Keras 3 migration

examples/nlp/ipynb/multiple_choice_task_with_transfer_learning.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "**Author:** Md Awsafur Rahman<br>\n",
     "**Date created:** 2023/09/14<br>\n",
-    "**Last modified:** 2023/09/14<br>\n",
+    "**Last modified:** 2024/01/10<br>\n",
     "**Description:** Use pre-trained nlp models for multiplechoice task."
    ]
   },
@@ -45,9 +45,10 @@
    },
    "outputs": [],
    "source": [
-    "import keras_nlp\n",
+    "\n",
     "import keras\n",
-    "import tensorflow as tf  # For tf.data only.\n",
+    "import keras_nlp\n",
+    "import tensorflow as tf\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -856,9 +857,9 @@
     "    question = row.startphrase\n",
     "    pred_answer = f\"ending{pred_answers[i]}\"\n",
     "    true_answer = f\"ending{true_answers[i]}\"\n",
-    "    print(f\"\u2753 Sentence {i+1}:\\n{question}\\n\")\n",
-    "    print(f\"\u2705 True Ending: {true_answer}\\n   >> {row[true_answer]}\\n\")\n",
-    "    print(f\"\ud83e\udd16 Predicted Ending: {pred_answer}\\n   >> {row[pred_answer]}\\n\")\n",
+    "    print(f\"\u2753  Sentence {i+1}:\\n{question}\\n\")\n",
+    "    print(f\"\u2705  True Ending: {true_answer}\\n   >> {row[true_answer]}\\n\")\n",
+    "    print(f\"\ud83e\udd16  Predicted Ending: {pred_answer}\\n   >> {row[pred_answer]}\\n\")\n",
     "    print(\"-\" * 90, \"\\n\")"
    ]
   },