| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: 100M__8397 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # 100M__8397 |
| |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 3.4643 |
| - Accuracy: 0.3759 |
| |
| ## 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: 32 |
| - eval_batch_size: 16 |
| - seed: 8397 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH 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 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Accuracy | Validation Loss | |
| |:-------------:|:-------:|:-----:|:--------:|:---------------:| |
| | 5.0934 | 0.1078 | 1000 | 0.2275 | 5.0225 | |
| | 4.5937 | 0.2156 | 2000 | 0.2702 | 4.5165 | |
| | 4.3092 | 0.3235 | 3000 | 0.2978 | 4.2461 | |
| | 4.1586 | 0.4313 | 4000 | 0.3127 | 4.0885 | |
| | 4.0492 | 0.5391 | 5000 | 0.3212 | 3.9914 | |
| | 3.9962 | 0.6469 | 6000 | 0.3279 | 3.9193 | |
| | 3.9219 | 0.7547 | 7000 | 0.3335 | 3.8632 | |
| | 3.8566 | 0.8625 | 8000 | 0.3377 | 3.8155 | |
| | 3.856 | 0.9704 | 9000 | 0.3408 | 3.7813 | |
| | 3.7649 | 1.0782 | 10000 | 0.3444 | 3.7518 | |
| | 3.769 | 1.1860 | 11000 | 0.3474 | 3.7232 | |
| | 3.7288 | 1.2938 | 12000 | 0.3493 | 3.6971 | |
| | 3.7086 | 1.4016 | 13000 | 0.3515 | 3.6748 | |
| | 3.703 | 1.5094 | 14000 | 0.3535 | 3.6584 | |
| | 3.664 | 1.6173 | 15000 | 0.3555 | 3.6374 | |
| | 3.6639 | 1.7251 | 16000 | 0.3572 | 3.6206 | |
| | 3.6383 | 1.8329 | 17000 | 0.3587 | 3.6051 | |
| | 3.6321 | 1.9407 | 18000 | 0.3600 | 3.5912 | |
| | 3.5672 | 2.0485 | 19000 | 0.3615 | 3.5821 | |
| | 3.5754 | 2.1563 | 20000 | 0.3624 | 3.5733 | |
| | 3.5637 | 2.2642 | 21000 | 0.3637 | 3.5613 | |
| | 3.5706 | 2.3720 | 22000 | 0.3646 | 3.5504 | |
| | 3.5408 | 2.4798 | 23000 | 0.3660 | 3.5402 | |
| | 3.532 | 2.5876 | 24000 | 0.3669 | 3.5312 | |
| | 3.527 | 2.6954 | 25000 | 0.3673 | 3.5218 | |
| | 3.5333 | 2.8032 | 26000 | 0.3685 | 3.5120 | |
| | 3.5442 | 2.9111 | 27000 | 0.3696 | 3.5054 | |
| | 3.4451 | 3.0189 | 28000 | 0.3700 | 3.5012 | |
| | 3.4295 | 3.1267 | 29000 | 0.3709 | 3.4969 | |
| | 3.4506 | 3.2345 | 30000 | 0.3713 | 3.4902 | |
| | 3.4582 | 3.3423 | 31000 | 0.3719 | 3.4849 | |
| | 3.4597 | 3.4501 | 32000 | 0.3733 | 3.4757 | |
| | 3.4639 | 3.5580 | 33000 | 0.3736 | 3.4722 | |
| | 3.4602 | 3.6658 | 34000 | 0.3735 | 3.4653 | |
| | 3.4613 | 3.7736 | 35000 | 0.3749 | 3.4591 | |
| | 3.4517 | 3.8814 | 36000 | 0.3755 | 3.4511 | |
| | 3.4488 | 3.9892 | 37000 | 0.3761 | 3.4457 | |
| | 3.3644 | 4.0970 | 38000 | 0.3764 | 3.4490 | |
| | 3.3636 | 4.2049 | 39000 | 0.3773 | 3.4452 | |
| | 3.3899 | 4.3127 | 40000 | 0.3775 | 3.4419 | |
| | 3.3969 | 4.4205 | 41000 | 0.3782 | 3.4341 | |
| | 3.3848 | 4.5283 | 42000 | 0.3786 | 3.4308 | |
| | 3.3952 | 4.6361 | 43000 | 0.3788 | 3.4244 | |
| | 3.3875 | 4.7439 | 44000 | 0.3793 | 3.4211 | |
| | 3.4071 | 4.8518 | 45000 | 0.3799 | 3.4152 | |
| | 3.3742 | 4.9596 | 46000 | 0.3803 | 3.4095 | |
| | 3.3158 | 5.0674 | 47000 | 0.3807 | 3.4110 | |
| | 3.3119 | 5.1752 | 48000 | 0.3812 | 3.4111 | |
| | 3.3434 | 5.2830 | 49000 | 0.3813 | 3.4074 | |
| | 3.3422 | 5.3908 | 50000 | 0.3820 | 3.4001 | |
| | 3.3445 | 5.4987 | 51000 | 0.3820 | 3.3994 | |
| | 3.3169 | 5.6065 | 52000 | 0.3826 | 3.3929 | |
| | 3.333 | 5.7143 | 53000 | 0.3832 | 3.3878 | |
| | 3.331 | 5.8221 | 54000 | 0.3835 | 3.3844 | |
| | 3.3342 | 5.9299 | 55000 | 0.3840 | 3.3803 | |
| | 3.2353 | 6.0377 | 56000 | 0.3840 | 3.3844 | |
| | 3.2567 | 6.1456 | 57000 | 0.3843 | 3.3837 | |
| | 3.2751 | 6.2534 | 58000 | 0.3847 | 3.3811 | |
| | 3.2877 | 6.3612 | 59000 | 0.3850 | 3.3768 | |
| | 3.2882 | 6.4690 | 60000 | 0.3854 | 3.3732 | |
| | 3.2801 | 6.5768 | 61000 | 0.3860 | 3.3683 | |
| | 3.2991 | 6.6846 | 62000 | 0.3863 | 3.3624 | |
| | 3.2791 | 6.7925 | 63000 | 0.3866 | 3.3601 | |
| | 3.288 | 6.9003 | 64000 | 0.3871 | 3.3546 | |
| | 3.1829 | 7.0081 | 65000 | 0.3871 | 3.3573 | |
| | 3.2234 | 7.1159 | 66000 | 0.3873 | 3.3607 | |
| | 3.2282 | 7.2237 | 67000 | 0.3876 | 3.3572 | |
| | 3.2267 | 7.3315 | 68000 | 0.3878 | 3.3537 | |
| | 3.218 | 7.4394 | 69000 | 0.3883 | 3.3486 | |
| | 3.2235 | 7.5472 | 70000 | 0.3885 | 3.3458 | |
| | 3.2512 | 7.6550 | 71000 | 0.3889 | 3.3421 | |
| | 3.2503 | 7.7628 | 72000 | 0.3895 | 3.3384 | |
| | 3.2303 | 7.8706 | 73000 | 0.3901 | 3.3343 | |
| | 3.2506 | 7.9784 | 74000 | 0.3901 | 3.3303 | |
| | 3.155 | 8.0863 | 75000 | 0.3902 | 3.3376 | |
| | 3.1552 | 8.1941 | 76000 | 0.3902 | 3.3350 | |
| | 3.1737 | 8.3019 | 77000 | 0.3907 | 3.3308 | |
| | 3.167 | 8.4097 | 78000 | 0.3908 | 3.3294 | |
| | 3.1798 | 8.5175 | 79000 | 0.3912 | 3.3247 | |
| | 3.1957 | 8.6253 | 80000 | 0.3918 | 3.3219 | |
| | 3.1876 | 8.7332 | 81000 | 0.3921 | 3.3189 | |
| | 3.1825 | 8.8410 | 82000 | 0.3922 | 3.3150 | |
| | 3.1641 | 8.9488 | 83000 | 0.3927 | 3.3115 | |
| | 3.1278 | 9.0566 | 84000 | 0.3927 | 3.3139 | |
| | 3.1192 | 9.1644 | 85000 | 0.3928 | 3.3138 | |
| | 3.1443 | 9.2722 | 86000 | 0.3932 | 3.3118 | |
| | 3.1211 | 9.3801 | 87000 | 0.3934 | 3.3095 | |
| | 3.1296 | 9.4879 | 88000 | 0.3936 | 3.3073 | |
| | 3.1073 | 9.5957 | 89000 | 0.3939 | 3.3058 | |
| | 3.1362 | 9.7035 | 90000 | 0.3942 | 3.3025 | |
| | 3.1261 | 9.8113 | 91000 | 0.3944 | 3.3008 | |
| | 3.1183 | 9.9191 | 92000 | 0.3945 | 3.2993 | |
| | 3.3293 | 10.0270 | 93000 | 3.4597 | 0.3782 | |
| | 3.364 | 10.1348 | 94000 | 3.4676 | 0.3759 | |
| | 3.3881 | 10.2426 | 95000 | 3.4643 | 0.3759 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.47.0.dev0 |
| - Pytorch 2.5.0+cu124 |
| - Datasets 3.0.2 |
| - Tokenizers 0.20.1 |
| |
