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null | transformers |
# UniSpeech-SAT-Base for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using ... | {"language": ["en"], "tags": ["speech"]} | microsoft/unispeech-sat-base-plus-sv | null | [
"transformers",
"pytorch",
"unispeech-sat",
"audio-xvector",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #audio-xvector #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #has_space #region-us
|
# UniSpeech-SAT-Base for Speaker Verification
Microsoft's UniSpeech
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of Libri-Light
- 10,... | [
"# UniSpeech-SAT-Base for Speaker Verification\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 60,000 hours of Libri... | [
"TAGS\n#transformers #pytorch #unispeech-sat #audio-xvector #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #has_space #region-us \n",
"# UniSpeech-SAT-Base for Speaker Verification\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled spee... |
null | transformers |
# UniSpeech-SAT-Base
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure tha... | {"language": ["en"], "tags": ["speech"]} | microsoft/unispeech-sat-base-plus | null | [
"transformers",
"pytorch",
"unispeech-sat",
"pretraining",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #pretraining #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us
|
# UniSpeech-SAT-Base
Microsoft's UniSpeech
The base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order t... | [
"# UniSpeech-SAT-Base\n\nMicrosoft's UniSpeech\n\nThe base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. ... | [
"TAGS\n#transformers #pytorch #unispeech-sat #pretraining #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us \n",
"# UniSpeech-SAT-Base\n\nMicrosoft's UniSpeech\n\nThe base model pretrained on 16kHz sampled speech audio with utterance and speaker c... |
null | transformers |
# UniSpeech-SAT-Base for Speaker Diarization
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using t... | {"language": ["en"], "tags": ["speech"], "datasets": ["librispeech_asr"]} | microsoft/unispeech-sat-base-sd | null | [
"transformers",
"pytorch",
"unispeech-sat",
"audio-frame-classification",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2110.05752",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #audio-frame-classification #speech #en #dataset-librispeech_asr #arxiv-2110.05752 #endpoints_compatible #has_space #region-us
|
# UniSpeech-SAT-Base for Speaker Diarization
Microsoft's UniSpeech
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 960 hours of LibriSpeech
Paper: U... | [
"# UniSpeech-SAT-Base for Speaker Diarization\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 960 hours of LibriSpee... | [
"TAGS\n#transformers #pytorch #unispeech-sat #audio-frame-classification #speech #en #dataset-librispeech_asr #arxiv-2110.05752 #endpoints_compatible #has_space #region-us \n",
"# UniSpeech-SAT-Base for Speaker Diarization\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utt... |
null | transformers |
# UniSpeech-SAT-Base for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using ... | {"language": ["en"], "tags": ["speech"], "datasets": ["librispeech_asr"]} | microsoft/unispeech-sat-base-sv | null | [
"transformers",
"pytorch",
"unispeech-sat",
"audio-xvector",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #audio-xvector #speech #en #dataset-librispeech_asr #arxiv-2110.05752 #endpoints_compatible #region-us
|
# UniSpeech-SAT-Base for Speaker Verification
Microsoft's UniSpeech
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 960 hours of LibriSpeech
Paper: ... | [
"# UniSpeech-SAT-Base for Speaker Verification\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 960 hours of LibriSpe... | [
"TAGS\n#transformers #pytorch #unispeech-sat #audio-xvector #speech #en #dataset-librispeech_asr #arxiv-2110.05752 #endpoints_compatible #region-us \n",
"# UniSpeech-SAT-Base for Speaker Verification\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker cont... |
null | transformers |
# UniSpeech-SAT-Base
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure tha... | {"language": ["en"], "tags": ["speech"], "datasets": ["librispeech_asr"]} | microsoft/unispeech-sat-base | null | [
"transformers",
"pytorch",
"unispeech-sat",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #pretraining #speech #en #dataset-librispeech_asr #arxiv-2110.05752 #endpoints_compatible #region-us
|
# UniSpeech-SAT-Base
Microsoft's UniSpeech
The base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order t... | [
"# UniSpeech-SAT-Base\n\nMicrosoft's UniSpeech\n\nThe base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. ... | [
"TAGS\n#transformers #pytorch #unispeech-sat #pretraining #speech #en #dataset-librispeech_asr #arxiv-2110.05752 #endpoints_compatible #region-us \n",
"# UniSpeech-SAT-Base\n\nMicrosoft's UniSpeech\n\nThe base model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using t... |
null | transformers |
# UniSpeech-SAT-Large for Speaker Diarization
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using ... | {"language": ["en"], "tags": ["speech"]} | microsoft/unispeech-sat-large-sd | null | [
"transformers",
"pytorch",
"unispeech-sat",
"audio-frame-classification",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #audio-frame-classification #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us
|
# UniSpeech-SAT-Large for Speaker Diarization
Microsoft's UniSpeech
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of Libri-Light
- 10,... | [
"# UniSpeech-SAT-Large for Speaker Diarization\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 60,000 hours of Libri... | [
"TAGS\n#transformers #pytorch #unispeech-sat #audio-frame-classification #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us \n",
"# UniSpeech-SAT-Large for Speaker Diarization\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled sp... |
null | transformers |
# UniSpeech-SAT-Large for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using... | {"language": ["en"], "tags": ["speech"]} | microsoft/unispeech-sat-large-sv | null | [
"transformers",
"pytorch",
"unispeech-sat",
"audio-xvector",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #audio-xvector #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #has_space #region-us
|
# UniSpeech-SAT-Large for Speaker Verification
Microsoft's UniSpeech
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of Libri-Light
- 10... | [
"# UniSpeech-SAT-Large for Speaker Verification\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 60,000 hours of Libr... | [
"TAGS\n#transformers #pytorch #unispeech-sat #audio-xvector #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #has_space #region-us \n",
"# UniSpeech-SAT-Large for Speaker Verification\n\nMicrosoft's UniSpeech\n\nThe model was pretrained on 16kHz sampled spe... |
null | transformers |
# UniSpeech-SAT-Large
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure t... | {"language": ["en"], "tags": ["speech"]} | microsoft/unispeech-sat-large | null | [
"transformers",
"pytorch",
"unispeech-sat",
"pretraining",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.05752"
] | [
"en"
] | TAGS
#transformers #pytorch #unispeech-sat #pretraining #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us
|
# UniSpeech-SAT-Large
Microsoft's UniSpeech
The large model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order... | [
"# UniSpeech-SAT-Large\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone... | [
"TAGS\n#transformers #pytorch #unispeech-sat #pretraining #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.05752 #endpoints_compatible #region-us \n",
"# UniSpeech-SAT-Large\n\nMicrosoft's UniSpeech\n\nThe large model pretrained on 16kHz sampled speech audio with utterance and speaker... |
null | transformers |
# WavLM-Base-Plus for Speaker Diarization
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-... | {"language": ["en"], "tags": ["speech"]} | microsoft/wavlm-base-plus-sd | null | [
"transformers",
"pytorch",
"wavlm",
"audio-frame-classification",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #audio-frame-classification #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #endpoints_compatible #region-us
|
# WavLM-Base-Plus for Speaker Diarization
Microsoft's WavLM
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of Libri-Light
- 10,000 hour... | [
"# WavLM-Base-Plus for Speaker Diarization\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on:\n\n- 60,000 hours of Libri-Light\n... | [
"TAGS\n#transformers #pytorch #wavlm #audio-frame-classification #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #endpoints_compatible #region-us \n",
"# WavLM-Base-Plus for Speaker Diarization\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with ... |
null | transformers |
# WavLM-Base-Plus for Speaker Verification
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This mo... | {"language": ["en"], "tags": ["speech"]} | microsoft/wavlm-base-plus-sv | null | [
"transformers",
"pytorch",
"wavlm",
"audio-xvector",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #audio-xvector #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #endpoints_compatible #has_space #region-us
|
# WavLM-Base-Plus for Speaker Verification
Microsoft's WavLM
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio ... | [
"# WavLM-Base-Plus for Speaker Verification\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained... | [
"TAGS\n#transformers #pytorch #wavlm #audio-xvector #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #endpoints_compatible #has_space #region-us \n",
"# WavLM-Base-Plus for Speaker Verification\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with u... |
feature-extraction | transformers |
# WavLM-Base-Plus
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. I... | {"language": ["en"], "tags": ["speech"], "inference": false} | microsoft/wavlm-base-plus | null | [
"transformers",
"pytorch",
"wavlm",
"feature-extraction",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #feature-extraction #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #has_space #region-us
|
# WavLM-Base-Plus
Microsoft's WavLM
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer sh... | [
"# WavLM-Base-Plus\n\nMicrosoft's WavLM\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a to... | [
"TAGS\n#transformers #pytorch #wavlm #feature-extraction #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #has_space #region-us \n",
"# WavLM-Base-Plus\n\nMicrosoft's WavLM\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your spee... |
null | transformers |
# WavLM-Base for Speaker Diarization
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-train... | {"language": ["en"], "tags": ["speech"]} | microsoft/wavlm-base-sd | null | [
"transformers",
"pytorch",
"wavlm",
"audio-frame-classification",
"speech",
"en",
"arxiv:2110.13900",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #audio-frame-classification #speech #en #arxiv-2110.13900 #endpoints_compatible #region-us
|
# WavLM-Base for Speaker Diarization
Microsoft's WavLM
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on 960h of Librispeech.
Paper: WavLM: Large-Scale Se... | [
"# WavLM-Base for Speaker Diarization\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on 960h of Librispeech.\n\nPaper: WavLM: La... | [
"TAGS\n#transformers #pytorch #wavlm #audio-frame-classification #speech #en #arxiv-2110.13900 #endpoints_compatible #region-us \n",
"# WavLM-Base for Speaker Diarization\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the mode... |
null | transformers |
# WavLM-Base for Speaker Verification
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trai... | {"language": ["en"], "tags": ["speech"]} | microsoft/wavlm-base-sv | null | [
"transformers",
"pytorch",
"wavlm",
"audio-xvector",
"speech",
"en",
"arxiv:2110.13900",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #audio-xvector #speech #en #arxiv-2110.13900 #endpoints_compatible #region-us
|
# WavLM-Base for Speaker Verification
Microsoft's WavLM
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on 960h of Librispeech.
Paper: WavLM: Large-Scale S... | [
"# WavLM-Base for Speaker Verification\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nThe model was pre-trained on 960h of Librispeech.\n\nPaper: WavLM: L... | [
"TAGS\n#transformers #pytorch #wavlm #audio-xvector #speech #en #arxiv-2110.13900 #endpoints_compatible #region-us \n",
"# WavLM-Base for Speaker Verification\n\nMicrosoft's WavLM\n\nThe model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure... |
feature-extraction | transformers |
# WavLM-Base
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In ord... | {"language": ["en"], "tags": ["speech"], "inference": false} | microsoft/wavlm-base | null | [
"transformers",
"pytorch",
"wavlm",
"feature-extraction",
"speech",
"en",
"arxiv:2110.13900",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #feature-extraction #speech #en #arxiv-2110.13900 #has_space #region-us
|
# WavLM-Base
Microsoft's WavLM
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should ... | [
"# WavLM-Base\n\nMicrosoft's WavLM\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokeniz... | [
"TAGS\n#transformers #pytorch #wavlm #feature-extraction #speech #en #arxiv-2110.13900 #has_space #region-us \n",
"# WavLM-Base\n\nMicrosoft's WavLM\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model doe... |
feature-extraction | transformers |
# WavLM-Large
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In o... | {"language": ["en"], "tags": ["speech"], "inference": false} | microsoft/wavlm-large | null | [
"transformers",
"pytorch",
"wavlm",
"feature-extraction",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.07875",
"2106.06909",
"2101.00390",
"2110.13900"
] | [
"en"
] | TAGS
#transformers #pytorch #wavlm #feature-extraction #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #has_space #region-us
|
# WavLM-Large
Microsoft's WavLM
The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer shoul... | [
"# WavLM-Large\n\nMicrosoft's WavLM\n\nThe large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. \n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a token... | [
"TAGS\n#transformers #pytorch #wavlm #feature-extraction #speech #en #arxiv-1912.07875 #arxiv-2106.06909 #arxiv-2101.00390 #arxiv-2110.13900 #has_space #region-us \n",
"# WavLM-Large\n\nMicrosoft's WavLM\n\nThe large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech ... |
fill-mask | transformers | # XLM-Align
**XLM-Align** (ACL 2021, [paper](https://aclanthology.org/2021.acl-long.265/), [repo](https://github.com/CZWin32768/XLM-Align), [model](https://huggingface.co/microsoft/xlm-align-base)) Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
XLM-Align is a pretrained cross-lingu... | {} | microsoft/xlm-align-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| XLM-Align
=========
XLM-Align (ACL 2021, paper, repo, model) Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
XLM-Align is a pretrained cross-lingual language model that supports 94 languages. See details in our paper.
Example
-------
Evaluation Results
------------------
XTR... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | ## xprophetnet-large-wiki100-cased-xglue-ntg
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401) and finetuned on xGLUE cross-lingual News Titles Generation task.
ProphetNet is a new pre-trained language model for sequence-to-se... | {} | microsoft/xprophetnet-large-wiki100-cased-xglue-ntg | null | [
"transformers",
"pytorch",
"xlm-prophetnet",
"text2text-generation",
"arxiv:2001.04063",
"arxiv:2004.01401",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.04063",
"2004.01401"
] | [] | TAGS
#transformers #pytorch #xlm-prophetnet #text2text-generation #arxiv-2001.04063 #arxiv-2004.01401 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## xprophetnet-large-wiki100-cased-xglue-ntg
Cross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset and finetuned on xGLUE cross-lingual News Titles Generation task.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-g... | [
"## xprophetnet-large-wiki100-cased-xglue-ntg\nCross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset and finetuned on xGLUE cross-lingual News Titles Generation task. \nProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called fut... | [
"TAGS\n#transformers #pytorch #xlm-prophetnet #text2text-generation #arxiv-2001.04063 #arxiv-2004.01401 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## xprophetnet-large-wiki100-cased-xglue-ntg\nCross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset and finetuned on xGLUE... |
text2text-generation | transformers | ## xprophetnet-large-wiki100-cased-xglue-ntg
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401) and finetuned on xGLUE cross-lingual Question Generation task.
ProphetNet is a new pre-trained language model for sequence-to-seque... | {} | microsoft/xprophetnet-large-wiki100-cased-xglue-qg | null | [
"transformers",
"pytorch",
"xlm-prophetnet",
"text2text-generation",
"arxiv:2001.04063",
"arxiv:2004.01401",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.04063",
"2004.01401"
] | [] | TAGS
#transformers #pytorch #xlm-prophetnet #text2text-generation #arxiv-2001.04063 #arxiv-2004.01401 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## xprophetnet-large-wiki100-cased-xglue-ntg
Cross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset and finetuned on xGLUE cross-lingual Question Generation task.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram... | [
"## xprophetnet-large-wiki100-cased-xglue-ntg\nCross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset and finetuned on xGLUE cross-lingual Question Generation task. \nProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future... | [
"TAGS\n#transformers #pytorch #xlm-prophetnet #text2text-generation #arxiv-2001.04063 #arxiv-2004.01401 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## xprophetnet-large-wiki100-cased-xglue-ntg\nCross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset and finetuned on xGLUE... |
text2text-generation | transformers |
## xprophetnet-large-wiki100-cased
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401).
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gra... | {"language": "multilingual"} | microsoft/xprophetnet-large-wiki100-cased | null | [
"transformers",
"pytorch",
"xlm-prophetnet",
"text2text-generation",
"multilingual",
"arxiv:2001.04063",
"arxiv:2004.01401",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2001.04063",
"2004.01401"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-prophetnet #text2text-generation #multilingual #arxiv-2001.04063 #arxiv-2004.01401 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## xprophetnet-large-wiki100-cased
Cross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future tokens with a ... | [
"## xprophetnet-large-wiki100-cased\nCross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset. \nProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. \nProphetNet is able to predict more future tokens ... | [
"TAGS\n#transformers #pytorch #xlm-prophetnet #text2text-generation #multilingual #arxiv-2001.04063 #arxiv-2004.01401 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## xprophetnet-large-wiki100-cased\nCross-lingual version ProphetNet, pretrained on wiki100 xGLUE dataset. \nProphetNet is ... |
text-classification | transformers |
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Tran... | {"language": "en", "license": "mit", "tags": ["text-classification"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/xtremedistil-l12-h384-uncased | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04563"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #text-classification #en #arxiv-2106.04563 #license-mit #endpoints_compatible #region-us
| XtremeDistilTransformers for Distilling Massive Neural Networks
===============================================================
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #text-classification #en #arxiv-2106.04563 #license-mit #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Tran... | {"language": "en", "license": "mit", "tags": ["text-classification"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/xtremedistil-l6-h256-uncased | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04563"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #text-classification #en #arxiv-2106.04563 #license-mit #endpoints_compatible #region-us
| XtremeDistilTransformers for Distilling Massive Neural Networks
===============================================================
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #text-classification #en #arxiv-2106.04563 #license-mit #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Tran... | {"language": "en", "license": "mit", "tags": ["text-classification"], "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png"} | microsoft/xtremedistil-l6-h384-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04563"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #text-classification #en #arxiv-2106.04563 #license-mit #endpoints_compatible #has_space #region-us
| XtremeDistilTransformers for Distilling Massive Neural Networks
===============================================================
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #text-classification #en #arxiv-2106.04563 #license-mit #endpoints_compatible #has_space #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": []}]} | mictiong85/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: 0.4635
* Wer: 0.3357
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... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_e2e_bart | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialog... |
text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_e2e_gpt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_e2e_mbart | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialo... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_e2e_pegasus | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dia... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_e2e_t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_h2e_bart | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialog... |
text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_h2e_gpt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_h2e_mbart | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialo... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_h2e_pegasus | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dia... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_h2e_t5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'... |
text2text-generation | transformers | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | {} | midas/gupshup_h2e_t5_mtl | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:1910.04073",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.04073"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Gupshup
=======
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: URL
Github: URL
### Dataset
Please request for the Gupshup data using this Google form.
Dataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e)... | [
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'(h2e) and 'English Dialogues to English Summarization'(e2e). For each task, Dialogues/conversastion have '.source'(URL) as file extension whereas Summary has '.tar... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-1910.04073 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Dataset\n\n\nPlease request for the Gupshup data using this Google form.\n\n\nDataset is available for 'Hinglish Dilaogues to English Summarization'... |
fill-mask | transformers |
# IceBERT-igc
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
| Dataset | Size | Tok... | {"language": "is", "license": "agpl-3.0", "tags": ["roberta", "icelandic", "masked-lm", "pytorch"], "widget": [{"text": "M\u00e1 bj\u00f3\u00f0a \u00fe\u00e9r <mask> \u00ed kv\u00f6ld?"}, {"text": "Forseti <mask> er \u00e1g\u00e6t."}, {"text": "S\u00fapan var <mask> \u00e1 brag\u00f0i\u00f0."}]} | mideind/IceBERT-igc | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"icelandic",
"masked-lm",
"is",
"arxiv:2201.05601",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.05601"
] | [
"is"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-igc
===========
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
Dataset: Icelandic Gigaword Corpus v20.05 (IGC), Size: 8.2 ... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# IceBERT
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
| Dataset | Size | Tokens ... | {"language": "is", "license": "agpl-3.0", "tags": ["roberta", "icelandic", "masked-lm", "pytorch"], "widget": [{"text": "M\u00e1 bj\u00f3\u00f0a \u00fe\u00e9r <mask> \u00ed kv\u00f6ld?"}, {"text": "Forseti <mask> er \u00e1g\u00e6t."}, {"text": "S\u00fapan var <mask> \u00e1 brag\u00f0i\u00f0."}]} | mideind/IceBERT | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"icelandic",
"masked-lm",
"is",
"arxiv:2201.05601",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.05601"
] | [
"is"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT
=======
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.
Dataset: Icelandic Gigaword Corpus v20.05 (IGC), Size: 8.2 GB, Toke... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #icelandic #masked-lm #is #arxiv-2201.05601 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | mikabeebee/Peterbot | 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
|
# Peter from Your Boyfriend Game.
| [
"# Peter from Your Boyfriend Game."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter from Your Boyfriend Game."
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# msft-regular-model
This model is a fine-tuned version of [](https://huggingface.co/) on the wikitext dataset.
It achieves the fo... | {"tags": ["generated_from_trainer"], "datasets": ["wikitext"], "model-index": [{"name": "msft-regular-model", "results": []}]} | mikaelsouza/msft-regular-model | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikitext",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| msft-regular-model
==================
This model is a fine-tuned version of [](URL on the wikitext dataset.
It achieves the following results on the evaluation set:
* Loss: 5.3420
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-wikitext #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\... |
text-generation | transformers |
# Neosh Bot1
This is a simplified version. Hopefully will train a more complex model in the future. | {"tags": ["conversational"]} | milayue/neosh-bot1 | 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
|
# Neosh Bot1
This is a simplified version. Hopefully will train a more complex model in the future. | [
"# Neosh Bot1\nThis is a simplified version. Hopefully will train a more complex model in the future."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Neosh Bot1\nThis is a simplified version. Hopefully will train a more complex model in the future."
] |
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-amazon-review
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilbert-base-uncased-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "data... | milyiyo/distilbert-base-uncased-finetuned-amazon-review | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"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-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-amazon-review
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3494
* Accuracy: 0.693
* F1: 0.7003
* Precision: 0.7... | [
"### 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-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-gen-finetuned-amazon-review
This model is a fine-tuned version of [mrm8488/electricidad-base-generator](https://hug... | {"tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "electra-base-gen-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_m... | milyiyo/electra-base-gen-finetuned-amazon-review | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-base-gen-finetuned-amazon-review
========================================
This model is a fine-tuned version of mrm8488/electricidad-base-generator on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8030
* Accuracy: 0.5024
* F1: 0.5063
* Precision: 0.51... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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: 7",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-small-finetuned-amazon-review
This model is a fine-tuned version of [google/electra-small-discriminator](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "electra-small-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"na... | milyiyo/electra-small-finetuned-amazon-review | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-small-finetuned-amazon-review
=====================================
This model is a fine-tuned version of google/electra-small-discriminator on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0560
* Accuracy: 0.5504
* F1: 0.5458
* Precision: 0.5429
* Re... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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\... |
text-classification | transformers |
Based model: [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased)
Dataset: [emotion](https://huggingface.co/datasets/emotion)
These are the results on the evaluation set:
| Attribute | Value |
| ------------------ | -------- |
| Training Loss | 0.163100 |
| ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["f1"], "model-index": [{"name": "minilm-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{... | milyiyo/minilm-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| Based model: microsoft/MiniLM-L12-H384-uncased
Dataset: emotion
These are the results on the evaluation set:
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multi-minilm-finetuned-amazon-review
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://hugg... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "multi-minilm-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "am... | milyiyo/multi-minilm-finetuned-amazon-review | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| multi-minilm-finetuned-amazon-review
====================================
This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2436
* Accuracy: 0.5422
* F1: 0.5435
* Precision: 0.5452
* ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# selectra-small-finetuned-amazon-review
This model is a fine-tuned version of [Recognai/selectra_small](https://huggingface.co/Re... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "selectra-small-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"n... | milyiyo/selectra-small-finetuned-amazon-review | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| selectra-small-finetuned-amazon-review
======================================
This model is a fine-tuned version of Recognai/selectra\_small on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6279
* Accuracy: 0.737
* F1: 0.7438
* Precision: 0.7525
* Recall: 0.7... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #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\... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Waynehills-NLP-doogie-AIHub-paper-summary
This model is a fine-tuned version of [mimi/Waynehills-NLP-doogie](https://huggingface... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Waynehills-NLP-doogie-AIHub-paper-summary", "results": []}]} | mimi/Waynehills-NLP-doogie-AIHub-paper-summary | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Waynehills-NLP-doogie-AIHub-paper-summary
This model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6206
- eval_runtime: 309.223
- eval_samples_per_second: 38.167
- eval_steps_per_second: 4.773
- epoch: 3.75
- step... | [
"# Waynehills-NLP-doogie-AIHub-paper-summary\n\nThis model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 2.6206\n- eval_runtime: 309.223\n- eval_samples_per_second: 38.167\n- eval_steps_per_second: 4.773\n- epoch: 3... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Waynehills-NLP-doogie-AIHub-paper-summary\n\nThis model is a fine-tuned version of mimi/Waynehills-NLP-doogie on the None dataset.\nIt achieves ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Waynehills-NLP-doogie
This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "Waynehills-NLP-doogie", "results": []}]} | mimi/Waynehills-NLP-doogie | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Waynehills-NLP-doogie
=====================
This model is a fine-tuned version of KETI-AIR/ke-t5-base-ko on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9188
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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 #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
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. -->
# wynehills-mimi-ASR
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluatio... | {"tags": ["generated_from_trainer"]} | mimi/wynehills-mimi-ASR | 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
| wynehills-mimi-ASR
==================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3822
* Wer: 0.6309
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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 #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: 8\n* eval\\_batch\\_s... |
object-detection | doctr |
# Faster-RCNN model
Pretrained on [DocArtefacts](https://mindee.github.io/doctr/datasets.html#doctr.datasets.DocArtefacts). The Faster-RCNN architecture was introduced in [this paper](https://arxiv.org/pdf/1506.01497.pdf).
## Model description
The core idea of the author is to unify Region Proposal with ... | {"license": "apache-2.0", "library_name": "doctr", "tags": ["object-detection", "pytorch"], "datasets": ["docartefacts"]} | mindee/fasterrcnn_mobilenet_v3_large_fpn | null | [
"doctr",
"pytorch",
"object-detection",
"dataset:docartefacts",
"arxiv:1506.01497",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1506.01497"
] | [] | TAGS
#doctr #pytorch #object-detection #dataset-docartefacts #arxiv-1506.01497 #license-apache-2.0 #has_space #region-us
|
# Faster-RCNN model
Pretrained on DocArtefacts. The Faster-RCNN architecture was introduced in this paper.
## Model description
The core idea of the author is to unify Region Proposal with the core detection module of Fast-RCNN.
## Installation
### Prerequisites
Python 3.6 (or higher) and pip ... | [
"# Faster-RCNN model\r\n\r\nPretrained on DocArtefacts. The Faster-RCNN architecture was introduced in this paper.",
"## Model description\r\n\r\nThe core idea of the author is to unify Region Proposal with the core detection module of Fast-RCNN.",
"## Installation",
"### Prerequisites\r\n\r\nPython 3.6 (or h... | [
"TAGS\n#doctr #pytorch #object-detection #dataset-docartefacts #arxiv-1506.01497 #license-apache-2.0 #has_space #region-us \n",
"# Faster-RCNN model\r\n\r\nPretrained on DocArtefacts. The Faster-RCNN architecture was introduced in this paper.",
"## Model description\r\n\r\nThe core idea of the author is to unif... |
image-classification | mindspore |
## MindSpore Image Classification models with MNIST on the 🤗Hub!
This repository contains the model from [this notebook on image classification with MNIST dataset using LeNet architecture](https://gitee.com/mindspore/mindspore/blob/r1.2/model_zoo/official/cv/lenet/README.md#).
## LeNet Description
Lenet-5 is one ... | {"license": "apache-2.0", "library_name": "mindspore", "tags": ["image-classification"], "datasets": ["mnist"]} | mindspore-ai/LeNet | null | [
"mindspore",
"image-classification",
"dataset:mnist",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#mindspore #image-classification #dataset-mnist #license-apache-2.0 #region-us
|
## MindSpore Image Classification models with MNIST on the Hub!
This repository contains the model from this notebook on image classification with MNIST dataset using LeNet architecture.
## LeNet Description
Lenet-5 is one of the earliest pre-trained models proposed by Yann LeCun and others in the year 1998, in th... | [
"## MindSpore Image Classification models with MNIST on the Hub! \n\nThis repository contains the model from this notebook on image classification with MNIST dataset using LeNet architecture.",
"## LeNet Description\nLenet-5 is one of the earliest pre-trained models proposed by Yann LeCun and others in the year 1... | [
"TAGS\n#mindspore #image-classification #dataset-mnist #license-apache-2.0 #region-us \n",
"## MindSpore Image Classification models with MNIST on the Hub! \n\nThis repository contains the model from this notebook on image classification with MNIST dataset using LeNet architecture.",
"## LeNet Description\nLene... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | minemile/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4718
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #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... |
null | null |
# ai-generated-pokemon-rudalle
A finetuned [ruDALL-E](https://github.com/sberbank-ai/ru-dalle) on Pokémon using the finetuning example Colab Notebook [linked in that repo](https://colab.research.google.com/drive/1Tb7J4PvvegWOybPfUubl5O7m5I24CBg5?usp=sharing). This model was used to create Pokémon th... | {"language": ["en"], "license": "mit", "tags": ["rudalle", "pokemon", "image-generation"]} | minimaxir/ai-generated-pokemon-rudalle | null | [
"pytorch",
"rudalle",
"pokemon",
"image-generation",
"en",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#pytorch #rudalle #pokemon #image-generation #en #license-mit #has_space #region-us
|
# ai-generated-pokemon-rudalle
A finetuned ruDALL-E on Pokémon using the finetuning example Colab Notebook linked in that repo. This model was used to create Pokémon that resulted in AI-Generated Pokémon that went viral (10k+ retweets on Twitter + 30k+ upvotes on Reddit)
The model used above was trained fo... | [
"# ai-generated-pokemon-rudalle\n\n\n\nA finetuned ruDALL-E on Pokémon using the finetuning example Colab Notebook linked in that repo. This model was used to create Pokémon that resulted in AI-Generated Pokémon that went viral (10k+ retweets on Twitter + 30k+ upvotes on Reddit)\n\nThe model used above was ... | [
"TAGS\n#pytorch #rudalle #pokemon #image-generation #en #license-mit #has_space #region-us \n",
"# ai-generated-pokemon-rudalle\n\n\n\nA finetuned ruDALL-E on Pokémon using the finetuning example Colab Notebook linked in that repo. This model was used to create Pokémon that resulted in AI-Generated Pokémo... |
text-generation | transformers | # magic-the-gathering
A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander 2021_.
The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card.
Examples of encoded cards:
```
<|t... | {} | minimaxir/magic-the-gathering | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # magic-the-gathering
A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander 2021_.
The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card.
Examples of encoded cards:
T... | [
"# magic-the-gathering\n\nA small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander 2021_.\n\nThe model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card.\n\nExamples of encoded card... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# magic-the-gathering\n\nA small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander... |
text-generation | transformers |
#Harry Potter DialoGPT-medium Model | {"tags": ["conversational"]} | minsiam/DialoGPT-medium-harrypotterbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT-medium Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | minsiam/DialoGPT-small-harrypotterbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | # BART base negative claim generation model
This is a BART-based model fine-tuned for negative claim generation. This model is used in the data augmentation process described in the paper [CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models](https://arxiv.org/abs/2109.15107). The mo... | {"language": ["en"], "license": "mit", "tags": ["text2text-generation"], "datasets": ["wikifactcheck"], "widget": [{"text": "Little Miss Sunshine was filmed over 30 days."}]} | minwhoo/bart-base-negative-claim-generation | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:wikifactcheck",
"arxiv:2109.15107",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.15107"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-wikifactcheck #arxiv-2109.15107 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # BART base negative claim generation model
This is a BART-based model fine-tuned for negative claim generation. This model is used in the data augmentation process described in the paper CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models. The model has been fine-tuned using the pa... | [
"# BART base negative claim generation model\n\nThis is a BART-based model fine-tuned for negative claim generation. This model is used in the data augmentation process described in the paper CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models. The model has been fine-tuned using... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-wikifactcheck #arxiv-2109.15107 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# BART base negative claim generation model\n\nThis is a BART-based model fine-tuned for negative claim generation. This model is used in... |
text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | miogfd1234/ll | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers | based on `sberbank-ai/rugpt3medium_based_on_gpt2`
finetuned for generate text description for notebook-devices | {} | mipatov/rugpt3_nb_descr | 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
| based on 'sberbank-ai/rugpt3medium_based_on_gpt2'
finetuned for generate text description for notebook-devices | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | based on `sberbank-ai/ruT5-large`
finetuned for generate text description for notebook-devices | {} | mipatov/rut5_nb_descr | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| based on 'sberbank-ai/ruT5-large'
finetuned for generate text description for notebook-devices | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers | # Sentiment Classification by pretraining bert-base-cased
A test repo exploring HF Model Hub by following https://huggingface.co/transformers/model_sharing.html | {} | mishig/my-awesome-model | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Sentiment Classification by pretraining bert-base-cased
A test repo exploring HF Model Hub by following URL | [
"# Sentiment Classification by pretraining bert-base-cased\n\nA test repo exploring HF Model Hub by following URL"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment Classification by pretraining bert-base-cased\n\nA test repo exploring HF Model Hub by following URL"
] |
audio-classification | null |
# Wav2Vec2-Base for Speaker Identification
## Model description
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which i... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav"}, {"example_title": "VoxCeleb Speaker id10004", "src"... | mishig/test_regex_searchreplace | null | [
"speech",
"audio",
"wav2vec2",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.01051"
] | [
"en"
] | TAGS
#speech #audio #wav2vec2 #audio-classification #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #region-us
| Wav2Vec2-Base for Speaker Identification
========================================
Model description
-----------------
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task.
The base model is wav2vec2-base, which is pretrained on 16kHz
sampled speech audio. When using the model ma... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#speech #audio #wav2vec2 #audio-classification #en #dataset-superb #arxiv-2105.01051 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
null | null | # Video demo on ModelCard
Please find [this file](https://huggingface.co/mishig/test_vid/blob/main/README.md) to see how to add a video to model card.
<video src="https://huggingface.co/mishig/test_vid/resolve/main/output.mp4" controls autoplay loop/> | {} | mishig/test_vid | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Video demo on ModelCard
Please find this file to see how to add a video to model card.
<video src="URL controls autoplay loop/> | [
"# Video demo on ModelCard\n\nPlease find this file to see how to add a video to model card.\n\n<video src=\"URL controls autoplay loop/>"
] | [
"TAGS\n#region-us \n",
"# Video demo on ModelCard\n\nPlease find this file to see how to add a video to model card.\n\n<video src=\"URL controls autoplay loop/>"
] |
fill-mask | transformers | A Transformer-based Persian Language Model Further Pretrained on Persian Poetry
ALBERT was first introduced by [Hooshvare](https://huggingface.co/HooshvareLab/albert-fa-zwnj-base-v2?text=%D8%B2+%D8%A2%D9%86+%D8%AF%D8%B1%D8%AF%D8%B4+%5BMASK%5D+%D9%85%DB%8C+%D8%B3%D9%88%D8%AE%D8%AA+%D8%AF%D8%B1+%D8%A8%D8%B1) with 30,000... | {} | mitra-mir/ALBERT-Persian-Poetry | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| A Transformer-based Persian Language Model Further Pretrained on Persian Poetry
ALBERT was first introduced by Hooshvare with 30,000 vocabulary size as lite BERT for self-supervised learning of language representations for the Persian language. Here we wanted to utilize its capabilities by pretraining it on a large co... | [] | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | BERT Language Model Further Pre-trained on Persian Poetry | {} | mitra-mir/BERT-Persian-Poetry | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERT Language Model Further Pre-trained on Persian Poetry | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# DialoGPT-medium-rickman2 | {"tags": ["conversational"]} | mittalnishit/DialoGPT-medium-rickman2 | 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
|
# DialoGPT-medium-rickman2 | [
"# DialoGPT-medium-rickman2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-medium-rickman2"
] |
text-generation | transformers |
# DialoGPT-small-rickman | {"tags": ["conversational"]} | mittalnishit/DialoGPT-small-rickman | 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
|
# DialoGPT-small-rickman | [
"# DialoGPT-small-rickman"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-small-rickman"
] |
text-generation | transformers |
# Samwise Gamgee DialoGPT Model | {"tags": ["conversational"]} | mjstamper/DialoGPT-small-samwise | 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
|
# Samwise Gamgee DialoGPT Model | [
"# Samwise Gamgee DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Samwise Gamgee DialoGPT Model"
] |
text-generation | transformers |
# yea | {"tags": ["conversational"]} | mk3smo/dialogpt-med-ahiru | 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
|
# yea | [
"# yea"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# yea"
] |
text-generation | transformers |
# Duck/Ahiru DialoGPT Model | {"tags": ["conversational"]} | mk3smo/dialogpt-med-duck2 | 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
|
# Duck/Ahiru DialoGPT Model | [
"# Duck/Ahiru DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Duck/Ahiru DialoGPT Model"
] |
text-generation | transformers |
# Duck/Ahiru DialoGPT Model | {"tags": ["conversational"]} | mk3smo/dialogpt-med-duck3 | 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
|
# Duck/Ahiru DialoGPT Model | [
"# Duck/Ahiru DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Duck/Ahiru DialoGPT Model"
] |
text-generation | transformers |
# Duck dialogpt model | {"tags": ["conversational"]} | mk3smo/dialogpt-med-duck5 | 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
|
# Duck dialogpt model | [
"# Duck dialogpt model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Duck dialogpt model"
] |
text-generation | transformers |
# yeah | {"tags": ["conversational"]} | mk3smo/dialogpt-med-duckfinal | 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
|
# yeah | [
"# yeah"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# yeah"
] |
text-generation | transformers |
# not writing shit here | {"tags": ["conversational"]} | mk3smo/dialogpt-med-stt3 | 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
|
# not writing shit here | [
"# not writing shit here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# not writing shit here"
] |
text-generation | transformers | # DEADPOOL DialoGPT Model | {"tags": ["conversational"]} | mklucifer/DialoGPT-medium-DEADPOOL | 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
| # DEADPOOL DialoGPT Model | [
"# DEADPOOL DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DEADPOOL DialoGPT Model"
] |
text-generation | transformers | # DEADPOOL DialoGPT Model | {"tags": ["conversational"]} | mklucifer/DialoGPT-small-DEADPOOL | 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
| # DEADPOOL DialoGPT Model | [
"# DEADPOOL DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DEADPOOL DialoGPT Model"
] |
sentence-similarity | sentence-transformers |
An XML-RoBERTa based cross-lingual Sentence-BERT model distilled to cover semantic textual similarity in Finnish in addition to English. At the time of creation there were no models performing better in Finnish STS that I was aware of.
# Usage instructions
This model is essentially an extended SentenceTransformer s... | {"language": ["fi", "en"], "tags": ["sentence-similarity", "sentence-transformers"], "widget": [{"source-sentence": "mik\u00e4 on teid\u00e4n paras telkkari"}]} | mkmoisio/xlm-r-cross-lingual-english-finnish-sts | null | [
"sentence-transformers",
"sentence-similarity",
"fi",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fi",
"en"
] | TAGS
#sentence-transformers #sentence-similarity #fi #en #endpoints_compatible #region-us
|
An XML-RoBERTa based cross-lingual Sentence-BERT model distilled to cover semantic textual similarity in Finnish in addition to English. At the time of creation there were no models performing better in Finnish STS that I was aware of.
# Usage instructions
This model is essentially an extended SentenceTransformer s... | [
"# Usage instructions\n\nThis model is essentially an extended SentenceTransformer so instructions described at URL apply.",
"# The other things\n\nThe training setup, data, optimizer parameters, limitations and evaluation is described in Ch 6 here and repository.",
"# Credit\n\nThis heavily builds on the work ... | [
"TAGS\n#sentence-transformers #sentence-similarity #fi #en #endpoints_compatible #region-us \n",
"# Usage instructions\n\nThis model is essentially an extended SentenceTransformer so instructions described at URL apply.",
"# The other things\n\nThe training setup, data, optimizer parameters, limitations and eva... |
null | null | Frequency Distribution of Free Text SIGs from medication orders in Allscripts | {} | mkrigba/FreeTextSIG | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Frequency Distribution of Free Text SIGs from medication orders in Allscripts | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
## City-Country-NER
A `bert-base-uncased` model finetuned on a custom dataset to detect `Country` and `City` names from a given sentence.
### Custom Dataset
We weakly supervised the [Ultra-Fine Entity Typing](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html) dataset to include the `City` and `Country... | {"language": ["en"], "tags": ["token-classification", "address-NER", "NER", "bert-base-uncased"], "datasets": ["Ultra Fine Entity Typing"], "metrics": ["Precision", "Recall", "F1 Score"], "widget": [{"text": "Hi, I am Kermit and I live in Berlin"}, {"text": "It is very difficult to find a house in Berlin, Germany."}, {... | ml6team/bert-base-uncased-city-country-ner | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"address-NER",
"NER",
"bert-base-uncased",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #address-NER #NER #bert-base-uncased #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## City-Country-NER
A 'bert-base-uncased' model finetuned on a custom dataset to detect 'Country' and 'City' names from a given sentence.
### Custom Dataset
We weakly supervised the Ultra-Fine Entity Typing dataset to include the 'City' and 'Country' information. We also did some extra preprocessing to remove fal... | [
"## City-Country-NER\n\nA 'bert-base-uncased' model finetuned on a custom dataset to detect 'Country' and 'City' names from a given sentence.",
"### Custom Dataset\nWe weakly supervised the Ultra-Fine Entity Typing dataset to include the 'City' and 'Country' information. We also did some extra preprocessing to re... | [
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"## City-Country-NER\n\nA 'bert-base-uncased' model finetuned on a custom dataset to detect 'Country' and 'City' names from a given sentence... |
text2text-generation | transformers | # ByT5 Dutch OCR Correction
This model is a finetuned byT5 model that corrects OCR mistakes found in dutch sentences. The [google/byt5-base](https://huggingface.co/google/byt5-base) model is finetuned on the dutch section of the [OSCAR](https://huggingface.co/datasets/oscar) dataset.
## Usage
```python
from trans... | {} | ml6team/byt5-base-dutch-ocr-correction | null | [
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # ByT5 Dutch OCR Correction
This model is a finetuned byT5 model that corrects OCR mistakes found in dutch sentences. The google/byt5-base model is finetuned on the dutch section of the OSCAR dataset.
## Usage
| [
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"## Usage"
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summarization | transformers |
# T&C Summarization Model
T&C Summarization Model based on [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6),
This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline
which is preceded by LSA (Latent Semantic Analysis) extractive summarizati... | {"language": ["en"], "tags": ["summarization", "t&c", "tos", "distilbart", "distilbart-6-6"], "datasets": ["tosdr"], "metrics": ["rouge1", "rouge2", "rougel"], "inference": {"parameters": {"min_length": 5, "max_length": 512, "do_sample": false}}, "widget": [{"text": "In addition, certain portions of the Web Site may be... | ml6team/distilbart-tos-summarizer-tosdr | null | [
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"en",
"dataset:tosdr",
"autotrain_compatible",
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# T&C Summarization Model
T&C Summarization Model based on sshleifer/distilbart-cnn-6-6,
This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline
which is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive
summarization shortens the T&C to b... | [
"# T&C Summarization Model \n\nT&C Summarization Model based on sshleifer/distilbart-cnn-6-6, \n\nThis abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline \nwhich is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive \nsummarization shortens th... | [
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"# T&C Summarization Model \n\nT&C Summarization Model based on sshleifer/distilbart-cnn-6-6, \n\nThis abstract... |
text-classification | transformers | # distilbert-base-dutch-toxic-comments
## Model description:
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we finetuned a multilingual distilbert model [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the tran... | {"language": ["nl"], "license": "apache-2.0", "tags": ["text-classification", "pytorch"], "metrics": ["Accuracy, F1 Score, Recall, Precision"], "widget": [{"text": "Ik heb je lief met heel mijn hart", "example_title": "Non toxic comment 1"}, {"text": "Dat is een goed punt, zo had ik het nog niet bekeken.", "example_tit... | ml6team/distilbert-base-dutch-cased-toxic-comments | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"nl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #distilbert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-dutch-toxic-comments
====================================
Model description:
------------------
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we finetuned a multilingual distilbert model distilbert-base-multilingual-cased on the translated... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
# German Toxic Comment Classification
## Model Description
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we fine-tuned a German DistilBERT model [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on a combination of five Ger... | {"language": ["de"], "tags": ["distilbert", "german", "classification"], "datasets": ["germeval21"], "widget": [{"text": "Das ist ein guter Punkt, so hatte ich das noch nicht betrachtet.", "example_title": "Agreement (non-toxic)"}, {"text": "Wow, was ein geiles Spiel. Gl\u00fcckwunsch.", "example_title": "Football (non... | ml6team/distilbert-base-german-cased-toxic-comments | null | [
"transformers",
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"distilbert",
"text-classification",
"german",
"classification",
"de",
"dataset:germeval21",
"arxiv:1701.08118",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1701.08118"
] | [
"de"
] | TAGS
#transformers #pytorch #distilbert #text-classification #german #classification #de #dataset-germeval21 #arxiv-1701.08118 #autotrain_compatible #endpoints_compatible #has_space #region-us
| German Toxic Comment Classification
===================================
Model Description
-----------------
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we fine-tuned a German DistilBERT model distilbert-base-german-cased on a combination of five German ... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #german #classification #de #dataset-germeval21 #arxiv-1701.08118 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers | This model has been finetuned on the [`Quotes-500K`](https://github.com/ShivaliGoel/Quotes-500K) dataset to generate quotes based on given topics. To generate a quote, use the following input prompt:
`Given Topics: topic 1 | topic 2 | ... | topic n. Related Quote: ` | {} | ml6team/gpt-2-medium-conditional-quote-generator | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| This model has been finetuned on the 'Quotes-500K' dataset to generate quotes based on given topics. To generate a quote, use the following input prompt:
'Given Topics: topic 1 | topic 2 | ... | topic n. Related Quote: ' | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Dutch finetuned GPT2 | {"language": "nl", "tags": ["adaption", "recycled", "gpt2-medium", "gpt2"], "widget": [{"text": "De regering heeft beslist dat"}], "pipeline_tag": "text-generation"} | ml6team/gpt2-medium-dutch-finetune-oscar | null | [
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"nl",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #adaption #recycled #gpt2-medium #nl #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Dutch finetuned GPT2 | [
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"# Dutch finetuned GPT2"
] |
text-generation | transformers |
# German finetuned GPT2 | {"language": "de", "tags": ["adaption", "recycled", "gpt2-medium", "gpt2"], "widget": [{"text": "es wird entschieden, dass es"}], "pipeline_tag": "text-generation"} | ml6team/gpt2-medium-german-finetune-oscar | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"adaption",
"recycled",
"gpt2-medium",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #adaption #recycled #gpt2-medium #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# German finetuned GPT2 | [
"# German finetuned GPT2"
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"# German finetuned GPT2"
] |
text-generation | transformers |
# Dutch finetuned GPT2
| {"language": "nl", "tags": ["adaption", "recycled", "gpt2-small"], "widget": [{"text": "De regering heeft beslist dat"}], "pipeline_tag": "text-generation"} | ml6team/gpt2-small-dutch-finetune-oscar | null | [
"transformers",
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"jax",
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"text-generation",
"adaption",
"recycled",
"gpt2-small",
"nl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #adaption #recycled #gpt2-small #nl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dutch finetuned GPT2
| [
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] |
text-generation | transformers |
# German finetuned GPT2 | {"language": "de", "tags": ["adaption", "recycled", "gpt2-small"], "widget": [{"text": "es wird entschieden, dass es"}], "pipeline_tag": "text-generation"} | ml6team/gpt2-small-german-finetune-oscar | null | [
"transformers",
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"jax",
"gpt2",
"text-generation",
"adaption",
"recycled",
"gpt2-small",
"de",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #adaption #recycled #gpt2-small #de #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# German finetuned GPT2 | [
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"# German finetuned GPT2"
] |
summarization | transformers | # mbart-large-cc25-cnn-dailymail-nl
## Model description
Finetuned version of [mbart](https://huggingface.co/facebook/mbart-large-cc25). We also wrote a **blog post** about this model [here](https://blog.ml6.eu/why-we-open-sourced-two-dutch-summarization-datasets-1047445abc97)
## Intended uses & limitations
It's meant ... | {"language": ["nl"], "tags": ["mbart", "bart", "summarization"], "datasets": ["ml6team/cnn_dailymail_nl"], "pipeline_tag": "summarization", "widget": [{"text": "Het jongetje werd eind april met zwaar letsel naar het ziekenhuis gebracht in Maastricht. Drie weken later overleed het kindje als gevolg van het letsel. Onder... | ml6team/mbart-large-cc25-cnn-dailymail-nl-finetune | null | [
"transformers",
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"mbart",
"text2text-generation",
"bart",
"summarization",
"nl",
"dataset:ml6team/cnn_dailymail_nl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #bart #summarization #nl #dataset-ml6team/cnn_dailymail_nl #autotrain_compatible #endpoints_compatible #region-us
| # mbart-large-cc25-cnn-dailymail-nl
## Model description
Finetuned version of mbart. We also wrote a blog post about this model here
## Intended uses & limitations
It's meant for summarizing Dutch news articles.
#### How to use
## Training data
Finetuned mbart with this dataset and another smaller dataset that we can'... | [
"# mbart-large-cc25-cnn-dailymail-nl",
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"## Intended uses & limitations\nIt's meant for summarizing Dutch news articles.",
"#### How to use",
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"# mbart-large-cc25-cnn-dailymail-nl",
"## Model description\nFinetuned version of mbart. We also wrote a blog post about this model h... |
summarization | transformers |
# mbart-large-cc25-cnn-dailymail-nl
## Model description
Finetuned version of [mbart](https://huggingface.co/facebook/mbart-large-cc25). We also wrote a **blog post** about this model [here](https://blog.ml6.eu/why-we-open-sourced-two-dutch-summarization-datasets-1047445abc97)
## Intended uses & limitations
It's mea... | {"language": ["nl"], "tags": ["mbart", "bart", "summarization"], "datasets": ["ml6team/cnn_dailymail_nl"], "pipeline_tag": "summarization", "widget": [{"text": "Het jongetje werd eind april met zwaar letsel naar het ziekenhuis gebracht in Maastricht. Drie weken later overleed het kindje als gevolg van het letsel. Onder... | ml6team/mbart-large-cc25-cnn-dailymail-nl | null | [
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"summarization",
"nl",
"dataset:ml6team/cnn_dailymail_nl",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #bart #summarization #nl #dataset-ml6team/cnn_dailymail_nl #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# mbart-large-cc25-cnn-dailymail-nl
## Model description
Finetuned version of mbart. We also wrote a blog post about this model here
## Intended uses & limitations
It's meant for summarizing Dutch news articles.
#### How to use
## Training data
Finetuned mbart with this dataset
| [
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"# mbart-large-cc25-cnn-dailymail-nl",
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summarization | transformers |
# mbart-large-cc25-cnn-dailymail-xsum-nl
## Model description
Finetuned version of [mbart](https://huggingface.co/facebook/mbart-large-cc25). We also wrote a **blog post** about this model [here](https://blog.ml6.eu/why-we-open-sourced-two-dutch-summarization-datasets-1047445abc97)
## Intended uses & limitations
It'... | {"language": ["nl"], "tags": ["mbart", "bart", "summarization"], "datasets": ["ml6team/cnn_dailymail_nl", "ml6team/xsum_nl"], "pipeline_tag": "summarization", "widget": [{"text": "Het jongetje werd eind april met zwaar letsel naar het ziekenhuis gebracht in Maastricht. Drie weken later overleed het kindje als gevolg va... | ml6team/mbart-large-cc25-cnn-dailymail-xsum-nl | null | [
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"dataset:ml6team/cnn_dailymail_nl",
"dataset:ml6team/xsum_nl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #mbart #text2text-generation #bart #summarization #nl #dataset-ml6team/cnn_dailymail_nl #dataset-ml6team/xsum_nl #autotrain_compatible #endpoints_compatible #region-us
|
# mbart-large-cc25-cnn-dailymail-xsum-nl
## Model description
Finetuned version of mbart. We also wrote a blog post about this model here
## Intended uses & limitations
It's meant for summarizing Dutch news articles.
#### How to use
## Training data
Finetuned mbart with this dataset and this dataset
| [
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summarization | transformers |
# mT5-small fine-tuned on German MLSUM
This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens.
It was fine-tuned on all German articles present in the train split of the [MLSUM dataset](https://huggingface.co/datasets/mlsum) having less than 384 "words" after spli... | {"language": "de", "tags": ["summarization"], "datasets": ["mlsum"]} | ml6team/mt5-small-german-finetune-mlsum | null | [
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"summarization",
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"dataset:mlsum",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #summarization #de #dataset-mlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5-small fine-tuned on German MLSUM
====================================
This model was finetuned for 3 epochs with a max\_len (input) of 768 tokens and target\_max\_len of 192 tokens.
It was fine-tuned on all German articles present in the train split of the MLSUM dataset having less than 384 "words" after split... | [] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #summarization #de #dataset-mlsum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | # RobBERT-dutch-base-toxic-comments
## Model description:
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we finetuned a dutch RobBerta-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the translated [Jigsaw Toxicity data... | {"language": ["nl"], "license": "apache-2.0", "tags": ["text-classification", "pytorch"], "metrics": ["Accuracy, F1 Score, Recall, Precision"], "widget": [{"text": "Ik heb je lief met heel mijn hart", "example_title": "Non toxic comment 1"}, {"text": "Dat is een goed punt, zo had ik het nog niet bekeken.", "example_tit... | ml6team/robbert-dutch-base-toxic-comments | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #roberta #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| RobBERT-dutch-base-toxic-comments
=================================
Model description:
------------------
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we finetuned a dutch RobBerta-based model called RobBERT on the translated Jigsaw Toxicity dataset.
T... | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | mlcorelib/deberta-base-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT base model (uncased)
=========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team rel... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\... |
fill-mask | transformers |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | mlcorelib/debertav2-base-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT base model (uncased)
=========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team rel... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #tf #jax #rust #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\... |
fill-mask | transformers | # GlassBERTa
## Language Modelling as Unsupervised Pre-Training for Glass Alloys
### Abstract:
Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously. This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model... | {"license": "mit", "tags": ["fill-mask", "alloys", "metallurgy"], "widget": [{"text": "Li 7 1 , <mask> 6 1 8 , Na 8 2 , P 2 0 9 , Pb 2 0"}]} | mldmm/GlassBERTa | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"alloys",
"metallurgy",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #alloys #metallurgy #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # GlassBERTa
## Language Modelling as Unsupervised Pre-Training for Glass Alloys
### Abstract:
Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously. This is modeled as a Supervised Task wherein Alloy Composition is provided for the Model... | [
"# GlassBERTa",
"## Language Modelling as Unsupervised Pre-Training for Glass Alloys",
"### Abstract:\nAlloy Property Prediction is a task under the sub field of Alloy Material Science wherein Machine Learning has been applied rigorously. This is modeled as a Supervised Task wherein Alloy Composition is provid... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #alloys #metallurgy #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# GlassBERTa",
"## Language Modelling as Unsupervised Pre-Training for Glass Alloys",
"### Abstract:\nAlloy Property Prediction is a task under the sub field of Alloy... |
text-classification | transformers | ## BERT Model for OGBV gendered text classification
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mlkorra/OGBV-gender-bert-hi-en")
model = AutoModelForSequenceClassification.from_pretrained("mlkorra/OGBV-gender-bert-h... | {} | mlkorra/OGBV-gender-bert-hi-en | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| BERT Model for OGBV gendered text classification
------------------------------------------------
How to use
----------
Model Performance
-----------------
Metric: Accuracy, dev: 0.88, test: 0.81
Metric: F1(weighted), dev: 0.86, test: 0.80
| [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Michael Scott DialoGPT model | {"tags": ["conversational"]} | mluengas/DialogGPT-small-michaelscott | 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
|
# Michael Scott DialoGPT model | [
"# Michael Scott DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT model"
] |
feature-extraction | transformers | # roberta-base-mld
This is a pretrained roberta-base model for machine learning domain documents.
| {} | mm/roberta-base-mld | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #feature-extraction #endpoints_compatible #region-us
| # roberta-base-mld
This is a pretrained roberta-base model for machine learning domain documents.
| [
"# roberta-base-mld\n\nThis is a pretrained roberta-base model for machine learning domain documents."
] | [
"TAGS\n#transformers #pytorch #jax #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# roberta-base-mld\n\nThis is a pretrained roberta-base model for machine learning domain documents."
] |
feature-extraction | transformers | # roberta-large-mld
This is a pretrained roberta-large model for machine learning domain documents.
| {} | mm/roberta-large-mld | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #feature-extraction #endpoints_compatible #region-us
| # roberta-large-mld
This is a pretrained roberta-large model for machine learning domain documents.
| [
"# roberta-large-mld\n\nThis is a pretrained roberta-large model for machine learning domain documents."
] | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# roberta-large-mld\n\nThis is a pretrained roberta-large model for machine learning domain documents."
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134442
- CO2 Emissions (in grams): 298.7849611952843
## Validation Metrics
- Loss: 0.21618066728115082
- Accuracy: 0.9393
- Precision: 0.9360730593607306
- Recall: 0.943
- AUC: 0.98362804
- F1: 0.9395237620803029
## Usage
You can us... | {"language": "en", "tags": "autonlp", "datasets": ["mmcquade11/autonlp-data-imdb-test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 298.7849611952843} | mmcquade11/autonlp-imdb-test-21134442 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-imdb-test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-mmcquade11/autonlp-data-imdb-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134442
- CO2 Emissions (in grams): 298.7849611952843
## Validation Metrics
- Loss: 0.21618066728115082
- Accuracy: 0.9393
- Precision: 0.9360730593607306
- Recall: 0.943
- AUC: 0.98362804
- F1: 0.9395237620803029
## Usage
You can us... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 21134442\n- CO2 Emissions (in grams): 298.7849611952843",
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... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-mmcquade11/autonlp-data-imdb-test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 21134442\n- CO2 Emissions (... |
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