BERT-large-cased fine-tuned on OntoNotes 5.0

This model is a fine-tuned version of google-bert/bert-large-cased on the English subset of the OntoNotes 5.0 (CoNLL-2012) dataset. It is optimized for high-precision Named Entity Recognition (NER) across 18 entity categories.

📊 Performance

The model achieves the following results on the OntoNotes 5.0 test set:

🛠 Training Details

• Architecture: BertForTokenClassification (Large)
• Tokenizer: BertTokenizerFast (using is_split_into_words=True)
• Epochs: 5
• Learning Rate: 1e-5
• Batch Size: 4 per device (2x V100 GPUs)
• Gradient Accumulation: 4 steps (Effective Batch Size = 32)
• Max Sequence Length: 128
• Weight Decay: 0.01
• Mixed Precision (FP16): Enabled

📂 Labels Mapping

The model identifies 18 entity types from OntoNotes 5.0: CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART.

📂 Project Assets

• GitHub Repository: https://github.com/Learnrr/ontonotes5_ner_evaluation.git

🚀 Usage

from transformers import pipeline

model_checkpoint = "learnrr/bert-large-ontonotes5-ner"
token_classifier = pipeline(
    "token-classification", 
    model=model_checkpoint, 
    aggregation_strategy="simple"
)

text = "The United Nations is headquartered in New York City."
results = token_classifier(text)

for entity in results:
    print(f"Entity: {entity['word']} | Label: {entity['entity_group']} | Score: {entity['score']:.4f}")