bert-paper-classifier
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the dataset from González-Márquez et al., 2023.
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.
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
