Duplicate from openbmb/UltraChat

Co-authored-by: Ning Ding <stingning@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+task_categories:
+- conversational
+- text-generation
+language:
+- en
+size_categories:
+- 1M<n<10M
+pretty_name: UltraChat
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, **we do not directly use any data available on the Internet as prompts**.
+To ensure generation quality, two separate ChatGPT Turbo APIs are adopted in generation, where one plays the role of the user to generate queries and the other generates the response. 
+We instruct the user model with carefully designed prompts to mimic human user behavior and call the two APIs iteratively. The generated dialogues undergo further post-processing and filtering.
+ULtraChat is composed of three sectors:
+
+- 🌏 **Questions about the World**: The dialogue data in this sector is derived from a wide range of inquiries related to concepts, entities, and objects from the real world. The topics covered are extensive, spanning areas such as technology, art, and entrepreneurship.
+- ✍🏻 **Writing and Creation**: The dialogue data in this sector is driven by the demands for writing/creation from scratch, and encompasses any tasks that an AI assistant may aid within the creative process, spanning from email composition to crafting narratives and plays, and beyond.
+- 📋 **Assistance on Existent Materials**: The dialogue data in this sector is generated based on existing materials, including but not limited to rewriting, continuation, summarization, and inference, covering a diverse range of topics.
+
+- Repository: [UltraChat](https://github.com/thunlp/UltraChat)
+- Explorer: [plain-explorer](http://39.101.77.220/), [Nomic-AI-Atlas-Explorer](https://atlas.nomic.ai/map/0ce65783-c3a9-40b5-895d-384933f50081/a7b46301-022f-45d8-bbf4-98107eabdbac)
+
+## Dataset Structure
+
+Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line.
+
+```
+{
+  "id": "0", 
+  "data": [
+    "How can cross training benefit groups like runners, swimmers, or weightlifters?", 
+    "Cross training can benefit groups like runners, swimmers, or weightlifters in the following ways: ...", 
+    "That makes sense. I've been wanting to improve my running time, but I never thought about incorporating strength training. Do you have any recommendations for specific exercises?", 
+    "Sure, here are some strength training exercises that can benefit runners: ...", 
+    "Hmm, I'm not really a fan of weightlifting though. Can I incorporate other forms of exercise into my routine to improve my running time?", 
+    "Yes, absolutely! ...",
+    "..."
+    ]
+}
+```
+
+
+### Citation Information
+
+```bibtex
+@article{ding2023enhancing,
+  title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations},
+  author={Ding, Ning and Chen, Yulin and Xu, Bokai and Qin, Yujia and Zheng, Zhi and Hu, Shengding and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen},
+  journal={arXiv preprint arXiv:2305.14233},
+  year={2023}
+}
+```

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