https://github.com/cgpotts/cs224u

Code for Stanford CS224u

CS224u: Natural Language Understanding

Code for the Stanford course.

Spring 2023

Christopher Potts

Core components

setup.ipynb

Details on how to get set up to work with this code.

hw_*.ipynb

The set of homeworks for the current run of the course.

tutorial_* notebooks

Introductions to Juypter notebooks, scientific computing with NumPy and friends, and PyTorch.

torch_*.py modules

A generic optimization class (torch_model_base.py) and subclasses for GloVe, Autoencoders, shallow neural classifiers, RNN classifiers, tree-structured networks, and grounded natural language generation.

tutorial_pytorch_models.ipynb shows how to use these modules as a general framework for creating original systems.

evaluation_*.ipynb and projects.md

Notebooks covering key experimental methods and practical considerations, and tips on writing up and presenting work in the field.

iit* and feature_attribution.ipynb

Part of our unit on explainability and model analysis.

np_*.py modules

This is now considered background material for the course.

Reference implementations for the torch_*.py models, designed to reveal more about how the optimization process works.

vsm_*

This is now considered background material for the course.

A unit on vector space models of meaning, covering traditional methods like PMI and LSA as well as newer methods like Autoencoders and GloVe. vsm.py provides a lot of the core functionality, and torch_glove.py and torch_autoencoder.py are the learned models that we cover. vsm_03_contextualreps.ipynb explores methods for deriving static representations from contextual models.

sst_*

This is now considered background material for the course.

A unit on sentiment analysis with the English Stanford Sentiment Treebank. The core code is sst.py, which includes a flexible experimental framework. All the PyTorch classifiers are put to use as well: torch_shallow_neural_network.py, torch_rnn_classifier.py, and torch_tree_nn.py.

finetuning.ipynb

This is now considered background material for the course.

Using pretrained parameters from Hugging Face for featurization and fine-tuning.

utils.py

Miscellaneous core functions used throughout the code.

test/

To run these tests, use

py.test -vv test/*

or, for just the tests in test_shallow_neural_classifiers.py,

py.test -vv test/test_shallow_neural_classifiers.py

If the above commands don't work, try

python3 -m pytest -vv test/test_shallow_neural_classifiers.py

License

The materials in this repo are licensed under the Apache 2.0 license and a Creative Commons Attribution-ShareAlike 4.0 International license.