| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: BiomedBERT-AC-LF-Classification |
| results: [] |
| datasets: |
| - surrey-nlp/PLOD-CW-25 |
| language: |
| - en |
| --- |
| |
| # BiomedBERT-AC-LF-Classification |
|
|
| This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the PLOD-CW-25 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2703 |
| - Precision: 0.7821 |
| - Recall: 0.8686 |
| - F1: 0.8231 |
| - Accuracy: 0.9204 |
|
|
| It achieves the following results on the test set: |
| - Loss: 0.1384 |
| - Precision: 0.8473 |
| - Recall: 0.9281 |
| - F1: 0.8858 |
| - Accuracy: 0.9529 |
|
|
| ## Model description |
|
|
| This model is a fine-tuned model, designed to detect abbreviations and long forms in biomedical text. Abbreviations and long forms are tagged in the BIO format, with the following labels, B-AC, B-LF, I-LF and O. |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.3341 | 1.0 | 125 | 0.2485 | 0.7727 | 0.8477 | 0.8084 | 0.9111 | |
| | 0.1633 | 2.0 | 250 | 0.2525 | 0.7767 | 0.8673 | 0.8195 | 0.9174 | |
| | 0.1293 | 3.0 | 375 | 0.2224 | 0.7855 | 0.8501 | 0.8165 | 0.9211 | |
| | 0.1081 | 4.0 | 500 | 0.2600 | 0.7780 | 0.8784 | 0.8252 | 0.9201 | |
| | 0.0938 | 5.0 | 625 | 0.2703 | 0.7821 | 0.8686 | 0.8231 | 0.9204 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.52.4 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.6.0 |
| - Tokenizers 0.21.1 |
