diff --git a/tsne_dim_reduction.ipynb b/tsne_dim_reduction.ipynb
index 0c471de..ac99eb5 100644
--- a/tsne_dim_reduction.ipynb
+++ b/tsne_dim_reduction.ipynb
@@ -51,12 +51,12 @@
     "tsne_normalized = scaler.fit_transform(tsne_results)\n",
     "\n",
     "# Add the normalized 2D coordinates back to your DataFrame\n",
-    "df['x_norm'] = tsne_normalized[:,0]\n",
+    "df['x'] = tsne_normalized[:,0]\n",
     "df['y'] = tsne_normalized[:,1]\n",
     "\n",
     "# Now you can plot the normalized results using matplotlib\n",
     "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(df['x_norm'], df['y'], s=1)  # You can also color the points by some label using the `c` argument\n",
+    "plt.scatter(df['x'], df['y'], s=1)  # You can also color the points by some label using the `c` argument\n",
     "plt.xlabel('Normalized t-SNE 2D One')\n",
     "plt.ylabel('Normalized t-SNE 2D Two')\n",
     "plt.title('2D t-SNE Visualization of Normalized Embeddings')\n",
@@ -94,7 +94,7 @@
        "      <th>TargetsNumber</th>\n",
        "      <th>Targets</th>\n",
        "      <th>jina-embeddings-v2-base-en</th>\n",
-       "      <th>x_norm</th>\n",
+       "      <th>x</th>\n",
        "      <th>y_norm</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -241,7 +241,7 @@
        "167   By 2020, enhance capacity-building support to...   \n",
        "168   By 2030, build on existing initiatives to dev...   \n",
        "\n",
-       "                            jina-embeddings-v2-base-en    x_norm    y_norm  \n",
+       "                            jina-embeddings-v2-base-en    x    y_norm  \n",
        "0    [0.013646188200000001, -0.8851571083, 0.273902...  0.474702  0.237202  \n",
        "1    [-0.5175303221, -0.1017927676, 0.1907873154000...  0.508123  0.238149  \n",
        "2    [-0.0685161054, -0.188393563, 0.4002646804, 0....  0.702829  0.147774  \n",