generated_from_trainer

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text_shortening_model_v78

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
1.7828 1.0 30 1.2876 0.8895 0.8866 0.8876 6.7595 16 2 10.6969 1.3497
1.3539 2.0 60 1.1852 0.8938 0.8896 0.8912 6.6074 16 2 10.6442 1.227
1.2004 3.0 90 1.1374 0.8942 0.891 0.8921 6.6896 16 2 10.7534 1.3497
1.1048 4.0 120 1.1165 0.8929 0.8931 0.8926 6.8025 16 2 10.9178 1.7178
1.0322 5.0 150 1.0995 0.8953 0.896 0.8952 6.827 17 2 11.0037 1.9632
0.9687 6.0 180 1.0822 0.894 0.8947 0.8939 6.9043 17 3 11.0147 2.2086
0.9076 7.0 210 1.0858 0.8976 0.8965 0.8966 6.7951 17 2 10.9804 1.8405
0.8602 8.0 240 1.0894 0.8977 0.897 0.8969 6.7902 17 2 10.9558 2.0859
0.8076 9.0 270 1.0945 0.8982 0.8974 0.8974 6.7669 17 2 10.9779 1.9632
0.7739 10.0 300 1.0843 0.8974 0.8959 0.8962 6.719 17 2 10.8564 1.9632
0.7361 11.0 330 1.0982 0.8972 0.8967 0.8965 6.789 17 3 10.9779 1.5951
0.7052 12.0 360 1.0953 0.8977 0.8958 0.8963 6.7055 17 2 10.8798 1.8405
0.6749 13.0 390 1.1098 0.8981 0.8967 0.897 6.7325 17 2 10.908 2.0859
0.6441 14.0 420 1.1158 0.8996 0.8981 0.8984 6.7043 17 2 10.8761 1.5951
0.623 15.0 450 1.1146 0.9009 0.8989 0.8994 6.7006 17 2 10.9104 1.3497
0.604 16.0 480 1.1267 0.902 0.8988 0.9 6.7104 17 2 10.8491 1.3497
0.583 17.0 510 1.1357 0.8999 0.8989 0.8989 6.7706 17 2 10.9767 2.0859
0.5605 18.0 540 1.1513 0.8996 0.899 0.8989 6.7534 17 2 11.0356 2.0859
0.5439 19.0 570 1.1643 0.8993 0.8988 0.8986 6.816 17 2 11.0638 1.8405
0.5281 20.0 600 1.1626 0.8998 0.8985 0.8987 6.7399 16 2 11.0025 1.4724
0.5165 21.0 630 1.1720 0.9002 0.8974 0.8983 6.6417 16 2 10.7816 1.5951
0.5002 22.0 660 1.1834 0.9005 0.8995 0.8996 6.7607 17 2 10.9693 2.2086
0.486 23.0 690 1.2005 0.9027 0.8979 0.8999 6.5853 17 2 10.7264 1.8405
0.4757 24.0 720 1.1916 0.9008 0.8991 0.8995 6.6994 16 2 10.8859 2.0859
0.4679 25.0 750 1.2023 0.8988 0.8985 0.8982 6.7853 16 2 10.984 2.0859
0.4502 26.0 780 1.2108 0.9012 0.8991 0.8997 6.7141 16 2 10.8577 2.3313
0.4452 27.0 810 1.2188 0.9015 0.8987 0.8996 6.6098 14 2 10.7853 1.8405
0.4351 28.0 840 1.2187 0.9005 0.8997 0.8996 6.7681 16 2 10.9877 2.0859
0.4223 29.0 870 1.2319 0.9001 0.8985 0.8989 6.6798 16 2 10.9031 1.7178
0.4186 30.0 900 1.2386 0.8987 0.8982 0.898 6.7509 16 2 10.9718 1.8405
0.4146 31.0 930 1.2399 0.9001 0.8989 0.8991 6.719 16 2 10.9067 1.8405
0.4039 32.0 960 1.2467 0.8997 0.8992 0.899 6.7632 16 2 11.0074 2.0859
0.3998 33.0 990 1.2425 0.8997 0.899 0.8989 6.7497 16 2 10.946 1.8405
0.396 34.0 1020 1.2474 0.9001 0.8982 0.8987 6.6945 16 2 10.8773 1.7178
0.3938 35.0 1050 1.2497 0.9003 0.8977 0.8985 6.6454 16 2 10.8245 0.9816
0.3828 36.0 1080 1.2511 0.9002 0.8978 0.8986 6.6798 16 2 10.8663 1.4724
0.3842 37.0 1110 1.2482 0.9002 0.898 0.8987 6.6883 16 2 10.8883 1.7178
0.391 38.0 1140 1.2507 0.9001 0.8974 0.8983 6.6577 16 2 10.8528 1.3497
0.3796 39.0 1170 1.2529 0.8994 0.8972 0.8979 6.6945 16 2 10.8933 1.5951
0.3807 40.0 1200 1.2534 0.8992 0.8971 0.8977 6.6982 16 2 10.8994 1.4724

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