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automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the sv unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Repr... | {"language": "sv", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-sv-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"sv",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sv"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #sv #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the sv unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaita... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the sv unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #sv #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the sv unlabeled subset of VoxPopuli corpus.\n\nPaper: *... |
null | transformers |
# Wav2Vec2-Base
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-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**: This model does not have a tokenizer as... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Base
Facebook's Wav2Vec2
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 audio alone. In order to use this model speech recognition, a tokenizer sh... | [
"# Wav2Vec2-Base \n\nFacebook's Wav2Vec2\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 pretrained on audio alone. In order to use this model speech recognition, a to... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Base \n\nFacebook's Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the 100k unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Note**: This model does not have a tokenizer as it was pretrained o... | {"language": "multilingual", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-100k-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"multilingual",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the 100k unlabeled subset of VoxPopuli corpus.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on la... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the 100k unlabeled subset of VoxPopuli corpus.\n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tun... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the 100k unlabeled subset of VoxPopuli ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the 10k unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for R... | {"language": "multilingual", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-10k-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"multilingual",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the 10k unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Cha... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the 10k unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, A... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the 10k unlabeled subset of VoxPopuli c... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-960h-Lv60 + Self-Training
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objecti... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-960h-lv60", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dat... | facebook/wav2vec2-large-960h-lv60-self | null | [
"transformers",
"pytorch",
"tf",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [
"2010.11430",
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-960h-Lv60 + Self-Training
========================================
Facebook's Wav2Vec2
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Training objective. When using the model make sure that your speech i... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #wav2vec2 #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.11430 #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-960h-Lv60
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-960h-lv60", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Librispeech (clean)", "type": "librispeech_asr... | facebook/wav2vec2-large-960h-lv60 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-960h-Lv60
========================
Facebook's Wav2Vec2
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Ab... | [] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-large-960h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Large-960h
===================
Facebook's Wav2Vec2
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **baltic** on **27.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled spe... | {"language": "baltic", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-baltic-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"baltic"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in baltic on 27.5 unlabeled datat of the VoxPopuli corpus.
The model is 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 a... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in baltic on 27.5 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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 t... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in baltic on 27.5 unlabeled datat of the VoxPopuli co... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **el** on **17.7** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech ... | {"language": "el", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-el-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"el",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #el #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in el on 17.7 unlabeled datat of the VoxPopuli corpus.
The model is 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... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in el on 17.7 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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 token... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #el #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in el on 17.7 unlabeled datat of the VoxPopuli co... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the es unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Re... | {"language": "es", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-es-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"es",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"es"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #es #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the es unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chai... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the es unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, An... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #es #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the es unlabeled subset of VoxPopuli c... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the fr unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Re... | {"language": "fr", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-fr-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"fr",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"fr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #fr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the fr unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chai... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the fr unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, An... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #fr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the fr unlabeled subset of VoxPopuli corpus.\n\nP... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the it unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Re... | {"language": "it", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-it-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"it",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"it"
] | TAGS
#transformers #pytorch #jax #safetensors #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #it #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the it unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chai... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the it unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, An... | [
"TAGS\n#transformers #pytorch #jax #safetensors #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #it #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the it unlabeled subset of VoxPopuli... |
null | transformers |
# Wav2Vec2-Large-LV60
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-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**: This model does not have a tokeniz... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-large-lv60 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-LV60
Facebook's Wav2Vec2
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 audio alone. In order to use this model speech recognition, a tokeniz... | [
"# Wav2Vec2-Large-LV60 \n\nFacebook's Wav2Vec2\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 pretrained on audio alone. In order to use this model speech recognition,... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-LV60 \n\nFacebook's Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **mt** on **9.1** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "mt", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-mt-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"mt",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"mt"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #mt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in mt on 9.1 unlabeled datat of the VoxPopuli corpus.
The model is 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 ... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in mt on 9.1 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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 tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #mt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in mt on 9.1 unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the nl unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Re... | {"language": "nl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-nl-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"nl",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"nl"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #nl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the nl unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chai... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the nl unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, An... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #nl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the nl unlabeled subset of VoxPopuli corpus.\n\nP... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **north_germanic** on **29.9** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sam... | {"language": "north_germanic", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-north_germanic-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"north_germanic"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in north_germanic on 29.9 unlabeled datat of the VoxPopuli corpus.
The model is 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 tok... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in north_germanic on 29.9 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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 ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in north_germanic on 29.9 unlabeled datat of the VoxP... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-Robust finetuned on Librispeech
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/).
This model is a fine-tuned version of the [wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) model.
It has been pretrained on:
... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["libri_light", "common_voice", "switchboard", "fisher", "librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {... | facebook/wav2vec2-large-robust-ft-libri-960h | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"dataset:librispeech_asr",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compati... | null | 2022-03-02T23:29:05+00:00 | [
"2104.01027"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #dataset-librispeech_asr #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-Robust finetuned on Librispeech
Facebook's Wav2Vec2.
This model is a fine-tuned version of the wav2vec2-large-robust model.
It has been pretrained on:
- Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data
- CommonVoice: crowd-source collected audio data; read-... | [
"# Wav2Vec2-Large-Robust finetuned on Librispeech\n\nFacebook's Wav2Vec2.\n\nThis model is a fine-tuned version of the wav2vec2-large-robust model.\nIt has been pretrained on:\n\n- Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data\n- CommonVoice: crowd-source collected audio... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #dataset-librispeech_asr #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-Ro... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-Robust finetuned on Switchboard
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/).
This model is a fine-tuned version of the [wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) model.
It has been pretrained on:
... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["libri_light", "common_voice", "switchboard", "fisher"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "L... | facebook/wav2vec2-large-robust-ft-swbd-300h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.01027"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-Robust finetuned on Switchboard
Facebook's Wav2Vec2.
This model is a fine-tuned version of the wav2vec2-large-robust model.
It has been pretrained on:
- Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data
- CommonVoice: crowd-source collected audio data; read-... | [
"# Wav2Vec2-Large-Robust finetuned on Switchboard\n\nFacebook's Wav2Vec2.\n\nThis model is a fine-tuned version of the wav2vec2-large-robust model.\nIt has been pretrained on:\n\n- Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data\n- CommonVoice: crowd-source collected audio... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-Robust finetuned on Switchboard\n\nFaceb... |
null | transformers |
# Wav2Vec2-Large-Robust
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained on 16kHz sampled speech audio.
Speech datasets from multiple domains were used to pretrain the model:
- [Libri-Light](https://github.com/facebookresearch... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["libri_light", "common_voice", "switchboard", "fisher"]} | facebook/wav2vec2-large-robust | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.01027"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-Robust
Facebook's Wav2Vec2
The large model pretrained on 16kHz sampled speech audio.
Speech datasets from multiple domains were used to pretrain the model:
- Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data
- CommonVoice: crowd-source collected audio data; ... | [
"# Wav2Vec2-Large-Robust\n\nFacebook's Wav2Vec2\n\nThe large model pretrained on 16kHz sampled speech audio. \nSpeech datasets from multiple domains were used to pretrain the model:\n- Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data\n- CommonVoice: crowd-source collected a... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-libri_light #dataset-common_voice #dataset-switchboard #dataset-fisher #arxiv-2104.01027 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-Robust\n\nFacebook's Wav2Vec2\n\nThe large model pretrained on 1... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **romance** on **101.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled s... | {"language": "romance", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-romance-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"romance"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in romance on 101.5 unlabeled datat of the VoxPopuli corpus.
The model is 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... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in romance on 101.5 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in romance on 101.5 unlabeled datat of the VoxPopuli ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **slavic** on **88.99999999999999** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kH... | {"language": "slavic", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-slavic-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"slavic"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in slavic on 88.99999999999999 unlabeled datat of the VoxPopuli corpus.
The model is 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 ... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in slavic on 88.99999999999999 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in slavic on 88.99999999999999 unlabeled datat of the... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the sv unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Re... | {"language": "sv", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-large-sv-voxpopuli | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"sv",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #sv #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-VoxPopuli
Facebook's Wav2Vec2 large model pretrained on the sv unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chai... | [
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the sv unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, An... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #sv #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-VoxPopuli\n\nFacebook's Wav2Vec2 large model pretrained on the sv unlabeled subset of VoxPopuli corpus.\n\nP... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **uralic** on **42.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled spe... | {"language": "uralic", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-uralic-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"uralic"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in uralic on 42.5 unlabeled datat of the VoxPopuli corpus.
The model is 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 a... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in uralic on 42.5 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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 t... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in uralic on 42.5 unlabeled datat of the VoxPopuli co... |
automatic-speech-recognition | transformers |
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **west_germanic** on **66.3** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz samp... | {"language": "west_germanic", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-large-west_germanic-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"west_germanic"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-large-VoxPopuli-V2
Facebook's Wav2Vec2 large model pretrained only in west_germanic on 66.3 unlabeled datat of the VoxPopuli corpus.
The model is 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 toke... | [
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in west_germanic on 66.3 unlabeled datat of the VoxPopuli corpus.\n\nThe model is 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 h... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-large-VoxPopuli-V2\n\nFacebook's Wav2Vec2 large model pretrained only in west_germanic on 66.3 unlabeled datat of the VoxPo... |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice NL Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-dutch"
device = "cuda"
chars_to_ignore_regex = '[\,\... | {"language": "nl", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-dutch | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"nl",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
## Evaluation on Common Voice NL Test
Result: 21.1 % | [
"## Evaluation on Common Voice NL Test\n\n\n\nResult: 21.1 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Evaluation on Common Voice NL Test\n\n\n\nResult: 21.1 %"
] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice FR Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-french"
device = "cuda"
chars_to_ignore_regex = '[\... | {"language": "fr", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-french | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"fr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #fr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## Evaluation on Common Voice FR Test
Result: 25.2 % | [
"## Evaluation on Common Voice FR Test\n\n\nResult: 25.2 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #fr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## Evaluation on Common Voice FR Test\n\n\nResult: 25.2 %"
] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice DE Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-german"
device = "cuda"
chars_to_ignore_regex = '[\,... | {"language": "de", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-german | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"de",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #de #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## Evaluation on Common Voice DE Test
Result: 18.5 % | [
"## Evaluation on Common Voice DE Test\n\nResult: 18.5 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #de #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## Evaluation on Common Voice DE Test\n\nResult: 18.5 %"
] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice IT Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-italian"
device = "cuda"
chars_to_ignore_regex = '[\... | {"language": "it", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-italian | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"it",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #it #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## Evaluation on Common Voice IT Test
Result: 22.1 % | [
"## Evaluation on Common Voice IT Test\n\nResult: 22.1 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #it #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## Evaluation on Common Voice IT Test\n\nResult: 22.1 %"
] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice PL Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-polish"
device = "cuda"
chars_to_ignore_regex = '[\,... | {"language": "nl", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-polish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"nl",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
## Evaluation on Common Voice PL Test
Result: 24.6 % | [
"## Evaluation on Common Voice PL Test\n\n\n\nResult: 24.6 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Evaluation on Common Voice PL Test\n\n\n\nResult: 24.6 %"
] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice PT Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-portuguese"
device = "cuda"
chars_to_ignore_regex = ... | {"language": "pt", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-portuguese | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"pt",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #pt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
## Evaluation on Common Voice PT Test
Result: 27.1 % | [
"## Evaluation on Common Voice PT Test\n\nResult: 27.1 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #pt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Evaluation on Common Voice PT Test\n\nResult: 27.1 %"
] |
automatic-speech-recognition | transformers |
## Evaluation on Common Voice ES Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
model_name = "facebook/wav2vec2-large-xlsr-53-spanish"
device = "cuda"
chars_to_ignore_regex = '[\... | {"language": "es", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53-spanish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
## Evaluation on Common Voice ES Test
Result: 17.6 % | [
"## Evaluation on Common Voice ES Test\n\nResult: 17.6 %"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #speech #audio #es #dataset-common_voice #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## Evaluation on Common Voice ES Test\n\nResult: 17.6 %"
] |
null | transformers |
# Wav2Vec2-XLSR-53
[Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-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 o... | {"language": "multilingual", "license": "apache-2.0", "tags": ["speech"], "datasets": ["common_voice"]} | facebook/wav2vec2-large-xlsr-53 | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"speech",
"multilingual",
"dataset:common_voice",
"arxiv:2006.13979",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.13979"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #pretraining #speech #multilingual #dataset-common_voice #arxiv-2006.13979 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-XLSR-53
Facebook's XLSR-Wav2Vec2
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, like Automatic Speech Recognition. Check out this blog for more informa... | [
"# Wav2Vec2-XLSR-53 \n\nFacebook's XLSR-Wav2Vec2\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 task, like Automatic Speech Recognition. Check out this blog for more... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #pretraining #speech #multilingual #dataset-common_voice #arxiv-2006.13979 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-XLSR-53 \n\nFacebook's XLSR-Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the m... |
null | transformers |
# Wav2Vec2-Large
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-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 dow... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-large | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large
Facebook's Wav2Vec2
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, like Automatic Speech Recognition. Check out this blog for more information.
... | [
"# Wav2Vec2-Large \n\nFacebook's Wav2Vec2\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 task, like Automatic Speech Recognition. Check out this blog for more inform... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large \n\nFacebook's Wav2Vec2\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-LV60 finetuned on multi-lingual Common Voice
This checkpoint leverages the pretrained checkpoint [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)
and is fine-tuned on [CommonVoice](https://huggingface.co/datasets/common_voice) to recognize phonetic labels in multiple langua... | {"language": "multilingual", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "phoneme-recognition"], "datasets": ["common_voice"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispe... | facebook/wav2vec2-lv-60-espeak-cv-ft | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"phoneme-recognition",
"multilingual",
"dataset:common_voice",
"arxiv:2109.11680",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.11680"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #phoneme-recognition #multilingual #dataset-common_voice #arxiv-2109.11680 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-LV60 finetuned on multi-lingual Common Voice
This checkpoint leverages the pretrained checkpoint wav2vec2-large-lv60
and is fine-tuned on CommonVoice to recognize phonetic labels in multiple languages.
When using the model make sure that your speech input is sampled at 16kHz.
Note that the model o... | [
"# Wav2Vec2-Large-LV60 finetuned on multi-lingual Common Voice\n\nThis checkpoint leverages the pretrained checkpoint wav2vec2-large-lv60 \nand is fine-tuned on CommonVoice to recognize phonetic labels in multiple languages.\n\nWhen using the model make sure that your speech input is sampled at 16kHz. \nNote that t... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #phoneme-recognition #multilingual #dataset-common_voice #arxiv-2109.11680 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-LV60 finetuned on multi-lingual Common Voice\n\nThis checkpoint lev... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-2b-21-EN
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model.
The ... | {"language": ["multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en"], "license": "apache-2.0", "tags": ["speech", "xls_r", "automatic-speech-recognition", "xls_r_translation"], "datasets": ["common_voice", "multilingual_librisp... | facebook/wav2vec2-xls-r-1b-21-to-en | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"speech",
"xls_r",
"xls_r_translation",
"multilingual",
"fr",
"de",
"es",
"ca",
"it",
"ru",
"zh",
"pt",
"fa",
"et",
"mn",
"nl",
"tr",
"ar",
"sv",
"lv",
"sl",
"ta",
"ja",
"id",
... | null | 2022-03-02T23:29:05+00:00 | [
"2111.09296"
] | [
"multilingual",
"fr",
"de",
"es",
"ca",
"it",
"ru",
"zh",
"pt",
"fa",
"et",
"mn",
"nl",
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] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-ap... |
# Wav2Vec2-XLS-R-2b-21-EN
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-1b' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently, the encoder-decod... | [
"# Wav2Vec2-XLS-R-2b-21-EN\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-1b' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\nConsequently, the ... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #lice... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-1B-EN-15
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model.
The ... | {"language": ["multilingual", "en", "de", "tr", "fa", "sv", "mn", "zh", "cy", "ca", "sl", "et", "id", "ar", "ta", "lv", "ja"], "license": "apache-2.0", "tags": ["speech", "xls_r", "automatic-speech-recognition", "xls_r_translation"], "datasets": ["common_voice", "multilingual_librispeech", "covost2"], "pipeline_tag": "... | facebook/wav2vec2-xls-r-1b-en-to-15 | null | [
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] | TAGS
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# Wav2Vec2-XLS-R-1B-EN-15
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-1b' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently, the encoder-decod... | [
"# Wav2Vec2-XLS-R-1B-EN-15\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-1b' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\nConsequently, the ... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #en #de #tr #fa #sv #mn #zh #cy #ca #sl #et #id #ar #ta #lv #ja #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-apache-2.0 #endpoint... |
null | transformers |
# Wav2Vec2-XLS-R-1B
[Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) counting **1 billion** parameters.
XLS-R is Facebook AI's large-scale multilin... | {"language": ["multilingual", "ab", "af", "sq", "am", "ar", "hy", "as", "az", "ba", "eu", "be", "bn", "bs", "br", "bg", "my", "yue", "ca", "ceb", "km", "zh", "cv", "hr", "cs", "da", "dv", "nl", "en", "eo", "et", "fo", "fi", "fr", "gl", "lg", "ka", "de", "el", "gn", "gu", "ht", "cnh", "ha", "haw", "he", "hi", "hu", "is"... | facebook/wav2vec2-xls-r-1b | null | [
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... | TAGS
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# Wav2Vec2-XLS-R-1B
Facebook's Wav2Vec2 XLS-R counting 1 billion parameters.
!model image
XLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the "XLM-R for Speech"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses ... | [
"# Wav2Vec2-XLS-R-1B\n\nFacebook's Wav2Vec2 XLS-R counting 1 billion parameters.\n\n!model image\n\nXLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the \"XLM-R for Speech\"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #xls_r #xls_r_pretrained #multilingual #ab #af #sq #am #ar #hy #as #az #ba #eu #be #bn #bs #br #bg #my #yue #ca #ceb #km #zh #cv #hr #cs #da #dv #nl #en #eo #et #fo #fi #fr #gl #lg #ka #de #el #gn #gu #ht #cnh #ha #haw #he #hi #hu #is #id #ia #ga #it #ja ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-2b-21-EN
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model.
The ... | {"language": ["multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en"], "license": "apache-2.0", "tags": ["speech", "xls_r", "automatic-speech-recognition", "xls_r_translation"], "datasets": ["common_voice", "multilingual_librisp... | facebook/wav2vec2-xls-r-2b-21-to-en | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-ap... |
# Wav2Vec2-XLS-R-2b-21-EN
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-2b' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently, the encoder-decod... | [
"# Wav2Vec2-XLS-R-2b-21-EN\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-2b' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\nConsequently, the ... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #lice... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-2B-22-16 (XLS-R-Any-to-Any)
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder... | {"language": ["multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en"], "license": "apache-2.0", "tags": ["speech", "xls_r", "automatic-speech-recognition", "xls_r_translation"], "datasets": ["common_voice", "multilingual_librisp... | facebook/wav2vec2-xls-r-2b-22-to-16 | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #license-apache-2.0 #endpoint... |
# Wav2Vec2-XLS-R-2B-22-16 (XLS-R-Any-to-Any)
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-2b' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently... | [
"# Wav2Vec2-XLS-R-2B-22-16 (XLS-R-Any-to-Any)\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-2b' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #license-apache-2.0 #en... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-2B-EN-15
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model.
The ... | {"language": ["multilingual", "en", "de", "tr", "fa", "sv", "mn", "zh", "cy", "ca", "sl", "et", "id", "ar", "ta", "lv", "ja"], "license": "apache-2.0", "tags": ["speech", "xls_r", "automatic-speech-recognition", "xls_r_translation"], "datasets": ["common_voice", "multilingual_librispeech", "covost2"], "pipeline_tag": "... | facebook/wav2vec2-xls-r-2b-en-to-15 | null | [
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"dataset:common_voice",
"data... | null | 2022-03-02T23:29:05+00:00 | [
"2111.09296"
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"lv",
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] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #en #de #tr #fa #sv #mn #zh #cy #ca #sl #et #id #ar #ta #lv #ja #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-apache-2.0 #endpoints_comp... |
# Wav2Vec2-XLS-R-2B-EN-15
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-2b' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently, the encoder-decod... | [
"# Wav2Vec2-XLS-R-2B-EN-15\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-2b' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\nConsequently, the ... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #en #de #tr #fa #sv #mn #zh #cy #ca #sl #et #id #ar #ta #lv #ja #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-apache-2.0 #endpoint... |
null | transformers |
# Wav2Vec2-XLS-R-2B
[Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) counting **2 billion** parameters.
XLS-R is Facebook AI's large-scale multilin... | {"language": ["multilingual", "ab", "af", "sq", "am", "ar", "hy", "as", "az", "ba", "eu", "be", "bn", "bs", "br", "bg", "my", "yue", "ca", "ceb", "km", "zh", "cv", "hr", "cs", "da", "dv", "nl", "en", "eo", "et", "fo", "fi", "fr", "gl", "lg", "ka", "de", "el", "gn", "gu", "ht", "cnh", "ha", "haw", "he", "hi", "hu", "is"... | facebook/wav2vec2-xls-r-2b | null | [
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"fr",
"gl",
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"ka",
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... | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #xls_r #xls_r_pretrained #multilingual #ab #af #sq #am #ar #hy #as #az #ba #eu #be #bn #bs #br #bg #my #yue #ca #ceb #km #zh #cv #hr #cs #da #dv #nl #en #eo #et #fo #fi #fr #gl #lg #ka #de #el #gn #gu #ht #cnh #ha #haw #he #hi #hu #is #id #ia #ga #it #ja #jv #k... |
# Wav2Vec2-XLS-R-2B
Facebook's Wav2Vec2 XLS-R counting 2 billion parameters.
!model image
XLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the "XLM-R for Speech"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses ... | [
"# Wav2Vec2-XLS-R-2B\n\nFacebook's Wav2Vec2 XLS-R counting 2 billion parameters.\n\n!model image\n\nXLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the \"XLM-R for Speech\"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #speech #xls_r #xls_r_pretrained #multilingual #ab #af #sq #am #ar #hy #as #az #ba #eu #be #bn #bs #br #bg #my #yue #ca #ceb #km #zh #cv #hr #cs #da #dv #nl #en #eo #et #fo #fi #fr #gl #lg #ka #de #el #gn #gu #ht #cnh #ha #haw #he #hi #hu #is #id #ia #ga #it #ja ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-300M-21-EN
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model.
Th... | {"language": ["multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en"], "license": "apache-2.0", "tags": ["speech", "xls_r", "automatic-speech-recognition", "xls_r_translation"], "datasets": ["common_voice", "multilingual_librisp... | facebook/wav2vec2-xls-r-300m-21-to-en | null | [
"transformers",
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"speech-encoder-decoder",
"automatic-speech-recognition",
"speech",
"xls_r",
"xls_r_translation",
"multilingual",
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"es",
"ca",
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"ru",
"zh",
"pt",
"fa",
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"nl",
"tr",
"ar",
"sv",
"lv",
"sl",
"ta",
"ja",
"id",
... | null | 2022-03-02T23:29:05+00:00 | [
"2111.09296"
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"it",
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"zh",
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"mn",
"nl",
"tr",
"ar",
"sv",
"lv",
"sl",
"ta",
"ja",
"id",
"cy",
"en"
] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-ap... |
# Wav2Vec2-XLS-R-300M-21-EN
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-300m' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently, the encoder-d... | [
"# Wav2Vec2-XLS-R-300M-21-EN\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-300m' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\nConsequently, ... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #fr #de #es #ca #it #ru #zh #pt #fa #et #mn #nl #tr #ar #sv #lv #sl #ta #ja #id #cy #en #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #lice... |
automatic-speech-recognition | transformers |
# Wav2Vec2-XLS-R-300M-EN-15
Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model.
Th... | {"language": ["multilingual", "en", "de", "tr", "fa", "sv", "mn", "zh", "cy", "ca", "sl", "et", "id", "ar", "ta", "lv", "ja"], "license": "apache-2.0", "tags": ["speech", "xls_r", "xls_r_translation", "automatic-speech-recognition"], "datasets": ["common_voice", "multilingual_librispeech", "covost2"], "pipeline_tag": "... | facebook/wav2vec2-xls-r-300m-en-to-15 | null | [
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"data... | null | 2022-03-02T23:29:05+00:00 | [
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] | TAGS
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# Wav2Vec2-XLS-R-300M-EN-15
Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.
!model image
This is a SpeechEncoderDecoderModel model.
The encoder was warm-started from the 'facebook/wav2vec2-xls-r-300m' checkpoint and
the decoder from the 'facebook/mbart-large-50' checkpoint.
Consequently, the encoder-d... | [
"# Wav2Vec2-XLS-R-300M-EN-15\n\nFacebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation.\n\n!model image\n\nThis is a SpeechEncoderDecoderModel model. \nThe encoder was warm-started from the 'facebook/wav2vec2-xls-r-300m' checkpoint and\nthe decoder from the 'facebook/mbart-large-50' checkpoint.\nConsequently, ... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #speech #xls_r #xls_r_translation #multilingual #en #de #tr #fa #sv #mn #zh #cy #ca #sl #et #id #ar #ta #lv #ja #dataset-common_voice #dataset-multilingual_librispeech #dataset-covost2 #arxiv-2111.09296 #license-apache-2.0 #endpoint... |
null | transformers |
# Wav2Vec2-XLS-R-300M
[Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) counting **300 million** parameters.
XLS-R is Facebook AI's large-scale mu... | {"language": ["multilingual", "ab", "af", "sq", "am", "ar", "hy", "as", "az", "ba", "eu", "be", "bn", "bs", "br", "bg", "my", "yue", "ca", "ceb", "km", "zh", "cv", "hr", "cs", "da", "dv", "nl", "en", "eo", "et", "fo", "fi", "fr", "gl", "lg", "ka", "de", "el", "gn", "gu", "ht", "cnh", "ha", "haw", "he", "hi", "hu", "is"... | facebook/wav2vec2-xls-r-300m | null | [
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... | TAGS
#transformers #pytorch #wav2vec2 #pretraining #speech #xls_r #xls_r_pretrained #multilingual #ab #af #sq #am #ar #hy #as #az #ba #eu #be #bn #bs #br #bg #my #yue #ca #ceb #km #zh #cv #hr #cs #da #dv #nl #en #eo #et #fo #fi #fr #gl #lg #ka #de #el #gn #gu #ht #cnh #ha #haw #he #hi #hu #is #id #ia #ga #it #ja #jv #k... |
# Wav2Vec2-XLS-R-300M
Facebook's Wav2Vec2 XLS-R counting 300 million parameters.
!model image
XLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the "XLM-R for Speech"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It u... | [
"# Wav2Vec2-XLS-R-300M\n\nFacebook's Wav2Vec2 XLS-R counting 300 million parameters.\n\n!model image\n\nXLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the \"XLM-R for Speech\"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLin... | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53 finetuned on multi-lingual Common Voice
This checkpoint leverages the pretrained checkpoint [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
and is fine-tuned on [CommonVoice](https://huggingface.co/datasets/common_voice) to recognize phonetic labels in multip... | {"language": "multi-lingual", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "phoneme-recognition"], "datasets": ["common_voice"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librisp... | facebook/wav2vec2-xlsr-53-espeak-cv-ft | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
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"multi-lingual"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53 finetuned on multi-lingual Common Voice
This checkpoint leverages the pretrained checkpoint wav2vec2-large-xlsr-53
and is fine-tuned on CommonVoice to recognize phonetic labels in multiple languages.
When using the model make sure that your speech input is sampled at 16kHz.
Note that the m... | [
"# Wav2Vec2-Large-XLSR-53 finetuned on multi-lingual Common Voice\n\nThis checkpoint leverages the pretrained checkpoint wav2vec2-large-xlsr-53 \nand is fine-tuned on CommonVoice to recognize phonetic labels in multiple languages.\n\nWhen using the model make sure that your speech input is sampled at 16kHz. \nNote ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #speech #audio #phoneme-recognition #dataset-common_voice #arxiv-2109.11680 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53 finetuned on multi-lingual Common Voice\n\nThis checkpoint leverages the ... |
translation | transformers |
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for de-en.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT sta... | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["translation", "wmt19", "facebook"], "datasets": ["wmt19"], "metrics": ["bleu"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/wmt19-de-en | null | [
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq wmt19 transformer for de-en.
For more details, please see, Facebook FAIR's WMT19 News Translation Task Submission.
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* wmt19-en-ru
* wmt19... | [
"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, content gets truncated\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by fairseq. For more details, ... | [
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"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this por... |
translation | transformers |
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for en-de.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT sta... | {"language": ["en", "de"], "license": "apache-2.0", "tags": ["translation", "wmt19", "facebook"], "datasets": ["wmt19"], "metrics": ["bleu"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/wmt19-en-de | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq wmt19 transformer for en-de.
For more details, please see, Facebook FAIR's WMT19 News Translation Task Submission.
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* wmt19-en-ru
* wmt19... | [
"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, content gets truncated\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by fairseq. For more details, ... | [
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"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) do... |
translation | transformers |
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for en-ru.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT sta... | {"language": ["en", "ru"], "license": "apache-2.0", "tags": ["translation", "wmt19", "facebook"], "datasets": ["wmt19"], "metrics": ["bleu"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/wmt19-en-ru | null | [
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"arxiv:1907.06616",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en",
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] | TAGS
#transformers #pytorch #fsmt #text2text-generation #translation #wmt19 #facebook #en #ru #dataset-wmt19 #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| FSMT
====
Model description
-----------------
This is a ported version of fairseq wmt19 transformer for en-ru.
For more details, please see, Facebook FAIR's WMT19 News Translation Task Submission.
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* wmt19-en-ru
* wmt19... | [
"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, content gets truncated\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by fairseq. For more details, ... | [
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"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) do... |
translation | transformers |
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for ru-en.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT sta... | {"language": ["ru", "en"], "license": "apache-2.0", "tags": ["translation", "wmt19", "facebook"], "datasets": ["wmt19"], "metrics": ["bleu"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/wmt19-ru-en | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
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"ru",
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| FSMT
====
Model description
-----------------
This is a ported version of fairseq wmt19 transformer for ru-en.
For more details, please see, Facebook FAIR's WMT19 News Translation Task Submission.
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* wmt19-en-ru
* wmt19... | [
"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, content gets truncated\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by fairseq. For more details, ... | [
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"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this por... |
translation | transformers |
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository.
The model can ... | {"language": ["multilingual", "ha", "is", "ja", "cs", "ru", "zh", "de", "en"], "license": "mit", "tags": ["translation", "wmt21"]} | facebook/wmt21-dense-24-wide-en-x | null | [
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"zh",
"de",
"en",
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"license:mit",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.03265"
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"is",
"ja",
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|
# WMT 21 En-X
WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this paper and first released in this repository.
The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), ... | [
"# WMT 21 En-X\nWMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanes... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #translation #wmt21 #multilingual #ha #is #ja #cs #ru #zh #de #en #arxiv-2108.03265 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# WMT 21 En-X\nWMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model... |
translation | transformers | # WMT 21 X-En
WMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository.
The model can d... | {"language": ["multilingual", "ha", "is", "ja", "cs", "ru", "zh", "de", "en"], "license": "mit", "tags": ["translation", "wmt21"]} | facebook/wmt21-dense-24-wide-x-en | null | [
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"zh",
"de",
"en",
"arxiv:2108.03265",
"license:mit",
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"endpoints_compatible",
"region:us"
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"2108.03265"
] | [
"multilingual",
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"is",
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"zh",
"de",
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#transformers #pytorch #m2m_100 #text2text-generation #translation #wmt21 #multilingual #ha #is #ja #cs #ru #zh #de #en #arxiv-2108.03265 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # WMT 21 X-En
WMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.
It was introduced in this paper and first released in this repository.
The model can directly translate text from 7 languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Rus... | [
"# WMT 21 X-En\nWMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation.\nIt was introduced in this paper and first released in this repository.\n\nThe model can directly translate text from 7 languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech ... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #translation #wmt21 #multilingual #ha #is #ja #cs #ru #zh #de #en #arxiv-2108.03265 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# WMT 21 X-En\nWMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained fo... |
text-generation | transformers |
# XGLM-1.7B
XGLM-1.7B is a multilingual autoregressive language model (with 1.7 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi V... | {"language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "license": "mit", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "inference": false} | facebook/xglm-1.7B | null | [
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] | TAGS
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| XGLM-1.7B
=========
XGLM-1.7B is a multilingual autoregressive language model (with 1.7 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylo... | [] | [
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] |
text-generation | transformers |
# XGLM-2.9B
XGLM-2.9B is a multilingual autoregressive language model (with 2.9 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi V... | {"language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "license": "mit", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "inference": false} | facebook/xglm-2.9B | null | [
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] | TAGS
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| XGLM-2.9B
=========
XGLM-2.9B is a multilingual autoregressive language model (with 2.9 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylo... | [] | [
"TAGS\n#transformers #pytorch #xglm #text-generation #multilingual #en #ru #zh #de #es #fr #ja #it #pt #el #ko #fi #id #tr #ar #vi #th #bg #ca #hi #et #bn #ta #ur #sw #te #eu #my #ht #qu #arxiv-2112.10668 #license-mit #autotrain_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# XGLM-4.5B
XGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\*, Todor Mihaylo... | {"language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "license": "mit", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "inference": false} | facebook/xglm-4.5B | null | [
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|
# XGLM-4.5B
XGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuoh... | [
"# XGLM-4.5B\n\nXGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang... | [
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"# XGLM-4.5B\n\nXGLM-4.5B is a mul... |
text-generation | transformers |
# XGLM-564M
XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by X... | {"language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "license": "mit", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "inference": false} | facebook/xglm-564M | null | [
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] | TAGS
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| XGLM-564M
=========
XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Miha... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #xglm #text-generation #multilingual #en #ru #zh #de #es #fr #ja #it #pt #el #ko #fi #id #tr #ar #vi #th #bg #ca #hi #et #bn #ta #ur #sw #te #eu #my #ht #qu #arxiv-2112.10668 #license-mit #autotrain_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# XGLM-7.5B
XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi V... | {"language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "license": "mit", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "inference": false} | facebook/xglm-7.5B | null | [
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] | TAGS
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| XGLM-7.5B
=========
XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylo... | [] | [
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] |
fill-mask | transformers |
# XLM-RoBERTa-XL (xlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anan... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | facebook/xlm-roberta-xl | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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"id",
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"i... | TAGS
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# XLM-RoBERTa-XL (xlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau and first r... | [
"# XLM-RoBERTa-XL (xlarge-sized model) \n\nXLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau and f... | [
"TAGS\n#transformers #pytorch #xlm-roberta-xl #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #... |
fill-mask | transformers |
# XLM-RoBERTa-XL (xxlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Ana... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | facebook/xlm-roberta-xxl | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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"id",
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"i... | TAGS
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# XLM-RoBERTa-XL (xxlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau and first ... | [
"# XLM-RoBERTa-XL (xxlarge-sized model) \n\nXLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau and ... | [
"TAGS\n#transformers #pytorch #xlm-roberta-xl #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #... |
audio-to-audio | fairseq | # xm_transformer_600m-en_ar-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Arabic
- Trained on MuST-C, CoVoS... | {"language": "en-ar", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c", "covost2"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_doma... | facebook/xm_transformer_600m-en_ar-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:covost2",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-ar"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-covost2 #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_ar-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-Arabic
- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-ar-cv7
## Usage
| [
"# xm_transformer_600m-en_ar-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Arabic\n- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-ar-cv7",
"## Usage"
] | [
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audio-to-audio | fairseq | # xm_transformer_600m-en_es-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Spanish
- Trained on MuST-C, Euro... | {"language": "en-es", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c", "europarl_st", "voxpopuli"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m... | facebook/xm_transformer_600m-en_es-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-es"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-europarl_st #dataset-voxpopuli #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_es-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-Spanish
- Trained on MuST-C, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-es-css10
## Usage
| [
"# xm_transformer_600m-en_es-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Spanish\n- Trained on MuST-C, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-es-css10",
"## Usa... | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-europarl_st #dataset-voxpopuli #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-en_es-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Spanish... |
audio-to-audio | fairseq | # xm_transformer_600m-en_fr-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-French
- Trained on MuST-C, EuroP... | {"language": "en-fr", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c", "europarl_st", "voxpopuli", "libritrans"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_tr... | facebook/xm_transformer_600m-en_fr-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:europarl_st",
"dataset:voxpopuli",
"dataset:libritrans",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-fr"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-europarl_st #dataset-voxpopuli #dataset-libritrans #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_fr-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-French
- Trained on MuST-C, EuroParl-ST, VoxPopuli, LibriTrans, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-fr-cv7_css10
## Us... | [
"# xm_transformer_600m-en_fr-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-French\n- Trained on MuST-C, EuroParl-ST, VoxPopuli, LibriTrans, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-fr-cv7_cs... | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-europarl_st #dataset-voxpopuli #dataset-libritrans #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-en_fr-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code)... |
audio-to-audio | fairseq | # xm_transformer_600m-en_ru-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Russian
- Trained on MuST-C, Mult... | {"language": "en-ru", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/... | facebook/xm_transformer_600m-en_ru-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-ru"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_ru-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-Russian
- Trained on MuST-C, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-ru-cv7_css10
## Usage
| [
"# xm_transformer_600m-en_ru-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Russian\n- Trained on MuST-C, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-ru-cv7_css10",
"## Usage"
] | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-en_ru-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Russian\n- Trained on MuST-C, Multilingual Libr... |
audio-to-audio | fairseq | # xm_transformer_600m-en_tr-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Turkish
- Trained on MuST-C, CoVo... | {"language": "en-tr", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c", "covost2"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_doma... | facebook/xm_transformer_600m-en_tr-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:covost2",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-tr"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-covost2 #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_tr-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-Turkish
- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-tr-cv7
## Usage
| [
"# xm_transformer_600m-en_tr-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Turkish\n- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-tr-cv7",
"## Usage"
] | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-covost2 #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-en_tr-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Turkish\n- Trained on MuST-C, ... |
audio-to-audio | fairseq | # xm_transformer_600m-en_vi-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Vietnamese
- Trained on MuST-C, M... | {"language": "en-vi", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/... | facebook/xm_transformer_600m-en_vi-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-vi"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_vi-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-Vietnamese
- Trained on MuST-C, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-vi-cv7
## Usage
| [
"# xm_transformer_600m-en_vi-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Vietnamese\n- Trained on MuST-C, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-vi-cv7",
"## Usage"
] | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-en_vi-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Vietnamese\n- Trained on MuST-C, Multilingual L... |
audio-to-audio | fairseq | # xm_transformer_600m-en_zh-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Chinese
- Trained on MuST-C, CoVo... | {"language": "en-zh", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["must_c", "covost2"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_doma... | facebook/xm_transformer_600m-en_zh-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:covost2",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"en-zh"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-covost2 #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-en_zh-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- English-Chinese
- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/tts_transformer-zh-cv7_css10
## Usage
| [
"# xm_transformer_600m-en_zh-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Chinese\n- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/tts_transformer-zh-cv7_css10",
"## Usage"
] | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-must_c #dataset-covost2 #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-en_zh-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- English-Chinese\n- Trained on MuST-C, ... |
audio-to-audio | fairseq | # xm_transformer_600m-es_en-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- Spanish-English
- Trained on mTEDx, CoVoS... | {"language": "es-en", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["mtedx", "covost2", "europarl_st", "voxpopuli"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transf... | facebook/xm_transformer_600m-es_en-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:mtedx",
"dataset:covost2",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"es-en"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #dataset-europarl_st #dataset-voxpopuli #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-es_en-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- Spanish-English
- Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/fastspeech2-en-ljspeech
## Usage
| [
"# xm_transformer_600m-es_en-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- Spanish-English\n- Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/fastspeech2-en-ljspeech",
... | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #dataset-europarl_st #dataset-voxpopuli #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-es_en-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n-... |
audio-to-audio | fairseq | # xm_transformer_600m-fr_en-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- French-English
- Trained on mTEDx, CoVoST... | {"language": "fr-en", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["mtedx", "covost2", "europarl_st", "voxpopuli"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transf... | facebook/xm_transformer_600m-fr_en-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:mtedx",
"dataset:covost2",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"fr-en"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #dataset-europarl_st #dataset-voxpopuli #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-fr_en-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- French-English
- Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/fastspeech2-en-ljspeech
## Usage
| [
"# xm_transformer_600m-fr_en-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- French-English\n- Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/fastspeech2-en-ljspeech",
... | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #dataset-europarl_st #dataset-voxpopuli #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-fr_en-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n-... |
audio-to-audio | fairseq | # xm_transformer_600m-ru_en-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- Russian-English
- Trained on mTEDx, CoVoS... | {"language": "ru-en", "library_name": "fairseq", "tags": ["fairseq", "audio", "audio-to-audio", "speech-to-speech-translation"], "datasets": ["mtedx", "covost2"], "task": "audio-to-audio", "widget": [{"example_title": "Common Voice sample 1", "src": "https://huggingface.co/facebook/xm_transformer_600m-ru_en-multi_domai... | facebook/xm_transformer_600m-ru_en-multi_domain | null | [
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:mtedx",
"dataset:covost2",
"arxiv:2010.05171",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171"
] | [
"ru-en"
] | TAGS
#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #arxiv-2010.05171 #has_space #region-us
| # xm_transformer_600m-ru_en-multi_domain
W2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):
- Russian-English
- Trained on mTEDx, CoVoST 2, OpenSTT, Common Voice v7 and CCMatrix
- Speech synthesis with facebook/fastspeech2-en-ljspeech
## Usage
| [
"# xm_transformer_600m-ru_en-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- Russian-English\n- Trained on mTEDx, CoVoST 2, OpenSTT, Common Voice v7 and CCMatrix\n- Speech synthesis with facebook/fastspeech2-en-ljspeech",
"## Usage"
] | [
"TAGS\n#fairseq #audio #audio-to-audio #speech-to-speech-translation #dataset-mtedx #dataset-covost2 #arxiv-2010.05171 #has_space #region-us \n",
"# xm_transformer_600m-ru_en-multi_domain\n\nW2V2-Transformer speech-to-text translation model from fairseq S2T (paper/code):\n- Russian-English\n- Trained on mTEDx, Co... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola-3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola-3", "results": []}]} | fadhilarkan/distilbert-base-uncased-finetuned-cola-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola-3
========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0002
* Matthews Correlation: 1.0
Label 0 : "AIMX"
Label 1 : "OWNX"
Label 2 : "CONT... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola-4", "results": []}]} | fadhilarkan/distilbert-base-uncased-finetuned-cola-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola-4
========================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0011
* Matthews Correlation: 1.0
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": []}]} | fadhilarkan/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0008
* Matthews Correlation: 1.0
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]} | fadhilarkan/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1523
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gq-indo-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- L... | {"metrics": ["rouge"]} | fadhilarkan/gq-indo-k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gq-indo-k
=========
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7905
* Rouge1: 22.5734
* Rouge2: 6.555
* Rougel: 20.9491
* Rougelsum: 20.9509
* Gen Len: 12.0767
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #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: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_siz... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-indo-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- L... | {} | fadhilarkan/qa-indo-k | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us
| qa-indo-k
=========
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4984
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and eva... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-indo-math-k-v2
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation ... | {} | fadhilarkan/qa-indo-math-k-v2 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| qa-indo-math-k-v2
=================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9328
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #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: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_siz... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-indo-math-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set... | {} | fadhilarkan/qa-indo-math-k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| qa-indo-math-k
==============
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8801
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Traini... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #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: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_siz... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "metrics": ["rouge"], "model_index": [{"name": "t5-small-finetuned-xsum-2", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "squad", "type": "squad", "args": ... | fadhilarkan/t5-small-finetuned-xsum-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"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 #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum-2
=========================
This model is a fine-tuned version of t5-small on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9536
* Rouge1: 28.8137
* Rouge2: 9.1265
* Rougel: 26.0238
* Rougelsum: 26.0217
* Gen Len: 13.854
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #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* ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]} | fadhilarkan/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"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 #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-xsum
This model is a fine-tuned version of t5-small on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The f... | [
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### T... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the squad dataset."... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-summarization
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation... | {"metrics": ["rouge"]} | fadhilarkan/test-summarization | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| test-summarization
==================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4740
* Rouge1: 28.3487
* Rouge2: 7.7836
* Rougel: 22.3307
* Rougelsum: 22.3357
* Gen Len: 18.8307
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #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: 2e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_siz... |
text-generation | transformers |
# test DialoGPT Model | {"tags": ["conversational"]} | faketermz/DialoGPT | 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
|
# test DialoGPT Model | [
"# test DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test DialoGPT Model"
] |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | {"title": "Test Space", "emoji": "\ud83d\udd25", "colorFrom": "indigo", "colorTo": "blue", "sdk": "gradio", "app_file": "app.py", "pinned": false} | omerXfaruq/test-space | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | [
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow,... | [
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbna... |
image-classification | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai", "image-classification"]} | fastai/fastbook_04_mnist_basics | null | [
"fastai",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#fastai #image-classification #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #image-classification #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai"]} | fastai/fastbook_06_multicat_Biwi_Kinect_Head_Pose | null | [
"fastai",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n... |
null | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"tags": ["fastai"]} | fastai/fastbook_06_multicat_PASCAL | null | [
"fastai",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n... |
text-generation | transformers |
# Hermione Granger DialoGPT Model | {"tags": ["conversational"]} | fatemaMeem98/DialoGPT-medium-HermioneGrangerBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Hermione Granger DialoGPT Model | [
"# Hermione Granger DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Hermione Granger DialoGPT Model"
] |
fill-mask | transformers |
# FERNET-C5
FERNET-C5 (**F**lexible **E**mbedding **R**epresentation **NET**work) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.
## Paper
https://link.springer.com/chapter/10.1007/978-3-030-89579-2_3
The preprint of our paper is av... | {"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["Czech", "KKY", "FAV"]} | fav-kky/FERNET-C5 | null | [
"transformers",
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"safetensors",
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"fill-mask",
"Czech",
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"cs",
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"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.10042"
] | [
"cs"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-C5
FERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.
## Paper
URL
The preprint of our paper is available at URL
If you find this model useful, please cite our paper:
| [
"# FERNET-C5\nFERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.",
"## Paper\nURL\n\nThe preprint of our paper is available at URL\n\nIf you find this model useful, please cite o... | [
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"# FERNET-C5\nFERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93... |
fill-mask | transformers |
# FERNET-CC_sk
FERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.
It is a Slovak version of our Czech [FERNET-C5](https://huggingface.co/fav-kky/FERNET-C5) model.
Preprint of our paper is available at https://arxiv.org/abs/2107.10042. | {"language": "sk", "license": "cc-by-nc-sa-4.0", "tags": ["Slovak", "KKY", "FAV"]} | fav-kky/FERNET-CC_sk | null | [
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"sk",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.10042"
] | [
"sk"
] | TAGS
#transformers #pytorch #tf #bert #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-CC_sk
FERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.
It is a Slovak version of our Czech FERNET-C5 model.
Preprint of our paper is available at URL | [
"# FERNET-CC_sk\nFERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.\n\nIt is a Slovak version of our Czech FERNET-C5 model.\n\nPreprint of our paper is available at URL"
] | [
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"# FERNET-CC_sk\nFERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.\n\nIt... |
fill-mask | transformers |
# FERNET-News
FERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.
Preprint of our paper is available at https://arxiv.org/abs/2107.10042. | {"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["Czech", "KKY", "FAV"]} | fav-kky/FERNET-News | null | [
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"tf",
"roberta",
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"Czech",
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"cs",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.10042"
] | [
"cs"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-News
FERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.
Preprint of our paper is available at URL | [
"# FERNET-News\nFERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.\n\nPreprint of our paper is available at URL"
] | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FERNET-News\nFERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.\n... |
fill-mask | transformers |
# FERNET-News_sk
FERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.
It is a Slovak version of our Czech [FERNET-News](https://huggingface.co/fav-kky/FERNET-News) model.
Preprint of our paper is available at https://arxiv.org/abs/2107.10042. | {"language": "sk", "license": "cc-by-nc-sa-4.0", "tags": ["Slovak", "KKY", "FAV"]} | fav-kky/FERNET-News_sk | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"Slovak",
"KKY",
"FAV",
"sk",
"arxiv:2107.10042",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.10042"
] | [
"sk"
] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-News_sk
FERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.
It is a Slovak version of our Czech FERNET-News model.
Preprint of our paper is available at URL | [
"# FERNET-News_sk\nFERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.\n\nIt is a Slovak version of our Czech FERNET-News model.\n\nPreprint of our paper is available at URL"
] | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FERNET-News_sk\nFERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news c... |
feature-extraction | transformers |
## Proc-RoBERTa
Proc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following [paper](https://arxiv.org/abs/2109.047... | {"language": ["en"], "datasets": ["pubmed", "chemical patent", "cooking recipe"]} | fbaigt/proc_roberta | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"en",
"arxiv:2109.04711",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04711"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us
|
## Proc-RoBERTa
Proc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:
## Usage
More usage detail... | [
"## Proc-RoBERTa\nProc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:",
"## Usage\n\n\nMore us... | [
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"## Proc-RoBERTa\nProc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/co... |
feature-extraction | transformers |
## ProcBERT
ProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following [paper](... | {"language": ["en"], "datasets": ["pubmed", "chemical patent", "cooking recipe"]} | fbaigt/procbert | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2109.04711",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04711"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us
|
## ProcBERT
ProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:
... | [
"## ProcBERT\nProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following pap... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us \n",
"## ProcBERT\nProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) cont... |
token-classification | transformers | This model is the fine-tuned model of "akdeniz27/bert-base-hungarian-cased-ner" using WikiANN-hu dataset. | {} | fdominik98/bert-base-hu-cased-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is the fine-tuned model of "akdeniz27/bert-base-hungarian-cased-ner" using WikiANN-hu dataset. | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | Magyar nyelvű token classification feladatra felkészített BERT modell. | {} | fdominik98/ner-hu-model-2021 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Magyar nyelvű token classification feladatra felkészített BERT modell. | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | federicopascual/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7480
* Matthews Correlation: 0.5370
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetune-sentiment-analysis-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "... | federicopascual/finetune-sentiment-analysis-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetune-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4558
- Accuracy: 0.8867
- F1: 0.8944
## Model description
More information needed
## Intended uses & limitations
... | [
"# finetune-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4558\n- Accuracy: 0.8867\n- F1: 0.8944",
"## Model description\n\nMore information needed",
"## Intended us... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetune-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-sentiment-analysis-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "precision", "recall"], "model-index": [{"name": "finetuned-sentiment-analysis-model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imd... | federicopascual/finetuned-sentiment-analysis-model | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuned-sentiment-analysis-model
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2868
- Accuracy: 0.909
- Precision: 0.8900
- Recall: 0.9283
## Model description
More information needed
## Intended uses & li... | [
"# finetuned-sentiment-analysis-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2868\n- Accuracy: 0.909\n- Precision: 0.8900\n- Recall: 0.9283",
"## Model description\n\nMore information needed",
"##... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuned-sentiment-analysis-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on t... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-analysis-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb",... | federicopascual/finetuning-sentiment-analysis-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3130
- Accuracy: 0.8733
- F1: 0.8812
## Model description
More information needed
## Intended uses & limitations... | [
"# finetuning-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3130\n- Accuracy: 0.8733\n- F1: 0.8812",
"## Model description\n\nMore information needed",
"## Intended ... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-bas... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples-testcopy
This model is a fine-tuned version of [distilbert-base-uncased](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples-testcopy", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb",... | federicopascual/finetuning-sentiment-model-3000-samples-testcopy | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples-testcopy
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3374
- Accuracy: 0.87
- F1: 0.8762
## Model description
More information needed
## Intended uses & limitations
... | [
"# finetuning-sentiment-model-3000-samples-testcopy\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3374\n- Accuracy: 0.87\n- F1: 0.8762",
"## Model description\n\nMore information needed",
"## Intended us... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples-testcopy\n\nThis model is a fine-tuned version of distilbert-bas... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | federicopascual/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3404
- Accuracy: 0.8667
- F1: 0.8734
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3404\n- Accuracy: 0.8667\n- F1: 0.8734",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
token-classification | transformers | # ✨ bert-restore-punctuation
[]()
This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews).
The model predicts the punctuation and upper-casing of plai... | {"language": ["en"], "license": "mit", "tags": ["punctuation"], "datasets": ["yelp_polarity"], "metrics": ["f1"]} | felflare/bert-restore-punctuation | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"punctuation",
"en",
"dataset:yelp_polarity",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-restore-punctuation
========================
![forthebadge]()
This a bert-base-uncased model finetuned for punctuation restoration on Yelp Reviews.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuat... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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