metadata
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exceptions_exp2_last_to_drop_frequency_40817
results: []
exceptions_exp2_last_to_drop_frequency_40817
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5595
- Accuracy: 0.3693
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: 0.0006
- train_batch_size: 16
- eval_batch_size: 16
- seed: 40817
- gradient_accumulation_steps: 5
- total_train_batch_size: 80
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 4.849 | 0.2912 | 1000 | 0.2538 | 4.7611 |
| 4.3402 | 0.5824 | 2000 | 0.2993 | 4.2887 |
| 4.1563 | 0.8736 | 3000 | 0.3152 | 4.0992 |
| 3.9798 | 1.1645 | 4000 | 0.3253 | 3.9908 |
| 3.9189 | 1.4557 | 5000 | 0.3321 | 3.9140 |
| 3.8836 | 1.7469 | 6000 | 0.3368 | 3.8585 |
| 3.7526 | 2.0379 | 7000 | 0.3411 | 3.8162 |
| 3.7682 | 2.3290 | 8000 | 0.3440 | 3.7890 |
| 3.7477 | 2.6202 | 9000 | 0.3469 | 3.7572 |
| 3.73 | 2.9114 | 10000 | 0.3494 | 3.7300 |
| 3.6397 | 3.2024 | 11000 | 0.3513 | 3.7153 |
| 3.6405 | 3.4936 | 12000 | 0.3531 | 3.6972 |
| 3.6412 | 3.7848 | 13000 | 0.3545 | 3.6827 |
| 3.5371 | 4.0757 | 14000 | 0.3560 | 3.6699 |
| 3.5768 | 4.3669 | 15000 | 0.3568 | 3.6631 |
| 3.577 | 4.6581 | 16000 | 0.3581 | 3.6506 |
| 3.5711 | 4.9493 | 17000 | 0.3594 | 3.6346 |
| 3.5066 | 5.2402 | 18000 | 0.3600 | 3.6377 |
| 3.5294 | 5.5314 | 19000 | 0.3611 | 3.6271 |
| 3.538 | 5.8226 | 20000 | 0.3619 | 3.6166 |
| 3.4415 | 6.1136 | 21000 | 0.3621 | 3.6199 |
| 3.475 | 6.4048 | 22000 | 0.3627 | 3.6099 |
| 3.4859 | 6.6959 | 23000 | 0.3636 | 3.6046 |
| 3.4951 | 6.9871 | 24000 | 0.3645 | 3.5929 |
| 3.4339 | 7.2781 | 25000 | 0.3643 | 3.5998 |
| 3.45 | 7.5693 | 26000 | 0.3647 | 3.5947 |
| 3.4628 | 7.8605 | 27000 | 0.3658 | 3.5823 |
| 3.3902 | 8.1514 | 28000 | 0.3655 | 3.5931 |
| 3.4052 | 8.4426 | 29000 | 0.3662 | 3.5856 |
| 3.4335 | 8.7338 | 30000 | 0.3667 | 3.5787 |
| 3.3189 | 9.0248 | 31000 | 0.3672 | 3.5799 |
| 3.3782 | 9.3159 | 32000 | 0.3669 | 3.5816 |
| 3.4026 | 9.6071 | 33000 | 0.3675 | 3.5727 |
| 3.4082 | 9.8983 | 34000 | 0.3684 | 3.5641 |
| 3.3356 | 10.1893 | 35000 | 0.3677 | 3.5779 |
| 3.3828 | 10.4805 | 36000 | 0.3682 | 3.5688 |
| 3.3911 | 10.7716 | 37000 | 0.3689 | 3.5626 |
| 3.2861 | 11.0626 | 38000 | 0.3686 | 3.5719 |
| 3.3361 | 11.3538 | 39000 | 0.3688 | 3.5682 |
| 3.361 | 11.6450 | 40000 | 0.3693 | 3.5595 |
| 3.382 | 11.9362 | 41000 | 0.3700 | 3.5506 |
| 3.3023 | 12.2271 | 42000 | 0.3694 | 3.5665 |
| 3.3451 | 12.5183 | 43000 | 0.3702 | 3.5592 |
| 3.3421 | 12.8095 | 44000 | 0.3703 | 3.5502 |
| 3.278 | 13.1005 | 45000 | 0.3697 | 3.5647 |
| 3.3183 | 13.3916 | 46000 | 0.3703 | 3.5587 |
| 3.3367 | 13.6828 | 47000 | 0.3709 | 3.5523 |
| 3.3425 | 13.9740 | 48000 | 0.3712 | 3.5452 |
| 3.2799 | 14.2650 | 49000 | 0.3707 | 3.5586 |
| 3.3193 | 14.5562 | 50000 | 0.3709 | 3.5554 |
| 3.3317 | 14.8474 | 51000 | 0.3714 | 3.5418 |
| 3.2427 | 15.1383 | 52000 | 0.3710 | 3.5579 |
| 3.2801 | 15.4295 | 53000 | 0.3715 | 3.5520 |
| 3.3063 | 15.7207 | 54000 | 0.3717 | 3.5437 |
| 3.2192 | 16.0116 | 55000 | 0.3716 | 3.5548 |
| 3.2589 | 16.3028 | 56000 | 0.3718 | 3.5538 |
| 3.2819 | 16.5940 | 57000 | 0.3720 | 3.5462 |
| 3.2991 | 16.8852 | 58000 | 0.3723 | 3.5384 |
| 3.2153 | 17.1762 | 59000 | 0.3717 | 3.5556 |
| 3.2642 | 17.4674 | 60000 | 0.3723 | 3.5492 |
| 3.2743 | 17.7585 | 61000 | 0.3721 | 3.5407 |
| 3.1953 | 18.0495 | 62000 | 0.3721 | 3.5563 |
| 3.2446 | 18.3407 | 63000 | 0.3721 | 3.5541 |
| 3.263 | 18.6319 | 64000 | 0.3727 | 3.5428 |
| 3.2807 | 18.9231 | 65000 | 0.3732 | 3.5372 |
| 3.2138 | 19.2140 | 66000 | 0.3722 | 3.5523 |
| 3.242 | 19.5052 | 67000 | 0.3728 | 3.5471 |
| 3.253 | 19.7964 | 68000 | 0.3732 | 3.5371 |
| 3.17 | 20.0874 | 69000 | 0.3724 | 3.5569 |
| 3.2225 | 20.3785 | 70000 | 0.3731 | 3.5482 |
| 3.237 | 20.6697 | 71000 | 0.3737 | 3.5437 |
| 3.263 | 20.9609 | 72000 | 0.3735 | 3.5322 |
| 3.2052 | 21.2519 | 73000 | 0.3727 | 3.5532 |
| 3.2214 | 21.5431 | 74000 | 0.3735 | 3.5449 |
| 3.2313 | 21.8343 | 75000 | 0.3740 | 3.5370 |
| 3.1508 | 22.1252 | 76000 | 0.3730 | 3.5523 |
| 3.2084 | 22.4164 | 77000 | 0.3734 | 3.5473 |
| 3.2192 | 22.7076 | 78000 | 0.3736 | 3.5417 |
| 3.2454 | 22.9988 | 79000 | 0.3740 | 3.5337 |
| 3.1837 | 23.2897 | 80000 | 0.3735 | 3.5482 |
| 3.1719 | 23.5809 | 81000 | 3.5576 | 0.3729 |
| 3.2133 | 23.8721 | 82000 | 3.5455 | 0.3735 |
| 3.1573 | 24.1634 | 83000 | 3.5548 | 0.3733 |
| 3.1941 | 24.4545 | 84000 | 3.5522 | 0.3734 |
| 3.2039 | 24.7457 | 85000 | 3.5436 | 0.3740 |
| 3.1082 | 25.0367 | 86000 | 3.5517 | 0.3735 |
| 3.1654 | 25.3279 | 87000 | 3.5514 | 0.3736 |
| 3.1816 | 25.6191 | 88000 | 3.5421 | 0.3740 |
| 3.2067 | 25.9103 | 89000 | 3.5390 | 0.3746 |
| 3.1412 | 26.2012 | 90000 | 3.5527 | 0.3737 |
| 3.1852 | 26.4924 | 91000 | 3.5452 | 0.3741 |
| 3.1961 | 26.7836 | 92000 | 3.5404 | 0.3746 |
| 3.1188 | 27.0745 | 93000 | 3.5554 | 0.3738 |
| 3.1557 | 27.3657 | 94000 | 3.5500 | 0.3741 |
| 3.1792 | 27.6569 | 95000 | 3.5387 | 0.3746 |
| 3.1917 | 27.9481 | 96000 | 3.5353 | 0.3747 |
| 3.1361 | 28.2391 | 97000 | 3.5542 | 0.3741 |
| 3.1525 | 28.5303 | 98000 | 3.5451 | 0.3744 |
| 3.1863 | 28.8214 | 99000 | 3.5396 | 0.3748 |
| 3.0999 | 29.1124 | 100000 | 3.5525 | 0.3739 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4