Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: https://github.com/google-research/electra
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More resources by HFL: https://github.com/ymcui/HFL-Anthology
from modelscope.models import Model
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == '__main__':
task = Tasks.fill_mask
sentence1 = '生活的真谛是[MASK]。'
model_id = 'dienstag/chinese-legal-electra-large-discriminator'
model = Model.from_pretrained(model_id)
pipeline_ins = pipeline(task=Tasks.fill_mask,
model=model,
model_revision='v1.0.0')
print(pipeline_ins(input=sentence1))
If you find our resource or paper is useful, please consider including the following citation in your paper.
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}