generated_from_keras_callback

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whisper_charsplit_new_round3__0063

This model is a fine-tuned version of bigmorning/whisper_charsplit_new_round2__0061 on an unknown 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

Train Loss Train Accuracy Train Wermet Validation Loss Validation Accuracy Validation Wermet Epoch
0.0009 0.0795 7.9492 0.5730 0.0769 7.2856 0
0.0015 0.0795 8.4221 0.5756 0.0769 7.1487 1
0.0012 0.0795 7.8476 0.5699 0.0769 6.5976 2
0.0010 0.0795 7.6843 0.5740 0.0769 6.9513 3
0.0014 0.0795 8.0796 0.5763 0.0768 7.4043 4
0.0019 0.0795 7.7274 0.5724 0.0769 6.4922 5
0.0008 0.0795 7.3468 0.5734 0.0769 6.1909 6
0.0009 0.0795 7.2393 0.5816 0.0769 6.5734 7
0.0010 0.0795 7.5822 0.5755 0.0769 6.6613 8
0.0004 0.0795 7.3807 0.5698 0.0770 7.0671 9
0.0001 0.0795 7.7157 0.5681 0.0771 6.8391 10
0.0001 0.0795 7.7540 0.5725 0.0771 6.9281 11
0.0001 0.0795 7.7721 0.5726 0.0771 6.8911 12
0.0000 0.0795 7.8163 0.5721 0.0771 6.8876 13
0.0000 0.0795 7.7745 0.5741 0.0771 6.8770 14
0.0000 0.0795 7.7277 0.5752 0.0771 6.8671 15
0.0000 0.0795 7.7355 0.5765 0.0771 6.8447 16
0.0000 0.0795 7.7109 0.5784 0.0771 6.8560 17
0.0000 0.0795 7.7427 0.5796 0.0771 6.8406 18
0.0003 0.0795 7.6709 0.6610 0.0762 7.0119 19
0.0115 0.0793 8.3288 0.5580 0.0769 7.1457 20
0.0013 0.0795 8.2537 0.5574 0.0770 6.7708 21
0.0004 0.0795 8.0507 0.5619 0.0770 7.0678 22
0.0003 0.0795 8.0534 0.5593 0.0771 7.0433 23
0.0002 0.0795 8.1738 0.5604 0.0771 7.1617 24
0.0001 0.0795 8.1494 0.5589 0.0771 7.1609 25
0.0000 0.0795 8.2151 0.5614 0.0771 7.1972 26
0.0000 0.0795 8.2332 0.5633 0.0771 7.1736 27
0.0000 0.0795 8.2573 0.5648 0.0771 7.2086 28
0.0000 0.0795 8.2571 0.5667 0.0771 7.1787 29
0.0000 0.0795 8.2607 0.5689 0.0771 7.2107 30
0.0000 0.0795 8.2992 0.5700 0.0772 7.2006 31
0.0000 0.0795 8.3059 0.5721 0.0772 7.2341 32
0.0000 0.0795 8.2872 0.5744 0.0772 7.2069 33
0.0080 0.0794 8.3693 0.5947 0.0762 7.3034 34
0.0063 0.0794 8.2517 0.5491 0.0769 7.1324 35
0.0008 0.0795 7.9115 0.5447 0.0771 6.9422 36
0.0002 0.0795 7.6265 0.5471 0.0771 6.8107 37
0.0001 0.0795 7.6685 0.5493 0.0771 6.6914 38
0.0001 0.0795 7.6100 0.5515 0.0771 6.7738 39
0.0000 0.0795 7.6623 0.5535 0.0771 6.7829 40
0.0000 0.0795 7.6768 0.5556 0.0771 6.8287 41
0.0000 0.0795 7.7199 0.5578 0.0772 6.8398 42
0.0000 0.0795 7.7423 0.5600 0.0772 6.8518 43
0.0000 0.0795 7.7561 0.5617 0.0772 6.8898 44
0.0000 0.0795 7.7766 0.5639 0.0772 6.8982 45
0.0000 0.0795 7.7962 0.5659 0.0772 6.9091 46
0.0000 0.0795 7.8106 0.5680 0.0772 6.9293 47
0.0000 0.0795 7.8387 0.5701 0.0772 6.9401 48
0.0000 0.0795 7.8480 0.5724 0.0772 6.9544 49
0.0000 0.0795 7.8755 0.5744 0.0772 6.9767 50
0.0000 0.0795 7.8924 0.5770 0.0772 6.9928 51
0.0000 0.0795 7.9169 0.5794 0.0772 7.0149 52
0.0000 0.0795 7.9400 0.5822 0.0772 7.0438 53
0.0000 0.0795 7.9697 0.5846 0.0772 7.0785 54
0.0000 0.0795 8.0061 0.5875 0.0772 7.0840 55
0.0000 0.0795 8.0364 0.5907 0.0772 7.0683 56
0.0113 0.0793 7.8674 0.5714 0.0768 6.0540 57
0.0030 0.0795 7.4853 0.5586 0.0770 6.6707 58
0.0009 0.0795 7.4969 0.5584 0.0771 6.7292 59
0.0004 0.0795 7.6676 0.5577 0.0771 6.7898 60
0.0002 0.0795 7.5238 0.5561 0.0772 6.6962 61
0.0002 0.0795 7.4915 0.5613 0.0772 6.6315 62

Framework versions