BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA.

Paper link

Abstract

The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.

Pre-Trained Models ( PyTorch )

• BioM-ELECTRA-Base-Discriminator Link
• BioM-ELECTRA-Base-Generator Link
• BioM-ELECTRA-Large-Discriminator Link
• BioM-ELECTRA-Large-Generator Link
• BioM-BERT-PubMed-PMC-Large Link
• BioM-ALBERT-xxlarge Link
• BioM-ALBERT-xxlarge-PMC Link ( +PMC for 64k steps)
• BioM-ELECTRA-Large-SQuAD2 Link
• BioM-ALBERT-xxlarge-SQuAD2 Link

Pre-Trained LM Models ( TensorFlow )

SQuAD Fine-Tuned Checkpoints ( TensorFlow )

We implement transferability between MNLI and SQuAD, which was explained in details by (Jeong, et al., 2020). We detailed our particpiation in BioASQ9B in this Paper. To check the performance of our systems (UDEL-LAB) from the official BioASQ leaderboard visit http://participants-area.bioasq.org/results/9b/phaseB/ .

GluonNLP (MXNet) Checkpoints

More information about GlounNLP https://github.com/dmlc/gluon-nlp

Colab Notebook Examples

BioM-ELECTRA-LARGE on NER and ChemProt Task

BioM-ELECTRA-Large on SQuAD2.0 and BioASQ7B Factoid tasks

BioM-ALBERT-xxlarge on SQuAD2.0 and BioASQ7B Factoid tasks

Text Classification Task With HuggingFace Transformers and PyTorchXLA on Free TPU

Fine-Tunning BioM-Transformers on Question Answering dataset with TPU and Torch XLA

Reproducing our BLURB results with JAX

Finetunning BioM-Transformers with Jax/Flax on TPUv3-8 with free Kaggle resource

Acknowledgment

We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.

Citation

BioM-Transfomers Model

@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}

Question Answering with BioM-Transformers

@article{alrowili2021large,
  title={Large biomedical question answering models with ALBERT and ELECTRA},
  author={Alrowili, Sultan and Shanker, K},
  url = "http://ceur-ws.org/Vol-2936/paper-14.pdf",
  journal={CLEF (Working Notes)},
  year={2021}
}

@inproceedings{alrowili2022exploring,
  title={Exploring Biomedical Question Answering with BioM-Transformers At BioASQ10B challenge: Findings and Techniques},
  author={Alrowili, Sultan and Vijay-Shanker, K},
  year={2022},
  organization={CEUR Workshop Bologna, Italy}
}