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IDEA-CCNL
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Erlangshen-UniMC-DeBERTa-v2-330M-Chinese

Transformers
PyTorch
deberta-v2
classification
zero-shot
Model card Files
xet
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@@ -43,8 +43,8 @@ We use full data to evaluate our model performance and compare it with RoBERTa
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  | Model | afqmc | tnews | iflytek | ocnli |
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  |------------|------------|----------|-----------|----------|
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- | roberta-base | 74.06 | 57.5 | 60.36 | 74.3 |
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- | roberta-large | 74.88 | 58.79 | 61.52 | 77.77 |
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  | [Erlangshen-UniMC-DeBERTa-v2-110M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-110M-Chinese/) | 74.49 | 57.28 | 61.42 | 76.98 |
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  | [Erlangshen-UniMC-DeBERTa-v2-330M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-330M-Chinese/) | **76.16** | 58.61 | - | **81.86** |
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  | Model | afqmc | tnews | iflytek | ocnli |
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  |------------|------------|----------|-----------|----------|
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+ | RoBERTa-110M | 74.06 | 57.5 | 60.36 | 74.3 |
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+ | RoBERTa-330M | 74.88 | 58.79 | 61.52 | 77.77 |
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  | [Erlangshen-UniMC-DeBERTa-v2-110M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-110M-Chinese/) | 74.49 | 57.28 | 61.42 | 76.98 |
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  | [Erlangshen-UniMC-DeBERTa-v2-330M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-DeBERTa-v2-330M-Chinese/) | **76.16** | 58.61 | - | **81.86** |
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