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Erlangshen-Roberta-110M-Sentiment

Text Classification
Transformers
PyTorch
Chinese
bert
roberta
NLU
Sentiment
Chinese
Inference Endpoints
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@@ -37,7 +37,7 @@ print(torch.nn.functional.softmax(output.logits,dim=-1))
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  | :--------: | :-----: | :----: | :-----: |
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  | Erlangshen-Roberta-110M-Sentiment | 97.77 | 97.31 | 96.61 |
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  | Erlangshen-Roberta-330M-Sentiment | 97.9 | 97.51 | 96.66 |
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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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  | :--------: | :-----: | :----: | :-----: |
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  | Erlangshen-Roberta-110M-Sentiment | 97.77 | 97.31 | 96.61 |
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  | Erlangshen-Roberta-330M-Sentiment | 97.9 | 97.51 | 96.66 |
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+ | Erlangshen-MegatronBert-1.3B-Sentiment | 98.1 | 97.8 | 97 |
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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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  ```