generated_from_trainer

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gpt2_winobias_finetuned

This model is a fine-tuned version of gpt2 on the None 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 Tp Tn Fp Fn
0.7128 0.8 20 0.7014 0.5 0.0 0.5 0.0 0.5
0.7384 1.6 40 0.7479 0.5 0.0 0.5 0.0 0.5
0.7142 2.4 60 0.7035 0.5 0.0 0.5 0.0 0.5
0.7004 3.2 80 0.7548 0.5 0.5 0.0 0.5 0.0
0.7353 4.0 100 0.7191 0.5 0.0 0.5 0.0 0.5
0.7041 4.8 120 0.7120 0.5 0.5 0.0 0.5 0.0
0.7012 5.6 140 0.7019 0.5 0.0 0.5 0.0 0.5
0.695 6.4 160 0.7264 0.5 0.0 0.5 0.0 0.5
0.7069 7.2 180 0.6932 0.5 0.5 0.0 0.5 0.0
0.7208 8.0 200 0.7370 0.5 0.5 0.0 0.5 0.0
0.7203 8.8 220 0.6935 0.5 0.0 0.5 0.0 0.5
0.6968 9.6 240 0.6944 0.5 0.5 0.0 0.5 0.0
0.7162 10.4 260 0.7056 0.5 0.5 0.0 0.5 0.0
0.6966 11.2 280 0.6942 0.5 0.0 0.5 0.0 0.5
0.705 12.0 300 0.6963 0.5 0.0 0.5 0.0 0.5
0.7007 12.8 320 0.7010 0.4956 0.4716 0.0240 0.4760 0.0284
0.7039 13.6 340 0.6973 0.4937 0.4697 0.0240 0.4760 0.0303
0.6864 14.4 360 0.7336 0.4937 0.3946 0.0991 0.4009 0.1054
0.7068 15.2 380 0.7135 0.4987 0.1073 0.3914 0.1086 0.3927
0.6626 16.0 400 0.7278 0.5019 0.0259 0.4760 0.0240 0.4741
0.6502 16.8 420 0.7612 0.5025 0.2879 0.2146 0.2854 0.2121
0.5627 17.6 440 0.7975 0.5404 0.1932 0.3472 0.1528 0.3068
0.5473 18.4 460 0.7218 0.5972 0.3005 0.2967 0.2033 0.1995
0.4772 19.2 480 0.7505 0.6490 0.3529 0.2961 0.2039 0.1471
0.388 20.0 500 0.7515 0.6812 0.3624 0.3188 0.1812 0.1376
0.3102 20.8 520 0.9149 0.6894 0.3819 0.3074 0.1926 0.1181
0.2433 21.6 540 0.7770 0.7020 0.3630 0.3390 0.1610 0.1370
0.2379 22.4 560 0.9499 0.7102 0.3422 0.3681 0.1319 0.1578
0.1669 23.2 580 0.9379 0.7077 0.3794 0.3283 0.1717 0.1206
0.1622 24.0 600 0.9364 0.7077 0.3902 0.3176 0.1824 0.1098
0.1455 24.8 620 1.1195 0.6970 0.3359 0.3611 0.1389 0.1641
0.114 25.6 640 1.1392 0.7102 0.3580 0.3523 0.1477 0.1420
0.0714 26.4 660 1.4233 0.7165 0.3447 0.3718 0.1282 0.1553
0.0739 27.2 680 1.5302 0.7159 0.3567 0.3592 0.1408 0.1433
0.0774 28.0 700 2.3741 0.7096 0.3636 0.3460 0.1540 0.1364
0.0672 28.8 720 1.3433 0.7096 0.3567 0.3529 0.1471 0.1433
0.0616 29.6 740 1.6716 0.7172 0.3718 0.3453 0.1547 0.1282
0.0432 30.4 760 1.2928 0.7109 0.3561 0.3548 0.1452 0.1439
0.0417 31.2 780 1.6960 0.7052 0.3447 0.3605 0.1395 0.1553
0.0538 32.0 800 1.8484 0.7102 0.3516 0.3586 0.1414 0.1484
0.0513 32.8 820 2.0704 0.6963 0.3485 0.3479 0.1521 0.1515
0.0428 33.6 840 1.8172 0.7090 0.3630 0.3460 0.1540 0.1370
0.0317 34.4 860 1.8815 0.7121 0.3674 0.3447 0.1553 0.1326
0.0375 35.2 880 1.7032 0.7121 0.3561 0.3561 0.1439 0.1439
0.032 36.0 900 2.1972 0.7128 0.3573 0.3554 0.1446 0.1427
0.0173 36.8 920 2.2502 0.7165 0.3662 0.3504 0.1496 0.1338
0.0117 37.6 940 2.1330 0.7184 0.3687 0.3497 0.1503 0.1313
0.0206 38.4 960 2.0618 0.7191 0.3725 0.3466 0.1534 0.1275
0.0146 39.2 980 1.9688 0.7172 0.3592 0.3580 0.1420 0.1408
0.0108 40.0 1000 2.0846 0.7153 0.3592 0.3561 0.1439 0.1408
0.0131 40.8 1020 2.3518 0.7140 0.3592 0.3548 0.1452 0.1408
0.0095 41.6 1040 2.5874 0.7197 0.3712 0.3485 0.1515 0.1288
0.0331 42.4 1060 2.5151 0.7159 0.3649 0.3510 0.1490 0.1351
0.0037 43.2 1080 2.3016 0.7153 0.3643 0.3510 0.1490 0.1357
0.0212 44.0 1100 2.1693 0.7121 0.3554 0.3567 0.1433 0.1446
0.0109 44.8 1120 2.1769 0.7134 0.3580 0.3554 0.1446 0.1420
0.0032 45.6 1140 2.2651 0.7146 0.3649 0.3497 0.1503 0.1351
0.0122 46.4 1160 2.3623 0.7172 0.3712 0.3460 0.1540 0.1288
0.0029 47.2 1180 2.4197 0.7197 0.3763 0.3434 0.1566 0.1237
0.0197 48.0 1200 2.4860 0.7159 0.3718 0.3441 0.1559 0.1282
0.0127 48.8 1220 2.4478 0.7146 0.3725 0.3422 0.1578 0.1275
0.0273 49.6 1240 2.4430 0.7153 0.3725 0.3428 0.1572 0.1275

Framework versions