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--- |
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library_name: peft |
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language: |
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- ko |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Whisper Small ko |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small ko |
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co./openai/whisper-large-v3-turbo) on the custom dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1904 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- training_steps: 2000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.831 | 0.0319 | 10 | 1.5891 | |
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| 0.8371 | 0.0639 | 20 | 1.5741 | |
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| 0.8014 | 0.0958 | 30 | 1.5449 | |
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| 0.7445 | 0.1278 | 40 | 1.4933 | |
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| 0.6808 | 0.1597 | 50 | 1.3938 | |
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| 0.5229 | 0.1917 | 60 | 1.1658 | |
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| 0.3057 | 0.2236 | 70 | 0.9399 | |
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| 0.2167 | 0.2556 | 80 | 0.8860 | |
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| 0.1855 | 0.2875 | 90 | 0.8494 | |
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| 0.1614 | 0.3195 | 100 | 0.8240 | |
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| 0.1227 | 0.3514 | 110 | 0.7880 | |
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| 0.1179 | 0.3834 | 120 | 0.7530 | |
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| 0.0939 | 0.4153 | 130 | 0.7150 | |
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| 0.0857 | 0.4473 | 140 | 0.6848 | |
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| 0.0653 | 0.4792 | 150 | 0.6642 | |
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| 0.0779 | 0.5112 | 160 | 0.6487 | |
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| 0.0644 | 0.5431 | 170 | 0.6472 | |
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| 0.0701 | 0.5751 | 180 | 0.6389 | |
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| 0.0545 | 0.6070 | 190 | 0.6243 | |
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| 0.0606 | 0.6390 | 200 | 0.6031 | |
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| 0.0581 | 0.6709 | 210 | 0.5788 | |
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| 0.0582 | 0.7029 | 220 | 0.5645 | |
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| 0.0507 | 0.7348 | 230 | 0.5589 | |
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| 0.0476 | 0.7668 | 240 | 0.5435 | |
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| 0.0431 | 0.7987 | 250 | 0.5336 | |
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| 0.0452 | 0.8307 | 260 | 0.5239 | |
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| 0.0425 | 0.8626 | 270 | 0.5211 | |
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| 0.035 | 0.8946 | 280 | 0.5237 | |
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| 0.0413 | 0.9265 | 290 | 0.5049 | |
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| 0.0642 | 0.9585 | 300 | 0.4803 | |
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| 0.0356 | 0.9904 | 310 | 0.4834 | |
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| 0.0461 | 1.0224 | 320 | 0.4719 | |
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| 0.0321 | 1.0543 | 330 | 0.4745 | |
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| 0.0384 | 1.0863 | 340 | 0.4579 | |
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| 0.0363 | 1.1182 | 350 | 0.4500 | |
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| 0.0301 | 1.1502 | 360 | 0.4383 | |
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| 0.0469 | 1.1821 | 370 | 0.4324 | |
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| 0.0347 | 1.2141 | 380 | 0.4253 | |
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| 0.0307 | 1.2460 | 390 | 0.4142 | |
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| 0.0341 | 1.2780 | 400 | 0.4111 | |
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| 0.0252 | 1.3099 | 410 | 0.4044 | |
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| 0.0372 | 1.3419 | 420 | 0.4048 | |
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| 0.0346 | 1.3738 | 430 | 0.4000 | |
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| 0.029 | 1.4058 | 440 | 0.3963 | |
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| 0.0277 | 1.4377 | 450 | 0.3899 | |
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| 0.0322 | 1.4696 | 460 | 0.3875 | |
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| 0.0241 | 1.5016 | 470 | 0.3878 | |
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| 0.0424 | 1.5335 | 480 | 0.3835 | |
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| 0.0323 | 1.5655 | 490 | 0.3781 | |
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| 0.0456 | 1.5974 | 500 | 0.3796 | |
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| 0.0326 | 1.6294 | 510 | 0.3735 | |
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| 0.0318 | 1.6613 | 520 | 0.3689 | |
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| 0.03 | 1.6933 | 530 | 0.3510 | |
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| 0.0307 | 1.7252 | 540 | 0.3461 | |
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| 0.0318 | 1.7572 | 550 | 0.3425 | |
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| 0.03 | 1.7891 | 560 | 0.3332 | |
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| 0.0299 | 1.8211 | 570 | 0.3359 | |
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| 0.0262 | 1.8530 | 580 | 0.3376 | |
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| 0.0337 | 1.8850 | 590 | 0.3369 | |
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| 0.0344 | 1.9169 | 600 | 0.3427 | |
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| 0.0236 | 1.9489 | 610 | 0.3365 | |
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| 0.0229 | 1.9808 | 620 | 0.3318 | |
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| 0.0211 | 2.0128 | 630 | 0.3369 | |
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| 0.0248 | 2.0447 | 640 | 0.3299 | |
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| 0.0346 | 2.0767 | 650 | 0.3179 | |
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| 0.0223 | 2.1086 | 660 | 0.3230 | |
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| 0.0251 | 2.1406 | 670 | 0.3253 | |
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| 0.0192 | 2.1725 | 680 | 0.3259 | |
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| 0.0219 | 2.2045 | 690 | 0.3240 | |
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| 0.0284 | 2.2364 | 700 | 0.3269 | |
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| 0.0246 | 2.2684 | 710 | 0.3208 | |
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| 0.0281 | 2.3003 | 720 | 0.3202 | |
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| 0.0277 | 2.3323 | 730 | 0.3147 | |
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| 0.0249 | 2.3642 | 740 | 0.3068 | |
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| 0.0184 | 2.3962 | 750 | 0.3018 | |
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| 0.0279 | 2.4281 | 760 | 0.2991 | |
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| 0.0178 | 2.4601 | 770 | 0.2980 | |
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| 0.0234 | 2.4920 | 780 | 0.2977 | |
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| 0.0231 | 2.5240 | 790 | 0.2951 | |
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| 0.0242 | 2.5559 | 800 | 0.2949 | |
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| 0.0279 | 2.5879 | 810 | 0.2947 | |
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| 0.0216 | 2.6198 | 820 | 0.2950 | |
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| 0.0192 | 2.6518 | 830 | 0.2924 | |
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| 0.0273 | 2.6837 | 840 | 0.2881 | |
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| 0.0192 | 2.7157 | 850 | 0.2865 | |
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| 0.0267 | 2.7476 | 860 | 0.2822 | |
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| 0.0276 | 2.7796 | 870 | 0.2771 | |
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| 0.0234 | 2.8115 | 880 | 0.2784 | |
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| 0.0236 | 2.8435 | 890 | 0.2826 | |
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| 0.0255 | 2.8754 | 900 | 0.2762 | |
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| 0.0306 | 2.9073 | 910 | 0.2703 | |
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| 0.0213 | 2.9393 | 920 | 0.2699 | |
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| 0.0242 | 2.9712 | 930 | 0.2692 | |
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| 0.0231 | 3.0032 | 940 | 0.2690 | |
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| 0.0184 | 3.0351 | 950 | 0.2697 | |
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| 0.0145 | 3.0671 | 960 | 0.2674 | |
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| 0.0196 | 3.0990 | 970 | 0.2671 | |
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| 0.0205 | 3.1310 | 980 | 0.2668 | |
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| 0.0212 | 3.1629 | 990 | 0.2666 | |
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| 0.0218 | 3.1949 | 1000 | 0.2618 | |
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| 0.0202 | 3.2268 | 1010 | 0.2658 | |
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| 0.0187 | 3.2588 | 1020 | 0.2593 | |
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| 0.0161 | 3.2907 | 1030 | 0.2588 | |
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| 0.0175 | 3.3227 | 1040 | 0.2603 | |
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| 0.0162 | 3.3546 | 1050 | 0.2572 | |
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| 0.0346 | 3.3866 | 1060 | 0.2437 | |
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| 0.0199 | 3.4185 | 1070 | 0.2499 | |
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| 0.0235 | 3.4505 | 1080 | 0.2497 | |
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| 0.0175 | 3.4824 | 1090 | 0.2467 | |
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| 0.0187 | 3.5144 | 1100 | 0.2458 | |
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| 0.0171 | 3.5463 | 1110 | 0.2461 | |
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| 0.0189 | 3.5783 | 1120 | 0.2446 | |
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| 0.0229 | 3.6102 | 1130 | 0.2440 | |
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| 0.021 | 3.6422 | 1140 | 0.2422 | |
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| 0.0163 | 3.6741 | 1150 | 0.2400 | |
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| 0.0223 | 3.7061 | 1160 | 0.2406 | |
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| 0.0241 | 3.7380 | 1170 | 0.2367 | |
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| 0.0166 | 3.7700 | 1180 | 0.2372 | |
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| 0.0187 | 3.8019 | 1190 | 0.2378 | |
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| 0.0286 | 3.8339 | 1200 | 0.2396 | |
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| 0.0244 | 3.8658 | 1210 | 0.2357 | |
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| 0.0239 | 3.8978 | 1220 | 0.2317 | |
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| 0.026 | 3.9297 | 1230 | 0.2311 | |
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| 0.0203 | 3.9617 | 1240 | 0.2312 | |
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| 0.0177 | 3.9936 | 1250 | 0.2275 | |
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| 0.0199 | 4.0256 | 1260 | 0.2284 | |
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| 0.0174 | 4.0575 | 1270 | 0.2299 | |
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| 0.0195 | 4.0895 | 1280 | 0.2284 | |
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| 0.0167 | 4.1214 | 1290 | 0.2288 | |
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| 0.0197 | 4.1534 | 1300 | 0.2278 | |
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| 0.0194 | 4.1853 | 1310 | 0.2258 | |
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| 0.0233 | 4.2173 | 1320 | 0.2188 | |
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| 0.018 | 4.2492 | 1330 | 0.2154 | |
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| 0.0181 | 4.2812 | 1340 | 0.2146 | |
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| 0.0177 | 4.3131 | 1350 | 0.2157 | |
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| 0.0172 | 4.3450 | 1360 | 0.2168 | |
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| 0.02 | 4.3770 | 1370 | 0.2166 | |
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| 0.0144 | 4.4089 | 1380 | 0.2127 | |
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| 0.0166 | 4.4409 | 1390 | 0.2121 | |
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| 0.0183 | 4.4728 | 1400 | 0.2131 | |
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| 0.0159 | 4.5048 | 1410 | 0.2126 | |
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| 0.0137 | 4.5367 | 1420 | 0.2128 | |
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| 0.0218 | 4.5687 | 1430 | 0.2130 | |
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| 0.0145 | 4.6006 | 1440 | 0.2106 | |
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| 0.0192 | 4.6326 | 1450 | 0.2061 | |
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| 0.0134 | 4.6645 | 1460 | 0.2058 | |
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| 0.0204 | 4.6965 | 1470 | 0.2062 | |
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| 0.0157 | 4.7284 | 1480 | 0.2050 | |
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| 0.0142 | 4.7604 | 1490 | 0.2054 | |
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| 0.0192 | 4.7923 | 1500 | 0.2051 | |
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| 0.0137 | 4.8243 | 1510 | 0.2047 | |
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| 0.0296 | 4.8562 | 1520 | 0.2062 | |
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| 0.0176 | 4.8882 | 1530 | 0.2060 | |
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| 0.0146 | 4.9201 | 1540 | 0.2050 | |
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| 0.0197 | 4.9521 | 1550 | 0.2036 | |
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| 0.0173 | 4.9840 | 1560 | 0.2026 | |
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| 0.0183 | 5.0160 | 1570 | 0.2031 | |
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| 0.0177 | 5.0479 | 1580 | 0.2034 | |
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| 0.0145 | 5.0799 | 1590 | 0.2035 | |
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| 0.015 | 5.1118 | 1600 | 0.2024 | |
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| 0.0173 | 5.1438 | 1610 | 0.2015 | |
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| 0.0201 | 5.1757 | 1620 | 0.2015 | |
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| 0.0138 | 5.2077 | 1630 | 0.2017 | |
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| 0.0141 | 5.2396 | 1640 | 0.2012 | |
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| 0.0164 | 5.2716 | 1650 | 0.2015 | |
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| 0.0166 | 5.3035 | 1660 | 0.2004 | |
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| 0.0147 | 5.3355 | 1670 | 0.1997 | |
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| 0.0216 | 5.3674 | 1680 | 0.1997 | |
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| 0.0132 | 5.3994 | 1690 | 0.1990 | |
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| 0.0113 | 5.4313 | 1700 | 0.1980 | |
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| 0.0159 | 5.4633 | 1710 | 0.1977 | |
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| 0.0125 | 5.4952 | 1720 | 0.1978 | |
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| 0.0138 | 5.5272 | 1730 | 0.1973 | |
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| 0.0099 | 5.5591 | 1740 | 0.1972 | |
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| 0.0296 | 5.5911 | 1750 | 0.1969 | |
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| 0.0224 | 5.6230 | 1760 | 0.1961 | |
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| 0.0156 | 5.6550 | 1770 | 0.1952 | |
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| 0.0238 | 5.6869 | 1780 | 0.1944 | |
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| 0.0112 | 5.7188 | 1790 | 0.1940 | |
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| 0.0133 | 5.7508 | 1800 | 0.1935 | |
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| 0.0261 | 5.7827 | 1810 | 0.1924 | |
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| 0.0146 | 5.8147 | 1820 | 0.1919 | |
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| 0.0136 | 5.8466 | 1830 | 0.1921 | |
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| 0.0118 | 5.8786 | 1840 | 0.1913 | |
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| 0.0163 | 5.9105 | 1850 | 0.1914 | |
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| 0.0199 | 5.9425 | 1860 | 0.1915 | |
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| 0.017 | 5.9744 | 1870 | 0.1914 | |
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| 0.0163 | 6.0064 | 1880 | 0.1912 | |
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| 0.0189 | 6.0383 | 1890 | 0.1910 | |
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| 0.0146 | 6.0703 | 1900 | 0.1910 | |
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| 0.0266 | 6.1022 | 1910 | 0.1909 | |
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| 0.0114 | 6.1342 | 1920 | 0.1908 | |
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| 0.017 | 6.1661 | 1930 | 0.1908 | |
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| 0.0147 | 6.1981 | 1940 | 0.1907 | |
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| 0.0125 | 6.2300 | 1950 | 0.1907 | |
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| 0.0201 | 6.2620 | 1960 | 0.1906 | |
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| 0.011 | 6.2939 | 1970 | 0.1905 | |
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| 0.0169 | 6.3259 | 1980 | 0.1905 | |
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| 0.0148 | 6.3578 | 1990 | 0.1904 | |
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| 0.0113 | 6.3898 | 2000 | 0.1904 | |
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### Framework versions |
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- PEFT 0.14.0 |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
