File size: 1,819 Bytes
f22e343 41af8ad f22e343 41af8ad f22e343 41af8ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
language:
- zh
license: apache-2.0
tags:
- bert
inference: true
widget:
- text: "中国首都位于[MASK]。"
---
# Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
The 186 million parameter deberta-V2 base model, using 180G Chinese data, 8 3090TI(24G) training for 21 days，which is a encoder-only transformer structure. Consumed totally 500M samples.
We pretrained a 128000 vocab from train datasets using sentence piece. And achieve a better in downstream task.
## Task Description
Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece 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-v2-186M-Chinese-SentencePiece', use_fast=False)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece')
text = '中国首都位于[MASK]。'
fillmask_pipe = FillMaskPipeline(model, tokenizer)
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-v2-186M-Chinese-SentencePiece** | 0.7625 | 0.81 |
## 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}},
}
```
|