generated_from_trainer

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20230826161130

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 0.6392 0.43
No log 2.0 50 0.1729 0.41
No log 3.0 75 0.1658 0.61
No log 4.0 100 0.1579 0.57
No log 5.0 125 0.1678 0.4
No log 6.0 150 0.1583 0.55
No log 7.0 175 0.1650 0.6
No log 8.0 200 0.1643 0.62
No log 9.0 225 0.1594 0.48
No log 10.0 250 0.1572 0.61
No log 11.0 275 0.1660 0.4
No log 12.0 300 0.1570 0.63
No log 13.0 325 0.1589 0.51
No log 14.0 350 0.1581 0.42
No log 15.0 375 0.1582 0.5
No log 16.0 400 0.1576 0.53
No log 17.0 425 0.1580 0.52
No log 18.0 450 0.1581 0.55
No log 19.0 475 0.1583 0.45
0.621 20.0 500 0.1606 0.52
0.621 21.0 525 0.1583 0.52
0.621 22.0 550 0.1573 0.49
0.621 23.0 575 0.1582 0.43
0.621 24.0 600 0.1581 0.53
0.621 25.0 625 0.1582 0.49
0.621 26.0 650 0.1582 0.5
0.621 27.0 675 0.1583 0.53
0.621 28.0 700 0.1586 0.47
0.621 29.0 725 0.1585 0.48
0.621 30.0 750 0.1584 0.46
0.621 31.0 775 0.1582 0.55
0.621 32.0 800 0.1582 0.53
0.621 33.0 825 0.1583 0.51
0.621 34.0 850 0.1585 0.39
0.621 35.0 875 0.1582 0.69
0.621 36.0 900 0.1583 0.48
0.621 37.0 925 0.1582 0.61
0.621 38.0 950 0.1580 0.63
0.621 39.0 975 0.1581 0.47
0.4969 40.0 1000 0.1582 0.49
0.4969 41.0 1025 0.1583 0.49
0.4969 42.0 1050 0.1583 0.47
0.4969 43.0 1075 0.1581 0.52
0.4969 44.0 1100 0.1584 0.47
0.4969 45.0 1125 0.1584 0.35
0.4969 46.0 1150 0.1582 0.56
0.4969 47.0 1175 0.1582 0.54
0.4969 48.0 1200 0.1582 0.53
0.4969 49.0 1225 0.1582 0.56
0.4969 50.0 1250 0.1582 0.54
0.4969 51.0 1275 0.1582 0.57
0.4969 52.0 1300 0.1582 0.52
0.4969 53.0 1325 0.1581 0.59
0.4969 54.0 1350 0.1582 0.55
0.4969 55.0 1375 0.1585 0.41
0.4969 56.0 1400 0.1584 0.45
0.4969 57.0 1425 0.1583 0.54
0.4969 58.0 1450 0.1583 0.41
0.4969 59.0 1475 0.1583 0.42
0.4428 60.0 1500 0.1583 0.4
0.4428 61.0 1525 0.1583 0.59
0.4428 62.0 1550 0.1582 0.65
0.4428 63.0 1575 0.1581 0.64
0.4428 64.0 1600 0.1581 0.59
0.4428 65.0 1625 0.1583 0.42
0.4428 66.0 1650 0.1582 0.5
0.4428 67.0 1675 0.1583 0.43
0.4428 68.0 1700 0.1584 0.39
0.4428 69.0 1725 0.1583 0.5
0.4428 70.0 1750 0.1583 0.49
0.4428 71.0 1775 0.1583 0.48
0.4428 72.0 1800 0.1584 0.29
0.4428 73.0 1825 0.1583 0.4
0.4428 74.0 1850 0.1582 0.59
0.4428 75.0 1875 0.1582 0.59
0.4428 76.0 1900 0.1582 0.53
0.4428 77.0 1925 0.1583 0.33
0.4428 78.0 1950 0.1583 0.35
0.4428 79.0 1975 0.1583 0.36
0.4082 80.0 2000 0.1582 0.39

Framework versions