biomedical_question_answering

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biomedical_question_answering

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on a custom dataset of question answer pairs annotated from research papers from Pubmed. It achieves the following results on the evaluation set:

• Loss: 2.6629

Model description

Model finetuned on PubmedBERT using custom daatset

Intended uses & limitations

For question answering related to biomedical research papers.

Training and evaluation data

Data https://huggingface.co/datasets/Shushant/BiomedicalQuestionAnsweringDataset

Training procedure

Finetuning using Trainer API

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.26.1

• Pytorch 1.13.1+cu116

• Datasets 2.9.0

• Tokenizers 0.13.2

• Paper Details

• If you want to know about the full implementation detials, please read the full paper here https://www.researchgate.net/publication/375011546_Question_Answering_on_Biomedical_Research_Papers_using_Transfer_Learning_on_BERT-Base_Models

  Citation Plain Text

  S. Pudasaini and S. Shakya, "Question Answering on Biomedical Research Papers using Transfer Learning on BERT-Base Models," 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 496-501, doi: 10.1109/I-SMAC58438.2023.10290240.

  Citation Bibtex

@INPROCEEDINGS{10290240, author={Pudasaini, Shushanta and Shakya, Subarna}, booktitle={2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)}, title={Question Answering on Biomedical Research Papers using Transfer Learning on BERT-Base Models}, year={2023}, volume={}, number={}, pages={496-501}, doi={10.1109/I-SMAC58438.2023.10290240}}