metadata
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exceptions_exp2_last_to_carry_frequency_2128
results: []
exceptions_exp2_last_to_carry_frequency_2128
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5554
- 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: 2128
- 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 |
|---|---|---|---|---|
| 4.8401 | 0.2913 | 1000 | 0.2522 | 4.7710 |
| 4.3331 | 0.5826 | 2000 | 0.2995 | 4.2837 |
| 4.1526 | 0.8739 | 3000 | 0.3154 | 4.0941 |
| 4.0002 | 1.1652 | 4000 | 0.3249 | 3.9890 |
| 3.9325 | 1.4565 | 5000 | 0.3318 | 3.9133 |
| 3.8836 | 1.7477 | 6000 | 0.3370 | 3.8557 |
| 3.7659 | 2.0390 | 7000 | 0.3412 | 3.8152 |
| 3.7532 | 2.3303 | 8000 | 0.3440 | 3.7830 |
| 3.7549 | 2.6216 | 9000 | 0.3470 | 3.7537 |
| 3.7257 | 2.9129 | 10000 | 0.3496 | 3.7279 |
| 3.631 | 3.2042 | 11000 | 0.3518 | 3.7140 |
| 3.6437 | 3.4955 | 12000 | 0.3532 | 3.6977 |
| 3.6449 | 3.7868 | 13000 | 0.3550 | 3.6775 |
| 3.5368 | 4.0781 | 14000 | 0.3562 | 3.6726 |
| 3.5699 | 4.3694 | 15000 | 0.3573 | 3.6574 |
| 3.5808 | 4.6606 | 16000 | 0.3580 | 3.6481 |
| 3.5669 | 4.9519 | 17000 | 0.3597 | 3.6345 |
| 3.4856 | 5.2432 | 18000 | 0.3601 | 3.6362 |
| 3.5372 | 5.5345 | 19000 | 0.3611 | 3.6238 |
| 3.5283 | 5.8258 | 20000 | 0.3621 | 3.6126 |
| 3.4499 | 6.1171 | 21000 | 0.3623 | 3.6183 |
| 3.478 | 6.4084 | 22000 | 0.3632 | 3.6113 |
| 3.4924 | 6.6997 | 23000 | 0.3641 | 3.5981 |
| 3.4865 | 6.9910 | 24000 | 0.3646 | 3.5887 |
| 3.4371 | 7.2823 | 25000 | 0.3647 | 3.5988 |
| 3.4552 | 7.5736 | 26000 | 0.3653 | 3.5886 |
| 3.4686 | 7.8648 | 27000 | 0.3659 | 3.5796 |
| 3.3643 | 8.1561 | 28000 | 0.3662 | 3.5864 |
| 3.4177 | 8.4474 | 29000 | 0.3664 | 3.5810 |
| 3.4214 | 8.7387 | 30000 | 0.3669 | 3.5738 |
| 3.3159 | 9.0300 | 31000 | 0.3674 | 3.5793 |
| 3.3668 | 9.3213 | 32000 | 0.3676 | 3.5774 |
| 3.3972 | 9.6126 | 33000 | 0.3682 | 3.5689 |
| 3.416 | 9.9039 | 34000 | 0.3687 | 3.5606 |
| 3.3313 | 10.1952 | 35000 | 0.3680 | 3.5746 |
| 3.3625 | 10.4865 | 36000 | 0.3689 | 3.5653 |
| 3.3663 | 10.7777 | 37000 | 0.3696 | 3.5588 |
| 3.2875 | 11.0690 | 38000 | 0.3689 | 3.5697 |
| 3.3281 | 11.3603 | 39000 | 0.3695 | 3.5642 |
| 3.3652 | 11.6516 | 40000 | 0.3699 | 3.5554 |
| 3.3736 | 11.9429 | 41000 | 0.3704 | 3.5485 |
| 3.2965 | 12.2342 | 42000 | 0.3702 | 3.5624 |
| 3.3183 | 12.5255 | 43000 | 0.3701 | 3.5566 |
| 3.3537 | 12.8168 | 44000 | 0.3706 | 3.5467 |
| 3.2595 | 13.1081 | 45000 | 0.3703 | 3.5585 |
| 3.3001 | 13.3994 | 46000 | 0.3707 | 3.5545 |
| 3.3331 | 13.6906 | 47000 | 0.3711 | 3.5460 |
| 3.3397 | 13.9819 | 48000 | 0.3718 | 3.5406 |
| 3.277 | 14.2732 | 49000 | 0.3712 | 3.5563 |
| 3.3188 | 14.5645 | 50000 | 0.3718 | 3.5446 |
| 3.3368 | 14.8558 | 51000 | 0.3720 | 3.5390 |
| 3.2346 | 15.1471 | 52000 | 0.3716 | 3.5561 |
| 3.2882 | 15.4384 | 53000 | 0.3719 | 3.5504 |
| 3.2962 | 15.7297 | 54000 | 0.3723 | 3.5404 |
| 3.1914 | 16.0210 | 55000 | 0.3723 | 3.5491 |
| 3.2381 | 16.3123 | 56000 | 0.3721 | 3.5517 |
| 3.2719 | 16.6036 | 57000 | 0.3724 | 3.5453 |
| 3.3051 | 16.8948 | 58000 | 0.3731 | 3.5334 |
| 3.2186 | 17.1861 | 59000 | 0.3722 | 3.5521 |
| 3.2536 | 17.4774 | 60000 | 0.3724 | 3.5459 |
| 3.2864 | 17.7687 | 61000 | 0.3732 | 3.5370 |
| 3.1842 | 18.0600 | 62000 | 0.3726 | 3.5499 |
| 3.2292 | 18.3513 | 63000 | 0.3727 | 3.5450 |
| 3.2734 | 18.6426 | 64000 | 0.3732 | 3.5386 |
| 3.2742 | 18.9339 | 65000 | 0.3739 | 3.5325 |
| 3.2181 | 19.2252 | 66000 | 0.3726 | 3.5493 |
| 3.2331 | 19.5165 | 67000 | 0.3730 | 3.5432 |
| 3.2519 | 19.8077 | 68000 | 0.3737 | 3.5357 |
| 3.1681 | 20.0990 | 69000 | 0.3733 | 3.5499 |
| 3.2174 | 20.3903 | 70000 | 0.3733 | 3.5480 |
| 3.2276 | 20.6816 | 71000 | 0.3740 | 3.5363 |
| 3.2571 | 20.9729 | 72000 | 0.3742 | 3.5296 |
| 3.1985 | 21.2642 | 73000 | 0.3734 | 3.5459 |
| 3.2095 | 21.5555 | 74000 | 0.3739 | 3.5399 |
| 3.2244 | 21.8468 | 75000 | 0.3743 | 3.5314 |
| 3.1653 | 22.1381 | 76000 | 0.3734 | 3.5493 |
| 3.1924 | 22.4294 | 77000 | 0.3739 | 3.5421 |
| 3.2211 | 22.7207 | 78000 | 0.3746 | 3.5349 |
| 3.148 | 23.0119 | 79000 | 0.3739 | 3.5430 |
| 3.1766 | 23.3032 | 80000 | 0.3740 | 3.5468 |
| 3.1807 | 23.5945 | 81000 | 3.5498 | 0.3738 |
| 3.2014 | 23.8858 | 82000 | 3.5405 | 0.3741 |
| 3.159 | 24.1771 | 83000 | 3.5526 | 0.3738 |
| 3.1787 | 24.4684 | 84000 | 3.5454 | 0.3739 |
| 3.2001 | 24.7597 | 85000 | 3.5354 | 0.3749 |
| 3.1291 | 25.0510 | 86000 | 3.5472 | 0.3742 |
| 3.1614 | 25.3423 | 87000 | 3.5451 | 0.3742 |
| 3.1755 | 25.6336 | 88000 | 3.5363 | 0.3748 |
| 3.2004 | 25.9248 | 89000 | 3.5326 | 0.3750 |
| 3.1347 | 26.2161 | 90000 | 3.5486 | 0.3740 |
| 3.1675 | 26.5074 | 91000 | 3.5394 | 0.3747 |
| 3.192 | 26.7987 | 92000 | 3.5304 | 0.3753 |
| 3.1155 | 27.0900 | 93000 | 3.5486 | 0.3744 |
| 3.1508 | 27.3813 | 94000 | 3.5469 | 0.3747 |
| 3.1786 | 27.6726 | 95000 | 3.5400 | 0.3750 |
| 3.1759 | 27.9639 | 96000 | 3.5315 | 0.3753 |
| 3.1214 | 28.2552 | 97000 | 3.5517 | 0.3744 |
| 3.1545 | 28.5465 | 98000 | 3.5401 | 0.3752 |
| 3.1694 | 28.8378 | 99000 | 3.5334 | 0.3756 |
| 3.095 | 29.1290 | 100000 | 3.5513 | 0.3744 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4