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--- |
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language: |
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- en |
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license: mit |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Shushant/BiomedicalQuestionAnsweringDataset |
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metrics: |
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- exact_match |
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- f1 |
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pipeline_tag: question-answering |
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base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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model-index: |
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- name: biomedical_question_answering |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# biomedical_question_answering |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6629 |
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## Model description |
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Model finetuned on PubmedBERT using custom daatset |
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## Intended uses & limitations |
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For question answering related to biomedical research papers. |
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## Training and evaluation data |
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Data https://huggingface.co/datasets/Shushant/BiomedicalQuestionAnsweringDataset |
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## Training procedure |
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Finetuning using Trainer API |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 1.0 | 236 | 1.6866 | |
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| No log | 2.0 | 472 | 1.5432 | |
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| 0.737 | 3.0 | 708 | 1.7998 | |
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| 0.737 | 4.0 | 944 | 1.9746 | |
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| 0.2893 | 5.0 | 1180 | 1.9510 | |
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| 0.2893 | 6.0 | 1416 | 2.1479 | |
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| 0.1562 | 7.0 | 1652 | 2.3304 | |
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| 0.1562 | 8.0 | 1888 | 2.5882 | |
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| 0.0823 | 9.0 | 2124 | 2.6494 | |
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| 0.0823 | 10.0 | 2360 | 2.6629 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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- # Paper Details |
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- If you want to know about the full implementation detials, please read the full paper here |
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https://www.researchgate.net/publication/375011546_Question_Answering_on_Biomedical_Research_Papers_using_Transfer_Learning_on_BERT-Base_Models |
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## Citation Plain Text |
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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. |
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## Citation Bibtex |
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@INPROCEEDINGS{10290240, |
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author={Pudasaini, Shushanta and Shakya, Subarna}, |
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booktitle={2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)}, |
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title={Question Answering on Biomedical Research Papers using Transfer Learning on BERT-Base Models}, |
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year={2023}, |
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volume={}, |
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number={}, |
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pages={496-501}, |
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doi={10.1109/I-SMAC58438.2023.10290240}} |
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