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Erlangshen-Roberta-110M-Sentiment

Text Classification
Transformers
PyTorch
Chinese
bert
roberta
NLU
Sentiment
Chinese
Inference Endpoints
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@@ -29,7 +29,7 @@ output=model(torch.tensor([tokenizer.encode(text)]))
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  print(torch.nn.functional.softmax(output.logits,dim=-1))
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  ```
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- ## Scores on downstream chinese tasks（The dev datasets of BUSTM and AFQMC may exist in the train set）
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  | Model | ASAP-SENT | ASAP-ASPECT | ChnSentiCorp |
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  | :--------: | :-----: | :----: | :-----: |
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  | Erlangshen-Roberta-110M-Sentiment | 97.77 | 97.31 | 96.61 |
 
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  print(torch.nn.functional.softmax(output.logits,dim=-1))
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  ```
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+ ## Scores on downstream chinese tasks
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  | Model | ASAP-SENT | ASAP-ASPECT | ChnSentiCorp |
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  | :--------: | :-----: | :----: | :-----: |
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  | Erlangshen-Roberta-110M-Sentiment | 97.77 | 97.31 | 96.61 |