generated_from_trainer

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20230826172956

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 25 0.2150 0.6
No log 2.0 50 0.1887 0.59
No log 3.0 75 0.1839 0.58
No log 4.0 100 0.1657 0.45
No log 5.0 125 0.1619 0.58
No log 6.0 150 0.1615 0.52
No log 7.0 175 0.1579 0.57
No log 8.0 200 0.1583 0.62
No log 9.0 225 0.1615 0.52
No log 10.0 250 0.1586 0.64
No log 11.0 275 0.1599 0.63
No log 12.0 300 0.1615 0.5
No log 13.0 325 0.1588 0.55
No log 14.0 350 0.1611 0.44
No log 15.0 375 0.1587 0.54
No log 16.0 400 0.1585 0.6
No log 17.0 425 0.1574 0.54
No log 18.0 450 0.1599 0.51
No log 19.0 475 0.1580 0.56
0.6147 20.0 500 0.1593 0.51
0.6147 21.0 525 0.1612 0.39
0.6147 22.0 550 0.1588 0.57
0.6147 23.0 575 0.1583 0.6
0.6147 24.0 600 0.1588 0.61
0.6147 25.0 625 0.1585 0.55
0.6147 26.0 650 0.1582 0.52
0.6147 27.0 675 0.1625 0.48
0.6147 28.0 700 0.1617 0.48
0.6147 29.0 725 0.1607 0.57
0.6147 30.0 750 0.1589 0.55
0.6147 31.0 775 0.1584 0.58
0.6147 32.0 800 0.1593 0.57
0.6147 33.0 825 0.1608 0.49
0.6147 34.0 850 0.1605 0.5
0.6147 35.0 875 0.1601 0.54
0.6147 36.0 900 0.1590 0.54
0.6147 37.0 925 0.1651 0.45
0.6147 38.0 950 0.1613 0.44
0.6147 39.0 975 0.1630 0.5
0.5279 40.0 1000 0.1598 0.48
0.5279 41.0 1025 0.1605 0.52
0.5279 42.0 1050 0.1598 0.46
0.5279 43.0 1075 0.1599 0.51
0.5279 44.0 1100 0.1611 0.5
0.5279 45.0 1125 0.1611 0.49
0.5279 46.0 1150 0.1602 0.56
0.5279 47.0 1175 0.1596 0.5
0.5279 48.0 1200 0.1605 0.59
0.5279 49.0 1225 0.1593 0.53
0.5279 50.0 1250 0.1584 0.51
0.5279 51.0 1275 0.1592 0.52
0.5279 52.0 1300 0.1588 0.49
0.5279 53.0 1325 0.1610 0.55
0.5279 54.0 1350 0.1591 0.53
0.5279 55.0 1375 0.1585 0.49
0.5279 56.0 1400 0.1591 0.46
0.5279 57.0 1425 0.1584 0.44
0.5279 58.0 1450 0.1612 0.47
0.5279 59.0 1475 0.1626 0.43
0.4515 60.0 1500 0.1607 0.46
0.4515 61.0 1525 0.1599 0.49
0.4515 62.0 1550 0.1590 0.49
0.4515 63.0 1575 0.1601 0.54
0.4515 64.0 1600 0.1606 0.49
0.4515 65.0 1625 0.1592 0.5
0.4515 66.0 1650 0.1605 0.52
0.4515 67.0 1675 0.1605 0.51
0.4515 68.0 1700 0.1603 0.54
0.4515 69.0 1725 0.1603 0.55
0.4515 70.0 1750 0.1604 0.56
0.4515 71.0 1775 0.1615 0.54
0.4515 72.0 1800 0.1593 0.5
0.4515 73.0 1825 0.1601 0.54
0.4515 74.0 1850 0.1603 0.57
0.4515 75.0 1875 0.1596 0.51
0.4515 76.0 1900 0.1608 0.54
0.4515 77.0 1925 0.1603 0.56
0.4515 78.0 1950 0.1600 0.55
0.4515 79.0 1975 0.1602 0.55
0.4114 80.0 2000 0.1602 0.54

Framework versions