Re-implementation of BiDAF(Bidirectional Attention Flow for Machine Comprehension, Minjoon Seo et al., ICLR 2017) on PyTorch.
Dataset: SQuAD v1.1
| Model(Single) | EM(%)(dev) | F1(%)(dev) |
|---|---|---|
| Re-implementation | 64.8 | 75.7 |
| Baseline(paper) | 67.7 | 77.3 |
- OS: Ubuntu 16.04 LTS (64bit)
- GPU: Nvidia Titan Xp
- Language: Python 3.6.2.
- Pytorch: 0.4.0
Please install the following library requirements specified in the requirements.txt first.
torch==0.4.0
nltk==3.2.4
tensorboardX==0.8
torchtext==0.2.3
python run.py --help
usage: run.py [-h] [--char-dim CHAR_DIM]
[--char-channel-width CHAR_CHANNEL_WIDTH]
[--char-channel-size CHAR_CHANNEL_SIZE]
[--dev-batch-size DEV_BATCH_SIZE] [--dev-file DEV_FILE]
[--dropout DROPOUT] [--epoch EPOCH] [--gpu GPU]
[--hidden-size HIDDEN_SIZE] [--learning-rate LEARNING_RATE]
[--print-freq PRINT_FREQ] [--train-batch-size TRAIN_BATCH_SIZE]
[--train-file TRAIN_FILE] [--word-dim WORD_DIM]
optional arguments:
-h, --help show this help message and exit
--char-dim CHAR_DIM
--char-channel-width CHAR_CHANNEL_WIDTH
--char-channel-size CHAR_CHANNEL_SIZE
--dev-batch-size DEV_BATCH_SIZE
--dev-file DEV_FILE
--dropout DROPOUT
--epoch EPOCH
--gpu GPU
--hidden-size HIDDEN_SIZE
--learning-rate LEARNING_RATE
--print-freq PRINT_FREQ
--train-batch-size TRAIN_BATCH_SIZE
--train-file TRAIN_FILE
--word-dim WORD_DIM