LahadMbacke/NER_WOLOF

# NER Wolof - Named Entity Recognition for Wolof Language A fine-tuned **GLiNER** model for Named Entity Recognition (NER) in Wolof, a language spoken primarily in Senegal, Gambia, and Mauritania. Fine-tuned on the **MasakhaNER** dataset from Hugging Face. ## 🎯 Project Overview This project provides: - A fine-tuned GLiNER model for Wolof NER - Training scripts to reproduce or improve the model ## 🤗 Fine-tuned Model The fine-tuned model is available on Hugging Face Hub: 👉 **Lahad/gliner_wolof_NER** ### Quick Usage ```python from gliner import GLiNER # Load the fine-tuned model model = GLiNER.from_pretrained("Lahad/gliner_wolof_NER") # Predict entities text = "Ousmane Sonko jàngae na ci Daaray Cheikh Anta Diop ci Dakar." labels = ["PER", "ORG", "LOC", "DATE"] entities = model.predict_entities(text, labels, threshold=0.5) for entity in entities: print(f"{entity['text']} => {entity['label']} (score: {entity['score']:.2f})") ``` **Output:** ``` Ousmane Sonko => PER (score: 0.95) Daaray Cheikh Anta Diop => ORG (score: 0.89) Dakar => LOC (score: 0.97) ``` ## 📊 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 | Entity Type | Precision | Recall | F1-Score | Support | |-------------|-----------|--------|----------|---------| | **DATE** | 30.77% | 22.86% | 26.23% | 70 | | **LOC** | 76.75% | 84.95% | 80.65% | 206 | | **ORG** | 41.89% | 56.36% | 48.06% | 55 | | **PER** | 53.02% | 70.69% | 60.59% | 174 | | **GLOBAL** | **58.87%**| **68.32%** | **63.24%** | 505 | ### ⚠️ …