generated_from_trainer

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20230830102643

This model is a fine-tuned version of bert-large-cased on the super_glue 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
No log 1.0 340 0.6910 0.5
0.7049 2.0 680 0.6698 0.5078
0.6976 3.0 1020 0.6913 0.5
0.6976 4.0 1360 0.7490 0.5
0.6898 5.0 1700 0.8592 0.5078
0.6423 6.0 2040 0.7080 0.5987
0.6423 7.0 2380 0.6940 0.5
0.6463 8.0 2720 0.6703 0.5
0.7054 9.0 3060 0.6741 0.5047
0.7054 10.0 3400 0.6784 0.5
0.7011 11.0 3740 0.6707 0.5
0.6895 12.0 4080 0.6941 0.5
0.6895 13.0 4420 0.7550 0.5
0.6898 14.0 4760 0.7095 0.5
0.6916 15.0 5100 0.6711 0.5
0.6916 16.0 5440 0.7051 0.5
0.6891 17.0 5780 0.6831 0.5
0.6875 18.0 6120 0.6733 0.5172
0.6875 19.0 6460 0.6707 0.5
0.6854 20.0 6800 0.6851 0.5
0.686 21.0 7140 0.6704 0.5
0.686 22.0 7480 0.6938 0.5
0.6874 23.0 7820 0.6848 0.5
0.6848 24.0 8160 0.6710 0.5
0.6811 25.0 8500 0.6783 0.4953
0.6811 26.0 8840 0.6886 0.5
0.6837 27.0 9180 0.7146 0.5
0.6851 28.0 9520 0.6703 0.5
0.6851 29.0 9860 0.6884 0.5
0.6813 30.0 10200 0.6704 0.5
0.6826 31.0 10540 0.6704 0.5
0.6826 32.0 10880 0.6970 0.5
0.6813 33.0 11220 0.6707 0.5
0.6786 34.0 11560 0.6840 0.5
0.6786 35.0 11900 0.6722 0.5
0.6821 36.0 12240 0.6706 0.5
0.6799 37.0 12580 0.6707 0.5
0.6799 38.0 12920 0.6824 0.4953
0.6803 39.0 13260 0.6995 0.5
0.6775 40.0 13600 0.6728 0.5
0.6775 41.0 13940 0.6711 0.4984
0.679 42.0 14280 0.6743 0.5
0.6775 43.0 14620 0.6742 0.5016
0.6775 44.0 14960 0.6724 0.5016
0.6766 45.0 15300 0.6884 0.5
0.6755 46.0 15640 0.6709 0.5
0.6755 47.0 15980 0.6811 0.5
0.6785 48.0 16320 0.6709 0.5
0.6765 49.0 16660 0.6813 0.5
0.6761 50.0 17000 0.6724 0.5
0.6761 51.0 17340 0.6713 0.5
0.6764 52.0 17680 0.6715 0.5016
0.6774 53.0 18020 0.6730 0.5
0.6774 54.0 18360 0.6730 0.5
0.673 55.0 18700 0.6716 0.5016
0.6718 56.0 19040 0.6714 0.5
0.6718 57.0 19380 0.6714 0.4984
0.6745 58.0 19720 0.6715 0.5016
0.6735 59.0 20060 0.6863 0.5
0.6735 60.0 20400 0.6710 0.4984
0.6725 61.0 20740 0.6718 0.5063
0.6734 62.0 21080 0.6717 0.5
0.6734 63.0 21420 0.6714 0.4984
0.6725 64.0 21760 0.6749 0.5
0.6719 65.0 22100 0.6723 0.5
0.6719 66.0 22440 0.6776 0.5
0.6714 67.0 22780 0.6716 0.5
0.6724 68.0 23120 0.6723 0.5
0.6724 69.0 23460 0.6717 0.5
0.6705 70.0 23800 0.6712 0.4984
0.6722 71.0 24140 0.6725 0.5
0.6722 72.0 24480 0.6775 0.5
0.6715 73.0 24820 0.6744 0.5
0.6708 74.0 25160 0.6762 0.5
0.6705 75.0 25500 0.6737 0.5
0.6705 76.0 25840 0.6745 0.5
0.6698 77.0 26180 0.6717 0.5
0.6691 78.0 26520 0.6735 0.5
0.6691 79.0 26860 0.6732 0.5
0.6693 80.0 27200 0.6732 0.5

Framework versions