Create README.md

README.md

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+---
+license: mit
+language:
+- en
+tags:
+- regmix
+pretty_name: regmix-data
+size_categories:
+- 10M<n<100M
+---
+
+# RegMix Data
+
+## Dataset Description
+
+The RegMix Data 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.
+
+### Key Features:
+
+- Size: Approximately 1TB disk space, 250B tokens
+- Distribution: Follows the natural token distribution of domain examples
+- Organization: Examples from different domains are separated into individual files
+
+## Dataset Structure
+
+The dataset is organized into two main directories: `train` and `valid`, each containing domain-specific JSONL files. The file naming convention is as follows:
+
+```
+[domain]-[identifier]-[number].jsonl
+```
+
+For example: `arxiv-10-74305611.jsonl`
+
+### Domains Included:
+
+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
+
+## Usage
+
+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).
+
+To download the dataset:
+
+```python
+from huggingface_hub import snapshot_download
+
+LOCAL_DIR = "regmix-data"
+snapshot_download(repo_id="sail/regmix-data", 
+                  repo_type='dataset',
+                  local_dir=LOCAL_DIR,
+                  local_dir_use_symlinks=False)
+```
+
+This will download the entire snapshot, containing 34 JSON line files (17 for train, and 17 for valid), to your specified local directory.
+
+## Data Preprocessing
+
+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).
+
+## Acknowledgements
+
+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.
+
+## Citation
+
+If you use this dataset in your research, please cite the RegMix paper:
+
+```
+@misc{liu2024regmix,
+      title={RegMix: Data Mixture as Regression for Language Model Pre-training}, 
+      author={Qian Liu and Xiaosen Zheng and Niklas Muennighoff and Guangtao Zeng and Longxu Dou and Tianyu Pang and Jing Jiang and Min Lin},
+      year={2024},
+      eprint={2407.01492},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2407.01492}, 
+}
+```
+
+For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492).