---
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