BERT Fine-Tuned for Named Entity Recognition (CoNLL-2003)
This model recognizes named entities in English text: People, Organizations,
Locations, and Miscellaneous entities.
Model Details
- Base model: bert-base-cased
- Dataset: CoNLL-2003 (14,041 training sentences from Reuters news)
- Task: Named Entity Recognition (token classification)
- Framework: PyTorch + HuggingFace Transformers
Entity Types
| Label | Meaning | Example |
|---|
| PER | Person names | Barack Obama, Elon Musk |
| ORG | Organizations | Apple Inc., United Nations |
| LOC | Locations | New York, Mount Everest |
| MISC | Miscellaneous | English, FIFA World Cup |
Performance (CoNLL-2003 Test Set)
| Metric | Score |
|---|
| F1 Score | 0.9116 |
| Precision | 0.9041 |
| Recall | 0.9192 |
| Accuracy | 0.9827 |
How to Use
from transformers import pipeline
# Load the model
ner = pipeline(
"token-classification",
model="samandar1105/named_entity-recognition",
aggregation_strategy="simple"
)
# Run inference
result = ner("Elon Musk founded SpaceX in Hawthorne, California.")
print(result)
# [
# {'entity_group': 'PER', 'word': 'Elon Musk', 'score': 0.998},
# {'entity_group': 'ORG', 'word': 'SpaceX', 'score': 0.997},
# {'entity_group': 'LOC', 'word': 'Hawthorne', 'score': 0.995},
# {'entity_group': 'LOC', 'word': 'California', 'score': 0.994},
# ]
Training Details
- Learning rate: 2e-5
- Epochs: 4
- Batch size: 16
- Max sequence length: 128
- Warmup ratio: 0.1
- Weight decay: 0.01
- Label alignment: First-subword strategy with -100 for continuation subwords
- Evaluation: seqeval (entity-level strict span matching)