Website update for v0.5 release

PiperOrigin-RevId: 492356336

Author: RyanMullins

docs/assets/css/new.css

@@ -544,7 +544,9 @@ h5 {
   margin-bottom: 20px;
 }
 
-.info-box-text, .info-box-text p {
+.info-box-text,
+.info-box-text p,
+.info-box-text ul {
   font-family: "Open Sans";
   font-style: normal;
   font-weight: normal;

docs/assets/images/actor_to_actress.png

@@ -76,7 +76,7 @@
   <div class="hero-container mdl-grid">
     <div class="mdl-cell--8-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
 
-      <div class="sub-title">Take the Learning Interpretability Tool for a spin!</div>
+      <div class="sub-title">Take LIT for a spin!</div>
       <div class="sub-copy">Get a feel for LIT in a variety of hosted demos.</div>
     </div>
   </div>
@@ -98,13 +98,13 @@
 </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/images.html" target="_blank">Image classification</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> images </span>  <span class="demo-tag"> mutli-class classification </span> 
+  <div class="demo-card-tags"> <span class="demo-tag"> images </span>  <span class="demo-tag"> multiclass classification </span> 
   </div>
   <div class="demo-card-data-source-title">DATA SOURCES</div>
   <div class="demo-card-data-source">
     Imagenette
   </div>
-  <div class="demo-card-copy">Analyze an image classification model with LIT, including muliple image salience techniques.</div>
+  <div class="demo-card-copy">Analyze an image classification model with LIT, including multiple image salience techniques.</div>
   <div class="demo-card-cta-button"><a href="/lit/demos/images.html"></a></div>
 </div>
 <div class="demo-card mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--4-col-phone">
@@ -115,7 +115,7 @@
   <div class="demo-card-data-source">
     Stanford Sentiment Treebank,  Multi-Genre NLI Corpus, Semantic Textual Similarity Benchmark
   </div>
-  <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-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 measuring text 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">
@@ -162,7 +162,19 @@
   <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="/lit/demos/is_eval.html" target="_blank">Evaluating input salience methods</a></div>
+  <div class="demo-card-tags"> <span class="demo-tag"> salience </span>  <span class="demo-tag"> evaluation </span> 
+  </div>
+  <div class="demo-card-data-source-title">DATA SOURCES</div>
+  <div class="demo-card-data-source">
+    Stanford Sentiment Treebank, Toxicity
   </div>
+  <div class="demo-card-copy">Explore the faithfulness of input salience methods across different datasets and artificial shortcuts.</div>
+  <div class="demo-card-cta-button"><a href="/lit/demos/is_eval.html"></a></div>
+</div>
+  </div>
+</div>
 
           </div>
           <div class="fixed-sub-navigation hide-me">

docs/assets/images/lit-coref-compare.png

@@ -0,0 +1,5 @@
+<html>
+  <script>
+    window.location.replace("http://34.86.17.245:5437/");
+  </script>
+</html>

docs/assets/images/lit-coref-data.png

@@ -1,5 +1,5 @@
 <html>
   <script>
-    window.location.replace("http://34.86.17.245:5433/");
+    window.location.replace("http://34.86.17.245:5434/");
   </script>
 </html>

docs/assets/images/lit-coref-metric-top.png

@@ -15,7 +15,8 @@
 </script>
 
     <title>Learning Interpretability Tool</title>
-    <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
+    
+        <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
     <link rel="stylesheet" href="/lit/assets/css/material.min.css">
     <link rel="stylesheet" href="/lit/assets/css/material.min.css">
     <link rel="stylesheet" href="/lit/assets/css/prism-material-light.css">
@@ -143,13 +144,13 @@ <h3>Framework agnostic</h3>
 </div>
 </div>
 <div class="spacer" style="height:50px;"></div>
-<h2>What’s the latest</h2>
+<h2>What's the latest</h2>
 <div class="mdl-grid no-padding">
 <div class="mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--8-col-phone">
   <img class="home-card-image" src="/lit/assets/images/LIT_Updates.png"/>
   <div class="home-card-action">UPDATES</div>
-  <h3 class="home-card-title">Version 0.4</h3>
-  <div class="home-card-desc">New features, updates, and improvements to LIT.</div>
+  <h3 class="home-card-title">Version 0.5</h3>
+  <div class="home-card-desc">Tabular feature attribution, Dive, and many more new features, updates, and improvements to LIT.</div>
   <div class="home-card-cta-button"><a href="https://github.com/PAIR-code/lit/blob/main/RELEASE.md" target="_blank" >See release notes</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
 </div>
 <div class="mdl-cell mdl-cell--6-col mdl-cell--4-col-tablet mdl-cell--8-col-phone">

docs/assets/images/lit-coref-metrics.png

@@ -105,7 +105,7 @@ <h1>Run the included demos</h1>
 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>
+<pre class="language-bash"><code class="language-bash">blaze run -c opt --config<span class="token operator">=</span>cuda examples:glue_demo -- --port<span class="token operator">=</span><span class="token number">5432</span> --quickstart</code></pre>
 <p>Navigate to http://localhost:5432 to access the LIT UI.</p>
 <p>Your default view will be a
 <a href="https://arxiv.org/abs/1908.08962">small BERT-based model</a> fine-tuned on the
@@ -114,7 +114,7 @@ <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.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">blaze run -c opt --config<span class="token operator">=</span>cuda 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>

docs/assets/images/lit-coref-pred.png

@@ -93,9 +93,17 @@ <h4 class="tutorial-link-title"><a href="/lit/tutorials/tour">A Tour of LIT</a><
 <div class="spacer" style="height:50px;"></div>
 <p><a name="analysis"></a></p>
 <h2>Conducting analysis in LIT</h2>
+<div class="tutorial-link-element">
+  <h4 class="tutorial-link-title"><a href="/lit/tutorials/text-salience">Salience Maps for Text</a></h4>
+  <div class="tutorial-link-copy">Learn how to use salience maps for text data in LIT.</div>
+</div>
+<div class="tutorial-link-element">
+  <h4 class="tutorial-link-title"><a href="/lit/tutorials/tab-feat-attr">Tabular Feature Attribution</a></h4>
+  <div class="tutorial-link-copy">Learn how to use the Kernel SHAP based Tabular Feature Attribution module in LIT.</div>
+</div>
 <div class="tutorial-link-element">
   <h4 class="tutorial-link-title"><a href="/lit/tutorials/tcav">Global Model Analysis with TCAV</a></h4>
-  <div class="tutorial-link-copy">Learn about examaning model behavior through user-curated concepts.</div>
+  <div class="tutorial-link-copy">Learn about examining model behavior through user-curated concepts.</div>
 </div>
 <div class="tutorial-link-element">
   <h4 class="tutorial-link-title"><a href="/lit/tutorials/sentiment">Exploring a Sentiment Classifier</a></h4>