YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
DistilBERT Query Classifier
Modèle de classification binaire pour distinguer les requêtes RAG des demandes d'envoi de messages.
Utilisation
from transformers import pipeline
# Charger le modèle
classifier = pipeline("text-classification", model="your-username/distilbert-query-classifier")
# Classifier une requête
result = classifier("What are the prerequisites for the machine learning course?")
print(result)
# [{'label': 'question_rag', 'score': 0.92}]
Classes
- question_rag (0): Questions nécessitant une recherche RAG
- send_message (1): Demandes d'envoi de messages
Exemples
queries = [
"What topics are covered in the Python course?", # → question_rag
"Send a message to John about the meeting", # → send_message
]
results = classifier(queries)
Détails techniques
- Modèle: distilbert-base-uncased
- Dataset: 98 exemples (50/50 split)
- Accuracy: 93% sur test set
- Couches entraînées: 2 dernières couches + classifier
- Epochs: 10
Limitations
- Petit dataset d'entraînement (98 exemples)
- Anglais uniquement
- Classification binaire seulement
- Downloads last month
- -
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support