Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
Paper β’ 2110.06696 β’ Published β’ 2
How to use Langboat/mengzi-bert-base-fin with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="Langboat/mengzi-bert-base-fin") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Langboat/mengzi-bert-base-fin")
model = AutoModelForMaskedLM.from_pretrained("Langboat/mengzi-bert-base-fin")Continue trained mengzi-bert-base with 20G financial news and research reports. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base-fin")
model = BertModel.from_pretrained("Langboat/mengzi-bert-base-fin")
If you find the technical report or resource is useful, please cite the following technical report in your paper.
@misc{zhang2021mengzi,
title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese},
author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
year={2021},
eprint={2110.06696},
archivePrefix={arXiv},
primaryClass={cs.CL}
}