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:

Split	Sentences	Description
Train	59,900	Sentences containing administrative place names
Validation	14,758	Used for hyperparameter tuning
Test	11,594	Held-out set with ~20% unseen entities per level

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

Parameter	Value
Base model	camembert-base
Learning rate	5e-5
Batch size	32
Epochs	70
Weight decay	0.01
Optimizer	AdamW
Frozen layers	Embedding layers only
Trainable parameters	85,062,923 / 110,039,819 (77.3%)

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:

Entity Type	Precision	Recall	F1-Score	Support
Country	0.99	0.99	0.99	4,648
Region	1.00	0.99	0.99	1,433
Province	0.99	0.98	0.99	541
Department	0.99	0.99	0.99	6,744
Village	0.94	0.93	0.94	3,236
Micro avg	0.98	0.98	0.98	16,602

Comparison with Baselines

Tested on 1,000 manually annotated news articles:

Model	Precision	Recall	F1-Score
Baseline CamemBERT (no fine-tuning)	0.41	0.81	0.55
GLiNER (zero-shot)	0.66	0.63	0.65

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: charles.ngom@inrae.fr
• Landy Rajaonarivo: landy.rajaonarivo@inrae.fr
• Sarah Valentin: sarah.valentin@cirad.fr
• Maguelonne Teisseire: maguelonne.teisseire@inrae.fr

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.