metadata

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

pretoxtm-sentence-classifier

This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on 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.

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