metadata

license: odc-by

TxT360: a globally deduplicated dataset for LLM pretraining

We introduce TxT360 (Trillion eXtracted Text) the first dataset to globally deduplicate 99 CommonCrawl snapshots and 14 commonly used non-web data sources (e.g. FreeLaw, PG-19, etc.) providing pretraining teams with a recipe to easily adjust data weighting and train the most performant models.

TxT360 Compared to Common Pretraining Datasets

Data Source	TxT360	FineWeb	RefinedWeb	PedPajamaV2	C4	Dolma	RedPajamaV1	The Pile
CommonCrawl Snapshots	99	96	90	84	1	24	5	0.6% of 74
Papers	5 Sources	-	-	-	-	1 Source	1 Source	4 Sources
Wikipedia	310+ Languages	-	-	-	-	Included	Included	English Only
FreeLaw	Included	-	-	-	-	-	-	Included
DM Math	Included	-	-	-	-	-	-	Included
USPTO	Included	-	-	-	-	-	-	Included
PG-19	Included	-	-	-	-	Included	Included	Included
HackerNews	Included	-	-	-	-	-	-	Included
Ubuntu IRC	Included	-	-	-	-	-	-	Included
EuroParl	Included	-	-	-	-	-	-	Included
StackExchange	Included	-	-	-	-	-	-	Included
Code	**	-	-	-	-	Included	Included	Included

** TxT360 does not include code. This decision was made due to the perceived low duplication code with other sources.

Complete details on the dataset can be found in our blog post here.

Upsampling Experiment with Comparison to FineWeb

To evaluate the training efficiency of our dataset, we sampled 1.5T tokens from both FineWeb and TxT360 (using the aforementioned weighting) and conducted a training ablation on an 8x8B Mixture-of-Experts architecture, similar to Mixtral. We compared the learning curves by tracking training loss, validation scores, and performance across a wide array of diverse evaluation benchmarks. The validation set was sampled independently from SlimPajama. Note that this experiment is done on a slightly earlier version of the dataset.

Initial Data Representation

To produce TxT360, a comprehensive and transparent data processing pipeline was designed to account for the nuances of both web and curated datasets. The pipeline presents a unified framework for processing both data types, making it convenient and easily adaptive for users to revise and fine-tune the pipeline for their own use cases.

Web datasets are inherently noisy and varied. The TxT360 pipeline implements sophisticated filtering and deduplication techniques to clean and remove redundancies while preserving data integrity.

Curated datasets are typically structured and consistently formatted. TxT360 filters these sources with selective steps to maintain their integrity while providing seamless integration into the larger dataset. Both data source types are globally deduplicated together resulting in 5.7T tokens of high-quality data. The table below shows the source distribution of TxT360 tokens.

Data Source	Raw Data Size	Token Count	Information Cut-Off Date
CommonCrawl	9.2 TB	4.83T	2024-30
Papers	712 GB	154.96B	Q4 2023
Wikipedia	210 GB	4.75B	-
Freelaw	23 GB	7.34B	Q1 2024
DM Math	22 GB	5.23B	-
USPTO	45 GB	4.95B	Q4 2023
PG-19	11 GB	2.94B	-
HackerNews	4.1 GB	1.08B	Q4 2023
Ubuntu IRC	4.7 GB	1.54B	Q4 2023
Europarl	6.1 GB	1.96B	-
StackExchange	45 GB	8.37B	Q4 2023

CommonCrawl Data Filtering

Follow this link to view all steps taken to filter the web data.

Curated Source Filtering

Each data source was filtered individually with respect to the underlying data. Full details and discussion on how each source is filter is covered here.

Global Deduplication

After the web and curated sources were filtered, they were globally deduplicated to create TxT360. The deduplication process is available here.

Citation

BibTeX:

@misc{txt360data2024,
      title={TxT360: a globally deduplicated dataset for LLM pretraining}, 
      author={Liping Tang, Nikhil Ranjan, Omkar Pangarkar, Zhen Wang, An Li, Zhoujun Cheng, Suqi Sun, Cun Mu, Victor Miller, Yue Peng, Eric P. Xing, Zhengzhong Liu},
      year={2024}
}