metadata

language:
  - zh
license: apache-2.0
tags:
  - bert
inference: true
widget:
  - text: 生活的真谛是[MASK]。

Erlangshen-Deberta-XLarge-710M-Chinese，one model of Fengshenbang-LM

The 710 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 21 days，which is a encoder-only transformer structure. Consumed totally 700M samples. Still training...

Task Description

Erlangshen-Deberta-XLarge-710M-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-XLarge-710M-Chinese', use_fast=false)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-Deberta-XLarge-710M-Chinese')
text = '生活的真谛是[MASK]。'
fillmask_pipe = FillMaskPipeline(model, tokenizer, device=-1)
print(fillmask_pipe(text, top_k=10))

Finetune

We present the dev results on some tasks.

Model	AFQMC	TNEWS1.1	IFLYTEK	OCNLI	CMNLI
RoBERTa-Large	0.7488	0.5879	0.6152	0.777	0.814
Erlangshen-Deberta-XLarge-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}},
}