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x4n4/
bert_conll2003_ner

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BERT NER Model (CoNLL-2003)

📌 Overview

This model is a fine-tuned version of bert-base-cased for the task of Named Entity Recognition (NER).

🎯 Task

Token Classification (Named Entity Recognition)

The model identifies the following entity types:

PER (Person)
ORG (Organization)
LOC (Location)
MISC (Miscellaneous)

📊 Dataset

CoNLL-2003 (English news dataset)
Only NER tags were used (BIO format)

🧠 Model Details

Base model: bert-base-cased
Architecture: Transformer Encoder (BERT)
Fine-tuning: Hugging Face Transformers
Training epochs: 3

📈 Performance

Test set results:

Precision: ~0.90
Recall: ~0.91
F1-score: ~0.91
Accuracy: ~0.98

⚙️ Usage Example

python

from transformers import pipeline

ner = pipeline(
    "ner",
    model="x4n4/bert-conll2003-ner",
    aggregation_strategy="simple"
)

text = "Barack Obama visited Google in New York"
print(ner(text))

x4n4/bert_conll2003_ner