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Updating publlic website for v0.4.1 release
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RyanMullins committed Dec 20, 2021
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2 changes: 1 addition & 1 deletion docs/demos/coref.html
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<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-title"><a href="/lithttps://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>
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<li>Does my model behave consistently if I change things like textual style, verb tense, or pronoun gender?</li>
</ul>
<p>LIT contains many built-in capabilities but is also customizable, with the ability to add custom interpretability techniques, metrics calculations, counterfactual generators, visualizations, and more.</p>
<p>LIT also contains preliminary support for non-language models, working with tabular and image data. For a similar tool built to explore general-purpose machine learning models, check out the <a href="https://whatif-tool.dev">What-If Tool</a>.</p>
<p>In addition to language, LIT also includes preliminary support for models operating on tabular and image data. For a similar tool built to explore general-purpose machine learning models, check out the <a href="https://whatif-tool.dev">What-If Tool</a>.</p>
<p>LIT can be run as a standalone server, or inside of python notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI Notebooks.</p>
</div>
<div class="spacer" style="height:50px;"></div>
Expand Down Expand Up @@ -155,7 +155,7 @@ <h3 class="home-card-title">Version 0.4</h3>
</div>
<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_Contribute.png"/>
<div class="home-card-action">CODE</div>
<div class="home-card-action">DOCS</div>
<h3 class="home-card-title">Documentation</h3>
<div class="home-card-desc">LIT is open-source and easily extensible to new models, tasks, and more.</div>
<div class="home-card-cta-button"><a href="https://github.com/PAIR-code/lit/wiki" target="_blank" >View documentation</a><img class="external-arrow" src="/lit/assets/images/arrow-link-out.png"/></div>
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Expand Up @@ -90,7 +90,7 @@ <h2>Debugging a Text Generator</h2>
from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.</p>
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<img class="tutorial-image" src="/lit/assets/images/lit-datatable-search.png"/>
<div class="tutorial-caption">Above: An example of the existance of "by former" in a similar datapoint from the training set.</div>
<div class="tutorial-caption">Above: An example of the existence of "by former" in a similar datapoint from the training set.</div>
</div>

</div>
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Expand Up @@ -118,9 +118,9 @@ <h4>Create a Concept</h4>
<div class="tutorial-caption">1: Selecting a slice as a concept and 2: running TCAV with the specified options</div>
</div>
<h4>Interpreting TCAV scores</h4>
<p>Once we run TCAV, we see an entry in the table in the TCAV module for each concept tested. Each concept gets a CAV (“Concept Activation Vector”) score between 0 and 1 describing the concept’s effect on the prediction of the class in question. What you want to look at is where the blue bar (CAV score) is relative to the black line (reference point). The reference point indicates the effect that slices made of randomly-chosen datapoints outside of the concept being tested has on prediction of the class. For a well-calibrated classifier, the reference point will usually be near 0.5 (i.e. no effect).</p>
<p>A blue bar extending beyond or falling short of the black line means the concept is influencing the prediction. If the blue bar extends beyond the black line, the concept is positively influencing the prediction; if it falls short, it is negatively influencing.</p>
<p>In our example, the CAV score of ~.85 indicates that our “acting” concept has a strong positive effect on the prediction of this class. So we have found that this concept has a positive effect on predicting positive sentiment for our classifier.</p>
<p>Once we run TCAV, we see an entry in the table in the TCAV module for each concept tested. Each concept gets a CAV (“Concept Activation Vector”) score between 0 and 1 describing the concept’s effect on the prediction of the class in question. What matters is where the blue bar (CAV score) is relative to the black line (reference point). The reference point indicates the effect that slices made of randomly-chosen datapoints outside of the concept being tested has on prediction of the class. For a well-calibrated classifier, the reference point will usually be near 0.5 (i.e. no effect).</p>
<p>A blue bar extending right or left of the black line means the concept is influencing the prediction. If the blue bar extends to the right of the black line, the concept is positively influencing the prediction. Conversely, if the bar extended to the left, it is negatively influencing. In either case, the larger the bar, the greater the influence.</p>
<p>In our example, the CAV score of ~0.91 indicates that our “acting” concept has a strong positive effect on the prediction of this class. So we have found that this concept has a positive effect on predicting positive sentiment for our classifier.</p>
<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
<img class="tutorial-image" src="/lit/assets/images/tcav_result.png"/>
<div class="tutorial-caption">TCAV results for our “acting” concept</div>
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Expand Up @@ -84,7 +84,7 @@ <h2>A Quick Tour of the Language Interpretability Tool</h2>
<h3>Building blocks - modules, groups, and workspaces</h3>
<p><strong>Modules, groups, and workspaces</strong> form the building blocks of LIT. Modules are discrete windows in which you can perform a specific set of tasks or analyses. Workspaces display combinations of modules known as groups, so you can view different visualizations and interpretability methods side-by-side.</p>
<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
<img class="tutorial-image" src="/lit/assets/images/lit-workspaces.jpg"/>
<img class="tutorial-image" src="/lit/assets/images/lit-workspaces.png"/>
<div class="tutorial-caption">Above: Building blocks of the Language Interpretability Tool: (1) Modules, (2) Groups, (3) Static workspace, (4) Group-based workspace.</div>
</div>
<p>LIT is divided into two workspaces - a Main workspace in the upper half of the interface, and a Group-based workspace in the lower half.</p>
Expand All @@ -108,7 +108,7 @@ <h3>Toolbars</h3>
<ul>
<li>Select data points by relationship, or by slice.</li>
<li>Choose a feature to color data points, across all modules.</li>
<li>Track the datapoint you’re looking at, navigate to the next, mark a datapoint as a favorite, or clear your selection.</li>
<li>Track the datapoint you are looking at, navigate to the next, mark a datapoint as a favorite, or clear your selection.</li>
<li>Select the active models and dataset, including multiple models to compare.</li>
</ul>
<div class="mdl-cell mdl-cell--12-col mdl-cell--6-col-tablet mdl-cell--4-col-phone">
Expand All @@ -123,7 +123,7 @@ <h3>Using Modules</h3>
<div class="info-box-title">Visualizations that scale</div>
<div class="info-box-text">Visualizations and results within modules can scale depending on if you're looking at one data point, multiple data points, one or multiple models. For instance, turning on the “compare datapoints” toggle allows you to compare a selected datapoint (known as the reference datapoint) to others by presenting individual results side-by-side within relevant modules.</div>
</div>
<p>Now that you’re familiar with LIT’s interface, take LIT for a spin in our <a href="../../demos">demos</a> or explore different <a href="../">case studies</a>.</p>
<p>Now that you are familiar with LIT’s interface, take LIT for a spin in our <a href="../../demos">demos</a> or explore different <a href="../">case studies</a>.</p>

</div>
<div class="mdl-cell--4-col hide-me">
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