generated_from_trainer

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

bert-base-uncased_winobias_classifieronly

This model is a fine-tuned version of bert-base-uncased 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
0.6962 0.8 20 0.6948 0.5019 0.2203 0.2816 0.2184 0.2797
0.6962 1.6 40 0.6944 0.4981 0.2696 0.2285 0.2715 0.2304
0.6967 2.4 60 0.6954 0.5019 0.4078 0.0941 0.4059 0.0922
0.6963 3.2 80 0.6944 0.5088 0.0581 0.4508 0.0492 0.4419
0.6949 4.0 100 0.6936 0.4987 0.2948 0.2039 0.2961 0.2052
0.7003 4.8 120 0.6944 0.5013 0.0322 0.4691 0.0309 0.4678
0.6948 5.6 140 0.6938 0.4987 0.3630 0.1357 0.3643 0.1370
0.695 6.4 160 0.6945 0.5032 0.0284 0.4747 0.0253 0.4716
0.6954 7.2 180 0.6939 0.5025 0.0739 0.4287 0.0713 0.4261
0.6914 8.0 200 0.6936 0.5019 0.3491 0.1528 0.3472 0.1509
0.6968 8.8 220 0.6945 0.4981 0.0234 0.4747 0.0253 0.4766
0.6961 9.6 240 0.6934 0.5069 0.2020 0.3049 0.1951 0.2980
0.6957 10.4 260 0.6932 0.5038 0.2917 0.2121 0.2879 0.2083
0.6929 11.2 280 0.6939 0.5032 0.0271 0.4760 0.0240 0.4729
0.7065 12.0 300 0.6934 0.4987 0.4009 0.0979 0.4021 0.0991
0.6986 12.8 320 0.6934 0.5025 0.0833 0.4192 0.0808 0.4167
0.6974 13.6 340 0.6945 0.4949 0.0221 0.4729 0.0271 0.4779
0.6991 14.4 360 0.6944 0.5044 0.4956 0.0088 0.4912 0.0044
0.6961 15.2 380 0.6932 0.5038 0.0751 0.4287 0.0713 0.4249
0.6982 16.0 400 0.6928 0.5057 0.1641 0.3415 0.1585 0.3359
0.6986 16.8 420 0.6928 0.5101 0.1351 0.375 0.125 0.3649
0.695 17.6 440 0.6927 0.5107 0.2702 0.2405 0.2595 0.2298
0.6954 18.4 460 0.6928 0.5095 0.3441 0.1654 0.3346 0.1559
0.6954 19.2 480 0.6926 0.5133 0.2626 0.2506 0.2494 0.2374
0.7003 20.0 500 0.6926 0.5183 0.2708 0.2475 0.2525 0.2292
0.6982 20.8 520 0.6926 0.5082 0.2652 0.2431 0.2569 0.2348
0.696 21.6 540 0.6927 0.5170 0.2456 0.2715 0.2285 0.2544
0.6928 22.4 560 0.6928 0.5038 0.3958 0.1080 0.3920 0.1042
0.6968 23.2 580 0.6927 0.5164 0.3062 0.2102 0.2898 0.1938
0.693 24.0 600 0.6926 0.5196 0.2702 0.2494 0.2506 0.2298
0.7 24.8 620 0.6927 0.5088 0.4148 0.0941 0.4059 0.0852
0.6993 25.6 640 0.6932 0.5044 0.0335 0.4710 0.0290 0.4665
0.7022 26.4 660 0.6926 0.5152 0.1806 0.3346 0.1654 0.3194
0.6954 27.2 680 0.6925 0.5170 0.2241 0.2929 0.2071 0.2759
0.6978 28.0 700 0.6925 0.5158 0.3289 0.1869 0.3131 0.1711
0.7013 28.8 720 0.6925 0.5145 0.2601 0.2544 0.2456 0.2399
0.7004 29.6 740 0.6931 0.5063 0.4729 0.0335 0.4665 0.0271
0.6913 30.4 760 0.6926 0.5164 0.1982 0.3182 0.1818 0.3018
0.6974 31.2 780 0.6926 0.5164 0.1900 0.3264 0.1736 0.3100
0.6904 32.0 800 0.6926 0.5107 0.3933 0.1174 0.3826 0.1067
0.6917 32.8 820 0.6925 0.5271 0.2961 0.2311 0.2689 0.2039
0.6956 33.6 840 0.6927 0.5114 0.1098 0.4015 0.0985 0.3902
0.6941 34.4 860 0.6927 0.5133 0.4350 0.0783 0.4217 0.0650
0.6935 35.2 880 0.6928 0.5114 0.4571 0.0543 0.4457 0.0429
0.6935 36.0 900 0.6925 0.5114 0.3643 0.1471 0.3529 0.1357
0.6935 36.8 920 0.6924 0.5177 0.2020 0.3157 0.1843 0.2980
0.6916 37.6 940 0.6925 0.5177 0.3314 0.1862 0.3138 0.1686
0.703 38.4 960 0.6925 0.5183 0.3220 0.1963 0.3037 0.1780
0.698 39.2 980 0.6925 0.5234 0.3005 0.2229 0.2771 0.1995
0.6966 40.0 1000 0.6925 0.5240 0.3150 0.2090 0.2910 0.1850
0.6942 40.8 1020 0.6925 0.5290 0.3049 0.2241 0.2759 0.1951
0.6916 41.6 1040 0.6924 0.5240 0.2999 0.2241 0.2759 0.2001
0.6946 42.4 1060 0.6925 0.5221 0.1862 0.3359 0.1641 0.3138
0.6925 43.2 1080 0.6926 0.5025 0.1155 0.3870 0.1130 0.3845
0.6977 44.0 1100 0.6925 0.5170 0.1559 0.3611 0.1389 0.3441
0.6938 44.8 1120 0.6924 0.5221 0.2008 0.3213 0.1787 0.2992
0.6972 45.6 1140 0.6924 0.5208 0.2449 0.2759 0.2241 0.2551
0.6938 46.4 1160 0.6924 0.5227 0.3213 0.2014 0.2986 0.1787
0.6924 47.2 1180 0.6924 0.5107 0.3396 0.1711 0.3289 0.1604
0.7034 48.0 1200 0.6924 0.5322 0.2980 0.2342 0.2658 0.2020
0.6935 48.8 1220 0.6924 0.5316 0.2809 0.2506 0.2494 0.2191
0.6971 49.6 1240 0.6924 0.5290 0.2614 0.2677 0.2323 0.2386

Framework versions