snips-intent-router

DistilBERT SNIPS Intent Router

A fine‑tuned distilbert-base-uncased model that classifies short user utterances into one of 7 customer‑support intents.

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Model Details

Model Description

This model was fine‑tuned on the SNIPS built‑in intents dataset for single‑label text classification. It takes a user query (e.g. “Book me a table for tonight”) and returns one of the predefined intents:

• AddToPlaylist
• BookRestaurant
• GetWeather
• PlayMusic
• RateBook
• SearchCreativeWork
• SearchScreeningEvent

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Uses

Direct Use

Route user requests in chatbots, voice assistants, or email triage systems into support categories for faster handling.

Out‑of‑Scope Use

• Long-form or multi‑sentence inputs; performance may degrade on utterances beyond ~20 words.
• Languages other than English.

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Bias, Risks, and Limitations

• Bias: Trained only on clear, synthetic voice‑assistant style utterances. May misclassify non‑standard phrasing or dialects.
• Risks: Misrouting critical user requests (e.g. emergency queries) if phrased unusually.
• Limitations:
  • Accuracy degrades on very short (“Hi”) or very long (“I’d like to…”) utterances.
  • No support for multi‑intent or slot filling.

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How to Get Started

from transformers import pipeline

intent_router = pipeline(
    "text-classification",
    model="YOUR_USERNAME/snips-intent-router",
    tokenizer="YOUR_USERNAME/snips-intent-router",
)

# Example
result = intent_router("Book me a table for two at an Italian restaurant tonight")
print(result)
# → [{'label':'BookRestaurant','score':0.99}]