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gpt2_sm_gen1
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.8305
- Accuracy: 0.798
- Precision: 0.6346
- Recall: 0.1528
- F1: 0.2463
- D-index: 1.4919
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 | 1.2636 | 0.706 | 0.25 | 0.1806 | 0.2097 | 1.3706 |
| 1.4845 | 2.0 | 500 | 0.7018 | 0.734 | 0.25 | 0.1157 | 0.1582 | 1.3887 |
| 1.4845 | 3.0 | 750 | 0.6251 | 0.784 | 0.5 | 0.0093 | 0.0182 | 1.4233 |
| 0.5185 | 4.0 | 1000 | 0.5824 | 0.786 | 0.6 | 0.0278 | 0.0531 | 1.4326 |
| 0.5185 | 5.0 | 1250 | 0.5811 | 0.789 | 0.6667 | 0.0463 | 0.0866 | 1.4432 |
| 0.4299 | 6.0 | 1500 | 0.6123 | 0.793 | 0.8 | 0.0556 | 0.1039 | 1.4520 |
| 0.4299 | 7.0 | 1750 | 0.5759 | 0.784 | 0.5 | 0.1389 | 0.2174 | 1.4677 |
| 0.3603 | 8.0 | 2000 | 0.7418 | 0.79 | 0.6154 | 0.0741 | 0.1322 | 1.4541 |
| 0.3603 | 9.0 | 2250 | 0.6740 | 0.766 | 0.4392 | 0.3009 | 0.3571 | 1.4963 |
| 0.2662 | 10.0 | 2500 | 0.8520 | 0.789 | 0.5385 | 0.1620 | 0.2491 | 1.4824 |
| 0.2662 | 11.0 | 2750 | 1.4823 | 0.79 | 0.6154 | 0.0741 | 0.1322 | 1.4541 |
| 0.1838 | 12.0 | 3000 | 1.2440 | 0.789 | 0.5203 | 0.2963 | 0.3776 | 1.5267 |
| 0.1838 | 13.0 | 3250 | 1.3491 | 0.781 | 0.4854 | 0.2315 | 0.3135 | 1.4944 |
| 0.1362 | 14.0 | 3500 | 1.6458 | 0.786 | 0.5093 | 0.2546 | 0.3395 | 1.5089 |
| 0.1362 | 15.0 | 3750 | 1.7559 | 0.794 | 0.5532 | 0.2407 | 0.3355 | 1.5155 |
| 0.1176 | 16.0 | 4000 | 2.3472 | 0.801 | 0.7073 | 0.1343 | 0.2257 | 1.4899 |
| 0.1176 | 17.0 | 4250 | 2.1587 | 0.793 | 0.5849 | 0.1435 | 0.2305 | 1.4818 |
| 0.0812 | 18.0 | 4500 | 2.1713 | 0.793 | 0.5584 | 0.1991 | 0.2935 | 1.5003 |
| 0.0812 | 19.0 | 4750 | 2.1631 | 0.793 | 0.5413 | 0.2731 | 0.3631 | 1.5247 |
| 0.065 | 20.0 | 5000 | 2.8305 | 0.798 | 0.6346 | 0.1528 | 0.2463 | 1.4919 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
