BF_NER

BF_NER: Burkina Faso Named Entity Recognition

Fine-tuned CamemBERT model for extracting geographic entities from French text, specialized for the Burkina Faso administrative hierarchy.

Model Description

This model is a fine-tuned version of camembert-base for Named Entity Recognition (NER) of geographic locations in French news articles. It recognizes five administrative levels specific to Burkina Faso:

• Country: Burkina Faso, regional country references
• Region: 13 regions (e.g., Centre, Hauts-Bassins, Sahel)
• Province: 45 provinces (e.g., Kadiogo, Houet, Soum)
• Department: 351 departments (e.g., Ouagadougou, Bobo-Dioulasso, Koudougou)
• Village: 7,936 villages (e.g., Pabre, Koubri, Sya)

Model Details

• Developed by: Charles Abdoulaye Ngom, Landy Rajaonarivo, Sarah Valentin, Maguelonne Teisseire
• Model type: Token Classification (NER)
• Language: French
• Base model: camembert-base
• License: MIT
• Paper: Spatio-Temporal Knowledge Graph from Unstructured Texts: A Multi-Scale Approach for Food Security Monitoring (AGILE 2026)
• DOI (model): 10.57967/hf/7766
• DOI (datasets): 10.57967/hf/7767

Intended Use

Primary Use Cases

• Food security monitoring: Extract location mentions from news articles to track food security events
• Geographic information extraction: Identify and classify locations in French West African texts
• Multi-scale spatial analysis: Enable analysis from village to country level
• Crisis mapping: Support humanitarian and development organizations in monitoring regional events

Out-of-Scope Use

• This model is NOT suitable for:
  • Named entity recognition in other countries (limited to Burkina Faso administrative entities)
  • Non-French languages
  • Person, organization, or other non-location entity types
  • Real-time applications without additional validation

Training Data

The model was trained using distant supervision on 15,000 French news articles from 2009:

Data Source: Official gazetteer from the 2022 Statistical Yearbook of Territorial Administration, Burkina Faso Ministry of Territorial Administration.

Annotation Scheme: BIO tagging (Begin-Inside-Outside)

• B-{type}: Beginning of an entity
• I-{type}: Inside/continuation of an entity
• O: Outside any entity

Entity types: country, region, province, departement, village

Training Procedure

Training Hyperparameters

Training Environment

• Hardware: NVIDIA RTX 3090 GPU
• Training time: ~2-3 hours
• Framework: Transformers 4.45.2, PyTorch 2.5.1

Evaluation

Test Set Performance

Evaluated on a held-out test set containing ~20% unseen entities at each hierarchical level:

Comparison with Baselines

Tested on 1,000 manually annotated news articles:

Usage

Installation

pip install transformers torch

Basic Usage

from transformers import CamembertTokenizerFast, CamembertForTokenClassification
import torch

# Load model and tokenizer
tokenizer = CamembertTokenizerFast.from_pretrained("CharlesAbdoulaye/BF_NER")
model = CamembertForTokenClassification.from_pretrained("CharlesAbdoulaye/BF_NER")
model.eval()

# Entity labels
label_list = [
    "O",
    "B-country", "I-country",
    "B-region", "I-region",
    "B-departement", "I-departement",
    "B-province", "I-province",
    "B-village", "I-village"
]
id2label = {i: label for i, label in enumerate(label_list)}

# Example text with all 5 entity types
text = "La crise alimentaire au Burkina Faso a frappe la region des Hauts-Bassins et la province du Houet. La ville de Bobo-Dioulasso et le village de Sya sont particulierement touches."

# Tokenize with offset mapping for span extraction
inputs = tokenizer(text, return_tensors="pt", truncation=True, return_offsets_mapping=True)
offset_mapping = inputs.pop("offset_mapping")[0].tolist()

with torch.no_grad():
    outputs = model(**inputs)

preds = torch.argmax(outputs.logits, dim=2)[0].tolist()
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])

# Reconstruct entities with character spans
entities, current = [], None
for idx, (pred_id, (start, end)) in enumerate(zip(preds, offset_mapping)):
    if start == 0 and end == 0:
        if current: entities.append(current); current = None
        continue
    label = id2label[pred_id]
    is_subword = not tokens[idx].startswith("\u2581")
    if label.startswith("B-"):
        if current: entities.append(current)
        current = {"type": label[2:], "start": start, "end": end}
    elif label.startswith("I-") or (is_subword and current):
        if current: current["end"] = end
    else:
        if current: entities.append(current); current = None
if current: entities.append(current)

# Merge consecutive same-type entities (handles hyphenated names)
merged = []
for ent in entities:
    if merged and merged[-1]["type"] == ent["type"]:
        gap = text[merged[-1]["end"]:ent["start"]]
        if gap in ("", "-", " -", "- "):
            merged[-1]["end"] = ent["end"]; continue
    merged.append(dict(ent))

for ent in merged:
    ent["text"] = text[ent["start"]:ent["end"]].rstrip(".,;:!?")
    print(f'{ent["text"]:20s} | {ent["type"]:15s} | span=({ent["start"]}, {ent["end"]})')

Expected output:

Burkina Faso         | country         | span=(24, 36)
Hauts-Bassins        | region          | span=(60, 73)
Houet                | province        | span=(92, 97)
Bobo-Dioulasso       | departement     | span=(111, 125)
Sya                  | village         | span=(143, 146)

Limitations

1. Geographic scope: The model is trained exclusively on Burkina Faso administrative entities. It will not recognize locations from other countries with the same accuracy.

2. Temporal coverage: Training data is from 2009. Administrative boundaries and place names may have changed since then.

3. Homonyms: Village names that exist in multiple provinces may be ambiguous. The model does not perform disambiguation based on context.

4. Spelling variations: West African toponyms exhibit significant spelling variability (e.g., "Ouagadougou" vs "Ouaga"). The model handles common variations but may miss rare spellings not present in training data.

5. Language: Only French text is supported. The model will not work on texts in local languages (Mooré, Dioula, Fulfulde, etc.).

Ethical Considerations

Potential Biases

• Media coverage bias: Urban areas (especially Ouagadougou and Bobo-Dioulasso) are overrepresented in news articles compared to rural villages.
• Administrative changes: Administrative boundaries and names may have changed since the 2022 gazetteer was published.
• Language bias: French-language bias excludes indigenous language place names and local toponyms.

Responsible Use

This model is intended for research and humanitarian applications:

• ✅ Food security monitoring and early warning systems
• ✅ Geographic information extraction for development organizations
• ✅ Academic research on crisis mapping and NLP

Citation

If you use this model in your research, please cite:

@article{ngom2026stkgfs,
  title={Spatio-Temporal Knowledge Graph from Unstructured Texts: A Multi-Scale Approach for Food Security Monitoring},
  author={Ngom, Charles Abdoulaye and Rajaonarivo, Landy and Valentin, Sarah and Teisseire, Maguelonne},
  journal={AGILE: GIScience Series},
  year={2026},
  
}

Contact

For questions about this model:

• Charles Abdoulaye Ngom: [email protected]
• Landy Rajaonarivo: [email protected]
• Sarah Valentin: [email protected]
• Maguelonne Teisseire: [email protected]

Acknowledgments

• Administrative hierarchy data: 2022 Statistical Yearbook, Burkina Faso Ministry of Territorial Administration
• Base model: CamemBERT (Martin et al., 2020)
• Geographic enrichment: Wikidata

License

This model is released under the MIT License. See the LICENSE file for details.