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Erlangshen-UniMC-RoBERTa-110M-Chinese

Fill-Mask
Transformers
PyTorch
Safetensors
bert
classification
zero-shot
Inference Endpoints
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@@ -65,6 +65,16 @@ avoiding problems in commonly used large generative models such as FLAN. It not
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  | [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **88.79** | 42.06 | 55.21 | 33.93 | **75.57** | **79.5** | **89.4** | 50.25 | **66.67** | **64.53** |
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  ## 使用 Usage
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  ```shell
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  git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
 
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  | [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **88.79** | 42.06 | 55.21 | 33.93 | **75.57** | **79.5** | **89.4** | 50.25 | **66.67** | **64.53** |
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+ **Full dataset**
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+ | Model | AFQMC | TNEWS1.1 | IFLYTEK | OCNLI | CMNLI | WSC1.1 | CSL | CHID | C3 |
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+ |--------------------------------------------|-------|----------|---------|-------|-------|--------|-------|-------|-------|
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+ | RoBERTa-Base | 74.06 | 57.5 | 60.36 | 74.3 | 79.73 | 83.48 | 85.37 | - | - |
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+ | RoBERTa-Large | 74.88 | 58.79 | 61.52 | 77.7 | 81.4 | 89.14 | 86 | - | - |
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+ | [Erlangshen-MegatronBert-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-MegatronBert-1.3B) 「Finetuning」 | 76.08 | 59.38 | 62.34 | 79.14 | 81 | 92.43 | 87.2 | 84.65 | 86.77 |
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+ | [Erlangshen-UniMC-MegatronBERT-1.3B-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | 77.09 | 60.4 | 62.67 | 83.05 | 84.76 | 93.74 | 87.67 | 85.93 | 86.54 |
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  ## 使用 Usage
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  ```shell
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  git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git