End of training
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README.md
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@@ -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.
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- Accuracy: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Binary: 0.
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs:
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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 | Accuracy | Precision | Recall | F1 | Binary |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| No log | 0.24 | 50 | 4.
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| No log | 0.48 | 100 | 4.
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| No log | 0.72 | 150 |
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| No log | 0.96 | 200 | 3.
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| 0.7001 | 9.1 | 1900 | 0.6618 | 0.8298 | 0.8457 | 0.8298 | 0.8269 | 0.8810 |
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| 0.7001 | 9.34 | 1950 | 0.6244 | 0.8426 | 0.8571 | 0.8426 | 0.8422 | 0.8900 |
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| 0.7001 | 9.58 | 2000 | 0.5802 | 0.8576 | 0.8681 | 0.8576 | 0.8569 | 0.8996 |
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| 0.7001 | 9.82 | 2050 | 0.5352 | 0.8688 | 0.8761 | 0.8688 | 0.8687 | 0.9072 |
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| 0.6288 | 10.06 | 2100 | 0.5347 | 0.8651 | 0.8773 | 0.8651 | 0.8637 | 0.9049 |
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| 0.6288 | 10.3 | 2150 | 0.6019 | 0.8546 | 0.8665 | 0.8546 | 0.8535 | 0.8986 |
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| 0.6288 | 10.54 | 2200 | 0.5699 | 0.8598 | 0.8670 | 0.8598 | 0.8571 | 0.9005 |
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| 0.6288 | 10.78 | 2250 | 0.5494 | 0.8748 | 0.8838 | 0.8748 | 0.8730 | 0.9118 |
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| 0.5959 | 11.02 | 2300 | 0.5471 | 0.8718 | 0.8804 | 0.8718 | 0.8714 | 0.9103 |
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| 0.5959 | 11.26 | 2350 | 0.5570 | 0.8628 | 0.8738 | 0.8628 | 0.8605 | 0.9042 |
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| 0.5959 | 11.5 | 2400 | 0.5300 | 0.8801 | 0.8875 | 0.8801 | 0.8791 | 0.9163 |
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| 0.5959 | 11.74 | 2450 | 0.5418 | 0.8643 | 0.8725 | 0.8643 | 0.8630 | 0.9039 |
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| 0.5959 | 11.98 | 2500 | 0.5418 | 0.8726 | 0.8822 | 0.8726 | 0.8715 | 0.9108 |
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| 0.5407 | 12.22 | 2550 | 0.5718 | 0.8658 | 0.8755 | 0.8658 | 0.8652 | 0.9058 |
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| 0.5407 | 12.46 | 2600 | 0.5686 | 0.8658 | 0.8725 | 0.8658 | 0.8643 | 0.9058 |
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| 0.5407 | 12.69 | 2650 | 0.6045 | 0.8658 | 0.8768 | 0.8658 | 0.8656 | 0.9053 |
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| 0.5407 | 12.93 | 2700 | 0.5571 | 0.8621 | 0.8715 | 0.8621 | 0.8607 | 0.9027 |
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| 0.5175 | 13.17 | 2750 | 0.5367 | 0.8756 | 0.8809 | 0.8756 | 0.8745 | 0.9131 |
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| 0.5175 | 13.41 | 2800 | 0.5241 | 0.8771 | 0.8827 | 0.8771 | 0.8755 | 0.9142 |
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| 0.5175 | 13.65 | 2850 | 0.5793 | 0.8703 | 0.8792 | 0.8703 | 0.8691 | 0.9095 |
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| 0.5175 | 13.89 | 2900 | 0.5608 | 0.8756 | 0.8843 | 0.8756 | 0.8751 | 0.9123 |
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| 0.4913 | 14.13 | 2950 | 0.5734 | 0.8711 | 0.8781 | 0.8711 | 0.8694 | 0.9100 |
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| 0.4913 | 14.37 | 3000 | 0.5916 | 0.8771 | 0.8821 | 0.8771 | 0.8758 | 0.9134 |
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| 0.4913 | 14.61 | 3050 | 0.5651 | 0.8696 | 0.8761 | 0.8696 | 0.8680 | 0.9082 |
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| 0.4913 | 14.85 | 3100 | 0.5535 | 0.8786 | 0.8831 | 0.8786 | 0.8771 | 0.9152 |
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| 0.4747 | 15.09 | 3150 | 0.5694 | 0.8741 | 0.8819 | 0.8741 | 0.8737 | 0.9118 |
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| 0.4747 | 15.33 | 3200 | 0.5759 | 0.8726 | 0.8794 | 0.8726 | 0.8720 | 0.9103 |
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| 0.4747 | 15.57 | 3250 | 0.5827 | 0.8666 | 0.8718 | 0.8666 | 0.8642 | 0.9070 |
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| 0.4747 | 15.81 | 3300 | 0.5497 | 0.8763 | 0.8838 | 0.8763 | 0.8758 | 0.9139 |
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| 0.4456 | 16.05 | 3350 | 0.5757 | 0.8838 | 0.8896 | 0.8838 | 0.8835 | 0.9192 |
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| 0.4456 | 16.29 | 3400 | 0.5547 | 0.8756 | 0.8830 | 0.8756 | 0.8731 | 0.9123 |
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| 0.4456 | 16.53 | 3450 | 0.5431 | 0.8808 | 0.8883 | 0.8808 | 0.8801 | 0.9168 |
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| 0.4456 | 16.77 | 3500 | 0.5459 | 0.8823 | 0.8883 | 0.8823 | 0.8815 | 0.9175 |
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| 0.4248 | 17.01 | 3550 | 0.5111 | 0.8891 | 0.8947 | 0.8891 | 0.8878 | 0.9220 |
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| 0.4248 | 17.25 | 3600 | 0.5371 | 0.8868 | 0.8922 | 0.8868 | 0.8860 | 0.9207 |
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| 0.4248 | 17.49 | 3650 | 0.5757 | 0.8748 | 0.8843 | 0.8748 | 0.8745 | 0.9131 |
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| 0.4248 | 17.72 | 3700 | 0.5509 | 0.8816 | 0.8880 | 0.8816 | 0.8804 | 0.9168 |
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| 0.4248 | 17.96 | 3750 | 0.5166 | 0.8853 | 0.8911 | 0.8853 | 0.8845 | 0.9197 |
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| 0.405 | 18.2 | 3800 | 0.5392 | 0.8823 | 0.8881 | 0.8823 | 0.8814 | 0.9173 |
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| 0.405 | 18.44 | 3850 | 0.5357 | 0.8793 | 0.8857 | 0.8793 | 0.8784 | 0.9155 |
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| 0.405 | 18.68 | 3900 | 0.5564 | 0.8748 | 0.8808 | 0.8748 | 0.8739 | 0.9120 |
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| 0.405 | 18.92 | 3950 | 0.5377 | 0.8853 | 0.8898 | 0.8853 | 0.8842 | 0.9202 |
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| 0.3925 | 19.16 | 4000 | 0.5489 | 0.8846 | 0.8902 | 0.8846 | 0.8832 | 0.9194 |
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| 0.3925 | 19.4 | 4050 | 0.5953 | 0.8726 | 0.8800 | 0.8726 | 0.8713 | 0.9115 |
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| 0.3925 | 19.64 | 4100 | 0.5802 | 0.8756 | 0.8812 | 0.8756 | 0.8738 | 0.9131 |
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| 0.3925 | 19.88 | 4150 | 0.6130 | 0.8756 | 0.8827 | 0.8756 | 0.8743 | 0.9121 |
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| 0.3707 | 20.12 | 4200 | 0.6210 | 0.8771 | 0.8828 | 0.8771 | 0.8760 | 0.9137 |
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| 0.3707 | 20.36 | 4250 | 0.6460 | 0.8786 | 0.8849 | 0.8786 | 0.8774 | 0.9154 |
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| 0.3707 | 20.6 | 4300 | 0.6255 | 0.8703 | 0.8780 | 0.8703 | 0.8694 | 0.9085 |
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| 0.3707 | 20.84 | 4350 | 0.6773 | 0.8658 | 0.8739 | 0.8658 | 0.8653 | 0.9056 |
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.6366
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- Accuracy: 0.8369
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- Precision: 0.8588
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- Recall: 0.8369
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- F1: 0.8354
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- Binary: 0.8865
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 100
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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 | Accuracy | Precision | Recall | F1 | Binary |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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| No log | 0.24 | 50 | 4.4216 | 0.0247 | 0.0070 | 0.0247 | 0.0085 | 0.1557 |
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| No log | 0.48 | 100 | 4.3485 | 0.0652 | 0.0204 | 0.0652 | 0.0249 | 0.2965 |
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| No log | 0.72 | 150 | 4.0225 | 0.0577 | 0.0118 | 0.0577 | 0.0151 | 0.3349 |
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| No log | 0.96 | 200 | 3.7163 | 0.0780 | 0.0180 | 0.0780 | 0.0245 | 0.3495 |
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| 4.2635 | 1.2 | 250 | 3.4497 | 0.1454 | 0.0633 | 0.1454 | 0.0699 | 0.3975 |
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| 4.2635 | 1.44 | 300 | 3.2114 | 0.1912 | 0.0913 | 0.1912 | 0.0994 | 0.4306 |
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| 4.2635 | 1.68 | 350 | 2.9633 | 0.2399 | 0.1697 | 0.2399 | 0.1533 | 0.4645 |
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| 4.2635 | 1.92 | 400 | 2.7158 | 0.2684 | 0.2035 | 0.2684 | 0.1801 | 0.4852 |
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| 3.2875 | 2.16 | 450 | 2.3488 | 0.4033 | 0.3287 | 0.4033 | 0.3141 | 0.5813 |
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| 3.2875 | 2.4 | 500 | 2.2100 | 0.4070 | 0.3778 | 0.4070 | 0.3362 | 0.5768 |
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| 3.2875 | 2.63 | 550 | 1.8435 | 0.5232 | 0.4362 | 0.5232 | 0.4500 | 0.6669 |
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| 3.2875 | 2.87 | 600 | 1.6225 | 0.5847 | 0.5594 | 0.5847 | 0.5397 | 0.7085 |
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| 2.2923 | 3.11 | 650 | 1.4552 | 0.6027 | 0.5886 | 0.6027 | 0.5546 | 0.7217 |
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| 2.2923 | 3.35 | 700 | 1.2618 | 0.6529 | 0.6733 | 0.6529 | 0.6201 | 0.7570 |
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| 2.2923 | 3.59 | 750 | 1.1393 | 0.6904 | 0.7135 | 0.6904 | 0.6672 | 0.7822 |
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| 2.2923 | 3.83 | 800 | 1.1629 | 0.6904 | 0.7141 | 0.6904 | 0.6713 | 0.7825 |
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| 1.6069 | 4.07 | 850 | 1.0495 | 0.7264 | 0.7485 | 0.7264 | 0.7120 | 0.8105 |
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| 1.6069 | 4.31 | 900 | 0.9086 | 0.7571 | 0.7644 | 0.7571 | 0.7409 | 0.8304 |
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| 1.6069 | 4.55 | 950 | 0.8281 | 0.7669 | 0.7855 | 0.7669 | 0.7580 | 0.8379 |
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| 1.6069 | 4.79 | 1000 | 0.8820 | 0.7534 | 0.7784 | 0.7534 | 0.7412 | 0.8272 |
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| 1.2646 | 5.03 | 1050 | 0.7262 | 0.7886 | 0.7982 | 0.7886 | 0.7817 | 0.8525 |
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| 1.2646 | 5.27 | 1100 | 0.7475 | 0.7954 | 0.8113 | 0.7954 | 0.7923 | 0.8577 |
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| 1.2646 | 5.51 | 1150 | 0.7535 | 0.7999 | 0.8152 | 0.7999 | 0.7960 | 0.8600 |
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| 1.2646 | 5.75 | 1200 | 0.7443 | 0.8021 | 0.8159 | 0.8021 | 0.7997 | 0.8624 |
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| 1.2646 | 5.99 | 1250 | 0.5991 | 0.8351 | 0.8471 | 0.8351 | 0.8301 | 0.8854 |
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| 1.0169 | 6.23 | 1300 | 0.6740 | 0.8193 | 0.8387 | 0.8193 | 0.8142 | 0.8741 |
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| 1.0169 | 6.47 | 1350 | 0.6129 | 0.8358 | 0.8535 | 0.8358 | 0.8326 | 0.8843 |
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| 1.0169 | 6.71 | 1400 | 0.6051 | 0.8358 | 0.8486 | 0.8358 | 0.8324 | 0.8851 |
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| 1.0169 | 6.95 | 1450 | 0.6603 | 0.8216 | 0.8388 | 0.8216 | 0.8176 | 0.8748 |
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| 0.89 | 7.19 | 1500 | 0.6105 | 0.8388 | 0.8499 | 0.8388 | 0.8363 | 0.8871 |
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| 0.89 | 7.43 | 1550 | 0.6328 | 0.8298 | 0.8433 | 0.8298 | 0.8265 | 0.8808 |
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| 0.89 | 7.66 | 1600 | 0.7041 | 0.8246 | 0.8405 | 0.8246 | 0.8203 | 0.8765 |
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| 0.89 | 7.9 | 1650 | 0.6693 | 0.8268 | 0.8453 | 0.8268 | 0.8257 | 0.8788 |
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| 0.7796 | 8.14 | 1700 | 0.6774 | 0.8223 | 0.8399 | 0.8223 | 0.8198 | 0.8768 |
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| 0.7796 | 8.38 | 1750 | 0.6875 | 0.8201 | 0.8334 | 0.8201 | 0.8181 | 0.8755 |
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| 0.7796 | 8.62 | 1800 | 0.6783 | 0.8253 | 0.8409 | 0.8253 | 0.8225 | 0.8795 |
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| 0.7796 | 8.86 | 1850 | 0.6815 | 0.8261 | 0.8431 | 0.8261 | 0.8241 | 0.8798 |
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### Framework versions
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runs/Jul27_06-05-30_LAPTOP-1GID9RGH/events.out.tfevents.1722035131.LAPTOP-1GID9RGH.2524.8
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size
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version https://git-lfs.github.com/spec/v1
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size 30629
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runs/Jul27_06-05-30_LAPTOP-1GID9RGH/events.out.tfevents.1722036088.LAPTOP-1GID9RGH.2524.9
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version https://git-lfs.github.com/spec/v1
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size 610
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