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--- |
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language: |
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- zh |
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license: apache-2.0 |
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tags: |
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- question-generation |
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- qg |
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- SQuAD |
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- nlg |
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- bart-base |
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datasets: |
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- chinesesquad |
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metrics: |
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- bleu |
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- rouge |
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- f1 |
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- meteor |
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- bleu_score |
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--- |
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# Randeng-BART-139M-QG-Chinese |
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- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/dev_yangqi/fengshen/examples/bart_qg) |
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- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) |
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## 简介 Brief Introduction |
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善于处理问题生成任务的中文版 BART-base 模型。 |
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Good at solving question generation tasks Bart-base Model (Chinese version). |
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## 模型分类 Model Taxonomy |
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| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | |
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| :----: | :----: | :----: | :----: | :----: | :----: | |
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| 通用 General | 自然语言转换 NLT | 燃灯 Randeng | BART | 139M | 问题生成任务-中文 QuestionGeneration-Chinese | |
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## 模型信息 Model Information |
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基于[IDEA-CCNL/Randeng-BART-139M](https://huggingface.co/IDEA-CCNL/Randeng-BART-139M)，我们在 [ChineseSQuAD](https://github.com/pluto-junzeng/ChineseSquad) 数据集上微调了问题生成任务版本。该数据集翻译了部分SQuAD数据集，包含约 67k 有答案的训练样本。 |
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Based on [IDEA-CCNL/Randeng-BART-139M](https://huggingface.co/IDEA-CCNL/Randeng-BART-139M), we fine-tuned a question generation version on [ChineseSQuAD](https://github.com/pluto-junzeng/ChineseSquad) datasets. The dataset is translated from SQuAD 2.0, with around 67k samples with answer. |
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### 下游效果 Performance |
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| Dataset | Size | BLEU-4 | METEOR | ROUGE-L| |
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| ------------ | ----- | -------- |--------- | ---------- | |
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| ChineseSQuAD | 139M | 22.17 | 40.38 | 38.17 | |
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## 使用 Usage |
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```python |
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from transformers import AutoTokenizer, BartForConditionalGeneration |
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tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Randeng-BART-139M-QG-Chinese",additional_special_tokens=["<ans>"]) |
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model = BartForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng-BART-139M-QG-Chinese") |
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context = "知识：1939年9月1日德国入侵波兰后，第二次世界大战开始，华沙一直被保卫到9月27日。波兰中部，包括华沙，都在德国纳粹殖民地政府总政府的统治下。所有的高等教育机构都立即关闭，华沙的犹太人口——几十万，约占城市的 <ans> ——全部涌入华沙的贫民区。回答：30%" |
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inputs = tokenizer.encode_plus( |
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context, |
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max_length=448, |
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padding="max_length", |
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truncation=True, |
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return_tensors='pt' |
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) |
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out = model.generate( |
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input_ids=inputs['input_ids'], |
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attention_mask=inputs['attention_mask'], |
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do_sample=True, |
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num_beams=5, |
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max_length=64, |
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top_p = 0.9, |
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) |
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print(pred = tokenizer.batch_decode(out,clean_up_tokenization_spaces=True, skip_special_tokens=True)[0]) |
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# 问题:华沙的犹太人口占城市的百分之多少? |
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``` |
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## 引用 Citation |
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如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)： |
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If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590): |
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```text |
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@article{unimc, |
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author = {Ping Yang and |
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Junjie Wang and |
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Ruyi Gan and |
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Xinyu Zhu and |
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Lin Zhang and |
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Ziwei Wu and |
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Xinyu Gao and |
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Jiaxing Zhang and |
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Tetsuya Sakai}, |
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title = {Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective}, |
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journal = {CoRR}, |
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volume = {abs/2210.08590}, |
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year = {2022} |
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} |
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``` |
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也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): |
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You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): |
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```text |
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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={2021}, |
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howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, |
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} |
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``` |
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