Metadata-Version: 2.1
Name: copynet-tf
Version: 0.1.5
Summary: CopyNet with TensorFlow 2.0
Home-page: https://github.com/pavanchhatpar/copynet-tf
Author: Pavan Chhatpar
Author-email: pavanchhatpar@gmail.com
License: UNKNOWN
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
        
        ![Python package](https://github.com/pavanchhatpar/copynet-tf/workflows/Python%20package/badge.svg)
        ![Upload Python Package](https://github.com/pavanchhatpar/copynet-tf/workflows/Upload%20Python%20Package/badge.svg)
        
        ## Environment to run examples
        ### Setup
        - 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,
           ```bash
           ./setup-env.sh [--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 `setup-env.sh` 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,
            ```
            ./setup-env.sh [--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,
           ```bash
           ./start-env.sh nb    # For Dockerized jupyter server
           ./start-env.sh bash  # For Dockerized bash
           ```
         - It is not necessary to use the `start-env.sh` 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
        
        ## References
         - Incorporating Copying Mechanism in Sequence-to-Sequence Learning: ([paper](https://arxiv.org/abs/1603.06393))
         - AllenNLP implementation: ([blog](https://medium.com/@epwalsh10/incorporating-a-copy-mechanism-into-sequence-to-sequence-models-40917280b89d)) ([code](https://github.com/epwalsh/nlp-models))
         - BLEU score metric: ([code](https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py))
Platform: UNKNOWN
Description-Content-Type: text/markdown
