tiny-clinicalbert-medical-text-classification
This model is a fine-tuned version of nlpie/tiny-clinicalbert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1588
- Accuracy: 0.236
- Precision: 0.2048
- Recall: 0.236
- F1: 0.2109
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 |
|---|---|---|---|---|---|---|---|
| 2.9902 | 1.0 | 250 | 3.0201 | 0.274 | 0.0880 | 0.274 | 0.1318 |
| 2.4558 | 2.0 | 500 | 2.6033 | 0.341 | 0.1392 | 0.341 | 0.1922 |
| 2.4627 | 3.0 | 750 | 2.4332 | 0.368 | 0.2003 | 0.368 | 0.2252 |
| 2.2983 | 4.0 | 1000 | 2.3126 | 0.365 | 0.2212 | 0.365 | 0.2495 |
| 2.1395 | 5.0 | 1250 | 2.2385 | 0.349 | 0.2100 | 0.349 | 0.2518 |
| 1.8919 | 6.0 | 1500 | 2.1892 | 0.339 | 0.2176 | 0.339 | 0.2490 |
| 1.9892 | 7.0 | 1750 | 2.1364 | 0.336 | 0.2310 | 0.336 | 0.2643 |
| 1.8569 | 8.0 | 2000 | 2.1441 | 0.321 | 0.2316 | 0.321 | 0.2532 |
| 1.9182 | 9.0 | 2250 | 2.1263 | 0.309 | 0.2185 | 0.309 | 0.2417 |
| 1.6594 | 10.0 | 2500 | 2.1234 | 0.262 | 0.2180 | 0.262 | 0.2245 |
| 1.5966 | 11.0 | 2750 | 2.1176 | 0.256 | 0.2149 | 0.256 | 0.2197 |
| 1.6185 | 12.0 | 3000 | 2.1578 | 0.243 | 0.2126 | 0.243 | 0.2140 |
| 1.3881 | 13.0 | 3250 | 2.1588 | 0.236 | 0.2048 | 0.236 | 0.2109 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2