tensorflow

Tensorflow implementation

How to

A model can be trained using `ref_model.py. All parameters can be tuned there too (see comments). In order to do this, both dataset and pretrained vectors need to be stored as for the Keras implementation.

python ref_model.py

Once a model is trained, it can be used interactively calling play_with.py. Model selection can be done using cv_model.py which implements grid search.

Contents

• README.md this file.
• utils/
  • data utils general data utility functions.
  • general utils other utility functions.
• reference parsing model contains the main RefModel model discussed in the paper, and can be run to train an instance (assumes the dataset and pretrained vectors are available).
• cross validation contains code to fot multiple models for model selection or fine tuning (assumes the dataset and pretrained vectors are available).
• play with contains code to load a model and use it with an interactive terminal.

Dependencies

• TensorFlow: 1.4.0
• Numpy: 1.13.3
• Sklearn : 0.19.1
• Python 3.5

Future work

• Add a conf file, ideally shared with the implementation in Keras.
• Add a multitask implementation.