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fill-mask | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-180g-base-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained mod... |
null | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-180g-large-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained mod... |
fill-mask | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-180g-large-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained mod... |
null | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-180g-small-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained m... |
null | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-180g-small-ex-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called E... |
fill-mask | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-180g-small-ex-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained mod... |
fill-mask | transformers |
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-180g-small-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further acce... | [
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor fu... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained mod... |
null | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-base-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BER... |
fill-mask | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-base-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance comp... |
null | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-large-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BER... |
fill-mask | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-large-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance comp... |
null | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-small-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BER... |
null | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-electra-small-ex-discriminator | null | [
"transformers",
"pytorch",
"tf",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its... |
fill-mask | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-small-ex-generator | null | [
"transformers",
"pytorch",
"tf",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to B... |
fill-mask | transformers |
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compare... | {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"} | hfl/chinese-electra-small-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to... | [
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and ... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance comp... |
null | transformers |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-legal-electra-base-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | [
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of th... | [
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"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact mode... |
fill-mask | transformers |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-legal-electra-base-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | [
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of th... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECT... |
null | transformers |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-legal-electra-large-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | [
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of th... | [
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact mode... |
fill-mask | transformers |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-legal-electra-large-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | [
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of th... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECT... |
null | transformers |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-legal-electra-small-discriminator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | [
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of th... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much... |
fill-mask | transformers |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-legal-electra-small-generator | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese ... | [
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of th... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECT... |
fill-mask | transformers | <p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&styl... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/chinese-macbert-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<a href="URL
<img alt="GitHub" src="URL
</a>
Please use 'Bert' related functions to load this model!
=======================================================
This repository contains the resources in our paper "Revisiting Pre-trained Models for Chinese Natural Language Processing", w... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | <p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&styl... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/chinese-macbert-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<a href="URL
<img alt="GitHub" src="URL
</a>
Please use 'Bert' related functions to load this model!
=======================================================
This repository contains the resources in our paper "Revisiting Pre-trained Models for Chinese Natural Language Processing", w... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT | {"language": ["zh"], "license": "cc-by-nc-sa-4.0"} | hfl/chinese-pert-base | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: URL | [
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
feature-extraction | transformers |
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT | {"language": ["zh"], "license": "cc-by-nc-sa-4.0"} | hfl/chinese-pert-large | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: URL | [
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
fill-mask | transformers |
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/chinese-roberta-wwm-ext-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bin... | [
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, T... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerati... |
fill-mask | transformers |
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/chinese-roberta-wwm-ext | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bin... | [
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, T... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerati... |
text-generation | transformers |
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-xlnet-base | null | [
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/... | [
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based... | [
"TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language proc... |
text-generation | transformers |
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/... | {"language": ["zh"], "license": "apache-2.0"} | hfl/chinese-xlnet-mid | null | [
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/... | [
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based... | [
"TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing reso... |
fill-mask | transformers |
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | {"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"} | hfl/cino-base-v2 | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | [
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R,... |
fill-mask | transformers |
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | {"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"} | hfl/cino-large-v2 | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | [
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R,... |
fill-mask | transformers |
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | {"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"} | hfl/cino-large | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | [
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R,... |
fill-mask | transformers |
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | {"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"} | hfl/cino-small-v2 | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack ... | [
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R,... |
feature-extraction | transformers |
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT | {"language": ["en"], "license": "cc-by-nc-sa-4.0"} | hfl/english-pert-base | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: URL | [
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more... |
feature-extraction | transformers |
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT | {"language": ["en"], "license": "cc-by-nc-sa-4.0"} | hfl/english-pert-large | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: URL | [
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more... |
fill-mask | transformers |
# This is a re-trained 3-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)** ... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"], "pipeline_tag": "fill-mask"} | hfl/rbt3 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This is a re-trained 3-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qi... | [
"# This is a re-trained 3-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 3-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natu... |
fill-mask | transformers | # This is a re-trained 4-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/rbt4 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # This is a re-trained 4-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin... | [
"# This is a re-trained 4-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 4-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natu... |
fill-mask | transformers | # This is a re-trained 6-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/rbt6 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # This is a re-trained 6-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin... | [
"# This is a re-trained 6-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting ... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 6-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natu... |
fill-mask | transformers | # This is a re-trained 3-layer RoBERTa-wwm-ext-large model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]} | hfl/rbtl3 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08101",
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # This is a re-trained 3-layer RoBERTa-wwm-ext-large model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bi... | [
"# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che,... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chines... |
image-classification | transformers |
# fruits
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | hgarg/fruits | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# fruits
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### apple
!apple
#### banana
!banana
#### mango
!mango
#### orange
!orange
#### tomato
!tomato | [
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### apple\n\n!apple",
"#### banana\n\n!banana",
"#### mango\n\n!mango",
"#### orange\n\n!orange... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with t... |
image-classification | transformers |
# indian-snacks
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggin... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | hgarg/indian-snacks | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# indian-snacks
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### dosa
!dosa
#### idli
!idli
#### naan
!naan
#### samosa
!samosa
#### vada
!vada | [
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dosa\n\n!dosa",
"#### idli\n\n!idli",
"#### naan\n\n!naan",
"#### samosa\n\n!samosa"... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-fa-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-fa-colab", "results": []}]} | hgharibi/wav2vec2-xls-r-300m-fa-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-fa-colab
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4404
* Wer: 0.4402
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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: 0.0003\n* t... |
text-classification | transformers | # BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include s... | {"language": ["es"], "license": "apache-2.0", "tags": ["es", "ticket classification"], "datasets": ["self made to classify whether text is related to technology or not."], "metrics": ["fscore", "accuracy", "precision", "recall"]} | hiiamsid/BETO_es_binary_classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"ticket classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
## Training procedure
I trai... | [
"# BETO(cased)\nThis model was built using pytorch.",
"## Model description\nInput for the model: Any spanish text\nOutput for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))",
"#### How to use\nHere is how to use this model to get the features of a given text in *PyTorch*:",
"## Tr... | [
"TAGS\n#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BETO(cased)\nThis model was built using pytorch.",
"## Model description\nInput for the model: Any spanish text\nOutput for the model:... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684327
- CO2 Emissions (in grams): 437.2441955971972
## Validation Metrics
- Loss: nan
- Rouge1: 3.7729
- Rouge2: 0.4152
- RougeL: 3.5066
- RougeLsum: 3.5167
- Gen Len: 5.0577
## Usage
You can use cURL to access this model:
```
$ curl -X P... | {"language": "es", "tags": "autonlp", "datasets": ["hiiamsid/autonlp-data-Summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 437.2441955971972} | hiiamsid/autonlp-Summarization-20684327 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684327
- CO2 Emissions (in grams): 437.2441955971972
## Validation Metrics
- Loss: nan
- Rouge1: 3.7729
- Rouge2: 0.4152
- RougeL: 3.5066
- RougeLsum: 3.5167
- Gen Len: 5.0577
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 Emissions (in grams): 437.2441955971972",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 3.7729\n- Rouge2: 0.4152\n- RougeL: 3.5066\n- RougeLsum: 3.5167\n- Gen Len: 5.0577",
"## Usage\n\nYou can use cURL to access thi... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 E... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684328
- CO2 Emissions (in grams): 1133.9679082840014
## Validation Metrics
- Loss: nan
- Rouge1: 9.4193
- Rouge2: 0.91
- RougeL: 7.9376
- RougeLsum: 8.0076
- Gen Len: 10.65
## Usage
You can use cURL to access this model:
```
$ curl -X POS... | {"language": "es", "tags": "autonlp", "datasets": ["hiiamsid/autonlp-data-Summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1133.9679082840014} | hiiamsid/autonlp-Summarization-20684328 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684328
- CO2 Emissions (in grams): 1133.9679082840014
## Validation Metrics
- Loss: nan
- Rouge1: 9.4193
- Rouge2: 0.91
- RougeL: 7.9376
- RougeLsum: 8.0076
- Gen Len: 10.65
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 Emissions (in grams): 1133.9679082840014",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 9.4193\n- Rouge2: 0.91\n- RougeL: 7.9376\n- RougeLsum: 8.0076\n- Gen Len: 10.65",
"## Usage\n\nYou can use cURL to access this ... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 E... |
text2text-generation | transformers | This is the finetuned model of hiiamsid/est5-base for Question Generation task.
* Here input is the context only and output is questions. No information regarding answers were given to model.
* Unfortunately, due to lack of sufficient resources it is fine tuned with batch_size=10 and num_seq_len=256. So, if too large ... | {"language": ["es"], "license": "mit", "tags": ["spanish", "question generation", "qg"], "Datasets": ["SQUAD"]} | hiiamsid/est5-base-qg | null | [
"transformers",
"pytorch",
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"text2text-generation",
"spanish",
"question generation",
"qg",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is the finetuned model of hiiamsid/est5-base for Question Generation task.
* Here input is the context only and output is questions. No information regarding answers were given to model.
* Unfortunately, due to lack of sufficient resources it is fine tuned with batch_size=10 and num_seq_len=256. So, if too large ... | [
"## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] |
text2text-generation | transformers | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Spanish embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Spanish tokens) ... | {"language": ["es"], "license": "mit", "tags": ["spanish"]} | hiiamsid/est5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"spanish",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a smaller version of the google/mt5-base model with only Spanish embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 25K (top 25K Spanish tokens) the number of model parameters reduced to ... | [
"## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
text2text-generation | transformers | This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only hindi embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Hindi tokens) the ... | {"language": ["hi"], "license": "mit", "tags": ["hindi"]} | hiiamsid/hit5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"hindi",
"hi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a smaller version of the google/mt5-base model with only hindi embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 25K (top 25K Hindi tokens) the number of model parameters reduced to 237M... | [
"## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
sentence-similarity | sentence-transformers |
# hiiamsid/sentence_similarity_hindi
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this mo... | {"language": ["hi"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | hiiamsid/sentence_similarity_hindi | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"hi",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us
|
# hiiamsid/sentence_similarity_hindi
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers instal... | [
"# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transform... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us \n",
"# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for t... |
sentence-similarity | sentence-transformers |
# hiiamsid/sentence_similarity_spanish_es
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using th... | {"language": ["es"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | hiiamsid/sentence_similarity_spanish_es | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"es",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# hiiamsid/sentence_similarity_spanish_es
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers i... | [
"# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tran... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dens... |
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": []}]} | hiiii23/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
text-generation | transformers | <br />
<div align="center">
<img src="https://raw.githubusercontent.com/himanshu-dutta/pycoder/master/docs/pycoder-logo-p.png">
<br/>
<img alt="Made With Python" src="http://ForTheBadge.com/images/badges/made-with-python.svg" height=28 style="display:inline; height:28px;" />
<img alt="Medium" src="https://img.shield... | {} | himanshu-dutta/pycoder-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <br />
<div align="center">
<img src="URL
<br/>
<img alt="Made With Python" src="URL height=28 style="display:inline; height:28px;" />
<img alt="Medium" src="URL height=28 style="display:inline; height:28px;"/>
<a href="URL
<img alt="WandB Dashboard" src="URL height=28 style="display:inline; height:28px;" />
</a>
... | [
"## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Transformers\" src=\"URL height=28 width=120 style=\"display:inline; background-color:white; height:28px; width:120px\"/>\n<img alt=\"Docker\" s... | [
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"## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Tr... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | hiraki/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3780
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
text-generation | transformers |
GPT-2 chatbot - talk to Ray Smuckles | {"tags": ["conversational"]} | hireddivas/DialoGPT-small-ray | 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
|
GPT-2 chatbot - talk to Ray Smuckles | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#GPT-2 model trained on Dana Scully's dialog. | {"tags": ["conversational"]} | hireddivas/DialoGPT-small-scully | 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
|
#GPT-2 model trained on Dana Scully's dialog. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
GPT-2 chatbot - talk to Fox Mulder | {"tags": ["conversational"]} | hireddivas/dialoGPT-small-mulder | 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
|
GPT-2 chatbot - talk to Fox Mulder | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
GPT-2 model trained on Phil from Eastenders | {"tags": ["conversational"]} | hireddivas/dialoGPT-small-phil | 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
|
GPT-2 model trained on Phil from Eastenders | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
GPT-2 chatbot - talk to Sonic | {"tags": ["conversational"]} | hireddivas/dialoGPT-small-sonic | 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
|
GPT-2 chatbot - talk to Sonic | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This pretrained model is almost the same as [cl-tohoku/bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2) but do not need `fugashi` or `unidic_lite`.
The only difference is in `word_token... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"]} | hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.
The only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tok... | [
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.\nThe only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-j... |
token-classification | spacy | Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ja_gsd_bert_wwm_unidic_lite` |
| **Version** | `3.1.1` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `transformer`, `parser`, `ner` |
| **Components** | `transformer`, `p... | {"language": ["ja"], "license": "CC-BY-SA-4.0", "tags": ["spacy", "token-classification"]} | hiroshi-matsuda-rit/ja_gsd_bert_wwm_unidic_lite | null | [
"spacy",
"token-classification",
"ja",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#spacy #token-classification #ja #model-index #region-us
| Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.
### Label Scheme
View label scheme (45 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ja #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)",
"### Accuracy"
] |
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... | histinct7002/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: 0.4600
* Matthews Correlation: 0.5291
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | histinct7002/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"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 #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0727
* Precision: 0.9334
* Recall: 0.9398
* F1: 0.9366
* Accuracy: 0.9845
Model des... | [
"### 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 #token-classification #generated_from_trainer #dataset-conll2003 #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* le... |
text-generation | transformers |
Note: this model was superceded by the [`load_in_8bit=True` feature in transformers](https://github.com/huggingface/transformers/pull/17901)
by Younes Belkada and Tim Dettmers. Please see [this usage example](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4#scrollTo=W8tQtyjp75O).
This legacy m... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]} | hivemind/gpt-j-6B-8bit | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"arxiv:2106.09685",
"arxiv:2110.02861",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09685",
"2110.02861"
] | [
"en"
] | TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2106.09685 #arxiv-2110.02861 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Note: this model was superceded by the 'load_in_8bit=True' feature in transformers
by Younes Belkada and Tim Dettmers. Please see this usage example.
This legacy model was built for transformers v4.15.0 and pytorch 1.11. Newer versions could work, but are not supported.
### Quantized EleutherAI/gpt-j-6b with 8-bit w... | [
"### Quantized EleutherAI/gpt-j-6b with 8-bit weights\n\nThis is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti).\n\nHere's how to run it: ![colab](URL\n\n__The original GPT-J__ takes 22+ G... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2106.09685 #arxiv-2110.02861 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Quantized EleutherAI/gpt-j-6b with 8-bit weights\n\nThis is a version of EleutherAI's GPT-J with 6 billion paramet... |
null | transformers | This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2.
Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers. | {} | hkunlp/T5_large_prefix_all_tasks_2upsample2 | null | [
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us
| This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2.
Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers. | [] | [
"TAGS\n#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | Convert from model .pt to transformer
Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
Bash:
```bash
pip install transformers[sentencepiece]
pip install fairseq -U
git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_ch... | {} | hoangbinhmta99/wav2vec-demo | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Convert from model .pt to transformer
Link: URL
Bash:
# install and upload model
| [
"# install and upload model"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# install and upload model"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "a... | hoanhkhoa/bert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0604
* Precision: 0.9247
* Recall: 0.9343
* F1: 0.9295
* Accuracy: 0.9854
Model description
--------------... | [
"### 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 #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "roberta-base-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "v... | hoanhkhoa/roberta-base-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0381
* Precision: 0.9469
* Recall: 0.9530
* F1: 0.9500
* Accuracy: 0.9915
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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 #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
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_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | hogger32/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"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_v2 #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\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7004
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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_v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\... |
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. -->
# xlmRoberta-for-VietnameseQA
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlmRoberta-for-VietnameseQA", "results": []}]} | hogger32/xlmRoberta-for-VietnameseQA | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
| xlmRoberta-for-VietnameseQA
===========================
This model is a fine-tuned version of xlm-roberta-base on the UIT-Viquad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8315
Model description
-----------------
Fine-tuned by Honganh Nguyen (FPTU AI Club).
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #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... |
text-generation | transformers |
# Zhongli, but not Zhongli | {"tags": ["conversational"]} | honguyenminh/old-zhongli | 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
|
# Zhongli, but not Zhongli | [
"# Zhongli, but not Zhongli"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Zhongli, but not Zhongli"
] |
null | null | dd | {} | hooni/bert-fine-tuned-cola | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| dd | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
#Joey DialoGPT Model | {"tags": ["conversational"]} | houssaineamzil/DialoGPT-small-joey | 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
|
#Joey DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | hrdipto/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4241
* Wer: 0.3381
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune
This model is a fine-tuned version of [hrdipto/wav2vec2-xls-r-300m-ba... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-300m-bangla-command-generated-data-finetune", "results": []}]} | hrdipto/wav2vec2-xls-r-300m-bangla-command-generated-data-finetune | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune
This model is a fine-tuned version of hrdipto/wav2vec2-xls-r-300m-bangla-command-data on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0099
- eval_wer: 0.0208
- eval_runtime: 2.5526
- eval_samples_per_second: 75... | [
"# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune\n\nThis model is a fine-tuned version of hrdipto/wav2vec2-xls-r-300m-bangla-command-data on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0099\n- eval_wer: 0.0208\n- eval_runtime: 2.5526\n- eval_samples_per_s... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-bangla-command-generated-data-finetune\n\nThis model is a fine-tuned version of hrdipto/wav2vec2-xls-r-300m-bangla-command-data on the None datase... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-tf-left-right-shuru-word-level
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-tf-left-right-shuru-word-level", "results": []}]} | hrdipto/wav2vec2-xls-r-tf-left-right-shuru-word-level | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-tf-left-right-shuru-word-level
=============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0504
* Wer: 0.6859
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-tf-left-right-shuru
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-tf-left-right-shuru", "results": []}]} | hrdipto/wav2vec2-xls-r-tf-left-right-shuru | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-tf-left-right-shuru
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0921
* Wer: 1.2628
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-tf-left-right-trainer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-tf-left-right-trainer", "results": []}]} | hrdipto/wav2vec2-xls-r-tf-left-right-trainer | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xls-r-tf-left-right-trainer
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0090
- eval_wer: 0.0037
- eval_runtime: 11.2686
- eval_samples_per_second: 71.703
- eval_steps_per_second: 8.963
- ep... | [
"# wav2vec2-xls-r-tf-left-right-trainer\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0090\n- eval_wer: 0.0037\n- eval_runtime: 11.2686\n- eval_samples_per_second: 71.703\n- eval_steps_per_second: ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-tf-left-right-trainer\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the f... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-timit-tokenizer-base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-timit-tokenizer-base", "results": []}]} | hrdipto/wav2vec2-xls-r-timit-tokenizer-base | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-timit-tokenizer-base
===================================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0828
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0003\n* train\\_batch\\_size: 1... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-timit-tokenizer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-timit-tokenizer", "results": []}]} | hrdipto/wav2vec2-xls-r-timit-tokenizer | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-timit-tokenizer
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4285
* Wer: 0.3662
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0003\n* train\\_batch\\_size: 1... |
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": "First Order Motion Model", "emoji": "\ud83d\udc22", "colorFrom": "blue", "colorTo": "yellow", "sdk": "gradio", "app_file": "app.py", "pinned": false} | hrushikute/DanceOnTune | 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... |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | hrv/DialoGPT-small-rick-morty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | hs788/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4125
* Wer: 0.3607
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\n* train\\_batch\\_size: 6... |
null | null | Hi, this is Taiwan_House_Prediction. | {} | huang0624/Taiwan_House_Prediction | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Hi, this is Taiwan_House_Prediction. | [] | [
"TAGS\n#region-us \n"
] |
null | transformers | ## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive... | {} | huawei-noah/DynaBERT_MNLI | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:2004.04037",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04037"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us
| ## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive... | [
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other similar-sized compressed models.\nThe training process of DynaBERT includes first training a width... | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us \n",
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other... |
null | transformers | ## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive... | {} | huawei-noah/DynaBERT_SST-2 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:2004.04037",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04037"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us
| ## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive... | [
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other similar-sized compressed models.\nThe training process of DynaBERT includes first training a width... | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-2004.04037 #endpoints_compatible #region-us \n",
"## DynaBERT: Dynamic BERT with Adaptive Width and Depth\n\n* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and \nthe subnetworks of it have competitive performances as other... |
null | null | # Overview
<p align="center">
<img src="https://avatars.githubusercontent.com/u/12619994?s=200&v=4" width="150">
</p>
<!-- -------------------------------------------------------------------------------- -->
JABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model.
JABER obtained rank one on [ALU... | {} | huawei-noah/JABER | null | [
"pytorch",
"arxiv:2112.04329",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.04329"
] | [] | TAGS
#pytorch #arxiv-2112.04329 #region-us
| # Overview
<p align="center">
<img src="URL width="150">
</p>
JABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model.
JABER obtained rank one on ALUE leaderboard at '01/09/2021'.
This model is only compatible with the code in this github repo (not supported by the Transformers library)
Plea... | [
"# Overview\n\n<p align=\"center\">\n <img src=\"URL width=\"150\">\n</p>\n\n\n\nJABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model. \nJABER obtained rank one on ALUE leaderboard at '01/09/2021'. \nThis model is only compatible with the code in this github repo (not supported by the Transfor... | [
"TAGS\n#pytorch #arxiv-2112.04329 #region-us \n",
"# Overview\n\n<p align=\"center\">\n <img src=\"URL width=\"150\">\n</p>\n\n\n\nJABER (Junior Arabic BERt) is a 12-layer Arabic pretrained Language Model. \nJABER obtained rank one on ALUE leaderboard at '01/09/2021'. \nThis model is only compatible with the cod... |
null | transformers | TinyBERT: Distilling BERT for Natural Language Understanding
========
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learni... | {} | huawei-noah/TinyBERT_General_4L_312D | null | [
"transformers",
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.10351"
] | [] | TAGS
#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #has_space #region-us
| TinyBERT: Distilling BERT for Natural Language Understanding
========
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learni... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #arxiv-1909.10351 #endpoints_compatible #has_space #region-us \n"
] |
null | null |
This is an Audacity wrapper for the model, forked from the repository `groadabike/ConvTasNet_DAMP-VSEP_enhboth`,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied directly from `groadabike/ConvTasNet_DAMP-VSEP_enhboth`:
### Description:
This... | {"tags": ["audacity"], "inference": false, "sample_rate": 8000} | hugggof/ConvTasNet-DAMP-Vocals | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
|
This is an Audacity wrapper for the model, forked from the repository 'groadabike/ConvTasNet_DAMP-VSEP_enhboth',
This model was trained using the Asteroid library: URL
The following info was copied directly from 'groadabike/ConvTasNet_DAMP-VSEP_enhboth':
### Description:
This model was trained by Gerardo Roa Dabik... | [
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile... | [
"TAGS\n#audacity #region-us \n",
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VS... |
null | null | This is an Audacity wrapper for the model, forked from the repository `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k`,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied directly from `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k`:
Description:
Thi... | {"tags": ["audacity"], "inference": false} | hugggof/ConvTasNet_Libri3Mix_sepnoisy_16k | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
| This is an Audacity wrapper for the model, forked from the repository 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k',
This model was trained using the Asteroid library: URL
The following info was copied directly from 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k':
Description:
This model was trained by Joris Cosentino ... | [] | [
"TAGS\n#audacity #region-us \n"
] |
null | null | This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean,
This model was trained using the Asteroid library: https://github.com/asteroid-team/asteroid.
The following info was copied from `mpariente/ConvTasNet_WHAM_sepclean`:
### Description:
This model was trained by M... | {"tags": ["audacity"], "inference": false} | hugggof/ConvTasNet_WHAM_sepclean | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
| This is an Audacity wrapper for the model, forked from the repository mpariente/ConvTasNet_WHAM_sepclean,
This model was trained using the Asteroid library: URL
The following info was copied from 'mpariente/ConvTasNet_WHAM_sepclean':
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasN... | [
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/ConvTasNet recipe in Asteroid.\nIt was trained on the 'sep_clean' task of the WHAM! dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_WHAM!_sepclean\" is a derivative of CSR-I (WSJ0) Com... | [
"TAGS\n#audacity #region-us \n",
"### Description:\nThis model was trained by Manuel Pariente \nusing the wham/ConvTasNet recipe in Asteroid.\nIt was trained on the 'sep_clean' task of the WHAM! dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_WHAM!_sepclean\" ... |
null | null |
## Music Source Separation in the Waveform Domain
This is the Demucs model, serialized from Facebook Research's pretrained models.
From Facebook research:
Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initializ... | {"tags": "audacity"} | hugggof/demucs_extra | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
|
## Music Source Separation in the Waveform Domain
This is the Demucs model, serialized from Facebook Research's pretrained models.
From Facebook research:
Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initializ... | [
"## Music Source Separation in the Waveform Domain\n\nThis is the Demucs model, serialized from Facebook Research's pretrained models. \n\nFrom Facebook research:\n\n Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific... | [
"TAGS\n#audacity #region-us \n",
"## Music Source Separation in the Waveform Domain\n\nThis is the Demucs model, serialized from Facebook Research's pretrained models. \n\nFrom Facebook research:\n\n Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM betwee... |
null | null |
# Labeler With Timestamps
## Being used for the `Audio Labeler` effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
```
{
"sample_rate": 48000,
"domain_tags": ["Music"],
"tags": ["Audio Labeler"],
"eff... | {"tags": ["audacity"], "inference": false} | hugggof/openl3-labeler-w-timestamps | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
|
# Labeler With Timestamps
## Being used for the 'Audio Labeler' effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
| [
"# Labeler With Timestamps",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nThis is a audio labeler model which is used in Audacity's labeler effect. \n\nmetadata:"
] | [
"TAGS\n#audacity #region-us \n",
"# Labeler With Timestamps",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nThis is a audio labeler model which is used in Audacity's labeler effect. \n\nmetadata:"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/9fd98af9a817af8cd78636f71895b6a... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/100-gecs"], "widget": [{"text": "I am"}]} | huggingartists/100-gecs | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/100-gecs",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/100-gecs #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 100 gecs.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## T... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/100-gecs #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/aa32202cc20d1dde62e57940a8b278b... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/21-savage"], "widget": [{"text": "I am"}]} | huggingartists/21-savage | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/21-savage",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/21-savage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 21 Savage.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## ... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/21-savage #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B repor... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4fedc5dd2830a874a5274bf1cac6200... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/25-17"], "widget": [{"text": "I am"}]} | huggingartists/25-17 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/25-17",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/25-17 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 25/17.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Trai... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/25-17 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/10f98dca7bcd1a31222e36374544cad... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/50-cent"], "widget": [{"text": "I am"}]} | huggingartists/50-cent | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/50-cent",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/50-cent #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 50 Cent.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Tr... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/50-cent #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/289ded19d51d41798be99217d6059eb... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/5nizza"], "widget": [{"text": "I am"}]} | huggingartists/5nizza | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/5nizza",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5nizza #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 5’Nizza.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Tr... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5nizza #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report."... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/c56dce03a151e17a9626e55e6c295bb... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/5opka"], "widget": [{"text": "I am"}]} | huggingartists/5opka | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/5opka",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5opka #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 5opka.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Trai... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/5opka #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/b2b164a7c6c02dd0843ad597df5dbf4... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/6ix9ine"], "widget": [{"text": "I am"}]} | huggingartists/6ix9ine | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/6ix9ine",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/6ix9ine #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from 6ix9ine.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"## Tr... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/6ix9ine #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/894021d09a748eef8c6d63ad898b814... | {"language": "en", "tags": ["huggingartists", "lyrics", "lm-head", "causal-lm"], "datasets": ["huggingartists/aaron-watson"], "widget": [{"text": "I am"}]} | huggingartists/aaron-watson | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/aaron-watson",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/aaron-watson #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL
</div>
</div>
<div style="text-align:... | [
"## How does it work?\n\nTo understand how the model was developed, check the W&B report.",
"## Training data\n\nThe model was trained on lyrics from Aaron Watson.\n\nDataset is available here.\nAnd can be used with:\n\n\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.",
"... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingartists #lyrics #lm-head #causal-lm #en #dataset-huggingartists/aaron-watson #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How does it work?\n\nTo understand how the model was developed, check the W&B re... |
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