|
--- |
|
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 models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658). |
|
|
|
You can download the 5 Chinese RoBERTa base models either from the links below: |
|
|
|
| corpus | Link | |
|
| :-----------: | :-------------------------------------------------------: | |
|
| **JD full** | [**roberta-base-finetuned-jd-full-chinese**][JD_full] | |
|
| **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][JD_binary] | |
|
| **Dianping** | [**roberta-base-finetuned-dianping-chinese**][Dianping] | |
|
| **Ifeng** | [**roberta-base-finetuned-ifeng-chinese**][Ifeng] | |
|
| **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][Chinanews] | |
|
|
|
|
|
|
|
## How to use |
|
|
|
You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese): |
|
|
|
```python |
|
>>> 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](https://github.com/zhangxiangxiao/glyph) project. |
|
|
|
|
|
|
|
## Training procedure |
|
|
|
Models are fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). 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](https://huggingface.co/uer/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. |
|
|
|
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 Glyph/Chinanews_train.txt \ |
|
--dev_path Glypg/Chinanews_test.txt \ |
|
--output_model_path models/Chinanews_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_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} |
|
} |
|
``` |
|
|
|
[JD_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese |
|
[JD_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese |
|
[Dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese |
|
[Ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese |
|
[Chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese |
