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| Model | Mat. Syn. | FabNER | SOFC | SOFC Slot | MatScholar | ChemdNER | Avg. |
|---|---|---|---|---|---|---|---|
| RoBERTa | 73.12 | 82.48 | 82.54 | 69.52 | 84.04 | 90.50 | 80.37 |
| MatSciBERT | 76.50 | 83.88 | 82.10 | 72.60 | 85.88 | 92.00 | 82.16 |
| ManufactuBERT | 75.04 | 84.00 | 84.40 | 73.68 | 86.76 | 91.92 | 82.63 |
| Model | Mat. Syn. (RE) | SOFC (SC) | Big Patent (SC) | Avg. |
|---|---|---|---|---|
| RoBERTa | 94.32 | 94.32 | 63.58 | 84.07 |
| MatSciBERT | 95.48 | 94.72 | 63.80 | 84.67 |
| ManufactuBERT | 94.62 | 94.68 | 65.80 | 85.03 |
| Model | GLUE Avg. |
|---|---|
| RoBERTa | 86.35 |
| MatSciBERT | 77.00 |
| SciBERT | 78.13 |
| ManufactuBERT | 81.78 |
1@misc{armingaud2025manufactubertefficientcontinualpretraining,
2 title={ManufactuBERT: Efficient Continual Pretraining for Manufacturing},
3 author={Robin Armingaud and Romaric Besançon},
4 year={2025},
5 eprint={2511.05135},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL},
8 url={https://arxiv.org/abs/2511.05135},
9}