generated_from_trainer

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

taNER-500-naamapdam-fine-tuned

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
0.37 0.51 1000 0.3241 0.7491 0.7745 0.7616 0.8951
0.3118 1.03 2000 0.3120 0.7631 0.7862 0.7745 0.8998
0.2877 1.54 3000 0.2994 0.7679 0.7918 0.7797 0.9027
0.284 2.06 4000 0.2977 0.7643 0.7960 0.7798 0.9026
0.2631 2.57 5000 0.2900 0.7716 0.8001 0.7856 0.9040
0.259 3.08 6000 0.2958 0.7739 0.8058 0.7895 0.9050
0.2426 3.6 7000 0.2850 0.7795 0.8042 0.7917 0.9072
0.2378 4.11 8000 0.2910 0.7740 0.8069 0.7901 0.9059
0.2231 4.63 9000 0.2878 0.7788 0.8106 0.7944 0.9077
0.2188 5.14 10000 0.2941 0.7752 0.8101 0.7923 0.9087
0.2066 5.66 11000 0.2928 0.7721 0.8147 0.7928 0.9079
0.2008 6.17 12000 0.3048 0.7798 0.8141 0.7966 0.9088
0.1902 6.68 13000 0.2987 0.7834 0.8108 0.7969 0.9099
0.1843 7.2 14000 0.3055 0.7784 0.8137 0.7957 0.9101
0.1775 7.71 15000 0.2991 0.7762 0.8155 0.7953 0.9096
0.1694 8.23 16000 0.3117 0.7876 0.8137 0.8004 0.9120
0.1631 8.74 17000 0.3085 0.7761 0.8210 0.7979 0.9121
0.1585 9.25 18000 0.3144 0.7851 0.8063 0.7955 0.9108
0.1528 9.77 19000 0.3086 0.7834 0.8169 0.7998 0.9124
0.1458 10.28 20000 0.3167 0.7773 0.8208 0.7985 0.9114
0.143 10.8 21000 0.3202 0.7822 0.8134 0.7975 0.9123
0.1368 11.31 22000 0.3299 0.7798 0.8176 0.7983 0.9112
0.1337 11.83 23000 0.3369 0.7857 0.8151 0.8002 0.9131
0.1289 12.34 24000 0.3366 0.7855 0.8148 0.7999 0.9128
0.1257 12.85 25000 0.3316 0.7837 0.8172 0.8001 0.9129
0.122 13.37 26000 0.3415 0.7880 0.8120 0.7998 0.9136
0.1191 13.88 27000 0.3414 0.7867 0.8182 0.8021 0.9139
0.1157 14.4 28000 0.3481 0.7863 0.8174 0.8016 0.9136
0.1153 14.91 29000 0.3466 0.7858 0.8156 0.8004 0.9134

Framework versions