BiDAF-pytorch

Re-implementation of BiDAF(Bidirectional Attention Flow for Machine Comprehension, Minjoon Seo et al., ICLR 2017) on PyTorch.

Results

Dataset: SQuAD v1.1

Model(Single)	EM(%)(dev)	F1(%)(dev)
Re-implementation	64.8	75.7
Baseline(paper)	67.7	77.3

Development Environment

• OS: Ubuntu 16.04 LTS (64bit)
• GPU: Nvidia Titan Xp
• Language: Python 3.6.2.
• Pytorch: 0.4.0

Requirements

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

Execution

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