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---
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}}
|