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README.md

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+---
+license: cc-by-4.0
+language:
+- zh
+tags:
+- medical consultation
+- finetuning
+pretty_name: ChatMed-Dataset
+task_categories:
+- text-generation
+---
+
+# Dataset Card for ChatMed
+
+## Dataset Description
+
+- **Homepage:** https://huggingface.co/datasets/michaelwzhu/ChatMed-Datasets
+- **Repository:** https://github.com/michael-wzhu/ChatMed
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** Wei Zhu
+
+### Dataset Summary
+
+ChatMed-Dataset is a dataset of 110,113 medical query-response pairs (in Chinese) generated by OpenAI's `GPT-3.5` engine. The queries are crawled from several online medical consultation sites, reflecting the medical needs in the real world. The responses are generated by the OpenAI engine. This dataset is designated to to inject medical knowledge into Chinese large language models. 
+
+The dataset size growing rapidly. Stay tuned for updates!  
+
+在大模型时代，模型都是趋同的，比如开源社区由于LlaMa的"开源"，出现了各种stanford-alpaca等一系列的开源模型。但是中文垂直领域上的AGI数据集相对匮乏。
+
+本数据集, ChatMed-Dataset, 中的query(或者是prompt)来自于互联网上的医疗问诊问题(110,113)，反映了真实世界的不同用户/患者的医疗问诊需求。目前response都是由OpenAI `GPT-3.5`引擎回答的。我们后续会对互联网上的医生回答与患者回答进行筛选甄别，择优选择，构建质量更优的数据集。
+
+
+### Supported Tasks and Leaderboards
+
+The ChatMed-Dataset designed for fine-tuning pretrained language models to perform better in automatic medical consultations.
+
+### Languages
+
+The data in ChatMed-Dataset are in Chinese (zh).
+
+## Dataset Structure
+
+### Data Instances
+
+The dataset is in json-line format and is very convenient to use! An example in the ChatMed-Dataset looks as follows:
+
+```json
+{
+  "query": "你好，视神经萎缩，黄斑变性。怎样缓解，及日常注意事项。谢谢。", 
+  "response": "视神经萎缩和黄斑变性是两种不同的疾病，需要分别进行治疗。\n\n视神经萎缩是指视神经逐渐受损，导致视力逐渐减退，常见的治疗方法包括：\n\n1.药物治疗：如促进视神经细胞再生的药物，改善视网膜供血的药物等。\n\n2.手术治疗：如视神经萎缩的手术治疗可以通过神经移植、修复和再生等手术方式来缓解症状。\n\n3.康复训练：通过视力训练等康复训练可以帮助患者恢复视力。\n\n黄斑变性则是指黄斑区域受损，导致中央视力减退，常见的治疗方法包括：\n\n1.药物治疗：如抗氧化剂、维生素等可以缓解症状。\n\n2.手术治疗：如黄斑脱离手术等可以恢复视力。\n\n3.日常注意事项：保持良好的生活习惯，如戒烟、控制血压、血糖等，避免暴晒和过度用眼，保持充足的睡眠和均衡的饮食等。\n\n需要根据具体情况选择合适的治疗方案，建议您咨询专业医生进行诊断和治疗。"
+}
+```
+
+### Data Fields
+
+The data fields are as follows:
+
+* `query`: real-life medical query from the web. 
+* `response`: response generated by the OpenAI `GPT-3.5` engine.
+
+### Data Splits
+
+|               | train |
+|---------------|------:|
+| ChatMed-Dataset |   110,113 |
+
+## 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
+
+### Social Impact of Dataset
+
+
+> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on how large language models responses to real-life medical query. 
+> More social impacts will be discussed soon!
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+The `ChatMed-Dataset` data is generated by a language model (`GPT-3.5`) 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]
+
+### Licensing Information
+
+The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
+
+
+### Citation Information
+
+```
+@misc{alpaca,
+  author = {Wei Zhu },
+  title = {ChatMed-Dataset: An GPT generated medical query-response datasets for medcial large language models},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/michael-wzhu/ChatMed}},
+}
+
+```
+
+### Contributions
+
+[More Information Needed]