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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- regmix |
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pretty_name: regmix-data-sample |
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size_categories: |
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- 100K<n<1M |
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--- |
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# RegMix Data Sample |
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## Dataset Description |
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The RegMix Data Sample is a curated dataset derived from the Pile-Uncopyrighted, specifically designed for the RegMix paper (https://huggingface.co/papers/2407.01492). This dataset aims to facilitate the automatic identification of high-performing data mixtures for language model pre-training by formulating it as a regression task. |
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### Key Features: |
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- Size: Approximately 20GB disk space, 5B tokens |
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- Distribution: Follows the natural token distribution of domain examples |
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- Organization: Examples from different domains are separated into individual files |
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## Dataset Structure |
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The dataset is organized into two main directories: `train` and `valid`, each containing domain-specific JSONL files. The file naming convention is as follows: |
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``` |
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[domain]-[identifier]-[number].jsonl |
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``` |
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For example: `arxiv-10-74305611.jsonl` |
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### Domains Included: |
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arxiv, gutenberg_pg_19, pubmed_central, dm_mathematics, hackernews, stackexchange, enron_emails, nih_exporter, ubuntu_irc, europarl, philpapers, uspto_backgrounds, freelaw, pile_cc, wikipedia_en, github, pubmed_abstracts |
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## Usage |
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We recommend downloading the entire dataset snapshot instead of using the traditional `load_dataset` function, as the RegMix code is integrated with the [TinyLlama framework](https://github.com/jzhang38/TinyLlama). |
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To download the dataset: |
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```python |
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from huggingface_hub import snapshot_download |
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LOCAL_DIR = "regmix-data-sample" |
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snapshot_download(repo_id="sail/regmix-data-sample", |
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repo_type='dataset', |
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local_dir=LOCAL_DIR, |
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local_dir_use_symlinks=False) |
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``` |
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This will download the entire snapshot, containing 34 JSON line files (17 for train, and 17 for valid), to your specified local directory. |
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## Data Preprocessing |
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Our [code](https://github.com/sail-sg/regmix) will preprocess these domain files into binary format with domain prefixes. It allows for random sampling of the dataset using user-defined data mixtures (i.e., domain weights). |
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## Acknowledgements |
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We extend our gratitude to the creators of the [Pile-Uncopyrighted dataset](https://huggingface.co/datasets/monology/pile-uncopyrighted) for their efforts in removing copyrighted content from the original Pile dataset, making this work possible. |
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## Citation |
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If you use this dataset in your research, please cite the RegMix paper: |
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``` |
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@article{liu2024regmix, |
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title={RegMix: Data Mixture as Regression for Language Model Pre-training}, |
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author={Liu, Qian and Zheng, Xiaosen and Muennighoff, Niklas and Zeng, Guangtao and Dou, Longxu and Pang, Tianyu and Jiang, Jing and Lin, Min}, |
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journal={arXiv preprint arXiv:2407.01492}, |
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year={2024} |
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} |
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``` |
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For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492). |
