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.
- 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
Model | Corpus | Vocab | Batch Size | Training Steps | Link |
---|---|---|---|---|---|
BioM-ELECTRA-Base | PubMed Abstracts | 29K PubMed | 1024 | 500K | link |
BioM-ELECTRA-Large | PubMed Abstracts | 29K PubMed | 4096 | 434K | link |
BioM-BERT-Large | PubMed Abstracts + PMC | 30K EN Wiki + Books Corpus | 4096 | 690K | link |
BioM-ALBERT-xxlarge | PubMed Abstracts | 30K PubMed | 8192 | 264k | link |
BioM-ALBERT-xxlarge-PMC | PubMed Abstracts + PMC | 30K PubMed | 8192 | +64k | link |
Model | Exact Match (EM) | F1 Score | Link |
---|---|---|---|
BioM-ELECTRA-Base-SQuAD2 | 81.35 | 84.20 | Link |
BioM-ELECTRA-Large-SQuAD2 | 85.48 | 88.27 | Link |
BioM-ELECTRA-Large-MNLI-SQuAD2 | 85.24 | 88.01 | Link |
BioM-ALBERT-xxlarge-SQuAD2 | 83.86 | 86.99 | Link |
BioM-ALBERT-xxlarge-MNLI-SQuAD2 | 84.35 | 87.31 | Link |
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/ .
More information about GlounNLP https://github.com/dmlc/gluon-nlp
Model | Link |
---|---|
BioM-ELECTRA-Base | Link |
BioM-ELECTRA-Large | Link |
Model | Exact Match (EM) | F1 Score | Link |
---|---|---|---|
BioM-ELECTRA-Base-SQuAD2 | 80.93 | 83.86 | Link |
BioM-ELECTRA-Large-SQuAD2 | 85.34 | 88.09 | Link |
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
We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
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.",
}
@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}
}