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nlu-engine

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This repository contains the code and resources for building a language understanding and question answering system using the Jeopardy! dataset. The project aims to train a language understanding model to extract meaning from open-ended questions and leverage that understanding to answer the questions using a dedicated QA system.

  • Updated Mar 18, 2024

Demo and benchmarks for building an NLU engine similar to those in voice assistants. Several intent classifiers are implemented and benchmarked. Conditional Random Fields (CRFs) are used for entity extraction.

  • Updated Apr 29, 2023
  • Jupyter Notebook

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.

  • Updated Jun 22, 2022
  • Python

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