Erlangshen-Deberta-97M-Chinese，one model of 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

Usage

from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
import torch

tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Deberta-97M-Chinese', use_fast=false)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-Deberta-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}},
}