Pytorch re-implementation of DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding.
This is an unofficial implementation. There is the implementation by the authors, which is implemented on Tensorflow.
Dataset: SNLI
| Model | Valid Acc(%) | Test Acc(%) |
|---|---|---|
| Baseline from the paper | - | 85.62 |
| Re-implemenation | 80.60 | 79.80 |
- OS: Ubuntu 16.04 LTS (64bit)
- Language: Python 3.6.6
- Pytorch: 0.4.0
Please install the following library requirements first.
nltk==3.3
tensorboardX==1.2
torch==0.4.0
torchtext==0.2.3
python train.py --help
usage: train.py [-h] [--batch-size BATCH_SIZE] [--data-type DATA_TYPE]
[--dropout DROPOUT] [--epoch EPOCH] [--gpu GPU]
[--learning-rate LEARNING_RATE] [--print-freq PRINT_FREQ]
[--weight-decay WEIGHT_DECAY] [--word-dim WORD_DIM]
[--d-e D_E] [--d-h D_H]
optional arguments:
-h, --help show this help message and exit
--batch-size BATCH_SIZE
--data-type DATA_TYPE
--dropout DROPOUT
--epoch EPOCH
--gpu GPU
--learning-rate LEARNING_RATE
--print-freq PRINT_FREQ
--weight-decay WEIGHT_DECAY
--word-dim WORD_DIM
--d-e D_E
--d-h D_H
Note:
- Only codes to use SNLI as training data are implemented.
- There must be some erros somewhere, as the performance is much lower. But I couldn't find...
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