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gpt2_sm_cv_defined_4
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4859
- Accuracy: 0.777
- Precision: 0.3889
- Recall: 0.2513
- F1: 0.3053
- D-index: 1.4874
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 | 1.2496 | 0.469 | 0.1830 | 0.4974 | 0.2676 | 1.1202 |
| 2.4045 | 2.0 | 500 | 0.6575 | 0.679 | 0.2056 | 0.2256 | 0.2152 | 1.3395 |
| 2.4045 | 3.0 | 750 | 0.5358 | 0.784 | 0.2439 | 0.0513 | 0.0847 | 1.4262 |
| 0.5054 | 4.0 | 1000 | 0.5534 | 0.786 | 0.3333 | 0.0974 | 0.1508 | 1.4457 |
| 0.5054 | 5.0 | 1250 | 0.5221 | 0.785 | 0.2727 | 0.0615 | 0.1004 | 1.4313 |
| 0.4682 | 6.0 | 1500 | 0.5396 | 0.765 | 0.3276 | 0.1949 | 0.2444 | 1.4510 |
| 0.4682 | 7.0 | 1750 | 0.5442 | 0.766 | 0.3415 | 0.2154 | 0.2642 | 1.4596 |
| 0.4097 | 8.0 | 2000 | 0.7828 | 0.79 | 0.2581 | 0.0410 | 0.0708 | 1.4309 |
| 0.4097 | 9.0 | 2250 | 0.6443 | 0.771 | 0.3607 | 0.2256 | 0.2776 | 1.4701 |
| 0.3341 | 10.0 | 2500 | 0.6839 | 0.76 | 0.3529 | 0.2769 | 0.3103 | 1.4727 |
| 0.3341 | 11.0 | 2750 | 0.7968 | 0.725 | 0.3095 | 0.3333 | 0.3210 | 1.4434 |
| 0.2456 | 12.0 | 3000 | 1.0615 | 0.771 | 0.3534 | 0.2103 | 0.2637 | 1.4648 |
| 0.2456 | 13.0 | 3250 | 1.7036 | 0.786 | 0.3797 | 0.1538 | 0.2190 | 1.4657 |
| 0.1537 | 14.0 | 3500 | 1.5848 | 0.741 | 0.3280 | 0.3128 | 0.3202 | 1.4587 |
| 0.1537 | 15.0 | 3750 | 1.5904 | 0.727 | 0.3125 | 0.3333 | 0.3226 | 1.4462 |
| 0.1323 | 16.0 | 4000 | 1.9229 | 0.685 | 0.3 | 0.4615 | 0.3636 | 1.4311 |
| 0.1323 | 17.0 | 4250 | 2.2383 | 0.785 | 0.3684 | 0.1436 | 0.2066 | 1.4607 |
| 0.1117 | 18.0 | 4500 | 2.4084 | 0.799 | 0.4167 | 0.0769 | 0.1299 | 1.4564 |
| 0.1117 | 19.0 | 4750 | 2.5225 | 0.798 | 0.4426 | 0.1385 | 0.2109 | 1.4769 |
| 0.0759 | 20.0 | 5000 | 2.4859 | 0.777 | 0.3889 | 0.2513 | 0.3053 | 1.4874 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
