Text-GCN and PyTorch_TextGCN
These two tools are competitors, as both are independent PyTorch implementations of the same "Graph Convolutional Networks for Text Classification" paper.
About Text-GCN
kenqgu/Text-GCN
A PyTorch implementation of "Graph Convolutional Networks for Text Classification." (AAAI 2019)
This project helps you categorize text documents quickly and accurately, even with limited training data. You provide a collection of documents and their assigned categories (or some of them), and it outputs a model that can predict categories for new documents. It also generates meaningful representations for your words and documents. This is ideal for data scientists, NLP practitioners, or researchers needing robust text classification.
About PyTorch_TextGCN
chengsen/PyTorch_TextGCN
The PyTorch 1.6 and Python 3.7 implementation for the paper Graph Convolutional Networks for Text Classification
This project helps machine learning engineers and researchers accurately categorize text documents. It takes raw text data as input and produces a trained model that can classify new documents into predefined categories, along with performance metrics. It's designed for those working with text classification tasks in natural language processing.
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