Update module_6.html

modules/module_6.html

@@ -50,8 +50,8 @@ <h1>Module 6: Named Entity Recognition and Text Classification</h1>
 
         <p>Module 6 will present different approaches to Named Entity Recognition/Extraction and Text Classification. Named Entity Recognition (NER) is a natural language processing task that identifies and classifies named entities (like people, organizations, locations, dates) in text. For example, in "Apple released iPhone in 2007", NER would identify "Apple" as an organization, "iPhone" as a product, and "2007" as a date. Text Classificaion Text classification assigns predefined categories/labels to text documents. Example: Categorizing emails as spam/not-spam, or news articles into topics like sports, politics, technology. We will use:</p>
         <ol>
-            <li>NER using models trained by the Impresso Project via the Impresso API</li>
-            <li>Question Answering based Namend Entiy Extraction using the small language model NuExtract</li>
+            <li>NER using models via the Impresso API (Impresso Project)</li>
+            <li>Question-Answering based Namend Entiy Extraction using the small language model NuExtract</li>
             <li>Text Classification using a small language model</li>
         </ol>