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nomnoos37/stt-turbo-1226-v1.4

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README.md ADDED
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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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+
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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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+
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+ # Whisper Small ko
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+ | 0.0145 | 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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+ | 0.0346 | 3.3866 | 1060 | 0.2437 |
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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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+ | 0.0223 | 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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+ | 0.0177 | 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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+ | 0.0138 | 5.5272 | 1730 | 0.1973 |
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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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+ | 0.0199 | 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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+ | 0.017 | 6.1661 | 1930 | 0.1908 |
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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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+
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+
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+ ### Framework versions
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+
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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
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