generated_from_trainer

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taNER-1k-V2

This model is a fine-tuned version of livinNector/tabert-1k 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
0.3724 0.49 1000 0.3865 0.7280 0.7372 0.7326 0.8758
0.3199 0.99 2000 0.3516 0.7524 0.7561 0.7543 0.8858
0.2911 1.48 3000 0.3436 0.7543 0.7765 0.7653 0.8906
0.2867 1.98 4000 0.3391 0.7522 0.7908 0.7710 0.8909
0.2654 2.47 5000 0.3262 0.7696 0.7845 0.7770 0.8961
0.2616 2.96 6000 0.3294 0.7784 0.7800 0.7792 0.8954
0.2422 3.46 7000 0.3191 0.7779 0.7934 0.7856 0.8999
0.2422 3.95 8000 0.3272 0.7735 0.7962 0.7847 0.8985
0.2208 4.44 9000 0.3252 0.7811 0.7952 0.7881 0.9012
0.2227 4.94 10000 0.3220 0.7789 0.7993 0.7890 0.9026
0.204 5.43 11000 0.3413 0.7904 0.7894 0.7899 0.9007
0.2036 5.93 12000 0.3329 0.7810 0.7984 0.7896 0.9009
0.1874 6.42 13000 0.3362 0.7872 0.7986 0.7929 0.9033
0.1877 6.91 14000 0.3414 0.7764 0.8029 0.7894 0.9013
0.172 7.41 15000 0.3463 0.7871 0.7997 0.7933 0.9032
0.1729 7.9 16000 0.3441 0.7863 0.8001 0.7931 0.9034
0.159 8.4 17000 0.3625 0.7856 0.7970 0.7912 0.9019
0.1585 8.89 18000 0.3575 0.7867 0.7980 0.7923 0.9030
0.1485 9.38 19000 0.3761 0.7850 0.7965 0.7907 0.9029
0.1468 9.88 20000 0.3658 0.7874 0.8019 0.7946 0.9037
0.1378 10.37 21000 0.3835 0.7851 0.8039 0.7944 0.9042
0.1364 10.86 22000 0.3852 0.7861 0.8019 0.7940 0.9043
0.1294 11.36 23000 0.3906 0.7854 0.7973 0.7913 0.9038
0.1277 11.85 24000 0.3947 0.7875 0.7988 0.7931 0.9030
0.1207 12.35 25000 0.4082 0.7841 0.7997 0.7918 0.9035
0.1199 12.84 26000 0.4137 0.7888 0.7993 0.7940 0.9049
0.1144 13.33 27000 0.4155 0.7875 0.7996 0.7935 0.9046
0.113 13.83 28000 0.4177 0.7840 0.8053 0.7945 0.9046
0.1103 14.32 29000 0.4280 0.7867 0.8021 0.7943 0.9042
0.1078 14.81 30000 0.4253 0.7866 0.8029 0.7947 0.9049

Framework versions