| # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA | |
| # 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. | |
| # Model Description | |
| This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 264K steps with a batch size of 8192 on TPUv3-512 unit. In order to help researchers with limited resources to fine-tune larger models, we created an example with PyTorch XLA. PyTorch XLA (https://github.com/pytorch/xla) is a library that allows you to use PyTorch on TPU units, which is provided for free by Google Colab and Kaggle. Follow this example to work with PyTorch/XLA [Link](https://github.com/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb) | |
| Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. We also updated this repo with a couple of examples on how to fine-tune LMs on text classification and questions answering tasks such as ChemProt, SQuAD, and BioASQ. | |
| # Colab Notebook Examples | |
| BioM-ELECTRA-LARGE on NER and ChemProt Task [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_NER_and_ChemProt_Task_on_TPU.ipynb) | |
| BioM-ELECTRA-Large on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ELECTRA_Large_on_TPU.ipynb) | |
| BioM-ALBERT-xxlarge on SQuAD2.0 and BioASQ7B Factoid tasks [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ALBERT_xxlarge_on_TPU.ipynb) | |
| Text Classification Task With HuggingFace Transformers and PyTorchXLA on Free TPU [![Open In Colab][COLAB]](https://colab.research.google.com/github/salrowili/BioM-Transformers/blob/main/examples/Fine_Tuning_Biomedical_Models_on_Text_Classification_Task_With_HuggingFace_Transformers_and_PyTorch_XLA.ipynb) | |
| Reproducing our BLURB results with JAX [![Open In Colab][COLAB]](https://github.com/salrowili/BioM-Transformers/blob/main/examples/BLURB_LeaderBoard_with_TPU_VM.ipynb) | |
| [COLAB]: https://colab.research.google.com/assets/colab-badge.svg | |
| # Acknowledgment | |
| We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units. | |
| # Citation | |
| ```bibtex | |
| @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.", | |
| } | |
| ``` |