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***** Test results *****
Thu Sep 22 07:44:16 2022
Task: ner
Model path: bert-base-uncased
Data path: ./data/ud/
Tokenizer: bert-base-uncased
Batch size: 32
Epoch: 7
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 0.7030 0.6225 0.6603 612
DATE 0.7036 0.7177 0.7106 1045
EVENT 0.4133 0.3875 0.4000 80
FAC 0.3661 0.4437 0.4012 151
GPE 0.8721 0.8667 0.8694 1936
LANGUAGE 0.5758 0.2468 0.3455 77
LAW 0.3621 0.3684 0.3652 57
LOC 0.4978 0.5115 0.5045 217
MONEY 0.5849 0.5082 0.5439 61
NORP 0.6927 0.7156 0.7040 422
ORDINAL 0.8035 0.8129 0.8081 171
ORG 0.5158 0.5893 0.5501 857
PERCENT 0.3878 0.5278 0.4471 36
PERSON 0.7476 0.7994 0.7726 1371
PRODUCT 0.2742 0.3469 0.3063 98
QUANTITY 0.3443 0.3962 0.3684 53
SEP] 0.0000 0.0000 0.0000 0
TIME 0.5816 0.6495 0.6137 214
WORK_OF_ART 0.3544 0.2154 0.2679 130
micro avg 0.6680 0.7080 0.6874 7588
macro avg 0.5148 0.5119 0.5073 7588
weighted avg 0.6955 0.7080 0.6998 7588
Special token predictions: 0
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