèle se base sur le modèle 'flaubert/flaubert_base_cased' et à été... |
summarization | transformers |
# Résumé automatique d'article de presses
Ce modèles est basé sur le modèle [`facebook/mbart-large-50`](https://huggingface.co/facebook/mbart-large-50) et été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM. L'hypothèse à été faite que les chapeaux des articles faisaient de bon résumés... | {"language": ["fr"], "license": "mit", "tags": ["summarization", "mbart", "bart"], "datasets": ["MLSUM"], "pipeline_tag": "summarization", "widget": [{"text": "\u00ab La veille de l\u2019ouverture, je vais faire venir un coach pour les salari\u00e9s qui reprendront le travail. Cela va me co\u00fbter 300 euros, mais apr... | lincoln/mbart-mlsum-automatic-summarization | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"text2text-generation",
"summarization",
"bart",
"fr",
"dataset:MLSUM",
"arxiv:2004.14900",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.14900"
] | [
"fr"
] | TAGS
#transformers #pytorch #tf #mbart #text2text-generation #summarization #bart #fr #dataset-MLSUM #arxiv-2004.14900 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Résumé automatique d'article de presses
Ce modèles est basé sur le modèle 'facebook/mbart-large-50' et été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM. L'hypothèse à été faite que les chapeaux des articles faisaient de bon résumés de référence.
## Entrainement
Nous avons testé... | [
"# Résumé automatique d'article de presses\n\nCe modèles est basé sur le modèle 'facebook/mbart-large-50' et été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM. L'hypothèse à été faite que les chapeaux des articles faisaient de bon résumés de référence.",
"## Entrainement\n\nNous ... | [
"TAGS\n#transformers #pytorch #tf #mbart #text2text-generation #summarization #bart #fr #dataset-MLSUM #arxiv-2004.14900 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Résumé automatique d'article de presses\n\nCe modèles est basé sur le modèle 'facebook/mbart-large-50' et été fine-tu... |
summarization | transformers |
## `bart-large-samsum`
This model was trained using Microsoft's [`Azure Machine Learning Service`](https://azure.microsoft.com/en-us/services/machine-learning). It was fine-tuned on the [`samsum`](https://huggingface.co/datasets/samsum) corpus from [`facebook/bart-large`](https://huggingface.co/facebook/bart-large) ch... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "azureml", "azure", "codecarbon", "bart"], "datasets": ["samsum"], "metrics": ["rouge"], "widget": [{"text": "Henry: Hey, is Nate coming over to watch the movie tonight?\nKevin: Yea, he said he'll be arriving a bit later at around 7 since he gets o... | linydub/bart-large-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"azureml",
"azure",
"codecarbon",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #azureml #azure #codecarbon #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| 'bart-large-samsum'
-------------------
This model was trained using Microsoft's 'Azure Machine Learning Service'. It was fine-tuned on the 'samsum' corpus from 'facebook/bart-large' checkpoint.
Usage (Inference)
-----------------
Fine-tune on AzureML
--------------------
![Deploy to Azure](URL ![Visualize](URL... | [
"### Carbon Emissions\n\n\nThese results were obtained using 'CodeCarbon'. The carbon emissions are estimated from training runtime only (excl. setup and evaluation runtimes).\n\n\n\nHyperparameters\n---------------\n\n\n* max\\_source\\_length: 512\n* max\\_target\\_length: 90\n* fp16: True\n* seed: 1\n* per\\_dev... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #azureml #azure #codecarbon #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Carbon Emissions\n\n\nThese results were obtained using 'CodeCarbon'. T... |
fill-mask | transformers | # CLIN-X-EN: a pre-trained language model for the English clinical domain
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike... | {} | llange/xlm-roberta-large-english-clinical | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2112.08754",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.08754"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2112.08754 #autotrain_compatible #endpoints_compatible #region-us
| CLIN-X-EN: a pre-trained language model for the English clinical domain
=======================================================================
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task trans... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2112.08754 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # CLIN-X-ES: a pre-trained language model for the Spanish clinical domain
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike... | {} | llange/xlm-roberta-large-spanish-clinical | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2112.08754",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.08754"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2112.08754 #autotrain_compatible #endpoints_compatible #region-us
| CLIN-X-ES: a pre-trained language model for the Spanish clinical domain
=======================================================================
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task trans... | [
"### Results for English concept extraction\n\n\nAs the CLIN-X-ES model is based on XLM-R, the model is still multilingual and we demonstrate the positive impact of cross-language domain adaptation by applying this model to five different English sequence labeling tasks from i2b2.\n\n\nWe found that further transfe... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #arxiv-2112.08754 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Results for English concept extraction\n\n\nAs the CLIN-X-ES model is based on XLM-R, the model is still multilingual and we demonstrate the positive impact of cross-language do... |
fill-mask | transformers | # Spanish XLM-R (from NLNDE-MEDDOPROF)
This Spanish language model was created for the MEDDOPROF shared task as part of the **NLNDE** team submission and outperformed all other participants in both sequence labeling tasks.
Details on the model, the pre-training corpus and the downstream task performance are given in ... | {} | llange/xlm-roberta-large-spanish | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Spanish XLM-R (from NLNDE-MEDDOPROF)
====================================
This Spanish language model was created for the MEDDOPROF shared task as part of the NLNDE team submission and outperformed all other participants in both sequence labeling tasks.
Details on the model, the pre-training corpus and the downstre... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## long-covid-classification
We fine-tuned bert-base-cased using a [manually curated dataset](https://huggingface.co/llangnickel/long-covid-classification-data) to train a Sequence Classification model able to distinguish between long COVID and non-long COVID-related documents.
## Used hyper parameters
|Parameter|Va... | {"license": "mit"} | llangnickel/long-covid-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| long-covid-classification
-------------------------
We fine-tuned bert-base-cased using a manually curated dataset to train a Sequence Classification model able to distinguish between long COVID and non-long COVID-related documents.
Used hyper parameters
---------------------
Metrics
-------
Precision [%]: 91.... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Game of thrones DialoGPT | {"tags": ["conversational"]} | cosmicroxks/DialoGPT-small-scott | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Game of thrones DialoGPT | [
"# Game of thrones DialoGPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Game of thrones DialoGPT"
] |
text-generation | transformers |
# harry potter DialogGPT Model | {"tags": ["conversational"]} | logube/DialogGPT_small_harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# harry potter DialogGPT Model | [
"# harry potter DialogGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# harry potter DialogGPT Model"
] |
token-classification | flair |
Published with ❤️ from [londogard](https://londogard.com).
## Swedish NER in Flair (SUC 3.0)
F1-Score: **85.6** (SUC 3.0)
Predicts 8 tags:
|**Tag**|**Meaning**|
|---|---|
| PRS| person name |
| ORG | organisation name|
| TME | time unit |
| WRK | building name |
| LOC | location na... | {"language": "sv", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["SUC 3.0"], "widget": [{"text": "Hampus bor i Sk\u00e5ne och har levererat denna model idag."}]} | londogard/flair-swe-ner | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"sv",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #sv #region-us
| Published with ️ from londogard.
Swedish NER in Flair (SUC 3.0)
------------------------------
F1-Score: 85.6 (SUC 3.0)
Predicts 8 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities... | [
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*Hampus*\" (labeled as a PRS), \"*Skåne*\" (labeled as a LOC), \"*idag*\" (labeled as a TME) are found in the sentence \"*Hampus bor i Skåne och har levererat denna model idag.*\"... | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #sv #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*Hampus*\" (labeled as a PRS), \"*Skåne*\" (labeled as a LOC), \"*idag*\" (labeled as a T... |
text-generation | transformers |
# Joshua DialoGPT Model | {"tags": ["conversational"]} | lonewanderer27/DialoGPT-small-Joshua | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Joshua DialoGPT Model | [
"# Joshua DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Joshua DialoGPT Model"
] |
text-generation | transformers |
# Camp Buddy - Keitaro - DialoGPTSmall Model | {"tags": ["conversational"]} | lonewanderer27/KeitaroBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Camp Buddy - Keitaro - DialoGPTSmall Model | [
"# Camp Buddy - Keitaro - DialoGPTSmall Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Camp Buddy - Keitaro - DialoGPTSmall Model"
] |
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