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---
language:
- zh
license: apache-2.0
# inference: false
# inference:
# parameters:
tags:
- question-generation
- qg
- SQuAD
- nlg
- bart-base
datasets:
- chinesesquad
metrics:
- bleu
- rouge
- f1
- meteor
- bleu_score
---
# Randeng-BART-139M-QG-Chinese
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/dev_yangqi/fengshen/examples/bart_qg)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
善于处理问题生成任务的中文版 BART-base 模型。
Good at solving question generation tasks Bart-base Model (Chinese version).
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 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 |
## 模型信息 Model Information
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。
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.
### 下游效果 Performance
| Dataset | Size | BLEU-4 | METEOR | ROUGE-L|
| ------------ | ----- | -------- |--------- | ---------- |
| ChineseSQuAD | 139M | 22.17 | 40.38 | 38.17 |
## 使用 Usage
```python
from transformers import AutoTokenizer, BartForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Randeng-BART-139M-QG-Chinese",additional_special_tokens=["<ans>"])
model = BartForConditionalGeneration.from_pretrained("IDEA-CCNL/Randeng-BART-139M-QG-Chinese")
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%"
inputs = tokenizer.encode_plus(
context,
max_length=448,
padding="max_length",
truncation=True,
return_tensors='pt'
)
out = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
do_sample=True,
num_beams=5,
max_length=64,
top_p = 0.9,
)
print(pred = tokenizer.batch_decode(out,clean_up_tokenization_spaces=True, skip_special_tokens=True)[0])
# 问题:华沙的犹太人口占城市的百分之多少?
```
## 引用 Citation
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)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{unimc,
author = {Ping Yang and
Junjie Wang and
Ruyi Gan and
Xinyu Zhu and
Lin Zhang and
Ziwei Wu and
Xinyu Gao and
Jiaxing Zhang and
Tetsuya Sakai},
title = {Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective},
journal = {CoRR},
volume = {abs/2210.08590},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
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