QANet-pytorch
Introduction
An implementation of QANet with PyTorch.
Any contributions are welcome!
Current performance
| F1 | EM | Got by | |----|----|--------| | 66 | ? | InitialBug| | 64 | 50 | BangLiu|
Usage
Install pytorch 0.4 for Python 3.6+
Run pip install -r requirements.txt
to install python dependencies.
Run download.sh
to download the dataset.
Run python main.py --mode data
to build tensors from the raw dataset.
Run python main.py --mode train
to train the model. After training, log/model.pt
will be generated.
Run python main.py --mode test
to test an pretrained model. Default model file is log/model.pt
Structure
preproc.py: downloads dataset and builds input tensors.
main.py: program entry; functions about training and testing.
models.py: QANet structure.
config.py: configurations.
Differences from the paper
The paper doesn't mention which activation function they used. I use relu.
I don't set the embedding of <UNK>
trainable.
The connector between embedding layers and embedding encoders may be different from the implementation of Google, since the description in the paper is inconsistent (residual block can't be used because the dimensions of input and output are different) and they don't say how they implemented it.
TODO
Contributors
InitialBug: found two bugs: (1) positional encodings require gradients; (2) wrong weight sharing among encoders.
linthieda: fixed one issue about dependencies and offered computing resources.
BangLiu: tested the model.
wlhgtc: (1) improved the calculation of Context-Question Attention; (2) fixed a bug that is compacting embeddings before highway nets.