cjiang2/VDCNN
Implementation of Very Deep Convolutional Neural Network for Text Classification
This project helps developers classify text by category using a Very Deep Convolutional Neural Network (VDCNN) architecture. It takes raw text data (like news articles or reviews) as input and outputs a classification for each text, such as its topic or sentiment. This is intended for machine learning engineers or researchers who are working with text classification tasks and prefer a TensorFlow 2 environment.
172 stars. No commits in the last 6 months.
Use this if you are a machine learning developer looking to implement or experiment with a VDCNN model for text classification within a TensorFlow 2 framework.
Not ideal if you are new to NLP text classification, as more modern and state-of-the-art methods like transformers or BERT are recommended, or if you prefer PyTorch for dynamic graphing and dataset support.
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172
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Language
Python
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Last pushed
Jun 28, 2022
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