Update README.md

README.md

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 ---
-license: apache-2.0
+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]