metadata
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exceptions_exp2_resemble_to_hit_frequency_5039
results: []
exceptions_exp2_resemble_to_hit_frequency_5039
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5570
- Accuracy: 0.3699
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: 5039
- 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 |
|---|---|---|---|---|
| 4.8402 | 0.2914 | 1000 | 0.2550 | 4.7538 |
| 4.3353 | 0.5828 | 2000 | 0.3000 | 4.2827 |
| 4.1411 | 0.8741 | 3000 | 0.3160 | 4.0942 |
| 3.994 | 1.1655 | 4000 | 0.3256 | 3.9890 |
| 3.9303 | 1.4569 | 5000 | 0.3321 | 3.9130 |
| 3.8992 | 1.7483 | 6000 | 0.3375 | 3.8558 |
| 3.7487 | 2.0396 | 7000 | 0.3416 | 3.8128 |
| 3.7619 | 2.3310 | 8000 | 0.3448 | 3.7829 |
| 3.74 | 2.6224 | 9000 | 0.3477 | 3.7515 |
| 3.7152 | 2.9138 | 10000 | 0.3499 | 3.7270 |
| 3.6367 | 3.2051 | 11000 | 0.3517 | 3.7141 |
| 3.642 | 3.4965 | 12000 | 0.3535 | 3.6977 |
| 3.6416 | 3.7879 | 13000 | 0.3547 | 3.6786 |
| 3.5382 | 4.0793 | 14000 | 0.3562 | 3.6716 |
| 3.5647 | 4.3706 | 15000 | 0.3577 | 3.6587 |
| 3.5807 | 4.6620 | 16000 | 0.3585 | 3.6452 |
| 3.5693 | 4.9534 | 17000 | 0.3599 | 3.6329 |
| 3.5152 | 5.2448 | 18000 | 0.3607 | 3.6340 |
| 3.5148 | 5.5361 | 19000 | 0.3614 | 3.6241 |
| 3.5384 | 5.8275 | 20000 | 0.3626 | 3.6128 |
| 3.4338 | 6.1189 | 21000 | 0.3628 | 3.6156 |
| 3.4762 | 6.4103 | 22000 | 0.3631 | 3.6075 |
| 3.4815 | 6.7016 | 23000 | 0.3637 | 3.6009 |
| 3.4942 | 6.9930 | 24000 | 0.3649 | 3.5895 |
| 3.4237 | 7.2844 | 25000 | 0.3649 | 3.5999 |
| 3.4501 | 7.5758 | 26000 | 0.3657 | 3.5900 |
| 3.4529 | 7.8671 | 27000 | 0.3663 | 3.5784 |
| 3.3725 | 8.1585 | 28000 | 0.3663 | 3.5908 |
| 3.4032 | 8.4499 | 29000 | 0.3666 | 3.5825 |
| 3.4275 | 8.7413 | 30000 | 0.3674 | 3.5740 |
| 3.3293 | 9.0326 | 31000 | 0.3673 | 3.5798 |
| 3.3803 | 9.3240 | 32000 | 0.3675 | 3.5790 |
| 3.3931 | 9.6154 | 33000 | 0.3682 | 3.5727 |
| 3.4063 | 9.9068 | 34000 | 0.3688 | 3.5588 |
| 3.3379 | 10.1981 | 35000 | 0.3684 | 3.5747 |
| 3.3768 | 10.4895 | 36000 | 0.3689 | 3.5660 |
| 3.3929 | 10.7809 | 37000 | 0.3693 | 3.5597 |
| 3.2905 | 11.0723 | 38000 | 0.3693 | 3.5673 |
| 3.3375 | 11.3636 | 39000 | 0.3695 | 3.5638 |
| 3.352 | 11.6550 | 40000 | 0.3699 | 3.5570 |
| 3.3692 | 11.9464 | 41000 | 0.3707 | 3.5468 |
| 3.3088 | 12.2378 | 42000 | 0.3698 | 3.5638 |
| 3.3395 | 12.5291 | 43000 | 0.3704 | 3.5564 |
| 3.3436 | 12.8205 | 44000 | 0.3708 | 3.5493 |
| 3.2689 | 13.1119 | 45000 | 0.3706 | 3.5606 |
| 3.2973 | 13.4033 | 46000 | 0.3709 | 3.5571 |
| 3.3302 | 13.6946 | 47000 | 0.3710 | 3.5503 |
| 3.3507 | 13.9860 | 48000 | 0.3717 | 3.5406 |
| 3.2781 | 14.2774 | 49000 | 0.3713 | 3.5558 |
| 3.3064 | 14.5688 | 50000 | 0.3715 | 3.5498 |
| 3.318 | 14.8601 | 51000 | 0.3719 | 3.5419 |
| 3.2508 | 15.1515 | 52000 | 0.3715 | 3.5566 |
| 3.2845 | 15.4429 | 53000 | 0.3717 | 3.5524 |
| 3.2991 | 15.7343 | 54000 | 0.3725 | 3.5421 |
| 3.21 | 16.0256 | 55000 | 0.3720 | 3.5520 |
| 3.2507 | 16.3170 | 56000 | 0.3717 | 3.5529 |
| 3.2887 | 16.6084 | 57000 | 0.3727 | 3.5447 |
| 3.2842 | 16.8998 | 58000 | 0.3732 | 3.5341 |
| 3.2203 | 17.1911 | 59000 | 0.3722 | 3.5538 |
| 3.2473 | 17.4825 | 60000 | 0.3724 | 3.5490 |
| 3.2853 | 17.7739 | 61000 | 0.3733 | 3.5380 |
| 3.1926 | 18.0653 | 62000 | 0.3726 | 3.5528 |
| 3.2341 | 18.3566 | 63000 | 0.3730 | 3.5460 |
| 3.2576 | 18.6480 | 64000 | 0.3732 | 3.5389 |
| 3.2692 | 18.9394 | 65000 | 0.3735 | 3.5341 |
| 3.2072 | 19.2308 | 66000 | 0.3730 | 3.5510 |
| 3.2441 | 19.5221 | 67000 | 0.3733 | 3.5430 |
| 3.2584 | 19.8135 | 68000 | 0.3737 | 3.5368 |
| 3.1789 | 20.1049 | 69000 | 0.3730 | 3.5511 |
| 3.2145 | 20.3963 | 70000 | 0.3732 | 3.5492 |
| 3.2446 | 20.6876 | 71000 | 0.3736 | 3.5399 |
| 3.2494 | 20.9790 | 72000 | 0.3745 | 3.5293 |
| 3.1886 | 21.2704 | 73000 | 0.3735 | 3.5465 |
| 3.2211 | 21.5618 | 74000 | 0.3739 | 3.5401 |
| 3.2288 | 21.8531 | 75000 | 0.3745 | 3.5336 |
| 3.1628 | 22.1445 | 76000 | 0.3734 | 3.5492 |
| 3.205 | 22.4359 | 77000 | 0.3739 | 3.5424 |
| 3.2246 | 22.7273 | 78000 | 0.3746 | 3.5349 |
| 3.1374 | 23.0186 | 79000 | 0.3741 | 3.5473 |
| 3.1667 | 23.3100 | 80000 | 0.3739 | 3.5483 |
| 3.1871 | 23.6014 | 81000 | 3.5499 | 0.3739 |
| 3.2005 | 23.8928 | 82000 | 3.5442 | 0.3742 |
| 3.1587 | 24.1841 | 83000 | 3.5505 | 0.3737 |
| 3.1966 | 24.4755 | 84000 | 3.5427 | 0.3742 |
| 3.211 | 24.7669 | 85000 | 3.5338 | 0.3745 |
| 3.1336 | 25.0583 | 86000 | 3.5483 | 0.3742 |
| 3.1568 | 25.3497 | 87000 | 3.5447 | 0.3744 |
| 3.1885 | 25.6410 | 88000 | 3.5370 | 0.3746 |
| 3.2063 | 25.9324 | 89000 | 3.5297 | 0.3752 |
| 3.1457 | 26.2238 | 90000 | 3.5491 | 0.3741 |
| 3.1848 | 26.5152 | 91000 | 3.5441 | 0.3744 |
| 3.1945 | 26.8065 | 92000 | 3.5328 | 0.3750 |
| 3.1137 | 27.0979 | 93000 | 3.5511 | 0.3744 |
| 3.1564 | 27.3893 | 94000 | 3.5458 | 0.3746 |
| 3.1807 | 27.6807 | 95000 | 3.5402 | 0.3749 |
| 3.1905 | 27.9720 | 96000 | 3.5301 | 0.3753 |
| 3.1347 | 28.2634 | 97000 | 3.5479 | 0.3746 |
| 3.1577 | 28.5548 | 98000 | 3.5432 | 0.3748 |
| 3.16 | 28.8462 | 99000 | 3.5354 | 0.3755 |
| 3.1113 | 29.1375 | 100000 | 3.5508 | 0.3747 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4