generated_from_trainer

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text_shortening_model_v70

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
2.1122 1.0 37 1.5033 0.8698 0.8713 0.87 6.6056 16 0 10.4705 3.003
1.5423 2.0 74 1.3536 0.8779 0.8809 0.8788 6.6286 16 2 10.5696 1.8018
1.4111 3.0 111 1.2926 0.8805 0.8849 0.8821 6.6446 16 2 10.5586 2.002
1.3269 4.0 148 1.2394 0.8852 0.8889 0.8865 6.5636 15 2 10.5175 1.4014
1.2566 5.0 185 1.2094 0.884 0.8889 0.8859 6.6787 16 2 10.6226 2.2022
1.1991 6.0 222 1.1820 0.8848 0.8899 0.8868 6.6747 16 2 10.6266 2.3023
1.1567 7.0 259 1.1627 0.8849 0.8916 0.8878 6.7908 16 2 10.7067 2.002
1.1126 8.0 296 1.1560 0.8866 0.8936 0.8896 6.7638 16 2 10.7598 2.1021
1.0722 9.0 333 1.1501 0.889 0.8944 0.8912 6.6807 16 2 10.6456 2.1021
1.0455 10.0 370 1.1384 0.8876 0.8946 0.8906 6.7117 16 2 10.7317 2.002
1.0111 11.0 407 1.1220 0.8907 0.8951 0.8924 6.5706 16 2 10.5425 1.5015
0.9804 12.0 444 1.1243 0.8912 0.8963 0.8933 6.5766 16 2 10.5836 1.6016
0.9509 13.0 481 1.1256 0.8898 0.8959 0.8924 6.6226 16 2 10.6366 1.9019
0.9295 14.0 518 1.1206 0.8896 0.8972 0.8929 6.7287 16 2 10.7788 2.3023
0.9137 15.0 555 1.1172 0.8917 0.895 0.8929 6.5175 16 2 10.4885 1.7017
0.9016 16.0 592 1.1218 0.8902 0.8978 0.8935 6.7237 16 2 10.7608 1.9019
0.8654 17.0 629 1.1171 0.8913 0.8966 0.8934 6.5946 16 2 10.6166 2.002
0.8562 18.0 666 1.1194 0.8916 0.8973 0.8939 6.6186 16 2 10.6607 2.002
0.8337 19.0 703 1.1235 0.8921 0.8989 0.895 6.7027 16 2 10.7658 1.8018
0.8323 20.0 740 1.1153 0.8914 0.8977 0.8941 6.6607 16 2 10.6917 1.8018
0.8146 21.0 777 1.1142 0.8929 0.8966 0.8943 6.5315 16 2 10.5536 1.7017
0.8053 22.0 814 1.1211 0.8923 0.8983 0.8948 6.6747 16 2 10.7407 2.1021
0.7858 23.0 851 1.1164 0.8928 0.8969 0.8944 6.5355 16 2 10.5706 1.6016
0.7795 24.0 888 1.1157 0.8942 0.8983 0.8958 6.5556 16 2 10.6016 1.7017
0.7603 25.0 925 1.1231 0.8935 0.8984 0.8955 6.5826 16 2 10.6486 1.7017
0.7709 26.0 962 1.1231 0.8932 0.8979 0.8951 6.6006 16 2 10.6396 2.002
0.7528 27.0 999 1.1217 0.8929 0.8973 0.8946 6.5506 16 2 10.5876 1.9019
0.7436 28.0 1036 1.1222 0.8933 0.8991 0.8957 6.5696 16 2 10.6587 2.002
0.7406 29.0 1073 1.1284 0.8928 0.898 0.8949 6.5636 16 2 10.6406 1.9019
0.7326 30.0 1110 1.1278 0.8932 0.8988 0.8956 6.6216 16 2 10.6977 1.9019
0.7253 31.0 1147 1.1273 0.893 0.8986 0.8953 6.5856 16 2 10.6537 1.7017
0.7245 32.0 1184 1.1274 0.8935 0.8985 0.8955 6.5586 16 2 10.6216 1.7017
0.7082 33.0 1221 1.1281 0.8935 0.8987 0.8956 6.5826 16 2 10.6597 1.8018
0.7011 34.0 1258 1.1265 0.8936 0.8985 0.8956 6.5696 16 2 10.6517 1.8018
0.7099 35.0 1295 1.1284 0.8935 0.8987 0.8956 6.5656 16 2 10.6396 1.6016
0.7064 36.0 1332 1.1281 0.8939 0.8992 0.896 6.5696 16 2 10.6476 1.8018
0.7045 37.0 1369 1.1286 0.8932 0.8978 0.895 6.5556 16 2 10.5976 1.8018
0.702 38.0 1406 1.1275 0.8934 0.8984 0.8954 6.5776 16 2 10.6296 1.8018
0.6952 39.0 1443 1.1284 0.8933 0.8981 0.8952 6.5586 16 2 10.6146 1.8018
0.6928 40.0 1480 1.1285 0.8931 0.8981 0.8951 6.5696 16 2 10.6276 1.8018

Framework versions