readme: ---
  language: 
    - zh
  license: apache-2.0
  
  tags:
  - bert
  - NLU
  - NLI
  
  inference: true
  
  widget:
  - text: "今天心情不好[SEP]今天很开心"
  
  ---
  # Erlangshen-Roberta-110M-Similarity, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
  We collect 20  paraphrace datasets in the Chinese domain for finetune, with a total of 2773880 samples. Our model is mainly based on [roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
  
  ## Usage
  ```python
  from transformers import BertForSequenceClassification
  from transformers import BertTokenizer
  import torch
  
  tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
  model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
  
  texta='今天的饭不好吃'
  textb='今天心情不好'
  
  output=model(torch.tensor([tokenizer.encode(texta,textb)]))
  print(torch.nn.functional.softmax(output.logits,dim=-1))
  
  ```
  ## Scores on downstream chinese tasks（The dev datasets of BUSTM and AFQMC may exist in the train set） 
  |    Model   | BQ    |  BUSTM  | AFQMC    |
  | :--------:    | :-----:  | :----:  | :-----:   | 
  | Erlangshen-Roberta-110M-Similarity | 85.41     |   95.18    | 81.72     |
  | Erlangshen-Roberta-330M-Similarity | 86.21      |   99.29    | 93.89      |  
  | Erlangshen-MegatronBert-1.3B-Similarity | 86.31      |   -    | -      |   
  
  ## Citation
  If you find the resource is useful, please cite the following website in your paper.
  ```
  @misc{Fengshenbang-LM,
    title={Fengshenbang-LM},
    author={IDEA-CCNL},
    year={2021},
    howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
  }
  ```