metadata
license: mit
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model
results: []
NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2644
- Precision: 0.5331
- Recall: 0.5170
- F1: 0.5249
- Accuracy: 0.9319
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 71 | 0.2155 | 0.4953 | 0.4904 | 0.4929 | 0.9230 |
| No log | 2.0 | 142 | 0.2250 | 0.5350 | 0.4910 | 0.5121 | 0.9314 |
| No log | 3.0 | 213 | 0.2293 | 0.5373 | 0.5071 | 0.5218 | 0.9327 |
| No log | 4.0 | 284 | 0.2374 | 0.5562 | 0.4978 | 0.5254 | 0.9344 |
| No log | 5.0 | 355 | 0.2644 | 0.5331 | 0.5170 | 0.5249 | 0.9319 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1