metadata
library_name: transformers
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: section-classification-v2
results: []
section-classification-v2
This model is a fine-tuned version of medicalai/ClinicalBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9659
- Accuracy: 0.8638
- Precision: 0.8715
- Recall: 0.8638
- F1: 0.8632
Model description
More information needed
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.3326 | 1.0 | 651 | 1.2205 | 0.7043 | 0.7794 | 0.7043 | 0.6578 |
| 1.2018 | 2.0 | 1302 | 1.0781 | 0.8172 | 0.8522 | 0.8172 | 0.8142 |
| 1.1063 | 3.0 | 1953 | 0.9935 | 0.8477 | 0.8594 | 0.8477 | 0.8455 |
| 0.9862 | 4.0 | 2604 | 0.9659 | 0.8638 | 0.8715 | 0.8638 | 0.8632 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1