ManufactuBERT: Efficient Continual Pretraining for Manufacturing

Robin Armingaud; Romaric Besançon

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arXiv · 2511.05135

ManufactuBERT: Efficient Continual Pretraining for Manufacturing

Robin Armingaud·Romaric Besançon

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Abstract

While large general-purpose Transformer-based encoders excel at general language understanding, their performance diminishes in specialized domains like manufacturing due to a lack of exposure to domain-specific terminology and semantics. In this paper, we address this gap by introducing ManufactuBERT, a RoBERTa model continually pretrained on a large-scale corpus curated for the manufacturing domain. We present a comprehensive data processing pipeline to create this corpus from web data, involving an initial domain-specific filtering step followed by a multi-stage deduplication process that removes redundancies. Our experiments show that ManufactuBERT establishes a new state-of-the-art on a range of manufacturing-related NLP tasks, outperforming strong specialized baselines. More importantly, we demonstrate that training on our carefully deduplicated corpus significantly accelerates convergence, leading to a 33\% reduction in training time and computational cost compared to training on the non-deduplicated dataset. The proposed pipeline offers a reproducible example for developing high-performing encoders in other specialized domains. We will release our model and curated corpus at https://huggingface.co/cea-list-ia.

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Robin Armingaud, Romaric Besançon. 2025-11-07. ManufactuBERT: Efficient Continual Pretraining for Manufacturing. https://arxiv.org/abs/2511.05135

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