Ner Roberta Base Ontonotesv5 Englishv4

🚀 (NER) roberta-base : conll2012_ontonotesv5-english-v4

This roberta-base NER model is fine - tuned on the conll2012_ontonotesv5 version english-v4 dataset, providing powerful named - entity recognition capabilities.

🚀 Quick Start

This roberta-base NER model was finetuned on conll2012_ontonotesv5 version english-v4 dataset.
Check out NER-System Repository for more information.

✨ Features

Dataset

• conll2012_ontonotesv5
  • Language : English
  • Version : v4

Evaluation

• Precision: 88.88
• Recall: 90.69
• F1-Score: 89.78

check out this eval.log file for evaluation metrics and classification report.

                precision    recall  f1-score   support

    CARDINAL       0.84      0.85      0.85       935
        DATE       0.85      0.90      0.87      1602
       EVENT       0.67      0.76      0.71        63
         FAC       0.74      0.72      0.73       135
         GPE       0.97      0.96      0.96      2240
    LANGUAGE       0.83      0.68      0.75        22
         LAW       0.66      0.62      0.64        40
         LOC       0.74      0.80      0.77       179
       MONEY       0.85      0.89      0.87       314
        NORP       0.93      0.96      0.95       841
     ORDINAL       0.81      0.89      0.85       195
         ORG       0.90      0.91      0.91      1795
     PERCENT       0.90      0.92      0.91       349
      PERSON       0.95      0.95      0.95      1988
     PRODUCT       0.74      0.83      0.78        76
    QUANTITY       0.76      0.80      0.78       105
        TIME       0.62      0.67      0.65       212
 WORK_OF_ART       0.58      0.69      0.63       166

   micro avg       0.89      0.91      0.90     11257
   macro avg       0.80      0.82      0.81     11257
weighted avg       0.89      0.91      0.90     11257

💻 Usage Examples

Basic Usage

from transformers import pipeline

ner_pipeline = pipeline(
    'token-classification', 
    model=r'djagatiya/ner-roberta-base-ontonotesv5-englishv4',
    aggregation_strategy='simple'
)

Advanced Usage

TEST 1

ner_pipeline("India is a beautiful country")

# Output
[{'entity_group': 'GPE',
  'score': 0.99186057,
  'word': ' India',
  'start': 0,
  'end': 5}]

TEST 2

ner_pipeline("On September 1st George won 1 dollar while watching Game of Thrones.")

# Output
[{'entity_group': 'DATE',
  'score': 0.99720246,
  'word': ' September 1st',
  'start': 3,
  'end': 16},
 {'entity_group': 'PERSON',
  'score': 0.99071586,
  'word': ' George',
  'start': 17,
  'end': 23},
 {'entity_group': 'MONEY',
  'score': 0.9872978,
  'word': ' 1 dollar',
  'start': 28,
  'end': 36},
 {'entity_group': 'WORK_OF_ART',
  'score': 0.9946732,
  'word': ' Game of Thrones',
  'start': 52,
  'end': 67}]