An assignment on creating a minimalist neural network toolkit for CS11-747

by Graham Neubig, Zhisong Zhang, and Divyansh Kaushik

This is an exercise in developing a minimalist neural network toolkit for NLP, part of Carnegie Mellon University’s CS11-747: Neural Networks for NLP.

The most important files it contains are the following:

1. minnn.py: This is what you’ll need to implement. It implements a very minimalist version of a dynamic neural network toolkit (like PyTorch or Dynet). Some code is provided, but important functionality is not included.
2. classifier.py: training code for a Deep Averaging Network for text classification using minnn. You can feel free to make any modifications to make it a better model, but the original version of classifier.py must also run with your minnn.py implementation.
3. setup.py: this is blank, but if your classifier implementation

    

    

    

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