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Erlangshen-Roberta-110M-NLI

Text Classification
Transformers
PyTorch
Chinese
bert
roberta
NLU
NLI
Chinese
Inference Endpoints
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@@ -38,6 +38,7 @@ print(torch.nn.functional.softmax(output.logits,dim=-1))
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  | :--------: | :-----: | :----: | :-----: |
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  | Erlangshen-Roberta-110M-NLI | 80.83 | 78.56 | 88.01 |
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  | Erlangshen-Roberta-330M-NLI | 82.25 | 79.82 | 88 |
 
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  ## Citation
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  If you find the resource is useful, please cite the following website in your paper.
 
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  | :--------: | :-----: | :----: | :-----: |
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  | Erlangshen-Roberta-110M-NLI | 80.83 | 78.56 | 88.01 |
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  | Erlangshen-Roberta-330M-NLI | 82.25 | 79.82 | 88 |
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+ | Erlangshen-MegatronBert-1.3B-NLI | 84.52 | 84.17 | 88.67 |
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  ## Citation
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  If you find the resource is useful, please cite the following website in your paper.