Rishabbh-Sahu/intent_and_slot_classification
One of the main NLU tasks is to understand the intents (sequence classification) and slots (entities within the sequence). This repo help classify both together using Joint Model (multitask model). BERT_SMALL is used which can be changed to any other BERT variant.
This project helps quickly classify customer queries or commands into their underlying purpose (intent) and extract key details (slots) within those queries. You provide text like "what is the weather in London," and it tells you the intent is "get weather" and the slot "London" is the location. This is for product managers, customer service leads, or anyone building conversational AI or automated assistants.
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Use this if you need to build or evaluate a natural language understanding system that accurately identifies user intent and extracts specific entities from text, without requiring massive computational resources or long training times.
Not ideal if you already have access to large-scale computing infrastructure and vast amounts of labeled data, where you might benefit from larger, more complex transformer models for potentially higher accuracy in very nuanced tasks.
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Jun 22, 2022
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