🎬 DistilBERT Intent Classifier for Movie & TV Assistant
This model is a fine-tuned DistilBERT-based intent classifier for a conversational movie recommendation assistant. It classifies user queries into one of three intents:
generic– general small talk or casual movie mentionsrecommendation– requests for movie or TV suggestionsfactual– questions about movie facts, cast, streaming availability, etc.
🧠 Model Details
- Base model:
distilbert-base-uncased - Training data: 6,000+ custom-labeled queries across all 3 intents
- Special augmentations:
- Out-of-domain recommendation phrasing (e.g. restaurants, gadgets)
- Thematic recommendation queries (e.g. “movies for Valentine’s Day”)
- Use case: Used inside a RAG-based chatbot for intent-routing and retrieval logic
✨ Example Predictions
| Input | Predicted Intent |
|---|---|
| "Can you recommend a good horror movie?" | recommendation |
| "Who directed Parasite?" | factual |
| "Do you like sci-fi shows?" | generic |
| "Suggest restaurants in Rome?" | generic (OOD) |
🚀 Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="your-username/intent-classifier-distilbert-moviebot")
query = "Can you suggest a good action movie?"
result = classifier(query, top_k=None)
print(result)
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