Material SciBERT (TPU): Improving language understanding in materials science
Work in progress
Introduction
SciBERT-based model pre-trained with materials science scientific fulltext
Authors
Luca Foppiano Pedro Ortiz Suarez
TLDR
- Collected full-text from ~700000 articles provided by the National Institute for Materials Science (NIMS) TDM platform (https://dice.nims.go.jp/services/TDM-PF/en/), dataset called ScienceCorpus (SciCorpus)
- We added to the SciBERT vocabulary (32k tokens), 100 domain-specific unknown words extracted from SciCorpus with a keywords modeler (KeyBERT)
- Starting conditions: original SciBERT weights
- Pre-train the model MatTpuSciBERT from on the Google Cloud with the TPU (Tensor Processing Unit) as follow:
- 800000 steps with batch_size: 256, max_seq_length:512
- 100000 steps with batch_size: 2048, max_seq_length:128
- Fine-tuning and testing on NER on superconductors (https://github.com/lfoppiano/grobid-superconductors) and physical quantities (https://github.com/kermitt2/grobid-quantities)
Related work
BERT Implementations
- BERT (the original) https://arxiv.org/abs/1810.04805
- RoBERTa (Re-implementation by Facebook) https://arxiv.org/abs/1907.11692
Relevant models
- SciBERT: BERT, from scratch, scientific articles (biology + CS) https://github.com/allenai/scibert
- MatSciBERT (Gupta): RoBERTa, from scratch, SciBERT vocab and weights, ~150 K paper limited to 4 MS families http://github.com/m3rg-iitd/matscibert
- MaterialBERT: Not yet published
- MatBERT (CEDER): BERT, from scratch, 2M documents on materials science (~60M paragraphs) https://github.com/lbnlp/MatBERT
- BatteryBERT (Cole): BERT, mixed from scratch and with predefined weights https://github.com/ShuHuang/batterybert/
Results
Results obtained via 10-fold cross-validation, using DeLFT (https://github.com/kermitt2/delft)
NER Superconductors
| Model | Precision | Recall | F1 |
|---|---|---|---|
| SciBERT (baseline) | 81.62% | 84.23% | 82.90% |
| MatSciBERT (Gupta) | 81.45% | 84.36% | 82.88% |
| MatTPUSciBERT | 82.13% | 85.15% | 83.61% |
| MatBERT (Ceder) | 81.25% | 83.99% | 82.60% |
| BatteryScibert-cased | 81.09% | 84.14% | 82.59% |
NER Quantities
| Model | Precision | Recall | F1 |
|---|---|---|---|
| SciBERT (baseline) | 88.73% | 86.76% | 87.73% |
| MatSciBERT (Gupta) | 84.98% | 90.12% | 87.47% |
| MatTPUSciBERT | 88.62% | 86.33% | 87.46% |
| MatBERT (Ceder) | 85.08% | 89.93% | 87.44% |
| BatteryScibert-cased | 85.02% | 89.30% | 87.11% |
| BatteryScibert-cased | 81.09% | 84.14% | 82.59% |
References
This work was supported by Google, through the researchers program https://cloud.google.com/edu/researchers
Acknowledgements
TBA