Merge pull request #158 from PAIR-code/dev

Merge dev into main

Author: iftenney

.gitignore

@@ -1,5 +1,6 @@
-lit_nlp/node_modules/**
 lit_nlp/yarn-error.log
+website/www/**
 **/build/**
+**/node_modules/**
 **/__pycache__/**
 **/*.pyc

Dockerfile

@@ -12,7 +12,7 @@ COPY . ./
 
 # Update Ubuntu packages and install basic utils
 RUN apt-get update
-RUN apt-get install -y wget curl gnupg2
+RUN apt-get install -y wget curl gnupg2 gcc g++
 
 # Install yarn
 RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add -

README.md

@@ -13,7 +13,7 @@ LIT is built to answer questions such as:
 *   **Does my model behave consistently** if I change things like textual style,
     verb tense, or pronoun gender?
 
-![Example of LIT UI](docs/images/figure-1.png)
+![Example of LIT UI](documentation/images/figure-1.png)
 
 LIT supports a variety of debugging workflows through a browser-based UI.
 Features include:
@@ -32,13 +32,13 @@ Features include:
 *   **Framework-agnostic** and compatible with TensorFlow, PyTorch, and more.
 
 For a broader overview, check out [our paper](https://arxiv.org/abs/2008.05122) and the
-[user guide](docs/user_guide.md).
+[user guide](documentation/user_guide.md).
 
 ## Documentation
 
-*   [User Guide](docs/user_guide.md)
-*   [Developer Guide](docs/development.md)
-*   [FAQ](docs/faq.md)
+*   [User Guide](documentation/user_guide.md)
+*   [Developer Guide](documentation/development.md)
+*   [FAQ](documentation/faq.md)
 *   [Release notes](./RELEASE.md)
 
 ## Download and Installation
@@ -61,8 +61,7 @@ conda install cudnn cupti  # optional, for GPU support
 conda install -c pytorch pytorch  # optional, for PyTorch
 
 # Build the frontend
-cd ~/lit/lit_nlp
-yarn && yarn build
+pushd lit_nlp; yarn && yarn build; popd
 ```
 
 Note: if you see [an error](https://github.com/yarnpkg/yarn/issues/2821)
@@ -84,25 +83,29 @@ running the demos.
 
 ## Running LIT
 
-### Quick-start: sentiment classifier
+### Quick-start: classification and regression
+
+To explore classification and regression models tasks from the popular [GLUE benchmark](https://gluebenchmark.com/):
 
 ```sh
-cd ~/lit
-python -m lit_nlp.examples.quickstart_sst_demo --port=5432
+python -m lit_nlp.examples.glue_demo --port=5432 --quickstart
 ```
 
-This will fine-tune a [BERT-tiny](https://arxiv.org/abs/1908.08962) model on the
+Navigate to http://localhost:5432 to access the LIT UI. 
+
+Your default view will be a 
+[small BERT-based model](https://arxiv.org/abs/1908.08962) fine-tuned on the
 [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/treebank.html),
-which should take less than 5 minutes on a GPU. After training completes, it'll
-start a LIT server on the development set; navigate to http://localhost:5432 for
-the UI.
+but you can switch to 
+[STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) or [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) using the toolbar or the gear icon in 
+the upper right.
+
 
 ### Quick start: language modeling
 
 To explore predictions from a pretrained language model (BERT or GPT-2), run:
 
 ```sh
-cd ~/lit
 python -m lit_nlp.examples.pretrained_lm_demo --models=bert-base-uncased \
   --port=5432
 ```
@@ -114,14 +117,13 @@ And navigate to http://localhost:5432 for the UI.
 See [lit_nlp/examples](./lit_nlp/examples). Run similarly to the above:
 
 ```sh
-cd ~/lit
 python -m lit_nlp.examples.<example_name> --port=5432 [optional --args]
 ```
 
 ## User Guide
 
-To learn about LIT's features, check out the [user guide](docs/user_guide.md), or
-watch this [short video](https://www.youtube.com/watch?v=j0OfBWFUqIE).
+To learn about LIT's features, check out the [user guide](documentation/user_guide.md), or
+watch this [video](https://www.youtube.com/watch?v=CuRI_VK83dU).
 
 ## Adding your own models or data
 
@@ -130,33 +132,34 @@ launcher, similar to those in [lit_nlp/examples](./lit_nlp/examples). The basic
 steps are:
 
 *   Write a data loader which follows the
-    [`Dataset` API](docs/python_api.md#datasets)
-*   Write a model wrapper which follows the [`Model` API](docs/python_api.md#models)
+    [`Dataset` API](documentation/python_api.md#datasets)
+*   Write a model wrapper which follows the [`Model` API](documentation/python_api.md#models)
 *   Pass models, datasets, and any additional
-    [components](docs/python_api.md#interpretation-components) to the LIT server
+    [components](documentation/python_api.md#interpretation-components) to the LIT server
     class
 
 For a full walkthrough, see
-[adding models and data](docs/python_api.md#adding-models-and-data).
+[adding models and data](documentation/python_api.md#adding-models-and-data).
 
 ## Extending LIT with new components
 
 LIT is easy to extend with new interpretability components, generators, and
 more, both on the frontend or the backend. See the
-[developer guide](docs/development.md) to get started.
+[developer guide](documentation/development.md) to get started.
 
 ## Citing LIT
 
-If you use LIT as part of your work, please cite [our paper](https://arxiv.org/abs/2008.05122):
+If you use LIT as part of your work, please cite [our EMNLP paper](https://arxiv.org/abs/2008.05122):
 
 ```
 @misc{tenney2020language,
-    title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models},
+    title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models},
     author={Ian Tenney and James Wexler and Jasmijn Bastings and Tolga Bolukbasi and Andy Coenen and Sebastian Gehrmann and Ellen Jiang and Mahima Pushkarna and Carey Radebaugh and Emily Reif and Ann Yuan},
-    year={2020},
-    eprint={2008.05122},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    pages = "107--118",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.15",
 }
 ```
 
@@ -165,7 +168,7 @@ If you use LIT as part of your work, please cite [our paper](https://arxiv.org/a
 This is not an official Google product.
 
 LIT is a research project, and under active development by a small team.
-There will be some bugs and rough edges, but we're releasing v0.1 because we
-think it's pretty useful already. We want LIT to be an open platform, not a
-walled garden, and we'd love your suggestions and feedback - drop us a line in
-the [issues](https://github.com/pair-code/lit/issues).
+There will be some bugs and rough edges, but we're releasing at an early stage
+because we think it's pretty useful already. We want LIT to be an open platform,
+not a walled garden, and we'd love your suggestions and feedback - drop us a
+line in the [issues](https://github.com/pair-code/lit/issues).

RELEASE.md

@@ -1,5 +1,14 @@
 # Language Interpretability Tool releases
 
+## Release 0.2
+
+This release of LIT coincides with the EMNLP 2020 conference, where the LIT
+paper was presented, and the publication of the LIT website, including tutorials
+and hosted demos.
+
+The UI has been slightly revamped, bugs have been fixed, and new capabilities
+have been added.
+
 ## Release 0.1.1
 
 This release of LIT adds a pip package for easy installation, cleans up some of