Instructions to use weiweishi/roc-bert-base-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use weiweishi/roc-bert-base-zh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="weiweishi/roc-bert-base-zh")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh") model = AutoModelForPreTraining.from_pretrained("weiweishi/roc-bert-base-zh") - Inference
- Notebooks
- Google Colab
- Kaggle
RoCBert
Introduction
RoCBert is a pretrained Chinese language model that is robust under various forms of adversarial attacks proposed by WeChatAI in 2022,
More detail: https://aclanthology.org/2022.acl-long.65.pdf
Pretrained code: https://github.com/sww9370/RoCBert
How to use
# pip install transformers>=4.25.1
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh")
model = AutoModel.from_pretrained("weiweishi/roc-bert-base-zh")
Citation
@inproceedings{su2022rocbert,
title={RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining},
author={Su, Hui and Shi, Weiwei and Shen, Xiaoyu and Xiao, Zhou and Ji, Tuo and Fang, Jiarui and Zhou, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={921--931},
year={2022}
}
- Downloads last month
- 104,819