language: Chinese
widget:
- text: 北京上个月召开了两会
Chinese RoBERTa-Base Models for Text Classification
Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by UER-py. You can download the 5 Chinese RoBERTa-Base classification models either from the UER-py Modelzoo page (in UER-py format), or via HuggingFace from the links below:
| Dataset | Link |
|---|---|
| JD full | roberta-base-finetuned-jd-full-chinese |
| JD binary | roberta-base-finetuned-jd-binary-chinese |
| Dianping | roberta-base-finetuned-dianping-chinese |
| Ifeng | roberta-base-finetuned-ifeng-chinese |
| Chinanews | roberta-base-finetuned-chinanews-chinese |
How to use
You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese):
>>> from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline
>>> model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
>>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese')
>>> text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> text_classification("北京上个月召开了两会")
[{'label': 'mainland China politics', 'score': 0.7211663722991943}]
Training data
We use 5 Chinese text classification datasets which are collected by Glyph project.
Training procedure
Models are fine-tuned by UER-py on Tencent Cloud. We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model chinese_roberta_L-12_H-768. At the end of each epoch, the model is saved when the best performance on development set is achieved. We use the same hyper-parameters on different models.
Taking the case of roberta-base-finetuned-chinanews-chinese
python3 run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \
--vocab_path models/google_zh_vocab.txt \
--train_path datasets/glyph/chinanews/train.tsv \
--dev_path datasets/glyph/chinanews/dev.tsv \
--output_model_path models/chinanews_classifier_model.bin \
--learning_rate 3e-5 --batch_size 32 --epochs_num 3 --seq_length 512 \
--embedding word_pos_seg --encoder transformer --mask fully_visible
Finally, we convert the pre-trained model into Huggingface's format:
python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/chinanews_classifier_model.bin \
--output_model_path pytorch_model.bin \
--layers_num 12
BibTeX entry and citation info
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}