louisowen6/GAN_BERT_CLINC150
Code Implementation of TDS Article "Semi-supervised Intent Classification with GAN-BERT"
This project helps machine learning engineers and NLP researchers efficiently categorize user intents from text data, even when only a small portion of the data is labeled. You provide a mix of labeled and unlabeled text examples, and it outputs a model capable of accurately classifying user intentions, such as 'book flight' or 'check balance'. This is ideal for those developing conversational AI, chatbots, or customer service automation systems.
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Use this if you need to build an intent classification model but have limited labeled data and a large amount of unlabeled text.
Not ideal if you have a fully labeled dataset or are looking for a simple, out-of-the-box solution without deep learning framework experience.
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Language
Python
License
Apache-2.0
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Last pushed
Aug 19, 2020
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