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library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# 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)) |