yunhenk/conv-knrm

Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

Conv-KNRM

This is an implementation of the paper: Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

Inspired by project K-NRM by the author.

Features

• python3.6 compatible
• latest tensorflow features.
  • ok with tensorflow 1.10 or later
• a Conv-KNRM implementation

Requirements

---

• Tensorflow
• Numpy
• traitlets

Run

To run the Conv-KNRM model, just append an argument '--convolution true', for example:

Training

python ./knrm/model/model_knrm.py config-file\
    --train \
    --train_file: path to training data\
    --validation_file: path to validation data\
    --train_size: size of training data (number of training samples)\
    --checkpoint_dir: directory to store/load model checkpoints\
    --load_model: True or False. Start with a new model or continue training \
    --convolution true

Testing:

python ./knrm/model/model_knrm.py config-file\
    --test \
    --test_file: path to testing data\
    --test_size: size of testing data (number of testing samples)\
    --checkpoint_dir: directory to load trained model\
    --output_score_file: file to output documents score\
    --convolution true

For more details,see the original README file.