readme: ---
  language: 
    - zh
  
  license: apache-2.0
  
  tags:
    - bert
  
  inference: true
  
  widget:
  - text: "生活的真谛是[MASK]。"
  ---
  # Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
  The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples.
  
  
  ## Task Description
  
  Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta [paper](https://readpaper.com/paper/3033187248)
  
  
  ## Usage
  ```python
  from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
  import torch
  
  tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese', use_fast=False)
  model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese')
  text = '生活的真谛是[MASK]。'
  fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7)
  print(fillmask_pipe(text, top_k=10))
  ```
  
  ## Finetune
  
  We present the dev results on some tasks.
  
  | Model                              | OCNLI | CMNLI  |
  | ---------------------------------- | ----- | ------ |
  | RoBERTa-base                       | 0.743 | 0.7973 |
  | **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807  |
  
  ## 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={2022},
    howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
  }
  ```