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--- |
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language: zh |
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tags: |
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- summarization |
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inference: False |
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--- |
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Randeng_Pegasus_523M_Summary model (Chinese)，which codes has merged into [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) |
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The 523M million parameter randeng_pegasus_large model, training with sampled gap sentence ratios on 180G Chinese data, and stochastically sample important sentences. The pretraining task just same as the paper [PEGASUS: Pre-training with Extracted Gap-sentences for |
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Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf) mentioned. |
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Different from the English version of pegasus, considering that the Chinese sentence piece is unstable, we use jieba and Bertokenizer as the tokenizer in chinese pegasus model. |
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This model we provided in hugging face hub is only the pretrained model, has not finetuned with downstream data yet. |
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We also pretained a base model, available with [Randeng_Pegasus_238M_Summary](https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_238M_Summary) |
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Task: Summarization |
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## Usage |
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```python |
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from transformers import PegasusForConditionalGeneration |
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# Need to download tokenizers_pegasus.py and other Python script from Fengshenbang-LM github repo in advance, |
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# or you can mv download in tokenizers_pegasus.py and data_utils.py in https://huggingface.co/IDEA-CCNL/Randeng_Pegasus_523M_Summary/tree/main |
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# Strongly recommend you git clone the Fengshenbang-LM repo: |
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# 1. git clone https://github.com/IDEA-CCNL/Fengshenbang-LM |
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# 2. cd Fengshenbang-LM/fengshen/examples/pegasus/ |
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# and then you will see the tokenizers_pegasus.py and data_utils.py which are needed by pegasus model |
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from tokenizers_pegasus import PegasusTokenizer |
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model = PegasusForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng_Pegasus_523M_Summary") |
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# You can find the vocab.txt in hugging face cache file. |
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# Or, you can download the vocab.txt manually |
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tokenizer = PegasusTokenizer.from_pretrained("/path/to/vocab.txt") |
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text = "在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中，中国选手谷爱凌夺得银牌。祝贺谷爱凌！今天上午，自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行，取选手最佳成绩排名决出奖牌。第一跳，中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后，谷爱凌又扮了个鬼脸，甚是可爱。第二轮中，谷爱凌在道具区第三个障碍处失误，落地时摔倒。获得16.98分。网友：摔倒了也没关系，继续加油！在第二跳失误摔倒的情况下，谷爱凌顶住压力，第三跳稳稳发挥，流畅落地！获得86.23分！此轮比赛，共12位选手参赛，谷爱凌第10位出场。网友：看比赛时我比谷爱凌紧张，加油！" |
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inputs = tokenizer(text, max_length=1024, return_tensors="pt") |
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# Generate Summary |
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summary_ids = model.generate(inputs["input_ids"]) |
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tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
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``` |
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## Citation |
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If you find the resource is useful, please cite the following website in your paper. |
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``` |
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@misc{Fengshenbang-LM, |
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title={Fengshenbang-LM}, |
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author={IDEA-CCNL}, |
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year={2022}, |
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howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, |
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} |
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``` |
