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bert_sm_cv_summarized_4
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9996
- Accuracy: 0.802
- Precision: 0.48
- Recall: 0.1846
- F1: 0.2667
- D-index: 1.4986
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: 8000
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4713 | 0.812 | 0.5814 | 0.1282 | 0.2101 | 1.4926 |
| 0.5708 | 2.0 | 500 | 0.4584 | 0.811 | 0.5625 | 0.1385 | 0.2222 | 1.4948 |
| 0.5708 | 3.0 | 750 | 0.4557 | 0.813 | 0.5769 | 0.1538 | 0.2429 | 1.5029 |
| 0.4231 | 4.0 | 1000 | 0.4700 | 0.81 | 0.5316 | 0.2154 | 0.3066 | 1.5202 |
| 0.4231 | 5.0 | 1250 | 0.4979 | 0.812 | 0.5385 | 0.2513 | 0.3427 | 1.5353 |
| 0.3292 | 6.0 | 1500 | 0.5337 | 0.816 | 0.5647 | 0.2462 | 0.3429 | 1.5389 |
| 0.3292 | 7.0 | 1750 | 0.6282 | 0.797 | 0.4615 | 0.2462 | 0.3211 | 1.5131 |
| 0.2218 | 8.0 | 2000 | 0.7182 | 0.805 | 0.5 | 0.2513 | 0.3345 | 1.5257 |
| 0.2218 | 9.0 | 2250 | 0.8488 | 0.809 | 0.5208 | 0.2564 | 0.3436 | 1.5329 |
| 0.1478 | 10.0 | 2500 | 0.9830 | 0.809 | 0.5294 | 0.1846 | 0.2738 | 1.5082 |
| 0.1478 | 11.0 | 2750 | 1.0302 | 0.79 | 0.4419 | 0.2923 | 0.3519 | 1.5193 |
| 0.077 | 12.0 | 3000 | 1.0467 | 0.795 | 0.4658 | 0.3487 | 0.3988 | 1.5452 |
| 0.077 | 13.0 | 3250 | 1.2609 | 0.803 | 0.4931 | 0.3641 | 0.4189 | 1.5612 |
| 0.0328 | 14.0 | 3500 | 1.4127 | 0.806 | 0.5044 | 0.2923 | 0.3701 | 1.5411 |
| 0.0328 | 15.0 | 3750 | 1.6626 | 0.802 | 0.4835 | 0.2256 | 0.3077 | 1.5128 |
| 0.0189 | 16.0 | 4000 | 1.7062 | 0.81 | 0.5362 | 0.1897 | 0.2803 | 1.5113 |
| 0.0189 | 17.0 | 4250 | 1.9225 | 0.809 | 0.54 | 0.1385 | 0.2204 | 1.4921 |
| 0.0214 | 18.0 | 4500 | 1.8228 | 0.81 | 0.5269 | 0.2513 | 0.3403 | 1.5325 |
| 0.0214 | 19.0 | 4750 | 1.9544 | 0.789 | 0.4355 | 0.2769 | 0.3386 | 1.5127 |
| 0.0184 | 20.0 | 5000 | 1.9996 | 0.802 | 0.48 | 0.1846 | 0.2667 | 1.4986 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
