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diff --git a/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html b/docs/posts/2021-06-27-Crude-ML-AI-Powered-Chatbot-Swift.html
index 17a7e90..de25fe8 100644
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- <h1>Making a Crude ML Powered Chatbot in Swift using CoreML</h1>
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+ <h1 id="making-a-crude-ml-powered-chatbot-in-swift-using-coreml">Making a Crude ML Powered Chatbot in Swift using CoreML</h1>
<p>A chatbot/virtual assistant, on paper, looks easy to build.
The user says something, the programs finds the best action, checks if additional input is required and sends back the output.
@@ -51,7 +83,7 @@ To do this in Swift, I used two separate ML Models created using Apple's Create
First is a Text Classifier to classify intent, and the other a word tagger for extracting input from the input message.
Disclaimer: This is a very crude proof-of-concept, but it does work.</p>
-<h2>Text Classifier</h2>
+<h2 id="text-classifier">Text Classifier</h2>
<p>I opened a CSV file and added some sample entries, with a corresponding label.</p>
@@ -83,7 +115,7 @@ i love you,banter
<p><img src="/assets/posts/swift-chatbot/create-intent.png" alt="Screenshot of Create ML Text Classifier" /></p>
-<h2>Word Tagging</h2>
+<h2 id="word-tagging">Word Tagging</h2>
<p>This is useful to extract the required variables directly from the user's input.
This model will be only called if the intent from the classifier is a custom action.
@@ -111,7 +143,7 @@ I created a sample JSON with only 3 examples (I know, very less, but works for a
<p><img src="/assets/posts/swift-chatbot/create-tagger.png" alt="Screenshot of Create ML Text Classifier" /></p>
-<h2>Time to Get Swift-y</h2>
+<h2 id="time-to-get-swift-y">Time to Get Swift-y</h2>
<p>The initial part is easy, importing CoreML and NaturalLanguage and then initializing the models and the tagger.</p>
@@ -220,14 +252,15 @@ Otherwise, it calls the custom action.</p>
<p>If I ever release a part-2, it will either be about implementing this in Tensorflow.JS or an iOS app using SwiftUI ;)</p>
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