Update README.md

README.md

@@ -1,3 +1,51 @@
----
-license: odc-by
----
+---
+license: odc-by
+task_categories:
+- fill-mask
+- text-generation
+language:
+- en
+tags:
+- manufacturing
+- engineering
+size_categories:
+- 1M<n<10M
+---
+# 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:
+```bibtex
+@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}, 
+}
+```