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hubert-classifier-aug

Audio Classification
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hubert
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  1. README.md +23 -83
  2. runs/Jul22_01-55-17_LAPTOP-1GID9RGH/events.out.tfevents.1721588118.LAPTOP-1GID9RGH.18988.0 +2 -2
  3. runs/Jul22_01-55-17_LAPTOP-1GID9RGH/events.out.tfevents.1721588593.LAPTOP-1GID9RGH.18988.1 +3 -0
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4804
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- - Accuracy: 0.8652
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- - Precision: 0.8836
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- - Recall: 0.8652
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- - F1: 0.8626
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- - Binary: 0.9049
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  ## Model description
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@@ -60,83 +60,23 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | No log | 0.19 | 50 | 3.9353 | 0.0539 | 0.0054 | 0.0539 | 0.0096 | 0.3232 |
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- | No log | 0.38 | 100 | 3.5299 | 0.0674 | 0.0168 | 0.0674 | 0.0214 | 0.3415 |
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- | No log | 0.58 | 150 | 3.2976 | 0.0809 | 0.0134 | 0.0809 | 0.0206 | 0.3550 |
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- | No log | 0.77 | 200 | 3.2171 | 0.0889 | 0.0148 | 0.0889 | 0.0244 | 0.3601 |
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- | No log | 0.96 | 250 | 3.0516 | 0.0970 | 0.0394 | 0.0970 | 0.0425 | 0.3674 |
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- | No log | 1.15 | 300 | 2.9416 | 0.1105 | 0.0476 | 0.1105 | 0.0455 | 0.3765 |
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- | No log | 1.34 | 350 | 2.8641 | 0.1348 | 0.0564 | 0.1348 | 0.0601 | 0.3911 |
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- | No log | 1.53 | 400 | 2.6831 | 0.2129 | 0.1140 | 0.2129 | 0.1256 | 0.4474 |
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- | No log | 1.73 | 450 | 2.4972 | 0.2534 | 0.1646 | 0.2534 | 0.1750 | 0.4757 |
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- | 3.3455 | 1.92 | 500 | 2.3968 | 0.2911 | 0.2066 | 0.2911 | 0.2067 | 0.4957 |
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- | 3.3455 | 2.11 | 550 | 2.2022 | 0.3720 | 0.2673 | 0.3720 | 0.2752 | 0.5606 |
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- | 3.3455 | 2.3 | 600 | 1.9843 | 0.4394 | 0.3474 | 0.4394 | 0.3584 | 0.6067 |
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- | 3.3455 | 2.49 | 650 | 1.8981 | 0.4582 | 0.3704 | 0.4582 | 0.3817 | 0.6175 |
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- | 3.3455 | 2.68 | 700 | 1.7742 | 0.4987 | 0.4631 | 0.4987 | 0.4378 | 0.6474 |
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- | 3.3455 | 2.88 | 750 | 1.5820 | 0.5337 | 0.4673 | 0.5337 | 0.4661 | 0.6749 |
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- | 3.3455 | 3.07 | 800 | 1.5259 | 0.5795 | 0.5610 | 0.5795 | 0.5335 | 0.7024 |
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- | 3.3455 | 3.26 | 850 | 1.3476 | 0.6361 | 0.5896 | 0.6361 | 0.5854 | 0.7477 |
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- | 3.3455 | 3.45 | 900 | 1.2645 | 0.6253 | 0.5957 | 0.6253 | 0.5700 | 0.7372 |
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- | 3.3455 | 3.64 | 950 | 1.2000 | 0.6577 | 0.6802 | 0.6577 | 0.6299 | 0.7598 |
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- | 2.2111 | 3.84 | 1000 | 1.1048 | 0.7170 | 0.7165 | 0.7170 | 0.6874 | 0.8003 |
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- | 2.2111 | 4.03 | 1050 | 1.0875 | 0.6900 | 0.6668 | 0.6900 | 0.6479 | 0.7798 |
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- | 2.2111 | 4.22 | 1100 | 1.0306 | 0.7197 | 0.7434 | 0.7197 | 0.6975 | 0.8040 |
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- | 2.2111 | 4.41 | 1150 | 0.9579 | 0.7520 | 0.7813 | 0.7520 | 0.7326 | 0.8261 |
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- | 2.2111 | 4.6 | 1200 | 0.8882 | 0.7358 | 0.7268 | 0.7358 | 0.7068 | 0.8143 |
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- | 2.2111 | 4.79 | 1250 | 0.9165 | 0.7574 | 0.7754 | 0.7574 | 0.7388 | 0.8286 |
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- | 2.2111 | 4.99 | 1300 | 0.8355 | 0.7763 | 0.7844 | 0.7763 | 0.7553 | 0.8447 |
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- | 2.2111 | 5.18 | 1350 | 0.7983 | 0.7871 | 0.8111 | 0.7871 | 0.7784 | 0.8523 |
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- | 2.2111 | 5.37 | 1400 | 0.7626 | 0.7978 | 0.8167 | 0.7978 | 0.7841 | 0.8569 |
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- | 2.2111 | 5.56 | 1450 | 0.7248 | 0.7898 | 0.7903 | 0.7898 | 0.7745 | 0.8553 |
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- | 1.6095 | 5.75 | 1500 | 0.7431 | 0.8005 | 0.8391 | 0.8005 | 0.7914 | 0.8598 |
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- | 1.6095 | 5.94 | 1550 | 0.7692 | 0.7790 | 0.8071 | 0.7790 | 0.7662 | 0.8466 |
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- | 1.6095 | 6.14 | 1600 | 0.6135 | 0.8302 | 0.8556 | 0.8302 | 0.8229 | 0.8814 |
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- | 1.6095 | 6.33 | 1650 | 0.6347 | 0.8221 | 0.8377 | 0.8221 | 0.8156 | 0.8757 |
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- | 1.6095 | 6.52 | 1700 | 0.6184 | 0.8221 | 0.8529 | 0.8221 | 0.8184 | 0.8747 |
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- | 1.6095 | 6.71 | 1750 | 0.6224 | 0.8221 | 0.8540 | 0.8221 | 0.8155 | 0.8757 |
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- | 1.6095 | 6.9 | 1800 | 0.6251 | 0.8194 | 0.8267 | 0.8194 | 0.8087 | 0.8728 |
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- | 1.6095 | 7.09 | 1850 | 0.5821 | 0.8383 | 0.8597 | 0.8383 | 0.8346 | 0.8860 |
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- | 1.6095 | 7.29 | 1900 | 0.6197 | 0.8059 | 0.8438 | 0.8059 | 0.8040 | 0.8644 |
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- | 1.6095 | 7.48 | 1950 | 0.5886 | 0.8275 | 0.8640 | 0.8275 | 0.8269 | 0.8784 |
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- | 1.3113 | 7.67 | 2000 | 0.5720 | 0.8410 | 0.8664 | 0.8410 | 0.8397 | 0.8889 |
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- | 1.3113 | 7.86 | 2050 | 0.6286 | 0.8248 | 0.8530 | 0.8248 | 0.8168 | 0.8765 |
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- | 1.3113 | 8.05 | 2100 | 0.5317 | 0.8329 | 0.8494 | 0.8329 | 0.8240 | 0.8822 |
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- | 1.3113 | 8.25 | 2150 | 0.4692 | 0.8571 | 0.8795 | 0.8571 | 0.8521 | 0.9003 |
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- | 1.3113 | 8.44 | 2200 | 0.5233 | 0.8598 | 0.8786 | 0.8598 | 0.8576 | 0.9011 |
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- | 1.3113 | 8.63 | 2250 | 0.5538 | 0.8329 | 0.8454 | 0.8329 | 0.8262 | 0.8822 |
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- | 1.3113 | 8.82 | 2300 | 0.5662 | 0.8167 | 0.8430 | 0.8167 | 0.8103 | 0.8712 |
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- | 1.3113 | 9.01 | 2350 | 0.5567 | 0.8491 | 0.8717 | 0.8491 | 0.8443 | 0.8946 |
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- | 1.3113 | 9.2 | 2400 | 0.5064 | 0.8464 | 0.8713 | 0.8464 | 0.8415 | 0.8916 |
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- | 1.3113 | 9.4 | 2450 | 0.5497 | 0.8544 | 0.8697 | 0.8544 | 0.8461 | 0.8973 |
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- | 1.1269 | 9.59 | 2500 | 0.5250 | 0.8544 | 0.8769 | 0.8544 | 0.8467 | 0.8981 |
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- | 1.1269 | 9.78 | 2550 | 0.5301 | 0.8571 | 0.8776 | 0.8571 | 0.8552 | 0.9003 |
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- | 1.1269 | 9.97 | 2600 | 0.4692 | 0.8733 | 0.8979 | 0.8733 | 0.8711 | 0.9105 |
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- | 1.1269 | 10.16 | 2650 | 0.5143 | 0.8544 | 0.8738 | 0.8544 | 0.8512 | 0.8965 |
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- | 1.1269 | 10.35 | 2700 | 0.5419 | 0.8383 | 0.8603 | 0.8383 | 0.8372 | 0.8860 |
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- | 1.1269 | 10.55 | 2750 | 0.6064 | 0.8275 | 0.8610 | 0.8275 | 0.8251 | 0.8784 |
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- | 1.1269 | 10.74 | 2800 | 0.4815 | 0.8760 | 0.9038 | 0.8760 | 0.8749 | 0.9124 |
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- | 1.1269 | 10.93 | 2850 | 0.4908 | 0.8652 | 0.8907 | 0.8652 | 0.8611 | 0.9059 |
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- | 1.1269 | 11.12 | 2900 | 0.5417 | 0.8491 | 0.8773 | 0.8491 | 0.8468 | 0.8935 |
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- | 1.1269 | 11.31 | 2950 | 0.5086 | 0.8518 | 0.8698 | 0.8518 | 0.8493 | 0.8954 |
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- | 1.0103 | 11.51 | 3000 | 0.5147 | 0.8518 | 0.8660 | 0.8518 | 0.8505 | 0.8954 |
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- | 1.0103 | 11.7 | 3050 | 0.5247 | 0.8571 | 0.8838 | 0.8571 | 0.8564 | 0.9000 |
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- | 1.0103 | 11.89 | 3100 | 0.4810 | 0.8625 | 0.8837 | 0.8625 | 0.8591 | 0.9040 |
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- | 1.0103 | 12.08 | 3150 | 0.4950 | 0.8706 | 0.8990 | 0.8706 | 0.8686 | 0.9097 |
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- | 1.0103 | 12.27 | 3200 | 0.4966 | 0.8544 | 0.8718 | 0.8544 | 0.8501 | 0.8965 |
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- | 1.0103 | 12.46 | 3250 | 0.4522 | 0.8598 | 0.8759 | 0.8598 | 0.8561 | 0.9030 |
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- | 1.0103 | 12.66 | 3300 | 0.5552 | 0.8437 | 0.8677 | 0.8437 | 0.8401 | 0.8916 |
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- | 1.0103 | 12.85 | 3350 | 0.5489 | 0.8248 | 0.8556 | 0.8248 | 0.8247 | 0.8776 |
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- | 1.0103 | 13.04 | 3400 | 0.5635 | 0.8598 | 0.8827 | 0.8598 | 0.8594 | 0.9011 |
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- | 1.0103 | 13.23 | 3450 | 0.5023 | 0.8652 | 0.8862 | 0.8652 | 0.8634 | 0.9067 |
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- | 0.912 | 13.42 | 3500 | 0.4804 | 0.8652 | 0.8836 | 0.8652 | 0.8626 | 0.9049 |
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- | 0.912 | 13.61 | 3550 | 0.4868 | 0.8598 | 0.8849 | 0.8598 | 0.8575 | 0.9013 |
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- | 0.912 | 13.81 | 3600 | 0.5493 | 0.8571 | 0.8852 | 0.8571 | 0.8533 | 0.8992 |
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- | 0.912 | 14.0 | 3650 | 0.5699 | 0.8437 | 0.8659 | 0.8437 | 0.8418 | 0.8908 |
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- | 0.912 | 14.19 | 3700 | 0.5606 | 0.8437 | 0.8692 | 0.8437 | 0.8395 | 0.8908 |
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- | 0.912 | 14.38 | 3750 | 0.5685 | 0.8491 | 0.8730 | 0.8491 | 0.8458 | 0.8935 |
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- | 0.912 | 14.57 | 3800 | 0.5088 | 0.8625 | 0.8792 | 0.8625 | 0.8606 | 0.9030 |
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- | 0.912 | 14.77 | 3850 | 0.5566 | 0.8437 | 0.8729 | 0.8437 | 0.8367 | 0.8898 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
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+ - Loss: 4.4217
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+ - Accuracy: 0.0135
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+ - Precision: 0.0002
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+ - Recall: 0.0135
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+ - F1: 0.0004
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+ - Binary: 0.1334
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.19 | 50 | 4.4268 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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+ | No log | 0.38 | 100 | 4.4280 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1261 |
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+ | No log | 0.58 | 150 | 4.4240 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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+ | No log | 0.77 | 200 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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+ | No log | 0.96 | 250 | 4.4229 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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+ | No log | 1.15 | 300 | 4.4210 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1280 |
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+ | No log | 1.34 | 350 | 4.4216 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1280 |
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+ | No log | 1.53 | 400 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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+ | No log | 1.73 | 450 | 4.4229 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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+ | 4.4254 | 1.92 | 500 | 4.4217 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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+ | 4.4254 | 2.11 | 550 | 4.4214 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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+ | 4.4254 | 2.3 | 600 | 4.4213 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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+ | 4.4254 | 2.49 | 650 | 4.4213 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1261 |
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+ | 4.4254 | 2.68 | 700 | 4.4228 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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+ | 4.4254 | 2.88 | 750 | 4.4209 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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+ | 4.4254 | 3.07 | 800 | 4.4210 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1261 |
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+ | 4.4254 | 3.26 | 850 | 4.4216 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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