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--- |
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language: zh |
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datasets: CLUECorpusSmall |
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widget: |
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- text: "内容丰富、版式设计考究、图片华丽、印制精美。[MASK]纸箱内还放了充气袋用于保护。" |
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--- |
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# Chinese Pegasus |
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## Model description |
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This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. |
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You can download the set of Chinese PEGASUS models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below: |
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| | Link | |
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| ----------------- | :----------------------------: | |
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| **PEGASUS-Base** | [**L=12/H=768 (Base)**][base] | |
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| **PEGASUS-Large** | [**L=16/H=1024 (Large)**][large] | |
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## How to use |
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You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base): |
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```python |
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>>> from transformers import BertTokenizer, PegasusForConditionalGeneration, Text2TextGenerationPipeline |
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>>> tokenizer = BertTokenizer.from_pretrained("uer/pegasus-base-chinese-cluecorpussmall") |
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>>> model = PegasusForConditionalGeneration.from_pretrained("uer/pegasus-base-chinese-cluecorpussmall") |
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>>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer) |
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>>> text2text_generator("内容丰富、版式设计考究、图片华丽、印制精美。[MASK]纸箱内还放了充气袋用于保护。", max_length=50, do_sample=False) |
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[{'generated_text': '书 的 质 量 很 好 。'}] |
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``` |
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## Training data |
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[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. |
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## Training procedure |
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The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 512. |
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Taking the case of PEGASUS-Base |
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``` |
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python3 preprocess.py --corpus_path corpora/cluecorpussmall_bert.txt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--dataset_path cluecorpussmall_pegasus_seq512_dataset.pt \ |
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--processes_num 32 --seq_length 512 \ |
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--data_processor gsg --sentence_selection_strategy random |
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``` |
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``` |
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python3 pretrain.py --dataset_path cluecorpussmall_pegasus_seq512_dataset.pt \ |
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--vocab_path models/google_zh_vocab.txt \ |
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--config_path models/pegasus/base_config.json \ |
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--output_model_path models/cluecorpussmall_pegasus_base_seq512_model.bin \ |
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--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ |
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--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ |
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--learning_rate 1e-4 --batch_size 8 |
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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_pegasus_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_pegasus_base_seq512_model.bin-1000000 \ |
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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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@inproceedings{zhang2020pegasus, |
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title={Pegasus: Pre-training with extracted gap-sentences for abstractive summarization}, |
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author={Zhang, Jingqing and Zhao, Yao and Saleh, Mohammad and Liu, Peter}, |
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booktitle={International Conference on Machine Learning}, |
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pages={11328--11339}, |
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year={2020}, |
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organization={PMLR} |
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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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@article{zhao2023tencentpretrain, |
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title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, |
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author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, |
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journal={ACL 2023}, |
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pages={217}, |
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year={2023} |
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``` |
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[base]:https://huggingface.co/uer/pegasus-base-chinese-cluecorpussmall |
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[large]:https://huggingface.co/uer/pegasus-large-chinese-cluecorpussmall |
