generated_from_trainer

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text_shortening_model_v69

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
1.6663 1.0 37 1.2012 0.8867 0.8851 0.8853 6.2943 15 1 10.2032 0.7007
1.228 2.0 74 1.1078 0.8922 0.8923 0.8918 6.3453 16 2 10.2863 1.5015
1.0592 3.0 111 1.0686 0.8922 0.8955 0.8933 6.5656 16 2 10.5856 0.8008
0.9195 4.0 148 1.0606 0.8958 0.8952 0.895 6.3323 16 1 10.3564 0.9009
0.8261 5.0 185 1.0630 0.897 0.8972 0.8967 6.3413 16 2 10.4134 0.7007
0.7476 6.0 222 1.0865 0.8946 0.8987 0.8962 6.5626 16 2 10.6527 1.1011
0.6731 7.0 259 1.1104 0.8962 0.8957 0.8955 6.3073 16 2 10.3063 0.9009
0.6166 8.0 296 1.1198 0.8976 0.9003 0.8985 6.4505 16 2 10.5255 0.8008
0.5618 9.0 333 1.1591 0.8959 0.9007 0.8978 6.5355 16 2 10.6957 1.7017
0.5272 10.0 370 1.1745 0.8982 0.9023 0.8998 6.5586 16 1 10.7227 1.7017
0.4796 11.0 407 1.1884 0.8971 0.899 0.8976 6.4244 16 1 10.5215 1.001
0.452 12.0 444 1.2263 0.9009 0.9004 0.9002 6.2342 16 1 10.3213 0.8008
0.4163 13.0 481 1.2370 0.8979 0.8987 0.8978 6.3113 16 1 10.3564 1.2012
0.3837 14.0 518 1.2830 0.8986 0.902 0.8998 6.4725 16 1 10.5786 1.7017
0.3544 15.0 555 1.2913 0.8991 0.8997 0.8989 6.3524 16 1 10.3984 1.001
0.3319 16.0 592 1.3335 0.8977 0.8987 0.8977 6.3423 16 2 10.4354 1.2012
0.313 17.0 629 1.3357 0.8996 0.9014 0.9 6.3854 15 2 10.5445 1.001
0.3005 18.0 666 1.3610 0.8999 0.8994 0.8991 6.1962 15 2 10.2753 0.6006
0.2789 19.0 703 1.3865 0.8991 0.9028 0.9005 6.4314 16 2 10.6406 1.001
0.2706 20.0 740 1.3929 0.8983 0.9015 0.8994 6.4244 15 2 10.5986 0.9009
0.2476 21.0 777 1.4228 0.8998 0.9004 0.8996 6.3023 16 2 10.4264 0.9009
0.2434 22.0 814 1.4307 0.8985 0.9002 0.8988 6.3223 16 2 10.4094 0.6006
0.2327 23.0 851 1.4522 0.8977 0.8995 0.8981 6.3473 16 2 10.4885 0.7007
0.2164 24.0 888 1.4538 0.8995 0.9006 0.8995 6.3203 16 2 10.4014 0.8008
0.2123 25.0 925 1.4741 0.8979 0.9006 0.8987 6.3934 16 2 10.6116 0.7007
0.2054 26.0 962 1.5038 0.8971 0.9006 0.8984 6.4314 16 2 10.6106 0.7007
0.1996 27.0 999 1.4962 0.8982 0.9015 0.8994 6.4525 16 2 10.6316 0.9009
0.1881 28.0 1036 1.5320 0.8993 0.9021 0.9002 6.4054 15 2 10.5876 0.7007
0.1814 29.0 1073 1.5209 0.8978 0.9007 0.8987 6.4535 16 2 10.6116 0.9009
0.1738 30.0 1110 1.5377 0.9003 0.9022 0.9008 6.3784 16 2 10.5315 0.7007
0.1761 31.0 1147 1.5387 0.8996 0.9021 0.9004 6.3744 16 2 10.5946 0.8008
0.1632 32.0 1184 1.5566 0.8995 0.9013 0.8999 6.3654 16 2 10.5105 1.001
0.1613 33.0 1221 1.5549 0.8994 0.9019 0.9001 6.3944 16 2 10.5696 1.001
0.1555 34.0 1258 1.5728 0.8993 0.9021 0.9002 6.4054 16 2 10.5936 1.1011
0.1604 35.0 1295 1.5671 0.8987 0.9018 0.8998 6.4274 16 2 10.5836 1.1011
0.1525 36.0 1332 1.5748 0.8991 0.901 0.8995 6.3664 16 2 10.5205 1.1011
0.1534 37.0 1369 1.5709 0.8993 0.9009 0.8996 6.3493 16 2 10.4775 0.8008
0.1478 38.0 1406 1.5777 0.8986 0.9008 0.8992 6.3894 16 2 10.5095 0.8008
0.1423 39.0 1443 1.5815 0.8986 0.9008 0.8992 6.3934 16 2 10.5295 0.8008
0.1474 40.0 1480 1.5824 0.8984 0.9007 0.899 6.3944 16 2 10.5305 0.8008

Framework versions