larryzhang23 / QANet-PyTorch

Re-implement "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension"

QANet-PyTorch

Introduction

Re-implement QANet with PyTorch. Contributions are welcomed!

Usage

Run python3 QANet_main.py --batch_size 32 --epochs 30 --with_cuda --use_ema to train model with cuda.

Run python3 QANet_main.py --batch_size 32 --epochs 3 --with_cuda --use_ema --debug to debug with small batches data.

Performance

With ema, 8 head attention, hidden size 128, QANet_andy.model, 30 epochs, batch_size 16:

F1: 80.49 EM: 71.24

Performance on validation set during 30 epochs:

Structure

QANet_main.py: code for training QANet.

trainer/QANet_trainer.py: trainer.

model/QANet_model.py: defines QANet.

data_loader/SQuAD.py: SQuAD 1.1 and 2.0 data loader.

Other codes are utils or neural network common modules library.

Acknowledge

1. The QANet structure implementation is mainly based on https://github.com/hengruo/QANet-pytorch and https://github.com/andy840314/QANet-pytorch- and https://github.com/hackiey/QAnet-pytorch.
2. For a TensorFlow implementation, please refer to https://github.com/NLPLearn/QANet.