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***** Test results *****
Thu Sep 22 09:50:02 2022
Task: ner
Model path: bert-base-uncased
Data path: ./data/ud/
Tokenizer: bert-base-uncased
Batch size: 32
Epoch: 9
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 0.6809 0.6520 0.6661 612
DATE 0.6870 0.7225 0.7043 1045
EVENT 0.3977 0.4375 0.4167 80
FAC 0.3404 0.4238 0.3776 151
GPE 0.8799 0.8549 0.8672 1936
LANGUAGE 0.4906 0.3377 0.4000 77
LAW 0.4062 0.4561 0.4298 57
LOC 0.5000 0.5023 0.5011 217
MONEY 0.5161 0.5246 0.5203 61
NORP 0.6817 0.7512 0.7148 422
ORDINAL 0.8276 0.8421 0.8348 171
ORG 0.5455 0.5741 0.5594 857
PERCENT 0.5476 0.6389 0.5897 36
PERSON 0.7531 0.7943 0.7732 1371
PRODUCT 0.2937 0.4286 0.3485 98
QUANTITY 0.3492 0.4151 0.3793 53
SEP] 0.0000 0.0000 0.0000 0
TIME 0.5748 0.6822 0.6239 214
WORK_OF_ART 0.2963 0.2462 0.2689 130
micro avg 0.6736 0.7127 0.6926 7588
macro avg 0.5141 0.5413 0.5250 7588
weighted avg 0.6958 0.7127 0.7033 7588
Special token predictions: 0
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