generated_from_trainer

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mtg_1000_subset_wav2vec2_100k_gtzan_model

This model is a fine-tuned version of facebook/wav2vec2-base-100k-voxpopuli on the gtzan dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2999 1.0 199 2.2945 0.135
2.2959 2.0 399 2.2945 0.135
2.2969 3.0 599 2.2945 0.135
2.2985 4.0 799 2.2938 0.12
2.1449 5.0 998 2.0673 0.32
2.1164 6.0 1198 2.0673 0.32
2.1542 7.0 1398 2.0673 0.32
2.0971 8.0 1598 2.0631 0.315
2.0703 9.0 1797 1.9101 0.375
1.939 10.0 1997 1.9101 0.375
1.9502 11.0 2197 1.9101 0.375
1.9831 12.0 2397 1.9083 0.365
1.7969 13.0 2596 1.7547 0.42
1.817 14.0 2796 1.7547 0.42
1.9083 15.0 2996 1.7547 0.42
1.7962 16.0 3196 1.7654 0.395
1.5644 17.0 3395 1.5647 0.47
1.5798 18.0 3595 1.5647 0.47
1.583 19.0 3795 1.5647 0.47
1.6343 20.0 3995 1.5708 0.455
1.4268 21.0 4194 1.6466 0.405
1.8691 22.0 4394 1.6466 0.405
1.5165 23.0 4594 1.6466 0.405
1.5306 24.0 4794 1.6108 0.43
1.4245 25.0 4993 1.3500 0.54
1.2792 26.0 5193 1.3500 0.54
1.3472 27.0 5393 1.3500 0.54
1.2189 28.0 5593 1.3610 0.525
1.2861 29.0 5792 1.2593 0.595
1.2574 30.0 5992 1.2593 0.595
1.342 31.0 6192 1.2593 0.595
1.1464 32.0 6392 1.2563 0.585
0.9877 33.0 6591 1.1095 0.69
0.9459 34.0 6791 1.1095 0.69
1.2336 35.0 6991 1.1095 0.69
1.1025 36.0 7191 1.1060 0.7
0.8704 37.0 7390 0.9817 0.715
0.8831 38.0 7590 0.9817 0.715
0.9367 39.0 7790 0.9817 0.715
0.899 40.0 7990 0.9881 0.705
0.7893 41.0 8189 0.9434 0.755
0.8003 42.0 8389 0.9434 0.755
0.715 43.0 8589 0.9434 0.755
0.8367 44.0 8789 0.9532 0.75
0.6512 45.0 8988 0.9307 0.77
0.6109 46.0 9188 0.9307 0.77
0.6644 47.0 9388 0.9307 0.77
0.562 48.0 9588 0.9356 0.765
0.5606 49.0 9787 0.8004 0.805
0.6194 50.0 9987 0.8004 0.805
0.5977 51.0 10187 0.8004 0.805
0.5307 52.0 10387 0.8003 0.81
0.7827 53.0 10586 0.8727 0.765
0.5433 54.0 10786 0.8727 0.765
0.3081 55.0 10986 0.8727 0.765
0.5239 56.0 11186 0.8528 0.765
0.284 57.0 11385 0.7730 0.8
0.2692 58.0 11585 0.7730 0.8
0.2465 59.0 11785 0.7730 0.8
0.3708 60.0 11985 0.7825 0.795
0.1754 61.0 12184 0.8742 0.785
0.2436 62.0 12384 0.8742 0.785
0.2889 63.0 12584 0.8742 0.785
0.2809 64.0 12784 0.8855 0.78
0.1971 65.0 12983 0.8661 0.8
0.2701 66.0 13183 0.8661 0.8
0.1 67.0 13383 0.8661 0.8
0.1668 68.0 13583 0.8615 0.8
0.1784 69.0 13782 0.8111 0.825
0.3054 70.0 13982 0.8111 0.825
0.0945 71.0 14182 0.8111 0.825
0.0784 72.0 14382 0.8075 0.83
0.2458 73.0 14581 0.9653 0.81
0.1893 74.0 14781 0.9653 0.81
0.0977 75.0 14981 0.9653 0.81
0.1417 76.0 15181 0.9600 0.81
0.0785 77.0 15380 0.8951 0.82
0.0768 78.0 15580 0.8951 0.82
0.1524 79.0 15780 0.8951 0.82
0.0456 80.0 15980 0.8952 0.82
0.1463 81.0 16179 1.0768 0.79
0.1568 82.0 16379 1.0768 0.79
0.0494 83.0 16579 1.0768 0.79
0.1716 84.0 16779 1.0751 0.79
0.0351 85.0 16978 0.9556 0.825
0.0329 86.0 17178 0.9556 0.825
0.3197 87.0 17378 0.9556 0.825
0.0317 88.0 17578 0.9561 0.825
0.0272 89.0 17777 1.0150 0.82
0.1779 90.0 17977 1.0150 0.82
0.1416 91.0 18177 1.0150 0.82
0.2224 92.0 18377 1.0154 0.82
0.1861 93.0 18576 1.0021 0.81
0.0288 94.0 18776 1.0021 0.81
0.1544 95.0 18976 1.0021 0.81
0.0629 96.0 19176 0.9993 0.815
0.1571 97.0 19375 0.9871 0.815
0.139 98.0 19575 0.9871 0.815
0.028 99.0 19775 0.9871 0.815
0.102 99.62 19900 0.9871 0.815

Framework versions