--- library_name: transformers tags: - generated_from_trainer model-index: - name: working results: [] --- # working 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: 4.2041 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | No log | 0.0257 | 50 | 8.3417 | | No log | 0.0513 | 100 | 7.5225 | | No log | 0.0770 | 150 | 7.2800 | | No log | 0.1026 | 200 | 7.1710 | | No log | 0.1283 | 250 | 7.0870 | | No log | 0.1539 | 300 | 7.0228 | | No log | 0.1796 | 350 | 6.9561 | | No log | 0.2052 | 400 | 6.9274 | | No log | 0.2309 | 450 | 6.8805 | | 7.373 | 0.2565 | 500 | 6.8446 | | 7.373 | 0.2822 | 550 | 6.7928 | | 7.373 | 0.3079 | 600 | 6.7473 | | 7.373 | 0.3335 | 650 | 6.7402 | | 7.373 | 0.3592 | 700 | 6.7083 | | 7.373 | 0.3848 | 750 | 6.6590 | | 7.373 | 0.4105 | 800 | 6.6615 | | 7.373 | 0.4361 | 850 | 6.6191 | | 7.373 | 0.4618 | 900 | 6.6050 | | 7.373 | 0.4874 | 950 | 6.5849 | | 6.7222 | 0.5131 | 1000 | 6.5876 | | 6.7222 | 0.5387 | 1050 | 6.5620 | | 6.7222 | 0.5644 | 1100 | 6.5360 | | 6.7222 | 0.5900 | 1150 | 6.5137 | | 6.7222 | 0.6157 | 1200 | 6.4960 | | 6.7222 | 0.6414 | 1250 | 6.5057 | | 6.7222 | 0.6670 | 1300 | 6.4713 | | 6.7222 | 0.6927 | 1350 | 6.4503 | | 6.7222 | 0.7183 | 1400 | 6.4650 | | 6.7222 | 0.7440 | 1450 | 6.4619 | | 6.5431 | 0.7696 | 1500 | 6.4230 | | 6.5431 | 0.7953 | 1550 | 6.4370 | | 6.5431 | 0.8209 | 1600 | 6.3983 | | 6.5431 | 0.8466 | 1650 | 6.3970 | | 6.5431 | 0.8722 | 1700 | 6.3728 | | 6.5431 | 0.8979 | 1750 | 6.3749 | | 6.5431 | 0.9236 | 1800 | 6.3552 | | 6.5431 | 0.9492 | 1850 | 6.3818 | | 6.5431 | 0.9749 | 1900 | 6.3363 | | 6.5431 | 1.0005 | 1950 | 6.3270 | | 6.403 | 1.0262 | 2000 | 6.3019 | | 6.403 | 1.0518 | 2050 | 6.3032 | | 6.403 | 1.0775 | 2100 | 6.3362 | | 6.403 | 1.1031 | 2150 | 6.2926 | | 6.403 | 1.1288 | 2200 | 6.3152 | | 6.403 | 1.1544 | 2250 | 6.2974 | | 6.403 | 1.1801 | 2300 | 6.2926 | | 6.403 | 1.2057 | 2350 | 6.2686 | | 6.403 | 1.2314 | 2400 | 6.2473 | | 6.403 | 1.2571 | 2450 | 6.2667 | | 6.3403 | 1.2827 | 2500 | 6.2516 | | 6.3403 | 1.3084 | 2550 | 6.2522 | | 6.3403 | 1.3340 | 2600 | 6.2326 | | 6.3403 | 1.3597 | 2650 | 6.2164 | | 6.3403 | 1.3853 | 2700 | 6.2078 | | 6.3403 | 1.4110 | 2750 | 6.2337 | | 6.3403 | 1.4366 | 2800 | 6.1851 | | 6.3403 | 1.4623 | 2850 | 6.2106 | | 6.3403 | 1.4879 | 2900 | 6.1793 | | 6.3403 | 1.5136 | 2950 | 6.1576 | | 6.232 | 1.5393 | 3000 | 6.1549 | | 6.232 | 1.5649 | 3050 | 6.1438 | | 6.232 | 1.5906 | 3100 | 6.1346 | | 6.232 | 1.6162 | 3150 | 6.1283 | | 6.232 | 1.6419 | 3200 | 6.1182 | | 6.232 | 1.6675 | 3250 | 6.1374 | | 6.232 | 1.6932 | 3300 | 6.0896 | | 6.232 | 1.7188 | 3350 | 6.0939 | | 6.232 | 1.7445 | 3400 | 6.0837 | | 6.232 | 1.7701 | 3450 | 6.0493 | | 6.1268 | 1.7958 | 3500 | 6.0319 | | 6.1268 | 1.8214 | 3550 | 6.0135 | | 6.1268 | 1.8471 | 3600 | 5.9833 | | 6.1268 | 1.8728 | 3650 | 5.9931 | | 6.1268 | 1.8984 | 3700 | 5.9830 | | 6.1268 | 1.9241 | 3750 | 5.9394 | | 6.1268 | 1.9497 | 3800 | 5.9464 | | 6.1268 | 1.9754 | 3850 | 5.9158 | | 6.1268 | 2.0010 | 3900 | 5.9190 | | 6.1268 | 2.0267 | 3950 | 5.8944 | | 6.0316 | 2.0523 | 4000 | 5.8898 | | 6.0316 | 2.0780 | 4050 | 5.8728 | | 6.0316 | 2.1036 | 4100 | 5.8521 | | 6.0316 | 2.1293 | 4150 | 5.7986 | | 6.0316 | 2.1550 | 4200 | 5.7913 | | 6.0316 | 2.1806 | 4250 | 5.7782 | | 6.0316 | 2.2063 | 4300 | 5.7479 | | 6.0316 | 2.2319 | 4350 | 5.7143 | | 6.0316 | 2.2576 | 4400 | 5.7298 | | 6.0316 | 2.2832 | 4450 | 5.6914 | | 5.845 | 2.3089 | 4500 | 5.7019 | | 5.845 | 2.3345 | 4550 | 5.6568 | | 5.845 | 2.3602 | 4600 | 5.6234 | | 5.845 | 2.3858 | 4650 | 5.6043 | | 5.845 | 2.4115 | 4700 | 5.5809 | | 5.845 | 2.4371 | 4750 | 5.5478 | | 5.845 | 2.4628 | 4800 | 5.5601 | | 5.845 | 2.4885 | 4850 | 5.5353 | | 5.845 | 2.5141 | 4900 | 5.5037 | | 5.845 | 2.5398 | 4950 | 5.4888 | | 5.6671 | 2.5654 | 5000 | 5.4820 | | 5.6671 | 2.5911 | 5050 | 5.4534 | | 5.6671 | 2.6167 | 5100 | 5.3811 | | 5.6671 | 2.6424 | 5150 | 5.3747 | | 5.6671 | 2.6680 | 5200 | 5.3791 | | 5.6671 | 2.6937 | 5250 | 5.3361 | | 5.6671 | 2.7193 | 5300 | 5.3293 | | 5.6671 | 2.7450 | 5350 | 5.3004 | | 5.6671 | 2.7707 | 5400 | 5.3009 | | 5.6671 | 2.7963 | 5450 | 5.2918 | | 5.4582 | 2.8220 | 5500 | 5.2683 | | 5.4582 | 2.8476 | 5550 | 5.2561 | | 5.4582 | 2.8733 | 5600 | 5.2350 | | 5.4582 | 2.8989 | 5650 | 5.2271 | | 5.4582 | 2.9246 | 5700 | 5.2199 | | 5.4582 | 2.9502 | 5750 | 5.1929 | | 5.4582 | 2.9759 | 5800 | 5.1695 | | 5.4582 | 3.0015 | 5850 | 5.1418 | | 5.4582 | 3.0272 | 5900 | 5.1523 | | 5.4582 | 3.0528 | 5950 | 5.1319 | | 5.3242 | 3.0785 | 6000 | 5.0999 | | 5.3242 | 3.1042 | 6050 | 5.1123 | | 5.3242 | 3.1298 | 6100 | 5.0591 | | 5.3242 | 3.1555 | 6150 | 5.0828 | | 5.3242 | 3.1811 | 6200 | 5.0369 | | 5.3242 | 3.2068 | 6250 | 5.0435 | | 5.3242 | 3.2324 | 6300 | 5.0053 | | 5.3242 | 3.2581 | 6350 | 5.0086 | | 5.3242 | 3.2837 | 6400 | 5.0027 | | 5.3242 | 3.3094 | 6450 | 4.9799 | | 5.144 | 3.3350 | 6500 | 4.9641 | | 5.144 | 3.3607 | 6550 | 4.9339 | | 5.144 | 3.3864 | 6600 | 4.9606 | | 5.144 | 3.4120 | 6650 | 4.9373 | | 5.144 | 3.4377 | 6700 | 4.9325 | | 5.144 | 3.4633 | 6750 | 4.9073 | | 5.144 | 3.4890 | 6800 | 4.9072 | | 5.144 | 3.5146 | 6850 | 4.8895 | | 5.144 | 3.5403 | 6900 | 4.8779 | | 5.144 | 3.5659 | 6950 | 4.8425 | | 5.0097 | 3.5916 | 7000 | 4.8450 | | 5.0097 | 3.6172 | 7050 | 4.8468 | | 5.0097 | 3.6429 | 7100 | 4.8333 | | 5.0097 | 3.6685 | 7150 | 4.8398 | | 5.0097 | 3.6942 | 7200 | 4.8169 | | 5.0097 | 3.7199 | 7250 | 4.7936 | | 5.0097 | 3.7455 | 7300 | 4.8094 | | 5.0097 | 3.7712 | 7350 | 4.7648 | | 5.0097 | 3.7968 | 7400 | 4.7333 | | 5.0097 | 3.8225 | 7450 | 4.7667 | | 4.8984 | 3.8481 | 7500 | 4.7508 | | 4.8984 | 3.8738 | 7550 | 4.7341 | | 4.8984 | 3.8994 | 7600 | 4.7046 | | 4.8984 | 3.9251 | 7650 | 4.7154 | | 4.8984 | 3.9507 | 7700 | 4.7260 | | 4.8984 | 3.9764 | 7750 | 4.6964 | | 4.8984 | 4.0021 | 7800 | 4.7233 | | 4.8984 | 4.0277 | 7850 | 4.6740 | | 4.8984 | 4.0534 | 7900 | 4.6793 | | 4.8984 | 4.0790 | 7950 | 4.6636 | | 4.8106 | 4.1047 | 8000 | 4.6204 | | 4.8106 | 4.1303 | 8050 | 4.6228 | | 4.8106 | 4.1560 | 8100 | 4.6408 | | 4.8106 | 4.1816 | 8150 | 4.6353 | | 4.8106 | 4.2073 | 8200 | 4.6116 | | 4.8106 | 4.2329 | 8250 | 4.6294 | | 4.8106 | 4.2586 | 8300 | 4.6225 | | 4.8106 | 4.2842 | 8350 | 4.5824 | | 4.8106 | 4.3099 | 8400 | 4.5927 | | 4.8106 | 4.3356 | 8450 | 4.6046 | | 4.7138 | 4.3612 | 8500 | 4.5761 | | 4.7138 | 4.3869 | 8550 | 4.5544 | | 4.7138 | 4.4125 | 8600 | 4.5403 | | 4.7138 | 4.4382 | 8650 | 4.5484 | | 4.7138 | 4.4638 | 8700 | 4.5567 | | 4.7138 | 4.4895 | 8750 | 4.5486 | | 4.7138 | 4.5151 | 8800 | 4.5225 | | 4.7138 | 4.5408 | 8850 | 4.5496 | | 4.7138 | 4.5664 | 8900 | 4.5178 | | 4.7138 | 4.5921 | 8950 | 4.5103 | | 4.6278 | 4.6178 | 9000 | 4.5105 | | 4.6278 | 4.6434 | 9050 | 4.4755 | | 4.6278 | 4.6691 | 9100 | 4.4663 | | 4.6278 | 4.6947 | 9150 | 4.4631 | | 4.6278 | 4.7204 | 9200 | 4.4652 | | 4.6278 | 4.7460 | 9250 | 4.4661 | | 4.6278 | 4.7717 | 9300 | 4.4558 | | 4.6278 | 4.7973 | 9350 | 4.4496 | | 4.6278 | 4.8230 | 9400 | 4.4307 | | 4.6278 | 4.8486 | 9450 | 4.4371 | | 4.529 | 4.8743 | 9500 | 4.4102 | | 4.529 | 4.8999 | 9550 | 4.4126 | | 4.529 | 4.9256 | 9600 | 4.4261 | | 4.529 | 4.9513 | 9650 | 4.3980 | | 4.529 | 4.9769 | 9700 | 4.3843 | | 4.529 | 5.0026 | 9750 | 4.4079 | | 4.529 | 5.0282 | 9800 | 4.3856 | | 4.529 | 5.0539 | 9850 | 4.3672 | | 4.529 | 5.0795 | 9900 | 4.3494 | | 4.529 | 5.1052 | 9950 | 4.3469 | | 4.455 | 5.1308 | 10000 | 4.3611 | | 4.455 | 5.1565 | 10050 | 4.3583 | | 4.455 | 5.1821 | 10100 | 4.3300 | | 4.455 | 5.2078 | 10150 | 4.3422 | | 4.455 | 5.2335 | 10200 | 4.3155 | | 4.455 | 5.2591 | 10250 | 4.3318 | | 4.455 | 5.2848 | 10300 | 4.3080 | | 4.455 | 5.3104 | 10350 | 4.3206 | | 4.455 | 5.3361 | 10400 | 4.3248 | | 4.455 | 5.3617 | 10450 | 4.2913 | | 4.3863 | 5.3874 | 10500 | 4.2628 | | 4.3863 | 5.4130 | 10550 | 4.2803 | | 4.3863 | 5.4387 | 10600 | 4.3030 | | 4.3863 | 5.4643 | 10650 | 4.2712 | | 4.3863 | 5.4900 | 10700 | 4.2587 | | 4.3863 | 5.5156 | 10750 | 4.2406 | | 4.3863 | 5.5413 | 10800 | 4.2384 | | 4.3863 | 5.5670 | 10850 | 4.2464 | | 4.3863 | 5.5926 | 10900 | 4.2406 | | 4.3863 | 5.6183 | 10950 | 4.2707 | | 4.3382 | 5.6439 | 11000 | 4.2268 | | 4.3382 | 5.6696 | 11050 | 4.2084 | | 4.3382 | 5.6952 | 11100 | 4.2366 | | 4.3382 | 5.7209 | 11150 | 4.2112 | | 4.3382 | 5.7465 | 11200 | 4.1928 | | 4.3382 | 5.7722 | 11250 | 4.1709 | | 4.3382 | 5.7978 | 11300 | 4.1960 | | 4.3382 | 5.8235 | 11350 | 4.1926 | | 4.3382 | 5.8492 | 11400 | 4.1710 | | 4.3382 | 5.8748 | 11450 | 4.1900 | | 4.2675 | 5.9005 | 11500 | 4.2041 | ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0