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Datasets:
shibing624
/
AdvertiseGen

Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
Chinese
Size:
100K - 1M
Tags:
text-generation
e-commerce advertise
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
AdvertiseGen
File size: 2,776 Bytes 
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---
license: cc-by-4.0
language:
- zh
tags:
- text-generation
- e-commerce advertise
pretty_name: AdvertiseGen
task_categories:
- text-generation
---

# Dataset Card for Alpaca-zh

- **formal url:** https://www.luge.ai/#/luge/dataDetail?id=9

## Dataset Description
数据集介绍

AdvertiseGen是电商广告文案生成数据集。

AdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。


- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；
- 数据规模：训练集114k，验证集1k，测试集3k；
- 数据来源：清华大学CoAI小组；

### Supported Tasks and Leaderboards

The Alpaca dataset designed for instruction training pretrained language models.

### Languages

The data in AdvertiseGen are in Chinese.

## Dataset Structure

### Data Instances

An example of "train" looks as follows:

```json
{
  "content": "类型#上衣*材质#牛仔布*颜色#白色*风格#简约*图案#刺绣*衣样式#外套*衣款式#破洞",
  "summary": "简约而不简单的牛仔外套，白色的衣身十分百搭。衣身多处有做旧破洞设计，打破单调乏味，增加一丝造型看点。衣身后背处有趣味刺绣装饰，丰富层次感，彰显别样时尚。"
}
```

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data


### Discussion of Biases

[More Information Needed]

### Other Known Limitations

The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.


## Additional Information

### Dataset Curators

[More Information Needed]

### Citation Information
数据集引用

如在学术论文中使用本数据集，请添加相关引用说明，具体如下：
```
Shao, Zhihong, et al. "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
```

### Contributions

[More Information Needed]