gliner_wolof_NER

GLiNER Wolof NER

A Named Entity Recognition (NER) model for Wolof language, fine-tuned from urchade/gliner_multi_pii-v1 on the MasakhaNER dataset.

Model Description

This model can identify the following entity types in Wolof text:

• PER - Person names
• ORG - Organizations
• LOC - Locations
• DATE - Dates

Usage

from gliner import GLiNER

# Load the model
model = GLiNER.from_pretrained("Lahad/gliner_wolof_NER")

# Define entity types
labels = ["PER", "ORG", "LOC", "DATE"]

# Predict entities
text = "Ousmane Sonko jàngae na ci Daaray Cheikh Anta Diop ci Dakar."
entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(f"{entity['text']} => {entity['label']} (score: {entity['score']:.2f})")

  → Ousmane Sonko => PER (score: 0.95)
  → Daaray Cheikh Anta Diop => ORG (score: 0.89)
  → Dakar => LOC (score: 0.97)

Training Details

• Base Model: urchade/gliner_multi_pii-v1
• Dataset: MasakhaNER (Wolof subset)
• Training samples: 5,143
• Validation samples: 643
• Epochs: 10
• Learning rate: 5e-6
• Batch size: 16

📊 Dataset

This project uses the MasakhaNER dataset, which provides high-quality NER annotations for 10 African languages including Wolof (wol).

Dataset Split:

• Train: 1,871 samples
• Validation: 267 samples
• Test: 539 samples

Entity Types:

• PER - Person names
• ORG - Organizations
• LOC - Locations
• DATE - Dates

📈 Evaluation Results

Evaluation on the test set:

• 539 sentences/examples
• 505 total annotated entities across these sentences

⚠️ Performance Note

The model was fine-tuned on a relatively limited dataset (MasakhaNER Wolof). Current performance reflects this constraint, particularly for DATE and ORG entity types which have fewer training examples.

Future Improvements:

• Collect and annotate more data in Wolof
• Increase source diversity (newspapers, social media, literature)
• Experiment with data augmentation techniques

With more annotated data, we expect to significantly improve the model's performance.

License

MIT