metadata
library_name: transformers
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
results: []
results
This model is a fine-tuned version of medicalai/ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8614
- Accuracy: 0.6145
- Precision: 0.6243
- Recall: 0.6145
- F1: 0.5971
- Roc Auc: 0.8073
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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 | Accuracy | Precision | Recall | F1 | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 42 | 1.0433 | 0.4458 | 0.4351 | 0.4458 | 0.3685 | 0.7162 |
| No log | 2.0 | 84 | 0.8946 | 0.5663 | 0.5641 | 0.5663 | 0.5559 | 0.7823 |
| No log | 3.0 | 126 | 0.9142 | 0.5783 | 0.6385 | 0.5783 | 0.5332 | 0.7896 |
| No log | 4.0 | 168 | 0.8497 | 0.6386 | 0.6434 | 0.6386 | 0.6299 | 0.8084 |
| No log | 5.0 | 210 | 0.8614 | 0.6145 | 0.6243 | 0.6145 | 0.5971 | 0.8073 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.5
- Tokenizers 0.20.3