camembert-ner-travel

camembert-ner-travel

CamemBERT-base fine-tuned for Named Entity Recognition on French travel sentences. The model extracts departure city (LOC_ORIGIN) and destination city (LOC_DEST) from user queries.

Model description

This model is part of the Travel Order Resolver project, a pipeline that:

1. Extracts origin/destination from a French sentence (this model)
2. Resolves city names against the SNCF station network
3. Computes the optimal train route (Dijkstra / A*)

Base model: camembert-base Task: Token classification (BIO tagging)

Labels

Training data

Synthetic dataset of French travel sentences generated from SNCF station data. Noise augmentation applied: lowercase, missing accents, typos (≈30% of samples).

Examples:

• "Je veux aller de Paris à Lyon" → Paris = LOC_ORIGIN, Lyon = LOC_DEST
• "un billet depuis marseille pour bordeaux" → noisy variant

Training procedure

Evaluation results

Test set

Validation set

Comparison with other runs

lr=5e-5 converges ~2× faster than lr=2e-5 (train loss 0.057 vs 0.129 at step 1000) with identical final F1.

Usage

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="Aldo26/camembert-ner-travel",
    aggregation_strategy="simple",
)

result = ner("Je voudrais un billet de Nantes pour Strasbourg")
# [
#   {'entity_group': 'LOC_ORIGIN', 'word': 'Nantes', ...},
#   {'entity_group': 'LOC_DEST',   'word': 'Strasbourg', ...}
# ]

Limitations

• Trained on synthetic data — may underperform on very unusual phrasings
• Handles one origin and one destination per sentence
• French only