Update website source

PiperOrigin-RevId: 366828734

docs/demos/index.html

@@ -96,6 +96,17 @@
   <div class="demo-card-copy">Use LIT with any of three tasks from the General Language Understanding Evaluation (GLUE) benchmark suite. This demo contains binary classification (for sentiment analysis, using SST2), multi-class classification (for textual entailment, using MultiNLI), and regression (for measuringtext similarity, using STS-B).</div>
   <div class="demo-card-cta-button"><a href="/lit/demos/glue.html"></a></div>
 </div>
+<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
+  <div class="demo-card-title"><a href="https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb" target="_blank">Notebook usage</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> binary classification </span>  <span class="demo-tag"> notebooks </span>
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    Stanford Sentiment Treebank
+  </div>
+  <div class="demo-card-copy">Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.</div>
+  <div class="demo-card-cta-button"><a href="https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb"></a></div>
+</div>
 <div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
   <div class="demo-card-title"><a href="/lit/demos/coref.html" target="_blank">Gender bias in coreference systems</a></div>
   <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> coreference </span>  <span class="demo-tag"> fairness </span>  <span class="demo-tag"> Winogender </span> 
@@ -128,17 +139,6 @@
   </div>
   <div class="demo-card-copy">Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.</div>
   <div class="demo-card-cta-button"><a href="/lit/demos/t5.html"></a></div>
-</div>
-<div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
-  <div class="demo-card-title"><a href="/lithttps://colab.research.google.com/github/pair-code/lit/blob/main/examples/notebooks/LIT_sentiment_classifier.ipynb" target="_blank">Using LIT in notebooks</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> Colab </span>  <span class="demo-tag"> notebooks </span>  <span class="demo-tag"> BERT </span>  <span class="demo-tag"> binary classification </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Stanford Sentiment Treebank
-  </div>
-  <div class="demo-card-copy">Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis from the General Language Understanding Evaluation (GLUE) benchmark suite.</div>
-  <div class="demo-card-cta-button"><a href="/lithttps://colab.research.google.com/github/pair-code/lit/blob/main/examples/notebooks/LIT_sentiment_classifier.ipynb"></a></div>
 </div>
   </div>
 

docs/index.html

@@ -134,6 +134,7 @@ <h3>Framework agnostic</h3>
 <p>TensorFlow 1.x</p>
 <p>TensorFlow 2.x</p>
 <p>PyTorch</p>
+<p>Notebook compatibility</p>
 <p>Custom inference code</p>
 <p>Remote Procedure Calls</p>
 <p>And more...</p>

docs/setup/index.html

@@ -99,6 +99,8 @@ <h2>Install from source</h2>
 <p><a name="demos"></a></p>
 <h1>Run the included demos</h1>
 <p>LIT ships with a number of demos that can easily be run after installation.</p>
+<p>LIT can be started on the command line and then viewed in a web browser.</p>
+<p>Alternatively, it can be run directly in a Colaboratory or Jupyter notebook and viewed in an output cell of the notebook.</p>
 <h2>Quick-start: Classification and regression</h2>
 <p>To explore classification and regression models tasks from the popular <a href="https://gluebenchmark.com/">GLUE benchmark</a>:</p>
 <pre class="language-bash"><code class="language-bash">python -m lit_nlp.examples.glue_demo --port<span class="token operator">=</span><span class="token number">5432</span> --quickstart</code></pre>
@@ -110,11 +112,13 @@ <h2>Quick-start: Classification and regression</h2>
 <a href="http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark">STS-B</a> or <a href="https://cims.nyu.edu/~sbowman/multinli/">MultiNLI</a> using the toolbar or the gear icon in
 the upper right.</p>
 <h2>Language modeling</h2>
-<pre class="language-bash"><code class="language-bash">python -m lit_nlp.examples.pretrained_lm_demo <span class="token punctuation">\</span><br>  --models<span class="token operator">=</span>bert-base-uncased --port<span class="token operator">=</span><span class="token number">5432</span></code></pre>
+<pre class="language-bash"><code class="language-bash">python -m lit_nlp.examples.lm_demo <span class="token punctuation">\</span><br>  --models<span class="token operator">=</span>bert-base-uncased --port<span class="token operator">=</span><span class="token number">5432</span></code></pre>
 <p>In this demo, you can explore predictions from a pretrained language model (i.e. fill in the blanks).
 Navigate to http://localhost:5432 for the UI.</p>
 <h2>More examples</h2>
 <p>The <a href="https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples">examples</a> directory contains additional examples to explore, all of which can be run similarly to those above.</p>
+<h2>Notebook usage</h2>
+<p>A simple colab demo can be found <a href="https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb">here</a>. Just run all the cells to see LIT on an example classification model right in the notebook.</p>
 <div class="spacer" style="height:50px;"></div>
 <p><a name="custom"></a></p>
 <h1>Use LIT on your own models and data</h1>

website/src/demos.md

@@ -19,7 +19,7 @@ color: "#49596c"
 
   {% include partials/demo-card c-title: "Notebook usage", link: "https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb",
   c-data-source: "Stanford Sentiment Treebank"
-  c-copy: "Use LIT directly inside of a colab notebook. This simple demo shows a binary classifier for sentiment analysis using SST2.", tags: "BERT, binary classification, notebook", external:"true" %}
+  c-copy: "Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.", tags: "BERT, binary classification, notebooks", external:"true" %}
 
   {% include partials/demo-card c-title: "Gender bias in coreference systems", link: "/demos/coref.html",
   c-data-source: "Winogender schemas", c-copy: "Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.", tags: "BERT, coreference, fairness, Winogender", external:"true" %}
@@ -29,7 +29,4 @@ color: "#49596c"
 
   {% include partials/demo-card c-title: "Text generation", link: "/demos/t5.html",
   c-data-source: "CNN / Daily Mail", c-copy: "Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.", tags: "T5, generation", external:"true" %}
-
-  {% include partials/demo-card c-title: "Using LIT in notebooks", link: "https://colab.research.google.com/github/pair-code/lit/blob/main/examples/notebooks/LIT_sentiment_classifier.ipynb",
-  c-data-source: "Stanford Sentiment Treebank", c-copy: "Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis from the General Language Understanding Evaluation (GLUE) benchmark suite.", tags: "Colab, notebooks, BERT, binary classification", external:"true" %}
   </div>

website/src/setup.md

@@ -89,7 +89,7 @@ the upper right.
 ## Language modeling
 
 ```bash
-python -m lit_nlp.examples.pretrained_lm_demo \
+python -m lit_nlp.examples.lm_demo \
   --models=bert-base-uncased --port=5432
 ```