Manu-FineWeb

Manu-FineWeb is a high-quality, large-scale corpus specifically curated for the manufacturing domain. It was extracted from the 15-trillion-token FineWeb dataset and refined to facilitate efficient domain-specific pretraining for models like ManufactuBERT.

Dataset Summary

• Developed by: Robin Armingaud and Romaric Besançon (Université Paris-Saclay, CEA, List)
• Statistics: 2B tokens/4,5 million documents

Construction & Curation

The dataset was built using a rigorous pipeline to ensure high relevance and low redundancy:

1. Domain-Specific Filtering

A fastText classifier was trained on a positive set of manufacturing-specific sources to filter the general FineWeb corpus. The training sources included:

• Elsevier: Abstracts from industrial and manufacturing engineering journals.
• ArXiv: Abstracts from categories like physics, computer science, and engineering related to industrial processes.
• Wikipedia: Articles from manufacturing and engineering categories.
• BigPatent: Patent descriptions containing "manufacturing" keywords.

2. Multi-Stage Deduplication

To improve training efficiency, the 10B token corpus was reduced by ~80% through:

• Lexical Deduplication (MinHash): Eliminating near-exact text duplicates.
• Semantic Deduplication (SemDeDup): Identifying and removing semantically redundant documents using sentence embeddings (all-MiniLM-L6-v2), leaving only the most representative data points.

Citation

If you use ManufactuBERT in your research, please cite:

@misc{armingaud2025manufactubertefficientcontinualpretraining,
      title={ManufactuBERT: Efficient Continual Pretraining for Manufacturing}, 
      author={Robin Armingaud and Romaric Besançon},
      year={2025},
      eprint={2511.05135},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.05135}, 
}