readme: ---
  
  tags:
  - token-classification
  task_ids:
  - named-entity-recognition
  datasets:
  - djagatiya/ner-ontonotes-v5-eng-v4
  widget:
  - text: "On September 1st George won 1 dollar while watching Game of Thrones."
  ---
  
  # (NER) bert-base-cased : conll2012_ontonotesv5-english-v4
  
  This `bert-base-cased` NER model was finetuned on `conll2012_ontonotesv5` version `english-v4` dataset. <br>
  Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information.
  
  ## Evaluation
  - Precision: 87.85
  - Recall: 89.63
  - F1-Score: 88.73
  
  > check out this [eval.log](eval.log) file for evaluation metrics and classification report.
  
  ```
                 precision    recall  f1-score   support
  
      CARDINAL       0.86      0.87      0.86       935
          DATE       0.84      0.88      0.86      1602
         EVENT       0.65      0.67      0.66        63
           FAC       0.69      0.71      0.70       135
           GPE       0.97      0.93      0.95      2240
      LANGUAGE       0.76      0.73      0.74        22
           LAW       0.54      0.55      0.54        40
           LOC       0.73      0.80      0.76       179
         MONEY       0.87      0.90      0.88       314
          NORP       0.93      0.96      0.94       841
       ORDINAL       0.80      0.87      0.83       195
           ORG       0.88      0.90      0.89      1795
       PERCENT       0.88      0.90      0.89       349
        PERSON       0.94      0.95      0.94      1988
       PRODUCT       0.62      0.76      0.69        76
      QUANTITY       0.74      0.81      0.77       105
          TIME       0.61      0.67      0.64       212
   WORK_OF_ART       0.56      0.66      0.61       166
  
     micro avg       0.88      0.90      0.89     11257
     macro avg       0.77      0.81      0.79     11257
  weighted avg       0.88      0.90      0.89     11257
  ```