generated_from_trainer

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20230830102630

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 0.6962 0.5
0.7003 2.0 680 0.6657 0.5345
0.6959 3.0 1020 0.6703 0.5
0.6959 4.0 1360 0.6845 0.5
0.6978 5.0 1700 0.6767 0.5
0.6854 6.0 2040 0.6876 0.5
0.6854 7.0 2380 0.6705 0.5
0.6851 8.0 2720 0.6806 0.5
0.6845 9.0 3060 0.6881 0.5
0.6845 10.0 3400 0.6737 0.5
0.6835 11.0 3740 0.6734 0.5
0.6821 12.0 4080 0.7058 0.5
0.6821 13.0 4420 0.7057 0.5
0.682 14.0 4760 0.7057 0.5
0.6827 15.0 5100 0.6771 0.5
0.6827 16.0 5440 0.6848 0.5
0.6803 17.0 5780 0.7044 0.5
0.6821 18.0 6120 0.6720 0.4984
0.6821 19.0 6460 0.6716 0.5
0.6784 20.0 6800 0.6855 0.5
0.6821 21.0 7140 0.6705 0.5
0.6821 22.0 7480 0.6753 0.5
0.6888 23.0 7820 0.6745 0.4953
0.6821 24.0 8160 0.6716 0.5
0.682 25.0 8500 0.6702 0.5
0.682 26.0 8840 0.6791 0.5
0.6829 27.0 9180 0.6771 0.5
0.6807 28.0 9520 0.6719 0.5
0.6807 29.0 9860 0.6739 0.5
0.6783 30.0 10200 0.6716 0.5
0.6789 31.0 10540 0.6706 0.5
0.6789 32.0 10880 0.7163 0.5
0.6798 33.0 11220 0.6703 0.5
0.6785 34.0 11560 0.6822 0.5
0.6785 35.0 11900 0.6715 0.5
0.6783 36.0 12240 0.6720 0.5
0.6781 37.0 12580 0.6733 0.5
0.6781 38.0 12920 0.6707 0.5
0.6798 39.0 13260 0.6950 0.5
0.6755 40.0 13600 0.6705 0.5
0.6755 41.0 13940 0.6715 0.5
0.6776 42.0 14280 0.6704 0.5
0.6772 43.0 14620 0.6789 0.5
0.6772 44.0 14960 0.6707 0.5
0.6755 45.0 15300 0.6925 0.5
0.6748 46.0 15640 0.6727 0.5
0.6748 47.0 15980 0.6801 0.5
0.6754 48.0 16320 0.6714 0.5
0.6762 49.0 16660 0.6882 0.5
0.6753 50.0 17000 0.6710 0.5
0.6753 51.0 17340 0.6707 0.5
0.6734 52.0 17680 0.6726 0.5063
0.678 53.0 18020 0.6727 0.5
0.678 54.0 18360 0.6751 0.5
0.6719 55.0 18700 0.6712 0.5
0.6726 56.0 19040 0.6721 0.5
0.6726 57.0 19380 0.6715 0.5
0.6732 58.0 19720 0.6717 0.5016
0.6736 59.0 20060 0.6819 0.5
0.6736 60.0 20400 0.6728 0.5141
0.6732 61.0 20740 0.6716 0.5016
0.6727 62.0 21080 0.6747 0.5
0.6727 63.0 21420 0.6715 0.4984
0.6726 64.0 21760 0.6737 0.5
0.6721 65.0 22100 0.6724 0.5
0.6721 66.0 22440 0.6744 0.5
0.6711 67.0 22780 0.6720 0.5
0.6725 68.0 23120 0.6722 0.4984
0.6725 69.0 23460 0.6722 0.4984
0.6713 70.0 23800 0.6722 0.4984
0.6708 71.0 24140 0.6743 0.5
0.6708 72.0 24480 0.6794 0.5
0.6703 73.0 24820 0.6756 0.5
0.6702 74.0 25160 0.6760 0.5
0.6688 75.0 25500 0.6741 0.4984
0.6688 76.0 25840 0.6753 0.5
0.67 77.0 26180 0.6730 0.4984
0.6688 78.0 26520 0.6751 0.4984
0.6688 79.0 26860 0.6750 0.4984
0.6685 80.0 27200 0.6751 0.4984

Framework versions