PyTorch_TextGCN
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The PyTorch 1.6 and Python 3.7 implementation for the paper Graph Convolutional Networks for Text Classification
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
- Process the data first, run
data_processor.py(Already done) - Generate graph, run
build_graph.py(Already done) - Training model, run
trainer.py
References
[1] Yao, L. , Mao, C. , & Luo, Y. . (2018). Graph convolutional networks for text classification.
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