task_categories:question-answering language:zh language:en license:other region:us
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task_categories:
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language:
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---


# Dataset Card for luotuo-QA-B

## Dataset Description

- **Homepage:** https://github.com/LC1332/Luotuo-Chinese-LLM
- **Repository:** https://github.com/LC1332/Luotuo-QA
- **Point of Contact:** qinyu_luo@163.com

### Dataset Summary

Anki_Card是一种用于记忆和学习的电子卡片系统。我们建立了一个类似于这种形式的问答数据集，旨在推动中英文语境下问答模型的研究和发展。

我们的数据集是在3个开源数据集之上生成构建的，这3个数据集分别是：

·Chinese Scientific Literature Dataset

·CNN-DailyMail News Text Summarization

·arXiv Dataset

您可以直接搜索这些原始数据集的名称或是从以下链接访问它们

  ·https://github.com/ydli-ai/CSL
  
  ·https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail
  
  ·https://www.kaggle.com/datasets/Cornell-University/arxiv

我们在这些数据集的基础上针对每一个摘要或新闻生成了5个“问题-答案”对。数据分布如下：

  ---从Chinese Scientific Literature Dataset(CSL)数据集中生成了25836条中文数据，共129180个问答对。

  ---从CNN-DailyMail News Text Summarization数据集中生成了2026条数据，共10130个问答对。

  ---从arXiv Dataset数据集中生成了3602条英文数据，共18010个问答对。


此外，由于此数据集是我们Luotuo-QA项目的一部分，我们将它叫做luotuo-QA-B。

您可以在这里查看Luotuo-QA项目：https://github.com/LC1332/Luotuo-QA

此数据集适用于训练和评估中文对话式问答模型。有益于推动中文自然语言处理领域的发展，同时也为研究人员和开发者提供了一个基准，用于比较不同模型的性能和探索新的方法。

我们希望这一工作能够促进全球范围内中文语境对话式问答任务的研究和进一步的创新。

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Anki_Card is an electronic flashcard system used for memory and learning. We have created a question-and-answer dataset in a similar format to facilitate research and development of question-answering models in both Chinese and English contexts.

Our dataset is constructed based on three open-source datasets:

·Chinese Scientific Literature Dataset

·CNN-DailyMail News Text Summarization

·arXiv Dataset

You can directly search for the names of these original datasets or access them from the following links:

·Chinese Scientific Literature Dataset (CSL): https://github.com/ydli-ai/CSL

·CNN-DailyMail News Text Summarization: https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail

·arXiv Dataset: https://www.kaggle.com/datasets/Cornell-University/arxiv

Based on these datasets, we have generated five "question-answer" pairs for each summary or news article. The data distribution is as follows:

---From the Chinese Scientific Literature Dataset (CSL), we generated 25,836 Chinese data points, resulting in a total of 129,180 question-answer pairs.

---From the CNN-DailyMail News Text Summarization dataset, we generated 2,026 data points, resulting in a total of 10,130 question-answer pairs.

---From the arXiv Dataset, we generated 3,602 English data points, resulting in a total of 18,010 question-answer pairs.

Furthermore, as this dataset is part of our Luotuo-QA project, we refer to it as luotuo-QA-B.

You can find the Luotuo-QA project here: https://github.com/LC1332/Luotuo-QA

This dataset is suitable for training and evaluating Chinese conversational question-answering models. It contributes to the development of Chinese natural language processing and provides researchers and developers with a benchmark for comparing the performance of different models and exploring new approaches.

We hope that this work will promote research and further innovation in Chinese conversational question-answering tasks on a global scale.

### Languages
CHINESE, ENGLISH
### Data Instances

中文数据样例：
```
{
  "story": "针对已有神经网络功放建模的建模精度不高,易陷入局部极值等问题,提出一种新的改进并行粒子群算法(Improved Parallel Particle Swarm Optimization,IPPSO)。该算法在并行粒子群算法的基础上引入自适应变异操作,防止陷入局部最优;在微粒的速度项中加入整体微粒群的全局最优位置,动态调节学习因子与线性递减惯性权重,加快微粒收敛。将该改进算法用于优化RBF神经网络参数,并用优化的网络对非线性功放进行建模仿真。结果表明,该算法能有效减小建模误差,且均方根误差提高19.08%,进一步提高了神经网络功放建模精度。",
  "QA": [
    {
      "question": "什么是IPPSO算法？",
      "answer": "IPPSO是一种改进的并行粒子群算法，引入自适应变异操作，防止陷入局部最优；在微粒的速度项中加入整体微粒群的全局最优位置，动态调节学习因子与线性递减惯性权重，加快微粒收敛。"
    },
    {
      "question": "IPPSO算法用于什么？",
      "answer": "IPPSO算法用于优化RBF神经网络参数，并用优化的网络对非线性功放进行建模仿真。"
    },
    {
      "question": "IPPSO算法的优点是什么？",
      "answer": "IPPSO算法能有效减小建模误差，且均方根误差提高19.08%，进一步提高了神经网络功放建模精度。"
    },
    {
      "question": "为什么需要改进并行粒子群算法？",
      "answer": "因为已有的神经网络功放建模的建模精度不高，易陷入局部极值等问题。"
    },
    {
      "question": "IPPSO算法如何防止陷入局部最优？",
      "answer": "IPPSO算法引入自适应变异操作，防止陷入局部最优；在微粒的速度项中加入整体微粒群的全局最优位置，动态调节学习因子与线性递减惯性权重，加快微粒收敛。"
    }
  ]
}
```
英文数据样例：
```
{
  "story": "We discuss an alternative non-perturbative proof of Bertrand's theorem that leads in a concise way directly to the two allowed fields: the newtonian and the isotropic harmonic oscillator central fields.",
  "QA": [
    {
      "question": "What is Bertrand's theorem?",
      "answer": "It is a theorem that leads to the two allowed fields: the newtonian and the isotropic harmonic oscillator central fields."
    },
    {
      "question": "What is the proof of Bertrand's theorem like?",
      "answer": "It is an alternative non-perturbative proof."
    },
    {
      "question": "How many central fields are allowed by Bertrand's theorem?",
      "answer": "Two central fields are allowed: the newtonian and the isotropic harmonic oscillator."
    },
    {
      "question": "What is the advantage of the proof discussed in the text?",
      "answer": "It leads directly to the allowed fields in a concise way."
    },
    {
      "question": "What are the two allowed fields according to Bertrand's theorem?",
      "answer": "The newtonian and the isotropic harmonic oscillator central fields."
    }
  ]
}
```


### Licensing Information
我们的协议与三个原始数据集的协议保持一致，请阅读以下内容。

·CSL数据集的协议是Apache License 2.0，除非遵守许可证，否则您不得使用此文件

·CNN-DailyMail News Text Summarization数据集的协议是 CC0: Public Domain

·arXiv数据集的协议是 CC0: Public Domain

-----------------------------------------------------------------------------------------------------------------------------------------------

Our agreements are consistent with the agreements of three original datasets. Please read the following information.

· The protocol for the CSL dataset is Apache License 2.0. You are not allowed to use this file unless you comply with the license.

· The protocol for the CNN-DailyMail News Text Summarization dataset is CC0: Public Domain.

· The protocol for the arXiv dataset is CC0: Public Domain.

### Citation Information
如果您在项目中使用了我们的模型、代码或者数据，请引用我们。

Please cite us if you use the data or code in this repo.

```bibtex
@misc{alpaca,
  author={Jianshen Liao, Ao Sun, Qinyu Luo, Hongsen Huang, Cheng Li},
  title = {Luotuo-QA: Better Conversational Question Answering Model with Answer Completion},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/LC1332/Luotuo-QA}},
}
```
