---
license: apache-2.0
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: pretoxtm-sentence-classifier
  results: []
datasets:
- javicorvi/pretoxtm-dataset
language:
- en
pipeline_tag: text-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# pretoxtm-sentence-classifier

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 [javicorvi/pretoxtm-dataset](https://huggingface.co/datasets/javicorvi/pretoxtm-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.1181
- Precision: 0.9788
- Recall: 0.9800
- Accuracy: 0.9795
- F1: 0.9794

## Model description

PretoxTM Sentence Classifier is a model trained on preclinical toxicology literature, designed to detect sentences that contain treatment-related findings.

## Training and evaluation data

The model was trained on [javicorvi/pretoxtm-dataset](https://huggingface.co/datasets/javicorvi/pretoxtm-dataset).

The dataset is divided in train, validation and test.

## Training procedure


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1.1848183151867784e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.2543        | 1.0   | 514  | 0.1181          | 0.9788    | 0.9800 | 0.9795   | 0.9794 |
| 0.1344        | 2.0   | 1028 | 0.1488          | 0.9767    | 0.9775 | 0.9773   | 0.9771 |
| 0.0419        | 3.0   | 1542 | 0.1520          | 0.9767    | 0.9775 | 0.9773   | 0.9771 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2