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---
language:
- zh
license: apache-2.0
tags:
- bert
- deberta
inference: true
widget:
- text: "桂林是世界闻名的旅游城市,它有[MASK]江。"
---
# Erlangshen-DeBERTa-v2-320M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
The 320 million parameter deberta-V2 base model, using 180G Chinese data, 8 A100(80G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 250M samples.
**our model is still training. And we will update our model once a week!**
## 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-v2-320M-Chinese', use_fast=False)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese')
text = '桂林是世界闻名的旅游城市,它有[MASK]江。'
fillmask_pipe = FillMaskPipeline(model, tokenizer, device=0)
print(fillmask_pipe(text, top_k=10))
```
## Finetune
We present the dev results on some tasks(dev set).
| Model | AFQMC | TNEWS1.1 | IFLYTEK | OCNLI | CMNLI |
| -------------------------------------------------------------------------------------------------------------------------------- | ------ | -------- | ------- | ------ | ------ |
| RoBERTa-base | 0.7406 | 0.575 | 0.6036 | 0.743 | 0.7973 |
| RoBERTa-large | 0.7488 | 0.5879 | 0.6152 | 0.777 | 0.814 |
| [IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece) | 0.7405 | 0.571 | 0.5977 | 0.7568 | 0.807 |
| **[IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese)** | 0.7498 | 0.5817 | 0.6042 | 0.8022 | 0.8301 |
| [IDEA-CCNL/Erlangshen-Deberta-XLarge-710M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese) | 0.7549 | 0.5873 | 0.6177 | 0.8012 | 0.8389 |
## 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}},
}
```
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