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--- |
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language: Chinese |
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widget: |
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- text: "北京上个月召开了两会" |
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--- |
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# Chinese RoBERTa-Base Models for Text Classification |
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## Model description |
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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: |
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| Dataset | Link | |
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| :-----------: | :-------------------------------------------------------: | |
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| **JD full** | [**roberta-base-finetuned-jd-full-chinese**][jd_full] | |
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| **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][jd_binary] | |
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| **Dianping** | [**roberta-base-finetuned-dianping-chinese**][dianping] | |
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| **Ifeng** | [**roberta-base-finetuned-ifeng-chinese**][ifeng] | |
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| **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][chinanews] | |
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## How to use |
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You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese): |
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```python |
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>>> from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline |
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>>> model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') |
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>>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') |
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>>> text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
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>>> text_classification("北京上个月召开了两会") |
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[{'label': 'mainland China politics', 'score': 0.7211663722991943}] |
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``` |
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## Training data |
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We use 5 Chinese text classification datasets which are collected by [Glyph](https://github.com/zhangxiangxiao/glyph) project. |
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## Training procedure |
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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. |
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Taking the case of roberta-base-finetuned-chinanews-chinese |
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``` |
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python3 run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--train_path datasets/glyph/chinanews/train.tsv \ |
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--dev_path datasets/glyph/chinanews/dev.tsv \ |
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--output_model_path models/chinanews_classifier_model.bin \ |
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--learning_rate 3e-5 --batch_size 32 --epochs_num 3 --seq_length 512 \ |
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--embedding word_pos_seg --encoder transformer --mask fully_visible |
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``` |
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Finally, we convert the pre-trained model into Huggingface's format: |
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``` |
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python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/chinanews_classifier_model.bin \ |
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--output_model_path pytorch_model.bin \ |
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--layers_num 12 |
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``` |
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### BibTeX entry and citation info |
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``` |
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@article{zhao2019uer, |
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title={UER: An Open-Source Toolkit for Pre-training Models}, |
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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}, |
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journal={EMNLP-IJCNLP 2019}, |
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pages={241}, |
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year={2019} |
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} |
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``` |
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[jd_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese |
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[jd_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese |
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[dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese |
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[ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese |
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[chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese |
