generated_from_trainer

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text_shortening_model_v76

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
1.8741 1.0 30 1.3873 0.8846 0.8811 0.8823 6.7558 15 2 10.6282 2.5767
1.4617 2.0 60 1.2781 0.8879 0.8867 0.8868 6.8613 16 2 10.773 0.9816
1.3352 3.0 90 1.2202 0.8908 0.8894 0.8896 6.8503 14 2 10.8245 0.9816
1.2484 4.0 120 1.1879 0.892 0.8902 0.8907 6.7816 17 1 10.7963 1.1043
1.1842 5.0 150 1.1657 0.893 0.8904 0.8913 6.6945 14 2 10.6822 0.6135
1.1263 6.0 180 1.1490 0.8932 0.8921 0.8921 6.8601 17 2 10.8663 1.7178
1.0859 7.0 210 1.1347 0.8909 0.8942 0.8921 7.0663 17 1 11.1975 2.3313
1.0511 8.0 240 1.1219 0.8925 0.8934 0.8925 6.865 17 1 11.0074 1.227
1.0023 9.0 270 1.1118 0.8936 0.8937 0.8931 6.8393 17 1 10.9963 1.7178
0.9795 10.0 300 1.1073 0.8939 0.8929 0.8929 6.7227 17 1 10.8528 0.8589
0.9489 11.0 330 1.1050 0.8932 0.8951 0.8937 6.9337 17 2 11.0969 1.5951
0.9275 12.0 360 1.1026 0.8945 0.8953 0.8945 6.8331 17 2 11.0135 1.4724
0.8829 13.0 390 1.0989 0.8946 0.8957 0.8947 6.8638 17 1 11.038 1.3497
0.8762 14.0 420 1.0975 0.8939 0.8962 0.8946 6.9239 17 1 11.1423 2.0859
0.8559 15.0 450 1.0988 0.8953 0.8953 0.8948 6.8049 16 1 10.9742 1.7178
0.8347 16.0 480 1.0960 0.8963 0.8972 0.8963 6.8233 16 1 11.0258 1.4724
0.8166 17.0 510 1.1009 0.8973 0.8974 0.8969 6.7914 16 2 11.0135 1.227
0.8054 18.0 540 1.1015 0.8957 0.8972 0.896 6.8896 17 1 11.0871 1.9632
0.786 19.0 570 1.1064 0.896 0.897 0.8961 6.8356 16 2 11.038 1.7178
0.7764 20.0 600 1.1000 0.8964 0.8965 0.896 6.7951 16 3 10.9804 1.5951
0.7526 21.0 630 1.1040 0.8961 0.8976 0.8964 6.8663 17 3 11.0748 1.7178
0.7467 22.0 660 1.1051 0.8953 0.8964 0.8954 6.8184 16 3 11.0221 1.5951
0.734 23.0 690 1.1057 0.8965 0.897 0.8963 6.8307 16 2 11.0049 1.5951
0.7268 24.0 720 1.1027 0.8956 0.8973 0.896 6.9301 17 3 11.1153 1.8405
0.718 25.0 750 1.1062 0.8965 0.8971 0.8963 6.8258 16 2 11.016 1.5951
0.7068 26.0 780 1.1058 0.8961 0.8967 0.896 6.816 16 2 11.0061 1.4724
0.6985 27.0 810 1.1120 0.8961 0.8977 0.8965 6.8933 16 2 11.1018 1.9632
0.6831 28.0 840 1.1130 0.8965 0.8968 0.8962 6.8184 16 2 11.0037 1.7178
0.6769 29.0 870 1.1144 0.8973 0.8975 0.897 6.7779 17 2 10.989 1.4724
0.6803 30.0 900 1.1139 0.8976 0.898 0.8974 6.8098 17 2 10.9779 1.5951
0.6618 31.0 930 1.1147 0.8973 0.8978 0.8971 6.8037 17 2 10.9902 1.227
0.6745 32.0 960 1.1157 0.8962 0.897 0.8961 6.8307 16 2 11.0135 1.4724
0.6618 33.0 990 1.1193 0.8963 0.897 0.8962 6.8123 17 2 10.9951 1.3497
0.6572 34.0 1020 1.1223 0.897 0.8977 0.8969 6.8209 16 2 11.0037 1.4724
0.6562 35.0 1050 1.1240 0.8963 0.8971 0.8963 6.854 17 2 11.0196 1.7178
0.6433 36.0 1080 1.1233 0.8969 0.8967 0.8964 6.8049 16 2 10.9632 1.4724
0.6405 37.0 1110 1.1236 0.8974 0.8977 0.8971 6.8245 16 2 11.011 1.5951
0.645 38.0 1140 1.1239 0.8967 0.897 0.8964 6.8135 16 2 10.9902 1.8405
0.6409 39.0 1170 1.1244 0.8967 0.897 0.8964 6.8086 16 2 10.9939 1.5951
0.6371 40.0 1200 1.1244 0.8967 0.8969 0.8964 6.8061 16 2 10.9902 1.5951

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