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bert_sm_gen1_cv_4
This model is a fine-tuned version of wiorz/bert_sm_gen1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4140
- Accuracy: 0.82
- Precision: 0.5758
- Recall: 0.2923
- F1: 0.3878
- D-index: 1.5600
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: 4
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|---|
| 0.962 | 1.0 | 1000 | 0.6663 | 0.821 | 0.5930 | 0.2615 | 0.3630 | 1.5509 |
| 0.5779 | 2.0 | 2000 | 0.6352 | 0.818 | 0.5481 | 0.3795 | 0.4485 | 1.5864 |
| 0.4922 | 3.0 | 3000 | 0.9985 | 0.819 | 0.6667 | 0.1436 | 0.2363 | 1.5076 |
| 0.2595 | 4.0 | 4000 | 1.3708 | 0.806 | 0.5062 | 0.2103 | 0.2971 | 1.5130 |
| 0.1417 | 5.0 | 5000 | 1.5550 | 0.811 | 0.5326 | 0.2513 | 0.3415 | 1.5339 |
| 0.1007 | 6.0 | 6000 | 1.8121 | 0.808 | 0.5185 | 0.2154 | 0.3043 | 1.5175 |
| 0.1046 | 7.0 | 7000 | 1.9016 | 0.818 | 0.5657 | 0.2872 | 0.3810 | 1.5556 |
| 0.1286 | 8.0 | 8000 | 1.8942 | 0.815 | 0.5714 | 0.2051 | 0.3019 | 1.5235 |
| 0.108 | 9.0 | 9000 | 1.9444 | 0.802 | 0.4895 | 0.3590 | 0.4142 | 1.5581 |
| 0.0547 | 10.0 | 10000 | 1.8634 | 0.802 | 0.4887 | 0.3333 | 0.3963 | 1.5495 |
| 0.0288 | 11.0 | 11000 | 2.0029 | 0.83 | 0.6761 | 0.2462 | 0.3609 | 1.5578 |
| 0.0185 | 12.0 | 12000 | 2.2107 | 0.803 | 0.4926 | 0.3436 | 0.4048 | 1.5543 |
| 0.0088 | 13.0 | 13000 | 2.1847 | 0.817 | 0.5517 | 0.3282 | 0.4116 | 1.5680 |
| 0.0018 | 14.0 | 14000 | 2.3947 | 0.808 | 0.5118 | 0.3333 | 0.4037 | 1.5576 |
| 0.0152 | 15.0 | 15000 | 2.3443 | 0.823 | 0.5957 | 0.2872 | 0.3875 | 1.5623 |
| 0.016 | 16.0 | 16000 | 2.3187 | 0.815 | 0.5385 | 0.3590 | 0.4308 | 1.5756 |
| 0.0 | 17.0 | 17000 | 2.3557 | 0.817 | 0.5536 | 0.3179 | 0.4039 | 1.5646 |
| 0.0001 | 18.0 | 18000 | 2.4107 | 0.816 | 0.5433 | 0.3538 | 0.4286 | 1.5752 |
| 0.0 | 19.0 | 19000 | 2.4105 | 0.82 | 0.5758 | 0.2923 | 0.3878 | 1.5600 |
| 0.0 | 20.0 | 20000 | 2.4140 | 0.82 | 0.5758 | 0.2923 | 0.3878 | 1.5600 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
