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wav2vec2-classifier-aug

Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Inference Endpoints
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  1. README.md +58 -58
  2. runs/Jul13_12-14-39_LAPTOP-1GID9RGH/events.out.tfevents.1720847680.LAPTOP-1GID9RGH.9408.0 +2 -2
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/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0124
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- - Accuracy: 0.7574
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- - Precision: 0.7773
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- - Recall: 0.7574
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- - F1: 0.7429
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- - Binary: 0.8286
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  ## Model description
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@@ -59,58 +59,58 @@ 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 | 4.2519 | 0.0566 | 0.0051 | 0.0566 | 0.0089 | 0.3305 |
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- | No log | 0.38 | 100 | 3.9645 | 0.0539 | 0.0068 | 0.0539 | 0.0107 | 0.3321 |
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- | No log | 0.58 | 150 | 3.7338 | 0.1105 | 0.0576 | 0.1105 | 0.0536 | 0.3717 |
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- | No log | 0.77 | 200 | 3.5648 | 0.1617 | 0.0751 | 0.1617 | 0.0846 | 0.4084 |
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- | No log | 0.96 | 250 | 3.4031 | 0.2183 | 0.1101 | 0.2183 | 0.1268 | 0.4488 |
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- | 3.9542 | 1.15 | 300 | 3.2478 | 0.2102 | 0.1080 | 0.2102 | 0.1281 | 0.4450 |
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- | 3.9542 | 1.34 | 350 | 3.0971 | 0.2992 | 0.1963 | 0.2992 | 0.2044 | 0.5081 |
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- | 3.9542 | 1.53 | 400 | 2.9647 | 0.2992 | 0.2154 | 0.2992 | 0.2194 | 0.5100 |
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- | 3.9542 | 1.73 | 450 | 2.8175 | 0.3774 | 0.3235 | 0.3774 | 0.3129 | 0.5628 |
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- | 3.9542 | 1.92 | 500 | 2.6989 | 0.4528 | 0.3689 | 0.4528 | 0.3734 | 0.6129 |
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- | 3.1802 | 2.11 | 550 | 2.5825 | 0.4798 | 0.4392 | 0.4798 | 0.4132 | 0.6345 |
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- | 3.1802 | 2.3 | 600 | 2.4777 | 0.4960 | 0.4381 | 0.4960 | 0.4257 | 0.6458 |
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- | 3.1802 | 2.49 | 650 | 2.3896 | 0.5067 | 0.4835 | 0.5067 | 0.4514 | 0.6534 |
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- | 3.1802 | 2.68 | 700 | 2.2850 | 0.5553 | 0.5319 | 0.5553 | 0.5060 | 0.6873 |
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- | 3.1802 | 2.88 | 750 | 2.1647 | 0.5660 | 0.5312 | 0.5660 | 0.5136 | 0.6938 |
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- | 2.6685 | 3.07 | 800 | 2.0787 | 0.5660 | 0.5682 | 0.5660 | 0.5299 | 0.6949 |
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- | 2.6685 | 3.26 | 850 | 1.9976 | 0.5849 | 0.5871 | 0.5849 | 0.5490 | 0.7078 |
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- | 2.6685 | 3.45 | 900 | 1.9318 | 0.6119 | 0.5932 | 0.6119 | 0.5737 | 0.7278 |
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- | 2.6685 | 3.64 | 950 | 1.8600 | 0.6065 | 0.5880 | 0.6065 | 0.5629 | 0.7240 |
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- | 2.6685 | 3.84 | 1000 | 1.7730 | 0.6496 | 0.6337 | 0.6496 | 0.6135 | 0.7550 |
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- | 2.2863 | 4.03 | 1050 | 1.7003 | 0.6765 | 0.6607 | 0.6765 | 0.6427 | 0.7720 |
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- | 2.2863 | 4.22 | 1100 | 1.6632 | 0.6658 | 0.6876 | 0.6658 | 0.6364 | 0.7655 |
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- | 2.2863 | 4.41 | 1150 | 1.6066 | 0.6739 | 0.6552 | 0.6739 | 0.6371 | 0.7712 |
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- | 2.2863 | 4.6 | 1200 | 1.5495 | 0.6900 | 0.6841 | 0.6900 | 0.6616 | 0.7825 |
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- | 2.2863 | 4.79 | 1250 | 1.5232 | 0.6631 | 0.6333 | 0.6631 | 0.6295 | 0.7644 |
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- | 2.2863 | 4.99 | 1300 | 1.4847 | 0.6900 | 0.6985 | 0.6900 | 0.6668 | 0.7825 |
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- | 2.0189 | 5.18 | 1350 | 1.4253 | 0.6927 | 0.6710 | 0.6927 | 0.6586 | 0.7833 |
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- | 2.0189 | 5.37 | 1400 | 1.4005 | 0.7008 | 0.6947 | 0.7008 | 0.6729 | 0.7900 |
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- | 2.0189 | 5.56 | 1450 | 1.3482 | 0.7143 | 0.7217 | 0.7143 | 0.6926 | 0.7984 |
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- | 2.0189 | 5.75 | 1500 | 1.3130 | 0.7089 | 0.7150 | 0.7089 | 0.6866 | 0.7957 |
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- | 2.0189 | 5.94 | 1550 | 1.2733 | 0.7332 | 0.7519 | 0.7332 | 0.7117 | 0.8127 |
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- | 1.8234 | 6.14 | 1600 | 1.2586 | 0.7278 | 0.7585 | 0.7278 | 0.7101 | 0.8089 |
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- | 1.8234 | 6.33 | 1650 | 1.2376 | 0.7251 | 0.7509 | 0.7251 | 0.7067 | 0.8059 |
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- | 1.8234 | 6.52 | 1700 | 1.2169 | 0.7251 | 0.7454 | 0.7251 | 0.7070 | 0.8059 |
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- | 1.8234 | 6.71 | 1750 | 1.2007 | 0.7358 | 0.7476 | 0.7358 | 0.7162 | 0.8135 |
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- | 1.8234 | 6.9 | 1800 | 1.1814 | 0.7278 | 0.7410 | 0.7278 | 0.7069 | 0.8078 |
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- | 1.6833 | 7.09 | 1850 | 1.1559 | 0.7358 | 0.7627 | 0.7358 | 0.7184 | 0.8135 |
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- | 1.6833 | 7.29 | 1900 | 1.1305 | 0.7574 | 0.7876 | 0.7574 | 0.7432 | 0.8286 |
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- | 1.6833 | 7.48 | 1950 | 1.1150 | 0.7385 | 0.7671 | 0.7385 | 0.7259 | 0.8173 |
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- | 1.6833 | 7.67 | 2000 | 1.1024 | 0.7412 | 0.7925 | 0.7412 | 0.7269 | 0.8173 |
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- | 1.6833 | 7.86 | 2050 | 1.0954 | 0.7358 | 0.7272 | 0.7358 | 0.7111 | 0.8135 |
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- | 1.5919 | 8.05 | 2100 | 1.0778 | 0.7547 | 0.7598 | 0.7547 | 0.7342 | 0.8267 |
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- | 1.5919 | 8.25 | 2150 | 1.0713 | 0.7385 | 0.7462 | 0.7385 | 0.7192 | 0.8164 |
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- | 1.5919 | 8.44 | 2200 | 1.0495 | 0.7574 | 0.7719 | 0.7574 | 0.7405 | 0.8286 |
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- | 1.5919 | 8.63 | 2250 | 1.0371 | 0.7574 | 0.7677 | 0.7574 | 0.7400 | 0.8296 |
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- | 1.5919 | 8.82 | 2300 | 1.0452 | 0.7655 | 0.7876 | 0.7655 | 0.7540 | 0.8353 |
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- | 1.5254 | 9.01 | 2350 | 1.0313 | 0.7601 | 0.7781 | 0.7601 | 0.7450 | 0.8315 |
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- | 1.5254 | 9.2 | 2400 | 1.0243 | 0.7628 | 0.7862 | 0.7628 | 0.7494 | 0.8334 |
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- | 1.5254 | 9.4 | 2450 | 1.0192 | 0.7520 | 0.7697 | 0.7520 | 0.7348 | 0.8259 |
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- | 1.5254 | 9.59 | 2500 | 1.0140 | 0.7628 | 0.7859 | 0.7628 | 0.7490 | 0.8323 |
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- | 1.5254 | 9.78 | 2550 | 1.0121 | 0.7601 | 0.7774 | 0.7601 | 0.7453 | 0.8305 |
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- | 1.5254 | 9.97 | 2600 | 1.0124 | 0.7574 | 0.7773 | 0.7574 | 0.7429 | 0.8286 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0269
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+ - Accuracy: 0.7790
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+ - Precision: 0.7971
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+ - Recall: 0.7790
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+ - F1: 0.7657
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+ - Binary: 0.8453
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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 | 4.2076 | 0.0512 | 0.0054 | 0.0512 | 0.0092 | 0.3245 |
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+ | No log | 0.38 | 100 | 3.9082 | 0.0566 | 0.0053 | 0.0566 | 0.0092 | 0.3364 |
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+ | No log | 0.58 | 150 | 3.7121 | 0.0809 | 0.0281 | 0.0809 | 0.0302 | 0.3553 |
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+ | No log | 0.77 | 200 | 3.5137 | 0.1671 | 0.0794 | 0.1671 | 0.0870 | 0.4164 |
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+ | No log | 0.96 | 250 | 3.3647 | 0.2049 | 0.1259 | 0.2049 | 0.1261 | 0.4429 |
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+ | 3.9294 | 1.15 | 300 | 3.2045 | 0.2534 | 0.1676 | 0.2534 | 0.1635 | 0.4765 |
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+ | 3.9294 | 1.34 | 350 | 3.0682 | 0.2695 | 0.1968 | 0.2695 | 0.1841 | 0.4879 |
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+ | 3.9294 | 1.53 | 400 | 2.9211 | 0.3154 | 0.2301 | 0.3154 | 0.2250 | 0.5191 |
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+ | 3.9294 | 1.73 | 450 | 2.7973 | 0.3720 | 0.2756 | 0.3720 | 0.2815 | 0.5615 |
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+ | 3.9294 | 1.92 | 500 | 2.6856 | 0.4232 | 0.3382 | 0.4232 | 0.3413 | 0.5973 |
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+ | 3.1851 | 2.11 | 550 | 2.5696 | 0.4582 | 0.4161 | 0.4582 | 0.3827 | 0.6208 |
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+ | 3.1851 | 2.3 | 600 | 2.4666 | 0.4987 | 0.4339 | 0.4987 | 0.4215 | 0.6501 |
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+ | 3.1851 | 2.49 | 650 | 2.3548 | 0.5202 | 0.4502 | 0.5202 | 0.4542 | 0.6633 |
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+ | 3.1851 | 2.68 | 700 | 2.2498 | 0.5229 | 0.4593 | 0.5229 | 0.4574 | 0.6660 |
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+ | 3.1851 | 2.88 | 750 | 2.1579 | 0.5660 | 0.5162 | 0.5660 | 0.4993 | 0.6962 |
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+ | 2.7 | 3.07 | 800 | 2.0626 | 0.5903 | 0.5465 | 0.5903 | 0.5332 | 0.7143 |
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+ | 2.7 | 3.26 | 850 | 1.9820 | 0.6092 | 0.5576 | 0.6092 | 0.5470 | 0.7264 |
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+ | 2.7 | 3.45 | 900 | 1.9158 | 0.6011 | 0.5636 | 0.6011 | 0.5466 | 0.7208 |
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+ | 2.7 | 3.64 | 950 | 1.8432 | 0.6092 | 0.5631 | 0.6092 | 0.5499 | 0.7264 |
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+ | 2.7 | 3.84 | 1000 | 1.7732 | 0.6280 | 0.5956 | 0.6280 | 0.5816 | 0.7396 |
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+ | 2.3404 | 4.03 | 1050 | 1.7214 | 0.6523 | 0.6167 | 0.6523 | 0.6000 | 0.7566 |
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+ | 2.3404 | 4.22 | 1100 | 1.6562 | 0.6550 | 0.6312 | 0.6550 | 0.6114 | 0.7585 |
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+ | 2.3404 | 4.41 | 1150 | 1.5909 | 0.6792 | 0.6402 | 0.6792 | 0.6301 | 0.7755 |
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+ | 2.3404 | 4.6 | 1200 | 1.5455 | 0.6900 | 0.6806 | 0.6900 | 0.6534 | 0.7830 |
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+ | 2.3404 | 4.79 | 1250 | 1.5123 | 0.6739 | 0.6415 | 0.6739 | 0.6330 | 0.7717 |
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+ | 2.3404 | 4.99 | 1300 | 1.4662 | 0.7089 | 0.6954 | 0.7089 | 0.6741 | 0.7962 |
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+ | 2.089 | 5.18 | 1350 | 1.4212 | 0.6981 | 0.6739 | 0.6981 | 0.6585 | 0.7887 |
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+ | 2.089 | 5.37 | 1400 | 1.3848 | 0.7008 | 0.6700 | 0.7008 | 0.6572 | 0.7906 |
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+ | 2.089 | 5.56 | 1450 | 1.3435 | 0.7305 | 0.7289 | 0.7305 | 0.7017 | 0.8113 |
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+ | 2.089 | 5.75 | 1500 | 1.3324 | 0.7251 | 0.7331 | 0.7251 | 0.7008 | 0.8075 |
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+ | 2.089 | 5.94 | 1550 | 1.3030 | 0.7116 | 0.7242 | 0.7116 | 0.6841 | 0.7981 |
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+ | 1.8929 | 6.14 | 1600 | 1.2662 | 0.7358 | 0.7356 | 0.7358 | 0.7065 | 0.8151 |
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+ | 1.8929 | 6.33 | 1650 | 1.2341 | 0.7332 | 0.7665 | 0.7332 | 0.7128 | 0.8132 |
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+ | 1.8929 | 6.52 | 1700 | 1.2299 | 0.7224 | 0.7255 | 0.7224 | 0.6950 | 0.8057 |
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+ | 1.8929 | 6.71 | 1750 | 1.1984 | 0.7574 | 0.7758 | 0.7574 | 0.7409 | 0.8302 |
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+ | 1.8929 | 6.9 | 1800 | 1.1810 | 0.7547 | 0.7710 | 0.7547 | 0.7390 | 0.8283 |
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+ | 1.7722 | 7.09 | 1850 | 1.1510 | 0.7763 | 0.7979 | 0.7763 | 0.7613 | 0.8434 |
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+ | 1.7722 | 7.29 | 1900 | 1.1476 | 0.7466 | 0.7541 | 0.7466 | 0.7259 | 0.8226 |
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+ | 1.7722 | 7.48 | 1950 | 1.1326 | 0.7601 | 0.7798 | 0.7601 | 0.7456 | 0.8321 |
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+ | 1.7722 | 7.67 | 2000 | 1.1218 | 0.7655 | 0.7778 | 0.7655 | 0.7493 | 0.8358 |
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+ | 1.7722 | 7.86 | 2050 | 1.0964 | 0.7736 | 0.7844 | 0.7736 | 0.7597 | 0.8415 |
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+ | 1.6621 | 8.05 | 2100 | 1.0895 | 0.7682 | 0.7778 | 0.7682 | 0.7505 | 0.8377 |
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+ | 1.6621 | 8.25 | 2150 | 1.0731 | 0.7682 | 0.7925 | 0.7682 | 0.7546 | 0.8377 |
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+ | 1.6621 | 8.44 | 2200 | 1.0673 | 0.7628 | 0.7771 | 0.7628 | 0.7497 | 0.8340 |
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+ | 1.6621 | 8.63 | 2250 | 1.0728 | 0.7682 | 0.7890 | 0.7682 | 0.7552 | 0.8377 |
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+ | 1.6621 | 8.82 | 2300 | 1.0449 | 0.7898 | 0.8032 | 0.7898 | 0.7779 | 0.8528 |
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+ | 1.6107 | 9.01 | 2350 | 1.0386 | 0.7871 | 0.8037 | 0.7871 | 0.7744 | 0.8509 |
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+ | 1.6107 | 9.2 | 2400 | 1.0398 | 0.7763 | 0.7912 | 0.7763 | 0.7635 | 0.8434 |
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+ | 1.6107 | 9.4 | 2450 | 1.0324 | 0.7844 | 0.8025 | 0.7844 | 0.7726 | 0.8491 |
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+ | 1.6107 | 9.59 | 2500 | 1.0323 | 0.7790 | 0.8006 | 0.7790 | 0.7651 | 0.8453 |
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+ | 1.6107 | 9.78 | 2550 | 1.0274 | 0.7790 | 0.7980 | 0.7790 | 0.7648 | 0.8453 |
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+ | 1.6107 | 9.97 | 2600 | 1.0269 | 0.7790 | 0.7971 | 0.7790 | 0.7657 | 0.8453 |
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  ### Framework versions
runs/Jul13_12-14-39_LAPTOP-1GID9RGH/events.out.tfevents.1720847680.LAPTOP-1GID9RGH.9408.0 CHANGED
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