---
title: ultrachat_200k
canonical_url: "https://www.modelscope.cn/datasets/swift/ultrachat_200k"
md_url: "https://www.modelscope.cn/datasets/swift/ultrachat_200k.md"
repository: swift/ultrachat_200k
last_updated: 2024-10-16
license: mit
storage_size: "1.5 GB"
domain:
  - size_categories
  - dataset_info
  - language
  - task_categories
  - pretty_name
tasks:
  - "100K<n<1M"
  - dataset_size
  - en
  - text-generation
  - "UltraChat 200k"
downloads: 13034
stars: 4
---

# ultrachat_200k

> ultrachat_200k - swift 在 ModelScope 开源的数据集。Dataset Card for UltraChat 200k

swift/ultrachat_200k 是 ModelScope 魔搭社区上的100K<n<1M、dataset_size、en数据集，涉及 size_categories、dataset_info、language 领域，存储大小 1.5 GB，采用 mit 许可。

- **Repository**: swift/ultrachat_200k
- **License**: mit
- **Tasks**: 100K<n<1M, dataset_size, en, text-generation, UltraChat 200k
- **Domain**: size_categories, dataset_info, language, task_categories, pretty_name
- **Storage size**: 1.5 GB
- **Downloads**: 13034
- **Stars**: 4
- **Last updated**: 2024-10-16

Source: https://www.modelscope.cn/datasets/swift/ultrachat_200k

---

# Dataset Card for UltraChat 200k

## Dataset Description

This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model.

The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic:

- Selection of a subset of data for faster supervised fine tuning.
- Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?"
- Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either.

## Dataset Structure

The dataset has four splits, suitable for:

* Supervised fine-tuning (`sft`).
* Generation ranking (`gen`) via techniques like rejection sampling or PPO.

The number of examples per split is shown as follows:


|  train_sft | test_sft  | train_gen | test_gen |
|:-------:|:-----------:|:-----:| :-----:|
|  207865 |       23110 | 256032 | 28304 |

The dataset is stored in parquet format with each entry using the following schema:
```

{
    "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...",
    "messages":[
        {
            "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...",
            "role": "user"
        },
        {
            "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...",
            "role": "assistant"
        },
        {
            "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details.  ...",
            "role": "user"
        },
        {
            "content": "Certainly! ....",
            "role": "assistant"
        },
        {
            "content": "That's really interesting! I would love to hear more...",
            "role": "user"
        }
        {
            "content": "Certainly! ....",
            "role": "assistant"
        },
    ],
    "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af"
}
```

## Citation

If you find this dataset is useful in your work, please cite the original UltraChat dataset:

```
@misc{ding2023enhancing,
      title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, 
      author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
      year={2023},
      eprint={2305.14233},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```