--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: reverseadd_lr5e-4_batch128_train1-16_eval20 results: [] --- # reverseadd_lr5e-4_batch128_train1-16_eval20 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: 0.0502 - Accuracy: 0.7989 ## 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: 0.0005 - train_batch_size: 128 - eval_batch_size: 512 - seed: 23452399 - 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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:| | No log | 0 | 0 | 2.6313 | 0.0 | | 2.3322 | 0.0064 | 100 | 2.3471 | 0.0 | | 2.1982 | 0.0128 | 200 | 2.2978 | 0.0 | | 2.1862 | 0.0192 | 300 | 2.2746 | 0.0 | | 2.0322 | 0.0256 | 400 | 2.2285 | 0.0 | | 1.9493 | 0.032 | 500 | 2.1695 | 0.0 | | 1.8439 | 0.0384 | 600 | 2.0763 | 0.0 | | 1.7229 | 0.0448 | 700 | 2.0266 | 0.0 | | 1.6624 | 0.0512 | 800 | 2.0584 | 0.0 | | 1.5239 | 0.0576 | 900 | 1.8663 | 0.0 | | 1.9185 | 0.064 | 1000 | 2.1340 | 0.0 | | 1.4731 | 0.0704 | 1100 | 1.7122 | 0.0 | | 1.355 | 0.0768 | 1200 | 1.5711 | 0.0 | | 1.4131 | 0.0832 | 1300 | 1.6069 | 0.0 | | 1.4976 | 0.0896 | 1400 | 1.5464 | 0.0 | | 1.2917 | 0.096 | 1500 | 1.5358 | 0.0 | | 1.2094 | 0.1024 | 1600 | 1.8685 | 0.0 | | 1.3588 | 0.1088 | 1700 | 1.9000 | 0.0 | | 1.3792 | 0.1152 | 1800 | 1.5154 | 0.0 | | 1.1533 | 0.1216 | 1900 | 1.5278 | 0.0 | | 1.051 | 0.128 | 2000 | 1.3225 | 0.0 | | 1.1368 | 0.1344 | 2100 | 1.4753 | 0.0004 | | 1.0412 | 0.1408 | 2200 | 1.3291 | 0.0 | | 1.1005 | 0.1472 | 2300 | 1.3304 | 0.0001 | | 1.2162 | 0.1536 | 2400 | 1.6520 | 0.0 | | 1.1055 | 0.16 | 2500 | 1.2969 | 0.0005 | | 1.0922 | 0.1664 | 2600 | 1.4005 | 0.0 | | 1.1883 | 0.1728 | 2700 | 1.2790 | 0.0 | | 1.0435 | 0.1792 | 2800 | 1.3838 | 0.0 | | 1.1645 | 0.1856 | 2900 | 1.3827 | 0.0002 | | 1.1591 | 0.192 | 3000 | 1.3217 | 0.0004 | | 1.1259 | 0.1984 | 3100 | 1.2118 | 0.0022 | | 1.0789 | 0.2048 | 3200 | 1.2462 | 0.0008 | | 1.2459 | 0.2112 | 3300 | 1.3497 | 0.0006 | | 1.1039 | 0.2176 | 3400 | 1.4554 | 0.0 | | 1.0148 | 0.224 | 3500 | 1.4382 | 0.0001 | | 1.0801 | 0.2304 | 3600 | 1.3569 | 0.0006 | | 1.0467 | 0.2368 | 3700 | 1.2601 | 0.0011 | | 1.074 | 0.2432 | 3800 | 1.2448 | 0.001 | | 0.9444 | 0.2496 | 3900 | 1.2555 | 0.0 | | 0.9905 | 0.256 | 4000 | 1.5620 | 0.0001 | | 1.1964 | 0.2624 | 4100 | 1.6136 | 0.0 | | 1.0417 | 0.2688 | 4200 | 1.3215 | 0.0002 | | 1.0259 | 0.2752 | 4300 | 1.1980 | 0.0 | | 0.8927 | 0.2816 | 4400 | 1.3055 | 0.0002 | | 0.7111 | 0.288 | 4500 | 1.0408 | 0.0026 | | 1.016 | 0.2944 | 4600 | 1.1094 | 0.0005 | | 0.5435 | 0.3008 | 4700 | 0.8169 | 0.0007 | | 0.5007 | 0.3072 | 4800 | 1.1938 | 0.0016 | | 0.4587 | 0.3136 | 4900 | 0.9865 | 0.0009 | | 0.3841 | 0.32 | 5000 | 0.8143 | 0.0043 | | 0.2693 | 0.3264 | 5100 | 1.5061 | 0.0009 | | 0.3136 | 0.3328 | 5200 | 0.9799 | 0.0019 | | 0.378 | 0.3392 | 5300 | 1.7552 | 0.0 | | 0.3777 | 0.3456 | 5400 | 0.8328 | 0.002 | | 0.2807 | 0.352 | 5500 | 0.7735 | 0.0026 | | 0.3366 | 0.3584 | 5600 | 0.8754 | 0.0059 | | 0.2533 | 0.3648 | 5700 | 1.1873 | 0.0009 | | 0.2903 | 0.3712 | 5800 | 0.6800 | 0.0039 | | 0.3856 | 0.3776 | 5900 | 0.8834 | 0.0143 | | 0.2595 | 0.384 | 6000 | 0.5487 | 0.0154 | | 0.3096 | 0.3904 | 6100 | 1.2200 | 0.002 | | 0.4554 | 0.3968 | 6200 | 0.8465 | 0.0031 | | 0.2255 | 0.4032 | 6300 | 0.7609 | 0.0203 | | 0.2355 | 0.4096 | 6400 | 0.5184 | 0.0078 | | 0.268 | 0.416 | 6500 | 1.0301 | 0.0144 | | 0.4108 | 0.4224 | 6600 | 1.5558 | 0.0022 | | 0.3565 | 0.4288 | 6700 | 0.8868 | 0.0034 | | 0.2181 | 0.4352 | 6800 | 1.0227 | 0.0155 | | 0.2712 | 0.4416 | 6900 | 1.0278 | 0.0122 | | 0.2636 | 0.448 | 7000 | 1.8009 | 0.0 | | 0.2927 | 0.4544 | 7100 | 1.0821 | 0.0019 | | 0.2169 | 0.4608 | 7200 | 0.7846 | 0.0058 | | 0.2804 | 0.4672 | 7300 | 0.6890 | 0.0218 | | 0.2207 | 0.4736 | 7400 | 0.8507 | 0.0012 | | 0.2335 | 0.48 | 7500 | 0.9050 | 0.0066 | | 0.2038 | 0.4864 | 7600 | 0.5769 | 0.0164 | | 0.2743 | 0.4928 | 7700 | 0.4535 | 0.0279 | | 0.224 | 0.4992 | 7800 | 1.6283 | 0.003 | | 0.2043 | 0.5056 | 7900 | 0.3530 | 0.0353 | | 0.244 | 0.512 | 8000 | 0.9512 | 0.0091 | | 0.2427 | 0.5184 | 8100 | 0.8703 | 0.0179 | | 0.2464 | 0.5248 | 8200 | 1.3465 | 0.0045 | | 0.2351 | 0.5312 | 8300 | 0.3625 | 0.0288 | | 0.3104 | 0.5376 | 8400 | 0.5255 | 0.0104 | | 0.1958 | 0.544 | 8500 | 0.2150 | 0.0639 | | 0.2227 | 0.5504 | 8600 | 0.7403 | 0.0096 | | 0.2132 | 0.5568 | 8700 | 0.6176 | 0.0277 | | 0.2519 | 0.5632 | 8800 | 0.8851 | 0.0244 | | 0.1964 | 0.5696 | 8900 | 0.5727 | 0.0335 | | 0.2111 | 0.576 | 9000 | 0.8698 | 0.0175 | | 0.1888 | 0.5824 | 9100 | 0.5645 | 0.024 | | 0.2028 | 0.5888 | 9200 | 0.9562 | 0.0265 | | 0.2037 | 0.5952 | 9300 | 0.4325 | 0.0321 | | 0.1995 | 0.6016 | 9400 | 0.4173 | 0.0303 | | 0.2006 | 0.608 | 9500 | 0.4584 | 0.028 | | 0.1882 | 0.6144 | 9600 | 0.3393 | 0.0478 | | 0.1817 | 0.6208 | 9700 | 0.6259 | 0.0347 | | 0.2151 | 0.6272 | 9800 | 0.6243 | 0.032 | | 0.2115 | 0.6336 | 9900 | 0.4065 | 0.0321 | | 0.225 | 0.64 | 10000 | 0.2680 | 0.0496 | | 0.3263 | 0.6464 | 10100 | 1.7611 | 0.004 | | 0.1918 | 0.6528 | 10200 | 0.6133 | 0.0203 | | 0.2033 | 0.6592 | 10300 | 0.6111 | 0.0441 | | 0.1866 | 0.6656 | 10400 | 0.7337 | 0.019 | | 0.0137 | 0.672 | 10500 | 0.2514 | 0.3302 | | 0.0075 | 0.6784 | 10600 | 0.4079 | 0.2665 | | 0.0011 | 0.6848 | 10700 | 1.4016 | 0.1568 | | 0.0257 | 0.6912 | 10800 | 0.5433 | 0.2545 | | 0.0003 | 0.6976 | 10900 | 0.7182 | 0.3316 | | 0.0108 | 0.704 | 11000 | 0.3807 | 0.3023 | | 0.0017 | 0.7104 | 11100 | 0.9152 | 0.0844 | | 0.0005 | 0.7168 | 11200 | 0.2532 | 0.4212 | | 0.0024 | 0.7232 | 11300 | 0.5469 | 0.1787 | | 0.0001 | 0.7296 | 11400 | 0.2340 | 0.4503 | | 0.0019 | 0.736 | 11500 | 0.3934 | 0.1499 | | 0.0006 | 0.7424 | 11600 | 0.9093 | 0.1823 | | 0.0114 | 0.7488 | 11700 | 0.2727 | 0.3235 | | 0.0005 | 0.7552 | 11800 | 0.3471 | 0.2692 | | 0.0193 | 0.7616 | 11900 | 0.2272 | 0.369 | | 0.0005 | 0.768 | 12000 | 0.4390 | 0.3516 | | 0.0145 | 0.7744 | 12100 | 0.6034 | 0.1464 | | 0.0001 | 0.7808 | 12200 | 0.4269 | 0.424 | | 0.001 | 0.7872 | 12300 | 1.3267 | 0.0162 | | 0.0142 | 0.7936 | 12400 | 1.4786 | 0.0842 | | 0.0108 | 0.8 | 12500 | 2.2668 | 0.0878 | | 0.0024 | 0.8064 | 12600 | 0.3531 | 0.3094 | | 0.0074 | 0.8128 | 12700 | 0.3830 | 0.2705 | | 0.0003 | 0.8192 | 12800 | 0.2046 | 0.4599 | | 0.0011 | 0.8256 | 12900 | 0.3602 | 0.4595 | | 0.0001 | 0.832 | 13000 | 0.1422 | 0.6938 | | 0.0019 | 0.8384 | 13100 | 0.5059 | 0.3255 | | 0.0001 | 0.8448 | 13200 | 0.0906 | 0.7197 | | 0.0001 | 0.8512 | 13300 | 0.0665 | 0.7852 | | 0.0003 | 0.8576 | 13400 | 0.1053 | 0.6684 | | 0.0002 | 0.864 | 13500 | 0.0420 | 0.8265 | | 0.0018 | 0.8704 | 13600 | 0.0174 | 0.9256 | | 0.0001 | 0.8768 | 13700 | 0.0299 | 0.8868 | | 0.0002 | 0.8832 | 13800 | 0.1543 | 0.6578 | | 0.0002 | 0.8896 | 13900 | 0.0247 | 0.8711 | | 0.0001 | 0.896 | 14000 | 0.0465 | 0.8384 | | 0.0001 | 0.9024 | 14100 | 0.1334 | 0.6616 | | 0.0001 | 0.9088 | 14200 | 0.0082 | 0.9529 | | 0.0 | 0.9152 | 14300 | 0.0151 | 0.9296 | | 0.0 | 0.9216 | 14400 | 0.0386 | 0.8415 | | 0.0 | 0.928 | 14500 | 0.0187 | 0.9052 | | 0.0 | 0.9344 | 14600 | 0.0216 | 0.8949 | | 0.0 | 0.9408 | 14700 | 0.1110 | 0.6488 | | 0.0 | 0.9472 | 14800 | 0.0766 | 0.7268 | | 0.0032 | 0.9536 | 14900 | 0.0567 | 0.788 | | 0.0 | 0.96 | 15000 | 0.0391 | 0.8443 | | 0.0 | 0.9664 | 15100 | 0.0359 | 0.8462 | | 0.0002 | 0.9728 | 15200 | 0.0423 | 0.8251 | | 0.0001 | 0.9792 | 15300 | 0.0469 | 0.8087 | | 0.0 | 0.9856 | 15400 | 0.0487 | 0.8031 | | 0.0 | 0.992 | 15500 | 0.0502 | 0.7988 | | 0.0 | 0.9984 | 15600 | 0.0502 | 0.7989 | ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1