generated_from_trainer

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text_shortening_model_v77

This model is a fine-tuned version of t5-small 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 Bert precision Bert recall Bert f1-score Average word count Max word count Min word count Average token count % shortened texts with length > 12
1.6557 1.0 30 1.2281 0.8905 0.8844 0.887 6.5926 15 1 10.4699 1.4724
1.2524 2.0 60 1.1200 0.8933 0.8902 0.8913 6.7227 15 2 10.8294 0.9816
1.1024 3.0 90 1.0914 0.8952 0.8931 0.8937 6.7706 16 2 10.811 1.1043
0.9921 4.0 120 1.0805 0.8935 0.8943 0.8935 6.9141 17 2 11.0454 1.3497
0.8865 5.0 150 1.1025 0.8971 0.8949 0.8956 6.7607 16 2 10.9828 1.4724
0.8273 6.0 180 1.1039 0.9005 0.8989 0.8993 6.7583 16 2 10.8982 1.9632
0.7585 7.0 210 1.0975 0.8981 0.8991 0.8982 6.9497 16 3 11.135 2.2086
0.7086 8.0 240 1.1068 0.8989 0.8971 0.8976 6.7374 15 2 10.9411 1.1043
0.6541 9.0 270 1.1340 0.898 0.902 0.8996 7.1239 17 2 11.4258 2.8221
0.6167 10.0 300 1.1316 0.8981 0.8996 0.8984 6.962 16 2 11.1436 2.6994
0.5817 11.0 330 1.1507 0.8984 0.8995 0.8985 6.9264 16 2 11.2466 2.3313
0.547 12.0 360 1.1416 0.899 0.8993 0.8988 6.8601 15 2 11.1865 1.5951
0.5181 13.0 390 1.1775 0.8987 0.8993 0.8986 6.8969 15 3 11.1571 1.8405
0.4874 14.0 420 1.2131 0.8969 0.8999 0.8979 7.0638 16 3 11.3877 2.454
0.461 15.0 450 1.2263 0.9009 0.9001 0.9001 6.8871 15 2 11.1497 1.1043
0.4449 16.0 480 1.2215 0.898 0.8998 0.8984 7.0184 15 2 11.3509 2.2086
0.4148 17.0 510 1.2528 0.8991 0.8997 0.899 6.8748 15 2 11.1939 1.227
0.3967 18.0 540 1.2512 0.8989 0.9006 0.8994 6.9853 15 2 11.3227 1.3497
0.385 19.0 570 1.2683 0.898 0.8992 0.8982 6.9755 17 2 11.3227 2.454
0.3667 20.0 600 1.2804 0.898 0.8982 0.8976 6.8528 15 2 11.1337 1.7178
0.3579 21.0 630 1.2892 0.9006 0.899 0.8993 6.762 15 2 11.0761 1.8405
0.3358 22.0 660 1.3081 0.8983 0.8996 0.8985 6.8957 15 2 11.3166 1.5951
0.3261 23.0 690 1.3189 0.8988 0.8997 0.8988 6.9571 15 2 11.2491 2.6994
0.3222 24.0 720 1.3116 0.8975 0.9 0.8983 7.027 15 2 11.3926 2.9448
0.2977 25.0 750 1.3295 0.8988 0.9005 0.8992 6.9755 15 2 11.308 1.9632
0.2977 26.0 780 1.3368 0.8972 0.9009 0.8986 7.0442 15 2 11.4184 2.5767
0.2795 27.0 810 1.3515 0.9015 0.9025 0.9015 6.9117 15 2 11.2601 2.2086
0.2758 28.0 840 1.3645 0.9018 0.9013 0.9011 6.8466 15 2 11.1264 1.9632
0.2696 29.0 870 1.3675 0.899 0.9002 0.8991 6.9399 15 2 11.2687 1.9632
0.261 30.0 900 1.3896 0.8986 0.901 0.8993 7.0282 17 2 11.3902 2.0859
0.2632 31.0 930 1.3802 0.8971 0.8988 0.8975 6.9546 15 2 11.2528 2.3313
0.2488 32.0 960 1.3919 0.8998 0.9011 0.9 6.9656 15 2 11.2748 2.454
0.2467 33.0 990 1.3973 0.8996 0.9013 0.9 6.9755 15 2 11.3031 2.2086
0.2384 34.0 1020 1.4041 0.8988 0.9001 0.899 6.9865 15 2 11.2785 2.3313
0.2369 35.0 1050 1.4044 0.8986 0.9005 0.8991 6.9595 15 2 11.2785 1.9632
0.2307 36.0 1080 1.4109 0.8985 0.9 0.8988 6.9436 15 2 11.2712 2.0859
0.2285 37.0 1110 1.4118 0.8987 0.8995 0.8987 6.9067 15 2 11.2037 1.7178
0.2294 38.0 1140 1.4171 0.8992 0.9007 0.8995 6.9436 15 2 11.2712 1.9632
0.2294 39.0 1170 1.4188 0.8988 0.9008 0.8994 6.9656 15 2 11.3006 2.2086
0.2261 40.0 1200 1.4183 0.8989 0.9008 0.8994 6.9571 15 2 11.2896 2.0859

Framework versions