Dataset Card for uplimit-synthetic-data-week-1-with-multi-turn

This dataset has been created with distilabel.

The pipeline script was uploaded to easily reproduce the dataset: colab_kernel_launcher.py.

It can be run directly using the CLI:

distilabel pipeline run --script "https://huggingface.co/datasets/ywilliam-face/uplimit-synthetic-data-week-1-with-multi-turn/raw/main/colab_kernel_launcher.py"

Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:

distilabel pipeline run --config "https://huggingface.co/datasets/ywilliam-face/uplimit-synthetic-data-week-1-with-multi-turn/raw/main/pipeline.yaml"

or explore the configuration:

distilabel pipeline info --config "https://huggingface.co/datasets/ywilliam-face/uplimit-synthetic-data-week-1-with-multi-turn/raw/main/pipeline.yaml"

Dataset structure

The examples have the following structure per configuration:

Configuration: default

---

{
    "conversation": [
        {
            "content": "I\u0027m trying to plan a trip to Japan for 10 days. I\u0027ve never been there before but have heard it\u0027s an amazing country. What should be my first steps? \n\n(Note: Please respond as if you\u0027re assisting a user who has not yet started planning their trip.)",
            "role": "user"
        },
        {
            "content": "Planning a trip to Japan can be both exciting and overwhelming, especially when considering all the options available. To get started, let\u0027s break down your goals into manageable tasks. First, we need to determine what type of experience you\u0027re looking for - culture, history, food, nature, or a mix of everything. Are you interested in exploring cities like Tokyo and Osaka, visiting historical landmarks such as Kyoto and Nara, enjoying traditional Japanese cuisine, relaxing onsen (hot springs) resorts, or experiencing the vibrant nightlife of Tokyo?\n\nOnce you have a general idea of what you want to do, we can start thinking about",
            "role": "assistant"
        },
        {
            "content": "What would be some good ways to narrow down these different aspects of travel to Japan?",
            "role": "user"
        },
        {
            "content": "To narrow down your options, consider the following factors: budget, time constraints, personal interests, and preferences. For instance, if you\u0027re traveling during peak season, you might prioritize experiences that offer unique cultural insights or scenic views. If you prefer more relaxed, laid-back environments, you could opt for smaller towns or rural areas.\n\nAnother approach is to think about the types of activities you enjoy doing in other countries. Do you love hiking, trying new foods, or taking part in festivals? Japan offers a wide range of activities, from hot spring baths to sumo wrestling tournaments. By identifying your interests, you\u0027ll be able",
            "role": "assistant"
        }
    ],
    "distilabel_metadata": {
        "statistics_magpie_generator_0": {
            "input_tokens": [
                50,
                110,
                238,
                255
            ],
            "output_tokens": [
                60,
                128,
                17,
                128
            ]
        }
    },
    "model_name": "HuggingFaceTB/SmolLM2-360M-Instruct"
}

This subset can be loaded as:

from datasets import load_dataset

ds = load_dataset("ywilliam-face/uplimit-synthetic-data-week-1-with-multi-turn", "default")

Or simply as it follows, since there's only one configuration and is named default:

from datasets import load_dataset

ds = load_dataset("ywilliam-face/uplimit-synthetic-data-week-1-with-multi-turn")

References

@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}