Merge pull request #1530 from PAIR-code/dev

Updating website

Author: bdu91

docs/demos/coref.html

@@ -98,17 +98,6 @@
   <div class="demo-card-copy">Analyze a tabular data model with LIT, including exploring partial dependence plots and automatically finding counterfactuals.</div>
   <div class="demo-card-cta-button"><a href="/lit/demos/penguins.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/images.html" target="_blank">Image classification</a></div>
-  <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 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">
   <div class="demo-card-title"><a href="/lit/demos/glue.html" target="_blank">Classification and regression models</a></div>
   <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> binary classification </span>  <span class="demo-tag"> multi-class classification </span>  <span class="demo-tag"> regression </span> 
@@ -130,50 +119,6 @@
   </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> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Winogender schemas
-  </div>
-  <div class="demo-card-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.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/coref.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/lm.html" target="_blank">Fill in the blanks</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <span class="demo-tag"> masked language model </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    Stanford Sentiment Treebank, Movie Reviews
-  </div>
-  <div class="demo-card-copy">Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.</div>
-  <div class="demo-card-cta-button"><a href="/lit/demos/lm.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/t5.html" target="_blank">Text generation</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> T5 </span>  <span class="demo-tag"> generation </span> 
-  </div>
-  <div class="demo-card-data-source-title">DATA SOURCES</div>
-  <div class="demo-card-data-source">
-    CNN / Daily Mail
-  </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="/lit/demos/is_eval.html" target="_blank">Evaluating input salience methods</a></div>
-  <div class="demo-card-tags"> <span class="demo-tag"> BERT </span>  <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 on a BERT-base model 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>