generated_from_trainer

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taNER-500-V2

This model is a fine-tuned version of livinNector/tabert-500 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 Precision Recall F1 Accuracy
0.3751 0.49 1000 0.3876 0.7294 0.7381 0.7337 0.8758
0.3211 0.99 2000 0.3530 0.7603 0.7427 0.7514 0.8851
0.2932 1.48 3000 0.3443 0.7501 0.7757 0.7627 0.8882
0.2884 1.98 4000 0.3404 0.7553 0.7878 0.7712 0.8907
0.268 2.47 5000 0.3241 0.7705 0.7888 0.7795 0.8959
0.2638 2.96 6000 0.3246 0.7823 0.7850 0.7836 0.8954
0.246 3.46 7000 0.3175 0.7769 0.7989 0.7878 0.8999
0.2457 3.95 8000 0.3216 0.7732 0.7934 0.7832 0.8999
0.2253 4.44 9000 0.3180 0.7792 0.7983 0.7887 0.8995
0.2271 4.94 10000 0.3250 0.7868 0.7895 0.7882 0.8996
0.2085 5.43 11000 0.3435 0.7838 0.7967 0.7902 0.8995
0.2091 5.93 12000 0.3300 0.7855 0.7958 0.7906 0.9009
0.1927 6.42 13000 0.3272 0.7771 0.7983 0.7876 0.9017
0.1932 6.91 14000 0.3310 0.7836 0.8060 0.7946 0.9047
0.1777 7.41 15000 0.3377 0.7882 0.8045 0.7963 0.9052
0.1785 7.9 16000 0.3406 0.7812 0.8042 0.7925 0.9036
0.1658 8.4 17000 0.3528 0.7892 0.7992 0.7942 0.9043
0.1651 8.89 18000 0.3419 0.7914 0.8072 0.7992 0.9068
0.1549 9.38 19000 0.3600 0.7931 0.7964 0.7948 0.9045
0.1539 9.88 20000 0.3525 0.7851 0.8091 0.7970 0.9052
0.1449 10.37 21000 0.3634 0.7881 0.7998 0.7939 0.9046
0.1436 10.86 22000 0.3736 0.7916 0.8058 0.7986 0.9069
0.1368 11.36 23000 0.3771 0.7892 0.8020 0.7955 0.9053
0.1347 11.85 24000 0.3800 0.7861 0.8060 0.7959 0.9045
0.1281 12.35 25000 0.3911 0.7852 0.8055 0.7952 0.9059
0.1272 12.84 26000 0.3919 0.7880 0.8005 0.7942 0.9052
0.1217 13.33 27000 0.4021 0.7887 0.7981 0.7934 0.9050
0.1202 13.83 28000 0.3959 0.7845 0.8057 0.7950 0.9056
0.1175 14.32 29000 0.4066 0.7864 0.8031 0.7947 0.9052
0.115 14.81 30000 0.4057 0.7870 0.8040 0.7954 0.9056

Framework versions