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CopyNet with TensorFlow 2.0

Project description

CopyNet implementation with TensorFlow 2

  • Incorporating Copying Mechanism in Sequence-to-Sequence Learning
  • Uses TensorFlow 2.0 and above APIs with tf.keras too
  • Adapted from AllenNLP's PyTorch implementation, their blog referenced below was very helpful to understand the math from an implementation perspective

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Environment to run examples


  • Copy sample.env to .env and enter appropriate values for the variables
  • A brief description of each is provided as a comment in that file
  • Post that run,
    ./ [--no-docker]
  • Uses env file to configure project environment
  • Builds required docker images (if you don't wanna use Docker then pass --no-docker option to the script)
  • Makes a python environment and installes required packages in it
  • Prepares an lock.env file. Do not edit/ delete it

Rebuilding environment

  • You may change environment config in the process of development
  • This includes adding a new python package to requirements.txt
  • After changing run,
    ./ [--no-docker]
  • If you do not want Docker, then pass --no-docker option similar to before

Start environment

  • At the end of setup script you will be shown the commands to start the environments
  • They are,
    ./ nb    # For Dockerized jupyter server
    ./ bash  # For Dockerized bash
  • It is not necessary to use the script for virtualenv, the regular source command to activate it is enough

Note on Dockerized environment

  • The dockerized environment is specifically helpful and recommended when using GPU
  • It takes care of many nuances involved in setting up CUDA. Your host machine should just have correct NVIDIA drivers and nothing else
  • It is recommended to run the examples in this environment to ensure all correct dependencies are met

Run examples

  • Instructions to run an example are detailed in its own folders respectively


  • Incorporating Copying Mechanism in Sequence-to-Sequence Learning: (paper)
  • AllenNLP implementation: (blog) (code)
  • BLEU score metric: (code)

Project details

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