metadata
base_model: nlpie/distil-clinicalbert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distil-clinicalbert-medical-text-classification
results: []
distil-clinicalbert-medical-text-classification
This model is a fine-tuned version of nlpie/distil-clinicalbert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8719
- Accuracy: 0.266
- Precision: 0.2357
- Recall: 0.266
- F1: 0.2427
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 2.5512 | 1.0 | 250 | 2.6302 | 0.335 | 0.1402 | 0.335 | 0.1911 |
| 2.0609 | 2.0 | 500 | 2.1857 | 0.357 | 0.2240 | 0.357 | 0.2474 |
| 1.9056 | 3.0 | 750 | 1.8964 | 0.321 | 0.2773 | 0.321 | 0.2812 |
| 1.5646 | 4.0 | 1000 | 1.8117 | 0.323 | 0.3183 | 0.323 | 0.2949 |
| 1.3789 | 5.0 | 1250 | 1.8869 | 0.302 | 0.2643 | 0.302 | 0.2701 |
| 1.3189 | 6.0 | 1500 | 1.8719 | 0.266 | 0.2357 | 0.266 | 0.2427 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2