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

> 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 mo...

## Metadata
- Authors: Sultan Alrowili, Vijay Shanker
- Published: 2021-01-01
- DOI: https://doi.org/10.18653/v1/2021.bionlp-1.24
- Citations: 68
- Source: OpenAlex
- Access: Open Access

## Technology Hub
- Hub: Large Language Models
- Discipline: Computer Science / AI
- Hub URL: https://science-database.com/technology/large-language-models
- Hub llms.txt: https://science-database.com/technology/large-language-models/llms.txt

## 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.

## Links
- DOI: https://doi.org/10.18653/v1/2021.bionlp-1.24
- OpenAlex: https://openalex.org/W3166204619
- PDF: https://aclanthology.org/2021.bionlp-1.24.pdf
- JSON API: https://science-database.com/api/v1/technology/large-language-models

---
Generated by science-database.com — The Knowledge Interface
Paper ID: oa-W3166204619 | Hub: large-language-models