# PyTorch_TextGCN

**Repository Path**: chengsen/PyTorch_TextGCN

## Basic Information

- **Project Name**: PyTorch_TextGCN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Graph Convolutional Networks for Text Classification in PyTorch

PyTorch 1.6 and Python 3.7 implementation of Graph Convolutional Networks for Text Classification [1].

Tested on the 20NG/R8/R52/Ohsumed/MR data set, the code on this repository can achieve the effect of the paper.

## Benchmark

| dataset       | 20NG | R8 | R52 | Ohsumed | MR  |
|---------------|----------|------|--------|--------|--------|
| TextGCN(official) | 0.8634    | 0.9707 | 0.9356   | 0.6836   | 0.7674   |
| This repo.    | 0.8618     | 0.9704 | 0.9354   | 0.6827  | 0.7643  |

NOTE: The result of the experiment is to repeat the run 10 times, and then take the average of accuracy.

## Requirements
* fastai==2.0.15
* PyTorch==1.6.0
* scipy==1.5.2
* pandas==1.0.1
* spacy==2.3.1
* nltk==3.5
* prettytable==1.0.0
* numpy==1.18.5
* networkx==2.5
* tqdm==4.49.0
* scikit_learn==0.23.2

## Usage
1. Process the data first, run `data_processor.py` (Already done)
2. Generate graph, run `build_graph.py` (Already done)
3. Training model, run `trainer.py`

## References
[1] [Yao, L. , Mao, C. , & Luo, Y. . (2018). Graph convolutional networks for text classification.](https://arxiv.org/abs/1809.05679)