Merge pull request cgpotts#124 from cgpotts/spring2023-prep

Spring2023 prep

.gitignore

@@ -69,3 +69,4 @@ rel_ext_data*
 .DS_Store
 ColBERT*
 experiments*
+cache*

README.md

@@ -2,77 +2,80 @@
 
 Code for [the Stanford course](http://web.stanford.edu/class/cs224u/).
 
-Spring 2022
+Spring 2023
 
 [Christopher Potts](http://web.stanford.edu/~cgpotts/)
 
 
-# Core components
+## Core components
 
 
-## `setup.ipynb`
+### `setup.ipynb`
 
 Details on how to get set up to work with this code.
 
 
-## `tutorial_*` notebooks
+### `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
+### `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.
 
 
-## `np_*.py` modules
-
-Reference implementations for the `torch_*.py` models, designed to reveal more about how the optimization process works.
+### `evaluation_*.ipynb` and `projects.md`
 
+Notebooks covering key experimental methods and practical considerations, and tips on writing up and presenting work in the field.
 
-## `vsm_*` and `hw_wordrelatedness.ipynb`
 
-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_retroffiting.ipynb` is an extension that uses `retrofitting.py`, and `vsm_04_contextualreps.ipynb` explores methods for deriving static representations from contextual models.
+### `iit*` and `feature_attribution.ipynb`
 
+Part of our unit on explainability and model analysis.
 
-## `sst_*` and `hw_sst.ipynb`
 
-A unit on sentiment analysis with the [English Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/treebank.html). 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`.
+### `np_*.py` modules
 
+This is now considered background material for the course.
 
-## `rel_ext*` and `hw_rel_ext.ipynb`
-
-A unit on relation extraction with distant supervision.
+Reference implementations for the `torch_*.py` models, designed to reveal more about how the optimization process works.
 
 
-## `nli_*` and `hw_wordentail.ipynb`
+### `vsm_*`
 
-A unit on Natural Language Inference. `nli.py` provides core interfaces to a variety of NLI dataset, and an experimental framework. All the PyTorch classifiers are again in heavy use: `torch_shallow_neural_network.py`, `torch_rnn_classifier.py`, and `torch_tree_nn.py`.
+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.
 
-## `colors*`, `torch_color_describer.py`, and `hw_colors.ipynb`
 
-A unit on grounded natural language generation, focused on generating context-dependent color descriptions using the [English Stanford Colors in Context dataset](https://cocolab.stanford.edu/datasets/colors.html).
+### `sst_*`
 
+This is now considered background material for the course.
 
-## `finetuning.ipynb`
+A unit on sentiment analysis with the [English Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/treebank.html). 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`.
 
-Using pretrained parameters from [Hugging Face](https://huggingface.co) for featurization and fine-tuning.
 
+### `finetuning.ipynb`
 
-## `evaluation_*.ipynb` and `projects.md`
+This is now considered background material for the course.
 
-Notebooks covering key experimental methods and practical considerations, and tips on writing up and presenting work in the field.
+Using pretrained parameters from [Hugging Face](https://huggingface.co) for featurization and fine-tuning.
 
 
-## `utils.py`
+### `utils.py`
 
 Miscellaneous core functions used throughout the code.
 
 
-## `test/`
+### `test/`
 
 To run these tests, use