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---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 430577854
num_examples: 77196
download_size: 264366766
dataset_size: 430577854
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
## Sampling Methodology
This dataset was created using **reservoir sampling**, a statistically unbiased random sampling algorithm that guarantees each sample from the source dataset has an equal probability of being included. This ensures the 100M token sample is representative of the full dataset's characteristics.
**Source Dataset**: [mlfoundations/dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0)
**Sample Size**: 100M tokens
**Content**: Filtered, diverse web content
Reservoir sampling enables rapid experimentation and ablation studies without processing the entire source dataset, while maintaining statistical validity of results.
For details on how this dataset was used in optimal pre-training data composition research, see the [blog post](https://huggingface.co/blog/codelion/optimal-dataset-mixing/).
## Citation
If you use this model/dataset, please cite:
```bibtex
@article{sharma2025billion,
title={The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix},
author={Sharma, Asankhaya},
year={2025},
url={https://huggingface.co/blog/codelion/optimal-dataset-mixing/}
}
```
For more details, see the [blog post](https://huggingface.co/blog/codelion/optimal-dataset-mixing/). |