Update pages/7_Advance_vectorization_techniques.py

pages/7_Advance_vectorization_techniques.py

@@ -328,7 +328,7 @@ if file_type == "Word2Vec":
                 </ul>
             </li>
             <li>Apply a <span class='highlight'>window size</span> of 2 (how many neighbors we consider).</li>
-            <li>Slide the window over the text with <span class='highlight'>stride = 1</span>.</li>
+            <li>Slide the window over the text with <span class='highlight'>slide = 1</span>.</li>
         </ul>
     """,
     unsafe_allow_html=True,
@@ -367,5 +367,80 @@ if file_type == "Word2Vec":
     """,
     unsafe_allow_html=True,
     )
-    
+
+    st.subheader(":red[Skipgram]")
+    st.markdown(
+    """
+    <div class='box'>
+        <h3 style='color: #6A0572;'>What is Skipgram?</h3>
+        <p><strong>Skipgram</strong> is a technique where we use focus words to predict the context words.</p>
+    </div>
+    """,
+    unsafe_allow_html=True,
+    )
+
+    st.markdown(
+    """
+        <h3 style='color: #6A0572;'>📂 Example Corpus</h3>
+        <ul>
+            <li><strong>d1:</strong> w1, w2, w3, w4, w5, w4</li>
+            <li><strong>d2:</strong> w3, w4, w5, w2, w1, w2, w3, w4</li>
+        </ul>
+        <p>We first preprocess the data to extract meaningful relationships.</p>
+    """,
+    unsafe_allow_html=True,
+    )
+
+    st.markdown(
+    """
+        <h3 style='color: #6A0572;'>📌 Steps to Process the Data</h3>
+        <ul>
+            <li>Create a <span class='highlight'>vocabulary</span> from the entire corpus: <pre style="background-color:#F7F7F7; padding: 10px; border-radius: 5px;">{w1, w2, w3, w4, w5}</pre></li>
+            <li>Generate a <strong>tabular dataset</strong> with:
+                <ul>
+                    <li><strong>Feature variables (Focus Words)</strong></li>
+                    <li><strong>Class variables (Context Words)</strong></li>
+                </ul>
+            </li>
+            <li>Apply a <span class='highlight'>window size</span> of 2 (how many neighbors we consider).</li>
+            <li>Slide the window over the text with <span class='highlight'>slide = 1</span>.</li>
+        </ul>
+    """,
+    unsafe_allow_html=True,
+    )
+
+    st.markdown(
+    """
+        <h3 style='color: #6A0572;'> Handling Variable Context Length</h3>
+        <ul>
+            <li>To ensure a consistent feature length, we use <strong>zero-padding</strong> when needed.</li>
+            <li>The model tries to understand relationships<span class='highlight'>focus words</span>.</li>
+        </ul>
+    """,
+    unsafe_allow_html=True,
+    )
+
+    st.markdown(
+    """
+        <strong>Mathematical Representation:</strong>
+        <pre style="background-color:#F7F7F7; padding: 10px; border-radius: 5px;">
+        y = f(xi)
+        where,
+        y = Context Word 
+        xi = Focus Words 
+        </pre>
+    """,
+    unsafe_allow_html=True,
+    )
+
+    st.markdown(
+    """
+        <h3 style='color: #6A0572;'> Training with Artificial Neural Networks</h3>
+        <p>The tabular data is passed to an <strong>Artificial Neural Network (ANN)</strong> which learns:</p>
+        <ul>
+            <li>How <span class='highlight'>focus words</span> are related with <span class='highlight'>context words</span>.</li>
+        </ul>
+    """,
+    unsafe_allow_html=True,
+    )