---
language:
- en
license: mit
library_name: transformers
tags:
- generated_from_trainer
datasets:
- Shushant/BiomedicalQuestionAnsweringDataset
metrics:
- exact_match
- f1
pipeline_tag: question-answering
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
model-index:
- name: biomedical_question_answering
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# biomedical_question_answering

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/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

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 236  | 1.6866          |
| No log        | 2.0   | 472  | 1.5432          |
| 0.737         | 3.0   | 708  | 1.7998          |
| 0.737         | 4.0   | 944  | 1.9746          |
| 0.2893        | 5.0   | 1180 | 1.9510          |
| 0.2893        | 6.0   | 1416 | 2.1479          |
| 0.1562        | 7.0   | 1652 | 2.3304          |
| 0.1562        | 8.0   | 1888 | 2.5882          |
| 0.0823        | 9.0   | 2124 | 2.6494          |
| 0.0823        | 10.0  | 2360 | 2.6629          |


### 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}}