metadata
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 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