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. -->

multiberts-seed_2-step_2000k_winobias_classifieronly

This model is a fine-tuned version of google/multiberts-seed_2-step_2000k 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
0.7201 0.8 20 0.7046 0.5044 0.0884 0.4160 0.0840 0.4116
0.7141 1.6 40 0.6997 0.5025 0.2001 0.3024 0.1976 0.2999
0.7113 2.4 60 0.6979 0.5063 0.2936 0.2128 0.2872 0.2064
0.7073 3.2 80 0.6967 0.5038 0.1225 0.3813 0.1187 0.3775
0.6922 4.0 100 0.6950 0.5019 0.1616 0.3403 0.1597 0.3384
0.7025 4.8 120 0.6953 0.5076 0.1313 0.3763 0.1237 0.3687
0.7029 5.6 140 0.6947 0.5019 0.2986 0.2033 0.2967 0.2014
0.6974 6.4 160 0.6952 0.5038 0.1092 0.3946 0.1054 0.3908
0.6992 7.2 180 0.6948 0.5088 0.1275 0.3813 0.1187 0.3725
0.6944 8.0 200 0.6939 0.4956 0.2557 0.2399 0.2601 0.2443
0.6953 8.8 220 0.6940 0.4912 0.1824 0.3087 0.1913 0.3176
0.6994 9.6 240 0.6942 0.4949 0.1503 0.3447 0.1553 0.3497
0.6955 10.4 260 0.6939 0.4949 0.2405 0.2544 0.2456 0.2595
0.6993 11.2 280 0.6942 0.5006 0.1446 0.3561 0.1439 0.3554
0.6925 12.0 300 0.6940 0.4975 0.1616 0.3359 0.1641 0.3384
0.6985 12.8 320 0.6938 0.4905 0.2008 0.2898 0.2102 0.2992
0.7014 13.6 340 0.6951 0.5051 0.0821 0.4230 0.0770 0.4179
0.6947 14.4 360 0.6939 0.4912 0.3150 0.1761 0.3239 0.1850
0.698 15.2 380 0.6940 0.5006 0.1654 0.3352 0.1648 0.3346
0.6912 16.0 400 0.6946 0.5032 0.1073 0.3958 0.1042 0.3927
0.6929 16.8 420 0.6946 0.5 0.1035 0.3965 0.1035 0.3965
0.6946 17.6 440 0.6938 0.4994 0.1951 0.3043 0.1957 0.3049
0.6955 18.4 460 0.6937 0.4962 0.2481 0.2481 0.2519 0.2519
0.7 19.2 480 0.6938 0.4994 0.1894 0.3100 0.1900 0.3106
0.6947 20.0 500 0.6938 0.4994 0.2008 0.2986 0.2014 0.2992
0.6978 20.8 520 0.6937 0.4937 0.2462 0.2475 0.2525 0.2538
0.7004 21.6 540 0.6937 0.4962 0.2677 0.2285 0.2715 0.2323
0.6977 22.4 560 0.6937 0.4975 0.2620 0.2355 0.2645 0.2380
0.6933 23.2 580 0.6937 0.4968 0.2342 0.2626 0.2374 0.2658
0.6991 24.0 600 0.6938 0.5 0.1824 0.3176 0.1824 0.3176
0.6961 24.8 620 0.6937 0.4987 0.2140 0.2847 0.2153 0.2860
0.7054 25.6 640 0.6944 0.5032 0.1029 0.4003 0.0997 0.3971
0.6991 26.4 660 0.6942 0.4943 0.1181 0.3763 0.1237 0.3819
0.702 27.2 680 0.6943 0.5013 0.1004 0.4009 0.0991 0.3996
0.6968 28.0 700 0.6941 0.4918 0.1206 0.3712 0.1288 0.3794
0.6939 28.8 720 0.6941 0.4912 0.1136 0.3775 0.1225 0.3864
0.6956 29.6 740 0.6936 0.5019 0.2361 0.2658 0.2342 0.2639
0.6956 30.4 760 0.6936 0.4968 0.2172 0.2797 0.2203 0.2828
0.6987 31.2 780 0.6937 0.4924 0.1679 0.3245 0.1755 0.3321
0.6935 32.0 800 0.6936 0.4912 0.1970 0.2942 0.2058 0.3030
0.6967 32.8 820 0.6936 0.4937 0.1913 0.3024 0.1976 0.3087
0.6982 33.6 840 0.6941 0.4968 0.1288 0.3681 0.1319 0.3712
0.7007 34.4 860 0.6937 0.4949 0.1824 0.3125 0.1875 0.3176
0.7 35.2 880 0.6936 0.4949 0.2197 0.2753 0.2247 0.2803
0.6904 36.0 900 0.6935 0.5025 0.2431 0.2595 0.2405 0.2569
0.6945 36.8 920 0.6936 0.4937 0.2096 0.2841 0.2159 0.2904
0.7025 37.6 940 0.6935 0.5019 0.2519 0.25 0.25 0.2481
0.6969 38.4 960 0.6935 0.4994 0.2235 0.2759 0.2241 0.2765
0.7038 39.2 980 0.6936 0.4949 0.1818 0.3131 0.1869 0.3182
0.698 40.0 1000 0.6937 0.4931 0.1736 0.3194 0.1806 0.3264
0.6973 40.8 1020 0.6938 0.5013 0.1540 0.3472 0.1528 0.3460
0.6964 41.6 1040 0.6939 0.5032 0.1408 0.3624 0.1376 0.3592
0.6999 42.4 1060 0.6939 0.5 0.1370 0.3630 0.1370 0.3630
0.7002 43.2 1080 0.6939 0.5006 0.1376 0.3630 0.1370 0.3624
0.6939 44.0 1100 0.6940 0.4956 0.1225 0.3731 0.1269 0.3775
0.6984 44.8 1120 0.6939 0.4994 0.1338 0.3655 0.1345 0.3662
0.6946 45.6 1140 0.6939 0.5019 0.1395 0.3624 0.1376 0.3605
0.6972 46.4 1160 0.6937 0.4962 0.1616 0.3346 0.1654 0.3384
0.694 47.2 1180 0.6937 0.4905 0.1679 0.3226 0.1774 0.3321
0.6974 48.0 1200 0.6937 0.4886 0.1648 0.3239 0.1761 0.3352
0.6956 48.8 1220 0.6937 0.4893 0.1648 0.3245 0.1755 0.3352
0.7032 49.6 1240 0.6937 0.4943 0.1629 0.3314 0.1686 0.3371

Framework versions