***** Test results *****
Thu Sep 22 06:41:21 2022
Task: ner
Model path: bert-base-uncased
Data path: ./data/ud/
Tokenizer: bert-base-uncased
Batch size: 32
Epoch: 6
Learning rate: 2e-05
LR Decay End Factor: 0.3LR Decay End Epoch: 5Sequence length: 96
Training: True
Num Threads: 24
Num Sentences: 0
Max Grad Norm: 0.0
Use GNN: False
Syntax graph style: dep
Use label weights: False
Clip value: 50
              precision    recall  f1-score   support

    CARDINAL     0.7133    0.6503    0.6803       612
        DATE     0.6922    0.7254    0.7084      1045
       EVENT     0.4429    0.3875    0.4133        80
         FAC     0.3390    0.3974    0.3659       151
         GPE     0.8456    0.8714    0.8583      1936
    LANGUAGE     0.5135    0.2468    0.3333        77
         LAW     0.4130    0.3333    0.3689        57
         LOC     0.5934    0.4977    0.5414       217
       MONEY     0.5370    0.4754    0.5043        61
        NORP     0.6211    0.7536    0.6809       422
     ORDINAL     0.8208    0.8304    0.8256       171
         ORG     0.5289    0.5869    0.5564       857
     PERCENT     0.3333    0.4722    0.3908        36
      PERSON     0.7192    0.7885    0.7523      1371
     PRODUCT     0.2705    0.3367    0.3000        98
    QUANTITY     0.3485    0.4340    0.3866        53
        SEP]     0.0000    0.0000    0.0000         0
        TIME     0.6071    0.6355    0.6210       214
 WORK_OF_ART     0.3000    0.2538    0.2750       130

   micro avg     0.6487    0.7110    0.6784      7588
   macro avg     0.5073    0.5093    0.5033      7588
weighted avg     0.6821    0.7110    0.6946      7588

 Special token predictions: 0