RedMod/mga_fineweb_edu

Massive Genre-Audience Augment Fineweb-Edu Corpus

This dataset is a synthetic pretraining corpus described in paper Reformulation for Pretraining Data Augmentation.

Overview of synthesis framework. Our method expands the original corpus through a two-stage synthesis process. Each document is reformulated to 5 new documents, achieving 3.9× token number expansion while maintaining diversity through massive (genre, audience) pairs.

We build MGACorpus based on SmolLM Corpus, expanding fineweb-edu-dedup source from 195B tokens to 770B tokens.

Performance

Our baseline is trained on SmolLM-Corpus dataset, and experiments use MGACorpus as incremental data.

Training dynamics of two common scenarios under data-constrained conditions:

• (1) expanding a 50B high-quality dataset to a 500B training budget (entire set repetition).
• (2) expanding a 500B mixed-quality dataset to a 700B training budget (subset repetition).

Dataset Schema

root
 |-- meta: struct (nullable = true)
 |    |-- chunk_id: string (nullable = true)
 |    |-- docid: string (nullable = true)
 |    |-- meta_extra: string (nullable = true)
 |    |-- source: string (nullable = true)
 |    |-- split: string (nullable = true)
 |    |-- genre: string (nullable = true)
 |    |-- audience: string (nullable = true)
 |    |-- raw_text: string (nullable = true)
 |-- content_split: string (nullable = true)

Loading the dataset

from datasets import load_dataset
ds = load_dataset("ByteDance-Seed/mga-fineweb-edu", split='train')
print(ds[0])

Data Source Statement

Content in the meta.raw_text field is derived from FineWeb-EDU-Dedup subset of SmolLM Corpus, licensed under ODC-By license. Other text fields follow the same license.

Disclaimer

Your access to and use of this dataset are at your own risk. We do not guarantee the accuracy of this dataset. The dataset is provided "as is" and we make no warranty or representation to you with respect to it and we expressly disclaim, and hereby expressly waive, all warranties, express, implied, statutory or otherwise. This includes, without limitation, warranties of quality, performance, merchantability or fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. In no event will we be liable to you on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this public license or use of the licensed material. The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.

Citation

@article{hao2025reformulation,
  title = {Reformulation for Pretraining Data Augmentation},
  author = {Hao, Xintong and Zhu, Ruijie and Zhang, Ge and Shen, Ke and Li, Chenggang},
  journal={arXiv preprint arXiv:2502.04235},
  url = {https://arxiv.org/abs/2502.04235}
}