---
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}},
}
```