---
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](https://arxiv.org/abs/1909.05658). You can download the 5 Chinese RoBERTa-Base classification models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from the links below:

|    Dataset     |                           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. 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}
}
```

[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