| license: mit | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract | |
| model-index: | |
| - name: bert-paper-classifier | |
| results: [] | |
| # bert-paper-classifier | |
| This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the dataset from [González-Márquez et al., 2023](https://www.biorxiv.org/content/10.1101/2023.04.10.536208v1). | |
| ## Intended uses & limitations | |
| This model is intended to predict the category given the paper title (and optionally its abstract) — for the biomedical papers. For example, it is likely to predict `virology` as a category for the paper with a title containing `COVID-19`. | |
| So far only a subset of the PubMed dataset has been used for training. Future improvements to this model can come with using the full dataset with a combination of titles and abstracts for the fine-tuning as well as extending the training set to the preprints from bioRxiv and/or arXiv. | |
| ## Training procedure | |
| The code for the model fine-tuning can be found [in the respective notebook](https://huggingface.co/oracat/bert-paper-classifier/blob/main/finetuning-pubmed.ipynb). | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 128 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - Pytorch 2.0.0+cu117 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 |