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README.md
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---
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language: Chinese
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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://arxiv.org/abs/1909.05658).
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## How to use
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You can use this model directly with a pipeline for text2text generation :
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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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```
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python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
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--vocab_path models/google_zh_vocab.txt \
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--dataset_path cluecorpussmall_bart_seq512_dataset.pt \
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--processes_num 32 --seq_length 512 \
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--dynamic_masking --target bart
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```
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```
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python3 pretrain.py --dataset_path cluecorpussmall_bart_seq512_dataset.pt \
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--vocab_path models/google_zh_vocab.txt \
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--config_path models/bart/base_config.json \
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--output_model_path models/cluecorpussmall_bart_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 16 \
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--span_masking --span_max_length 3 \
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--embedding word_pos --tgt_embedding word_pos \
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--encoder transformer --mask fully_visible --decoder transformer \
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--target bart --tie_weights --has_lmtarget_bias
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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_bart_from_uer_to_huggingface.py --input_model_path cluecorpussmall_bart_base_seq512_model.bin-250000 \
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--output_model_path pytorch_model.bin \
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--layers_num 6
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```
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### BibTeX entry and citation info
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```
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@article{lewis2019bart,
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title={Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension},
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author={Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Ves and Zettlemoyer, Luke},
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journal={arXiv preprint arXiv:1910.13461},
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year={2019}
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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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