t5-small-intent-slot

T5 Small Intent-Slot Model

This is a fine-tuned T5 model designed for Intent Detection and Slot Filling — a core task in natural language understanding (NLU) for chatbots, virtual assistants, and conversational AI.

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What does this model do?

Imagine you’re teaching a smart assistant to understand user requests like:

• “Book a hotel in London for 3 nights.”
• “Find me an Italian restaurant nearby.”
• “What’s the weather tomorrow in Paris?”

This model reads the input sentence and simultaneously figures out:

• The user's intent (e.g., booking, searching)
• The slots (key details like location, date, type)

It outputs a structured sequence that identifies these elements, so your app can respond intelligently.

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Model Details

• Based on T5 small architecture (6 layers, 512 hidden size)
• Trained for conditional generation of intents and slots from text
• Uses SentencePiece tokenizer with custom added tokens
• Model weights stored as safetensors for efficiency and safety

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Files in this repository

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