diff --git "a/kpa-t5-improved.ipynb" "b/kpa-t5-improved.ipynb"
deleted file mode 100644--- "a/kpa-t5-improved.ipynb"
+++ /dev/null
@@ -1,11068 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f2b60989",
-   "metadata": {
-    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
-    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:05:28.652668Z",
-     "iopub.status.busy": "2025-12-11T04:05:28.652028Z",
-     "iopub.status.idle": "2025-12-11T04:05:30.226465Z",
-     "shell.execute_reply": "2025-12-11T04:05:30.225650Z"
-    },
-    "papermill": {
-     "duration": 1.582329,
-     "end_time": "2025-12-11T04:05:30.227781",
-     "exception": false,
-     "start_time": "2025-12-11T04:05:28.645452",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/kaggle/input/ibm-debator-kpa/combined_argkp.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
-    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
-    "# For example, here's several helpful packages to load\n",
-    "\n",
-    "import numpy as np # linear algebra\n",
-    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
-    "\n",
-    "# Input data files are available in the read-only \"../input/\" directory\n",
-    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
-    "\n",
-    "import os\n",
-    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
-    "    for filename in filenames:\n",
-    "        print(os.path.join(dirname, filename))\n",
-    "\n",
-    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
-    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6c807ffd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:05:30.237671Z",
-     "iopub.status.busy": "2025-12-11T04:05:30.237059Z",
-     "iopub.status.idle": "2025-12-11T04:07:31.218492Z",
-     "shell.execute_reply": "2025-12-11T04:07:31.217615Z"
-    },
-    "papermill": {
-     "duration": 120.988001,
-     "end_time": "2025-12-11T04:07:31.220119",
-     "exception": false,
-     "start_time": "2025-12-11T04:05:30.232118",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.1/162.1 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
-      "bigframes 2.12.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\r\n",
-      "opentelemetry-proto 1.37.0 requires protobuf<7.0,>=5.0, but you have protobuf 3.20.3 which is incompatible.\r\n",
-      "onnx 1.18.0 requires protobuf>=4.25.1, but you have protobuf 3.20.3 which is incompatible.\r\n",
-      "a2a-sdk 0.3.10 requires protobuf>=5.29.5, but you have protobuf 3.20.3 which is incompatible.\r\n",
-      "ray 2.51.1 requires click!=8.3.0,>=7.0, but you have click 8.3.0 which is incompatible.\r\n",
-      "bigframes 2.12.0 requires rich<14,>=12.4.4, but you have rich 14.2.0 which is incompatible.\r\n",
-      "tensorflow-metadata 1.17.2 requires protobuf>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\r\n",
-      "pydrive2 1.21.3 requires cryptography<44, but you have cryptography 46.0.3 which is incompatible.\r\n",
-      "pydrive2 1.21.3 requires pyOpenSSL<=24.2.1,>=19.1.0, but you have pyopenssl 25.3.0 which is incompatible.\r\n",
-      "ydf 0.13.0 requires protobuf<7.0.0,>=5.29.1, but you have protobuf 3.20.3 which is incompatible.\r\n",
-      "grpcio-status 1.71.2 requires protobuf<6.0dev,>=5.26.1, but you have protobuf 3.20.3 which is incompatible.\r\n",
-      "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2025.10.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.7/130.7 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.5/8.5 MB\u001b[0m \u001b[31m77.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
-      "sentence-transformers 4.1.0 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.38.2 which is incompatible.\u001b[0m\u001b[31m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m70.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m81.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m42.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m69.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
-      "libcugraph-cu12 25.6.0 requires libraft-cu12==25.6.*, but you have libraft-cu12 25.2.0 which is incompatible.\r\n",
-      "pylibcugraph-cu12 25.6.0 requires pylibraft-cu12==25.6.*, but you have pylibraft-cu12 25.2.0 which is incompatible.\r\n",
-      "pylibcugraph-cu12 25.6.0 requires rmm-cu12==25.6.*, but you have rmm-cu12 25.2.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 MB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
-      "bigframes 2.12.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\r\n",
-      "pylibcudf-cu12 25.2.2 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\r\n",
-      "cudf-cu12 25.2.2 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\r\n",
-      "bigframes 2.12.0 requires rich<14,>=12.4.4, but you have rich 14.2.0 which is incompatible.\r\n",
-      "cudf-polars-cu12 25.6.0 requires pylibcudf-cu12==25.6.*, but you have pylibcudf-cu12 25.2.2 which is incompatible.\u001b[0m\u001b[31m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.1/61.1 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
-      "\u001b[?25h"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install -q protobuf==3.20.*\n",
-    "!pip install -q transformers==4.38.2\n",
-    "!pip install -q sentencepiece\n",
-    "!pip install -q accelerate\n",
-    "!pip install -q datasets\n",
-    "!pip install -q evaluate\n",
-    "!pip install transformers datasets evaluate rouge-score bert-score -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ae1f7913",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:31.283633Z",
-     "iopub.status.busy": "2025-12-11T04:07:31.282784Z",
-     "iopub.status.idle": "2025-12-11T04:07:42.792505Z",
-     "shell.execute_reply": "2025-12-11T04:07:42.791521Z"
-    },
-    "papermill": {
-     "duration": 11.542119,
-     "end_time": "2025-12-11T04:07:42.793870",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:31.251751",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ All dependencies imported successfully!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 1: INSTALL AND IMPORT DEPENDENCIES\n",
-    "# ============================================================================\n",
-    "\n",
-    "# Uncomment if running on Kaggle and packages are missing\n",
-    "# !pip install transformers datasets evaluate rouge-score bert-score -q\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "import torch\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "from transformers import (\n",
-    "    T5Tokenizer, \n",
-    "    T5ForConditionalGeneration,\n",
-    "    AdamW,\n",
-    "    get_linear_schedule_with_warmup\n",
-    ")\n",
-    "from tqdm.auto import tqdm\n",
-    "import random\n",
-    "import warnings\n",
-    "from collections import Counter\n",
-    "import re\n",
-    "\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "# Evaluation metrics\n",
-    "try:\n",
-    "    from rouge_score import rouge_scorer\n",
-    "    from bert_score import score as bert_score\n",
-    "    ROUGE_AVAILABLE = True\n",
-    "    BERT_SCORE_AVAILABLE = True\n",
-    "except:\n",
-    "    print(\"⚠️ ROUGE or BERTScore not available. Install with: pip install rouge-score bert-score\")\n",
-    "    ROUGE_AVAILABLE = False\n",
-    "    BERT_SCORE_AVAILABLE = False\n",
-    "\n",
-    "print(\"✅ All dependencies imported successfully!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "49eed528",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:42.858417Z",
-     "iopub.status.busy": "2025-12-11T04:07:42.857428Z",
-     "iopub.status.idle": "2025-12-11T04:07:42.949123Z",
-     "shell.execute_reply": "2025-12-11T04:07:42.948148Z"
-    },
-    "papermill": {
-     "duration": 0.12516,
-     "end_time": "2025-12-11T04:07:42.950447",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:42.825287",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🖥️  Using device: cuda\n",
-      "   GPU: Tesla P100-PCIE-16GB\n",
-      "   Memory: 17.06 GB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 2: SETUP AND CONFIGURATION\n",
-    "# ============================================================================\n",
-    "\n",
-    "# Set random seeds for reproducibility\n",
-    "def set_seed(seed=42):\n",
-    "    random.seed(seed)\n",
-    "    np.random.seed(seed)\n",
-    "    torch.manual_seed(seed)\n",
-    "    if torch.cuda.is_available():\n",
-    "        torch.cuda.manual_seed_all(seed)\n",
-    "\n",
-    "set_seed(42)\n",
-    "\n",
-    "# Configuration\n",
-    "class Config:\n",
-    "    MODEL_NAME = 't5-base'  # Using base model for better quality\n",
-    "    BATCH_SIZE = 8  # Reduced for larger model\n",
-    "    EPOCHS = 5  # More epochs for better learning\n",
-    "    LEARNING_RATE = 3e-5  # Lower learning rate for stability\n",
-    "    MAX_INPUT_LENGTH = 128\n",
-    "    MAX_TARGET_LENGTH = 256\n",
-    "    AUGMENTATION_RATIO = 0.9  # Better balance\n",
-    "    VAL_SPLIT = 0.15\n",
-    "    TEST_SPLIT = 0.1\n",
-    "    MIN_ARGUMENT_LENGTH = 50  # Filter short arguments (characters)\n",
-    "    MIN_WORD_COUNT = 8  # Minimum words in argument\n",
-    "    \n",
-    "config = Config()\n",
-    "\n",
-    "# Check device\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "print(f\"🖥️  Using device: {device}\")\n",
-    "if torch.cuda.is_available():\n",
-    "    print(f\"   GPU: {torch.cuda.get_device_name(0)}\")\n",
-    "    print(f\"   Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "68cd8a9b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:43.014687Z",
-     "iopub.status.busy": "2025-12-11T04:07:43.014161Z",
-     "iopub.status.idle": "2025-12-11T04:07:44.091705Z",
-     "shell.execute_reply": "2025-12-11T04:07:44.090770Z"
-    },
-    "papermill": {
-     "duration": 1.11139,
-     "end_time": "2025-12-11T04:07:44.092979",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:42.981589",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📂 Loading dataset...\n",
-      "   Removed 0 rows with missing values\n",
-      "   After length filter: 34762 samples\n",
-      "   After quality filter: 34726 samples\n",
-      "   Total removed: 2074 low-quality arguments\n",
-      "✅ Loaded 34726 high-quality samples\n",
-      "\n",
-      "📊 Dataset Statistics:\n",
-      "   Total samples: 34726\n",
-      "   Unique topics: 41\n",
-      "   Average argument length: 111 characters\n",
-      "\n",
-      "📊 Stance Distribution:\n",
-      "   Positive: 18816 (54.2%)\n",
-      "   Negative: 15910 (45.8%)\n",
-      "\n",
-      "📊 Top 5 Topics:\n",
-      "topic\n",
-      "We should legalize cannabis                1226\n",
-      "The USA is a good country to live in       1151\n",
-      "We should legalize sex selection           1107\n",
-      "The vow of celibacy should be abandoned    1056\n",
-      "We should ban the use of child actors      1032\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "📋 Sample Data:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>topic</th>\n",
-       "      <th>stance</th>\n",
-       "      <th>argument</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Assisted suicide should be a criminal offence</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>`people reach their limit when it comes to the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Assisted suicide should be a criminal offence</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>a cure or treatment may be discovered shortly ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Assisted suicide should be a criminal offence</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>a cure or treatment may be discovered shortly ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Assisted suicide should be a criminal offence</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>a cure or treatment may be discovered shortly ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Assisted suicide should be a criminal offence</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>a cure or treatment may be discovered shortly ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           topic    stance  \\\n",
-       "0  Assisted suicide should be a criminal offence  negative   \n",
-       "1  Assisted suicide should be a criminal offence  positive   \n",
-       "2  Assisted suicide should be a criminal offence  positive   \n",
-       "3  Assisted suicide should be a criminal offence  positive   \n",
-       "4  Assisted suicide should be a criminal offence  positive   \n",
-       "\n",
-       "                                            argument  \n",
-       "0  `people reach their limit when it comes to the...  \n",
-       "1  a cure or treatment may be discovered shortly ...  \n",
-       "2  a cure or treatment may be discovered shortly ...  \n",
-       "3  a cure or treatment may be discovered shortly ...  \n",
-       "4  a cure or treatment may be discovered shortly ...  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 3: DATA LOADING AND EXPLORATION\n",
-    "# ============================================================================\n",
-    "\n",
-    "def load_and_preprocess_data(file_path):\n",
-    "    \"\"\"\n",
-    "    Load CSV and extract only topic, stance, and argument columns\n",
-    "    Convert stance to readable format (positive/negative)\n",
-    "    Filter out low-quality arguments\n",
-    "    \"\"\"\n",
-    "    print(\"📂 Loading dataset...\")\n",
-    "    df = pd.read_csv(file_path)\n",
-    "    \n",
-    "    # Keep only required columns\n",
-    "    df = df[['topic', 'stance', 'argument']].copy()\n",
-    "    \n",
-    "    # Remove rows with missing values\n",
-    "    original_len = len(df)\n",
-    "    df = df.dropna()\n",
-    "    print(f\"   Removed {original_len - len(df)} rows with missing values\")\n",
-    "    \n",
-    "    # Filter out very short arguments (likely low quality)\n",
-    "    df = df[df['argument'].str.len() >= config.MIN_ARGUMENT_LENGTH].copy()\n",
-    "    df = df[df['argument'].str.split().str.len() >= config.MIN_WORD_COUNT].copy()\n",
-    "    print(f\"   After length filter: {len(df)} samples\")\n",
-    "    \n",
-    "    # Remove arguments that just repeat the topic\n",
-    "    def is_low_quality(row):\n",
-    "        topic_words = set(row['topic'].lower().split())\n",
-    "        arg_words = set(row['argument'].lower().split())\n",
-    "        overlap = len(topic_words & arg_words) / max(len(arg_words), 1)\n",
-    "        # If more than 70% of argument words are from topic, it's likely low quality\n",
-    "        if overlap > 0.7:\n",
-    "            return True\n",
-    "        \n",
-    "        # Filter out arguments with common filler patterns\n",
-    "        arg_lower = row['argument'].lower()\n",
-    "        \n",
-    "        # Remove arguments that are too generic or illogical\n",
-    "        bad_patterns = [\n",
-    "            'waste of time and money',\n",
-    "            'waste of money and time',\n",
-    "            'good thing',\n",
-    "            'bad thing',\n",
-    "            'for example',\n",
-    "            'etc. etc.',\n",
-    "            'and so on',\n",
-    "        ]\n",
-    "        \n",
-    "        # Count how many bad patterns appear\n",
-    "        pattern_count = sum(1 for pattern in bad_patterns if pattern in arg_lower)\n",
-    "        if pattern_count >= 2:  # If 2+ generic patterns, likely low quality\n",
-    "            return True\n",
-    "            \n",
-    "        return False\n",
-    "    \n",
-    "    df = df[~df.apply(is_low_quality, axis=1)].copy()\n",
-    "    print(f\"   After quality filter: {len(df)} samples\")\n",
-    "    print(f\"   Total removed: {original_len - len(df)} low-quality arguments\")\n",
-    "    \n",
-    "    # Convert stance from 1/-1 to positive/negative\n",
-    "    df['stance'] = df['stance'].apply(lambda x: 'positive' if x == 1 else 'negative')\n",
-    "    \n",
-    "    print(f\"✅ Loaded {len(df)} high-quality samples\")\n",
-    "    \n",
-    "    return df\n",
-    "\n",
-    "# Load the data\n",
-    "df = load_and_preprocess_data('/kaggle/input/ibm-debator-kpa/combined_argkp.csv')\n",
-    "\n",
-    "# Display basic statistics\n",
-    "print(\"\\n📊 Dataset Statistics:\")\n",
-    "print(f\"   Total samples: {len(df)}\")\n",
-    "print(f\"   Unique topics: {df['topic'].nunique()}\")\n",
-    "print(f\"   Average argument length: {df['argument'].str.len().mean():.0f} characters\")\n",
-    "\n",
-    "print(\"\\n📊 Stance Distribution:\")\n",
-    "stance_counts = df['stance'].value_counts()\n",
-    "for stance, count in stance_counts.items():\n",
-    "    percentage = (count / len(df)) * 100\n",
-    "    print(f\"   {stance.capitalize()}: {count} ({percentage:.1f}%)\")\n",
-    "\n",
-    "print(\"\\n📊 Top 5 Topics:\")\n",
-    "print(df['topic'].value_counts().head())\n",
-    "\n",
-    "# Display sample data\n",
-    "print(\"\\n📋 Sample Data:\")\n",
-    "display(df.head())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "19562a17",
-   "metadata": {
-    "papermill": {
-     "duration": 0.03176,
-     "end_time": "2025-12-11T04:07:44.156596",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:44.124836",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "220c9b00",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:44.222264Z",
-     "iopub.status.busy": "2025-12-11T04:07:44.221645Z",
-     "iopub.status.idle": "2025-12-11T04:07:44.845490Z",
-     "shell.execute_reply": "2025-12-11T04:07:44.844519Z"
-    },
-    "papermill": {
-     "duration": 0.658442,
-     "end_time": "2025-12-11T04:07:44.846871",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:44.188429",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🔄 Balancing dataset with data augmentation...\n",
-      "   Majority class (positive): 18816\n",
-      "   Minority class (negative): 15910\n",
-      "   Target minority count: 16934\n",
-      "   Generating 1024 augmented samples...\n",
-      "✅ Balanced dataset created with 35750 samples\n",
-      "📊 New stance distribution:\n",
-      "   Positive: 18816 (52.6%)\n",
-      "   Negative: 16934 (47.4%)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 5: DATA AUGMENTATION\n",
-    "# ============================================================================\n",
-    "\n",
-    "def augment_minority_class(df, target_ratio=0.9):\n",
-    "    \"\"\"\n",
-    "    Balance dataset by augmenting the minority stance class\n",
-    "    Uses advanced paraphrasing techniques and back-translation style augmentation\n",
-    "    \"\"\"\n",
-    "    print(\"\\n🔄 Balancing dataset with data augmentation...\")\n",
-    "    \n",
-    "    # Count samples per stance\n",
-    "    stance_counts = df['stance'].value_counts()\n",
-    "    majority_class = stance_counts.idxmax()\n",
-    "    minority_class = stance_counts.idxmin()\n",
-    "    \n",
-    "    majority_count = stance_counts[majority_class]\n",
-    "    minority_count = stance_counts[minority_class]\n",
-    "    \n",
-    "    # Calculate how many samples to generate\n",
-    "    target_minority_count = int(majority_count * target_ratio)\n",
-    "    samples_to_generate = max(0, target_minority_count - minority_count)\n",
-    "    \n",
-    "    print(f\"   Majority class ({majority_class}): {majority_count}\")\n",
-    "    print(f\"   Minority class ({minority_class}): {minority_count}\")\n",
-    "    print(f\"   Target minority count: {target_minority_count}\")\n",
-    "    print(f\"   Generating {samples_to_generate} augmented samples...\")\n",
-    "    \n",
-    "    if samples_to_generate == 0:\n",
-    "        print(\"   ✅ Dataset already balanced!\")\n",
-    "        return df\n",
-    "    \n",
-    "    # Get minority class samples\n",
-    "    minority_df = df[df['stance'] == minority_class].copy()\n",
-    "    \n",
-    "    # More sophisticated augmentation patterns\n",
-    "    def augment_text(text):\n",
-    "        \"\"\"Apply multiple augmentation techniques\"\"\"\n",
-    "        \n",
-    "        # Pattern 1: Synonym replacement\n",
-    "        synonyms = {\n",
-    "            'should': ['must', 'ought to', 'needs to'],\n",
-    "            'can': ['could', 'may', 'might'],\n",
-    "            'people': ['individuals', 'persons', 'citizens'],\n",
-    "            'because': ['since', 'as', 'due to the fact that'],\n",
-    "            'many': ['numerous', 'several', 'a lot of'],\n",
-    "            'important': ['crucial', 'essential', 'vital', 'significant'],\n",
-    "            'good': ['beneficial', 'positive', 'advantageous'],\n",
-    "            'bad': ['harmful', 'negative', 'detrimental', 'adverse'],\n",
-    "            'make': ['create', 'produce', 'generate'],\n",
-    "            'use': ['utilize', 'employ', 'apply'],\n",
-    "            'help': ['assist', 'aid', 'support'],\n",
-    "            'need': ['require', 'necessitate'],\n",
-    "            'want': ['desire', 'wish', 'seek'],\n",
-    "            'think': ['believe', 'consider', 'feel'],\n",
-    "            'know': ['understand', 'recognize', 'realize'],\n",
-    "            'get': ['obtain', 'acquire', 'receive'],\n",
-    "            'give': ['provide', 'offer', 'supply'],\n",
-    "        }\n",
-    "        \n",
-    "        words = text.split()\n",
-    "        for i, word in enumerate(words):\n",
-    "            word_lower = word.lower().strip('.,!?;:')\n",
-    "            if word_lower in synonyms and random.random() < 0.3:  # 30% chance\n",
-    "                replacement = random.choice(synonyms[word_lower])\n",
-    "                # Preserve capitalization\n",
-    "                if word[0].isupper():\n",
-    "                    replacement = replacement.capitalize()\n",
-    "                words[i] = word.replace(word_lower, replacement)\n",
-    "        \n",
-    "        augmented = ' '.join(words)\n",
-    "        \n",
-    "        # Pattern 2: Sentence restructuring (simple reordering)\n",
-    "        if 'because' in augmented.lower() and random.random() < 0.3:\n",
-    "            parts = augmented.split(' because ')\n",
-    "            if len(parts) == 2:\n",
-    "                augmented = f\"Because {parts[1].strip()}, {parts[0].lower().strip()}\"\n",
-    "        \n",
-    "        return augmented\n",
-    "    \n",
-    "    augmented_samples = []\n",
-    "    \n",
-    "    # Generate augmented samples with variety\n",
-    "    for i in range(samples_to_generate):\n",
-    "        # Select a sample from minority class (cycle through)\n",
-    "        sample = minority_df.iloc[i % len(minority_df)].copy()\n",
-    "        \n",
-    "        # Apply augmentation 1-2 times\n",
-    "        augmented_text = sample['argument']\n",
-    "        for _ in range(random.randint(1, 2)):\n",
-    "            augmented_text = augment_text(augmented_text)\n",
-    "        \n",
-    "        sample['argument'] = augmented_text\n",
-    "        augmented_samples.append(sample)\n",
-    "    \n",
-    "    # Create augmented dataframe\n",
-    "    augmented_df = pd.DataFrame(augmented_samples)\n",
-    "    \n",
-    "    # Combine original and augmented data\n",
-    "    balanced_df = pd.concat([df, augmented_df], ignore_index=True)\n",
-    "    \n",
-    "    # Shuffle the dataset\n",
-    "    balanced_df = balanced_df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
-    "    \n",
-    "    print(f\"✅ Balanced dataset created with {len(balanced_df)} samples\")\n",
-    "    print(f\"📊 New stance distribution:\")\n",
-    "    new_counts = balanced_df['stance'].value_counts()\n",
-    "    for stance, count in new_counts.items():\n",
-    "        percentage = (count / len(balanced_df)) * 100\n",
-    "        print(f\"   {stance.capitalize()}: {count} ({percentage:.1f}%)\")\n",
-    "    \n",
-    "    return balanced_df\n",
-    "\n",
-    "# Apply data augmentation\n",
-    "balanced_df = augment_minority_class(df, target_ratio=config.AUGMENTATION_RATIO)\n",
-    "\n",
-    "# Visualize before/after augmentation\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
-    "\n",
-    "# Before augmentation\n",
-    "df['stance'].value_counts().plot(kind='bar', ax=ax1, color=['#FF6B6B', '#4ECDC4'])\n",
-    "ax1.set_title('Before Augmentation', fontsize=14, fontweight='bold')\n",
-    "ax1.set_xlabel('Stance')\n",
-    "ax1.set_ylabel('Count')\n",
-    "ax1.tick_params(axis='x', rotation=0)\n",
-    "\n",
-    "# After augmentation\n",
-    "balanced_df['stance'].value_counts().plot(kind='bar', ax=ax2, color=['#FF6B6B', '#4ECDC4'])\n",
-    "ax2.set_title('After Augmentation', fontsize=14, fontweight='bold')\n",
-    "ax2.set_xlabel('Stance')\n",
-    "ax2.set_ylabel('Count')\n",
-    "ax2.tick_params(axis='x', rotation=0)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a46aa2da",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:44.913226Z",
-     "iopub.status.busy": "2025-12-11T04:07:44.912303Z",
-     "iopub.status.idle": "2025-12-11T04:07:44.985179Z",
-     "shell.execute_reply": "2025-12-11T04:07:44.984025Z"
-    },
-    "papermill": {
-     "duration": 0.107449,
-     "end_time": "2025-12-11T04:07:44.986725",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:44.879276",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "✂️  Splitting dataset into train/val/test sets...\n",
-      "   Training samples: 26812 (75.0%)\n",
-      "   Validation samples: 5363 (15.0%)\n",
-      "   Test samples: 3575 (10.0%)\n",
-      "\n",
-      "📊 Stance distribution across splits:\n",
-      "   Train: Positive=14111, Negative=12701\n",
-      "   Val: Positive=2823, Negative=2540\n",
-      "   Test: Positive=1882, Negative=1693\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 6: TRAIN/VAL/TEST SPLIT\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n✂️  Splitting dataset into train/val/test sets...\")\n",
-    "\n",
-    "# First split: separate test set\n",
-    "train_val_df, test_df = train_test_split(\n",
-    "    balanced_df,\n",
-    "    test_size=config.TEST_SPLIT,\n",
-    "    random_state=42,\n",
-    "    stratify=balanced_df['stance']\n",
-    ")\n",
-    "\n",
-    "# Second split: separate train and validation\n",
-    "train_df, val_df = train_test_split(\n",
-    "    train_val_df,\n",
-    "    test_size=config.VAL_SPLIT / (1 - config.TEST_SPLIT),  # Adjust for already removed test set\n",
-    "    random_state=42,\n",
-    "    stratify=train_val_df['stance']\n",
-    ")\n",
-    "\n",
-    "print(f\"   Training samples: {len(train_df)} ({len(train_df)/len(balanced_df)*100:.1f}%)\")\n",
-    "print(f\"   Validation samples: {len(val_df)} ({len(val_df)/len(balanced_df)*100:.1f}%)\")\n",
-    "print(f\"   Test samples: {len(test_df)} ({len(test_df)/len(balanced_df)*100:.1f}%)\")\n",
-    "\n",
-    "print(\"\\n📊 Stance distribution across splits:\")\n",
-    "for name, split_df in [(\"Train\", train_df), (\"Val\", val_df), (\"Test\", test_df)]:\n",
-    "    counts = split_df['stance'].value_counts()\n",
-    "    print(f\"   {name}: Positive={counts.get('positive', 0)}, Negative={counts.get('negative', 0)}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1a6c62a9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:45.061315Z",
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-     "iopub.status.idle": "2025-12-11T04:07:45.069063Z",
-     "shell.execute_reply": "2025-12-11T04:07:45.068277Z"
-    },
-    "papermill": {
-     "duration": 0.046444,
-     "end_time": "2025-12-11T04:07:45.070257",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:45.023813",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Dataset class defined successfully!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 7: DATASET CLASS\n",
-    "# ============================================================================\n",
-    "\n",
-    "class ArgumentDataset(Dataset):\n",
-    "    \"\"\"\n",
-    "    Custom Dataset for argument generation\n",
-    "    Input format: \"generate argument: topic: [TOPIC] | stance: [STANCE]\"\n",
-    "    Output format: \"[ARGUMENT]\"\n",
-    "    \"\"\"\n",
-    "    def __init__(self, dataframe, tokenizer, max_input_length=128, max_target_length=256):\n",
-    "        self.data = dataframe.reset_index(drop=True)\n",
-    "        self.tokenizer = tokenizer\n",
-    "        self.max_input_length = max_input_length\n",
-    "        self.max_target_length = max_target_length\n",
-    "    \n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "    \n",
-    "    def __getitem__(self, idx):\n",
-    "        row = self.data.iloc[idx]\n",
-    "        \n",
-    "        # Create input text with stronger stance conditioning\n",
-    "        stance_instruction = \"support\" if row['stance'] == 'positive' else \"oppose\"\n",
-    "        input_text = f\"Task: {stance_instruction} the following position with a logical argument. Topic: {row['topic']} | Generate argument:\"\n",
-    "        target_text = row['argument']\n",
-    "        \n",
-    "        # Tokenize input\n",
-    "        input_encoding = self.tokenizer(\n",
-    "            input_text,\n",
-    "            max_length=self.max_input_length,\n",
-    "            padding='max_length',\n",
-    "            truncation=True,\n",
-    "            return_tensors='pt'\n",
-    "        )\n",
-    "        \n",
-    "        # Tokenize target\n",
-    "        target_encoding = self.tokenizer(\n",
-    "            target_text,\n",
-    "            max_length=self.max_target_length,\n",
-    "            padding='max_length',\n",
-    "            truncation=True,\n",
-    "            return_tensors='pt'\n",
-    "        )\n",
-    "        \n",
-    "        # Get input_ids and attention_mask, remove batch dimension\n",
-    "        input_ids = input_encoding['input_ids'].squeeze()\n",
-    "        attention_mask = input_encoding['attention_mask'].squeeze()\n",
-    "        labels = target_encoding['input_ids'].squeeze()\n",
-    "        \n",
-    "        # Replace padding token ids in labels with -100 (ignored by loss function)\n",
-    "        labels[labels == self.tokenizer.pad_token_id] = -100\n",
-    "        \n",
-    "        return {\n",
-    "            'input_ids': input_ids,\n",
-    "            'attention_mask': attention_mask,\n",
-    "            'labels': labels\n",
-    "        }\n",
-    "\n",
-    "print(\"✅ Dataset class defined successfully!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0f83500f",
-   "metadata": {
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-    },
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-     "exception": false,
-     "start_time": "2025-12-11T04:07:45.103029",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🤖 Loading model and tokenizer...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "336d4cddc1004f71bfb6bff7d3d53646",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
-      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c1f11116bda948079e6274f4025592e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bce20354a10d48b4a0efcccb4c0ffd9a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Model loaded: t5-base\n",
-      "   Total parameters: 222,903,552\n",
-      "   Trainable parameters: 222,903,552\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 8: INITIALIZE MODEL AND TOKENIZER\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n🤖 Loading model and tokenizer...\")\n",
-    "tokenizer = T5Tokenizer.from_pretrained(config.MODEL_NAME)\n",
-    "model = T5ForConditionalGeneration.from_pretrained(config.MODEL_NAME)\n",
-    "model.to(device)\n",
-    "\n",
-    "# Count model parameters\n",
-    "total_params = sum(p.numel() for p in model.parameters())\n",
-    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
-    "\n",
-    "print(f\"✅ Model loaded: {config.MODEL_NAME}\")\n",
-    "print(f\"   Total parameters: {total_params:,}\")\n",
-    "print(f\"   Trainable parameters: {trainable_params:,}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0aeb4d66",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:51.253996Z",
-     "iopub.status.busy": "2025-12-11T04:07:51.253194Z",
-     "iopub.status.idle": "2025-12-11T04:07:51.262197Z",
-     "shell.execute_reply": "2025-12-11T04:07:51.261355Z"
-    },
-    "papermill": {
-     "duration": 0.044239,
-     "end_time": "2025-12-11T04:07:51.263400",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:51.219161",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📦 Creating datasets and dataloaders...\n",
-      "✅ Dataloaders created:\n",
-      "   Train batches: 3352\n",
-      "   Val batches: 671\n",
-      "   Test batches: 447\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 9: CREATE DATALOADERS\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n📦 Creating datasets and dataloaders...\")\n",
-    "\n",
-    "train_dataset = ArgumentDataset(train_df, tokenizer, config.MAX_INPUT_LENGTH, config.MAX_TARGET_LENGTH)\n",
-    "val_dataset = ArgumentDataset(val_df, tokenizer, config.MAX_INPUT_LENGTH, config.MAX_TARGET_LENGTH)\n",
-    "test_dataset = ArgumentDataset(test_df, tokenizer, config.MAX_INPUT_LENGTH, config.MAX_TARGET_LENGTH)\n",
-    "\n",
-    "train_loader = DataLoader(train_dataset, batch_size=config.BATCH_SIZE, shuffle=True)\n",
-    "val_loader = DataLoader(val_dataset, batch_size=config.BATCH_SIZE)\n",
-    "test_loader = DataLoader(test_dataset, batch_size=config.BATCH_SIZE)\n",
-    "\n",
-    "print(f\"✅ Dataloaders created:\")\n",
-    "print(f\"   Train batches: {len(train_loader)}\")\n",
-    "print(f\"   Val batches: {len(val_loader)}\")\n",
-    "print(f\"   Test batches: {len(test_loader)}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "820c26f9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:51.332733Z",
-     "iopub.status.busy": "2025-12-11T04:07:51.332093Z",
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-    },
-    "papermill": {
-     "duration": 0.048094,
-     "end_time": "2025-12-11T04:07:51.345276",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:51.297182",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 10: TRAINING FUNCTION\n",
-    "# ============================================================================\n",
-    "\n",
-    "def train_model(model, train_loader, val_loader, device, epochs=3, learning_rate=5e-5):\n",
-    "    \"\"\"\n",
-    "    Train the T5 model on argument generation task\n",
-    "    Uses AdamW optimizer with linear warmup schedule\n",
-    "    Tracks and returns training history\n",
-    "    \"\"\"\n",
-    "    print(f\"\\n🚀 Starting training on {device}...\")\n",
-    "    \n",
-    "    # Optimizer\n",
-    "    optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
-    "    \n",
-    "    # Learning rate scheduler with warmup\n",
-    "    total_steps = len(train_loader) * epochs\n",
-    "    warmup_steps = int(0.1 * total_steps)  # 10% warmup\n",
-    "    scheduler = get_linear_schedule_with_warmup(\n",
-    "        optimizer,\n",
-    "        num_warmup_steps=warmup_steps,\n",
-    "        num_training_steps=total_steps\n",
-    "    )\n",
-    "    \n",
-    "    # Track history\n",
-    "    history = {\n",
-    "        'train_loss': [],\n",
-    "        'val_loss': [],\n",
-    "        'learning_rate': []\n",
-    "    }\n",
-    "    \n",
-    "    # Training loop\n",
-    "    for epoch in range(epochs):\n",
-    "        print(f\"\\n{'='*60}\")\n",
-    "        print(f\"📚 Epoch {epoch + 1}/{epochs}\")\n",
-    "        print(f\"{'='*60}\")\n",
-    "        \n",
-    "        # Training phase\n",
-    "        model.train()\n",
-    "        train_loss = 0\n",
-    "        train_pbar = tqdm(train_loader, desc=\"Training\")\n",
-    "        \n",
-    "        for batch in train_pbar:\n",
-    "            # Move batch to device\n",
-    "            input_ids = batch['input_ids'].to(device)\n",
-    "            attention_mask = batch['attention_mask'].to(device)\n",
-    "            labels = batch['labels'].to(device)\n",
-    "            \n",
-    "            # Forward pass\n",
-    "            outputs = model(\n",
-    "                input_ids=input_ids,\n",
-    "                attention_mask=attention_mask,\n",
-    "                labels=labels\n",
-    "            )\n",
-    "            \n",
-    "            loss = outputs.loss\n",
-    "            train_loss += loss.item()\n",
-    "            \n",
-    "            # Backward pass\n",
-    "            optimizer.zero_grad()\n",
-    "            loss.backward()\n",
-    "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)  # Gradient clipping\n",
-    "            optimizer.step()\n",
-    "            scheduler.step()\n",
-    "            \n",
-    "            # Update progress bar\n",
-    "            train_pbar.set_postfix({\n",
-    "                'loss': f'{loss.item():.4f}',\n",
-    "                'lr': f'{scheduler.get_last_lr()[0]:.2e}'\n",
-    "            })\n",
-    "        \n",
-    "        avg_train_loss = train_loss / len(train_loader)\n",
-    "        \n",
-    "        # Validation phase\n",
-    "        model.eval()\n",
-    "        val_loss = 0\n",
-    "        val_pbar = tqdm(val_loader, desc=\"Validation\")\n",
-    "        \n",
-    "        with torch.no_grad():\n",
-    "            for batch in val_pbar:\n",
-    "                input_ids = batch['input_ids'].to(device)\n",
-    "                attention_mask = batch['attention_mask'].to(device)\n",
-    "                labels = batch['labels'].to(device)\n",
-    "                \n",
-    "                outputs = model(\n",
-    "                    input_ids=input_ids,\n",
-    "                    attention_mask=attention_mask,\n",
-    "                    labels=labels\n",
-    "                )\n",
-    "                \n",
-    "                val_loss += outputs.loss.item()\n",
-    "                val_pbar.set_postfix({'loss': f'{outputs.loss.item():.4f}'})\n",
-    "        \n",
-    "        avg_val_loss = val_loss / len(val_loader)\n",
-    "        \n",
-    "        # Store history\n",
-    "        history['train_loss'].append(avg_train_loss)\n",
-    "        history['val_loss'].append(avg_val_loss)\n",
-    "        history['learning_rate'].append(scheduler.get_last_lr()[0])\n",
-    "        \n",
-    "        # Print epoch summary\n",
-    "        print(f\"\\n📊 Epoch {epoch + 1} Summary:\")\n",
-    "        print(f\"   Train Loss: {avg_train_loss:.4f}\")\n",
-    "        print(f\"   Val Loss: {avg_val_loss:.4f}\")\n",
-    "        print(f\"   Learning Rate: {scheduler.get_last_lr()[0]:.2e}\")\n",
-    "    \n",
-    "    print(f\"\\n{'='*60}\")\n",
-    "    print(\"🎉 Training completed!\")\n",
-    "    print(f\"{'='*60}\")\n",
-    "    \n",
-    "    return model, history\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "489b4cda",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T04:07:51.415003Z",
-     "iopub.status.busy": "2025-12-11T04:07:51.414669Z",
-     "iopub.status.idle": "2025-12-11T06:17:21.727760Z",
-     "shell.execute_reply": "2025-12-11T06:17:21.726994Z"
-    },
-    "papermill": {
-     "duration": 7770.350052,
-     "end_time": "2025-12-11T06:17:21.729398",
-     "exception": false,
-     "start_time": "2025-12-11T04:07:51.379346",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🚀 Starting training on cuda...\n",
-      "\n",
-      "============================================================\n",
-      "📚 Epoch 1/5\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bbc477d17ab0497caef18b45ceecd0ce",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training:   0%|          | 0/3352 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a9267c24f46549a8aea150d89873cd1a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation:   0%|          | 0/671 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Epoch 1 Summary:\n",
-      "   Train Loss: 2.8585\n",
-      "   Val Loss: 2.4069\n",
-      "   Learning Rate: 2.67e-05\n",
-      "\n",
-      "============================================================\n",
-      "📚 Epoch 2/5\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2d5aa2090a6f48f5b3dc4eb87495001c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training:   0%|          | 0/3352 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ad3a6ebcf3a4d75b3133bbf42aba5db",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation:   0%|          | 0/671 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Epoch 2 Summary:\n",
-      "   Train Loss: 2.4697\n",
-      "   Val Loss: 2.2124\n",
-      "   Learning Rate: 2.00e-05\n",
-      "\n",
-      "============================================================\n",
-      "📚 Epoch 3/5\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8161e90bb5b44cc8ecafae9ae7e9293",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training:   0%|          | 0/3352 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f158f58c255464aa0989eeb722f3127",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation:   0%|          | 0/671 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Epoch 3 Summary:\n",
-      "   Train Loss: 2.3143\n",
-      "   Val Loss: 2.0962\n",
-      "   Learning Rate: 1.33e-05\n",
-      "\n",
-      "============================================================\n",
-      "📚 Epoch 4/5\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e225f6e4bbd04242ad29233b872f5ef3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training:   0%|          | 0/3352 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9565c09f1974a2ba25c85b8f4461678",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation:   0%|          | 0/671 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Epoch 4 Summary:\n",
-      "   Train Loss: 2.2197\n",
-      "   Val Loss: 2.0285\n",
-      "   Learning Rate: 6.67e-06\n",
-      "\n",
-      "============================================================\n",
-      "📚 Epoch 5/5\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8b40396922884dc3ae5e3ba3b06fc264",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training:   0%|          | 0/3352 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f3ee7ffa48c45f8871bd37d6cdd0f65",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation:   0%|          | 0/671 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Epoch 5 Summary:\n",
-      "   Train Loss: 2.1696\n",
-      "   Val Loss: 2.0056\n",
-      "   Learning Rate: 0.00e+00\n",
-      "\n",
-      "============================================================\n",
-      "🎉 Training completed!\n",
-      "============================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1500x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 11: TRAIN THE MODEL\n",
-    "# ============================================================================\n",
-    "\n",
-    "model, history = train_model(\n",
-    "    model, \n",
-    "    train_loader, \n",
-    "    val_loader, \n",
-    "    device, \n",
-    "    epochs=config.EPOCHS,\n",
-    "    learning_rate=config.LEARNING_RATE\n",
-    ")\n",
-    "\n",
-    "# Plot training history\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
-    "\n",
-    "# Loss curves\n",
-    "epochs_range = range(1, config.EPOCHS + 1)\n",
-    "ax1.plot(epochs_range, history['train_loss'], 'b-o', label='Train Loss', linewidth=2)\n",
-    "ax1.plot(epochs_range, history['val_loss'], 'r-o', label='Val Loss', linewidth=2)\n",
-    "ax1.set_xlabel('Epoch', fontsize=12)\n",
-    "ax1.set_ylabel('Loss', fontsize=12)\n",
-    "ax1.set_title('Training and Validation Loss', fontsize=14, fontweight='bold')\n",
-    "ax1.legend()\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Learning rate\n",
-    "ax2.plot(epochs_range, history['learning_rate'], 'g-o', linewidth=2)\n",
-    "ax2.set_xlabel('Epoch', fontsize=12)\n",
-    "ax2.set_ylabel('Learning Rate', fontsize=12)\n",
-    "ax2.set_title('Learning Rate Schedule', fontsize=14, fontweight='bold')\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "ax2.ticklabel_format(style='scientific', axis='y', scilimits=(0,0))\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "aa36a444",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T06:17:21.791124Z",
-     "iopub.status.busy": "2025-12-11T06:17:21.790888Z",
-     "iopub.status.idle": "2025-12-11T06:17:21.797719Z",
-     "shell.execute_reply": "2025-12-11T06:17:21.797094Z"
-    },
-    "papermill": {
-     "duration": 0.03889,
-     "end_time": "2025-12-11T06:17:21.798773",
-     "exception": false,
-     "start_time": "2025-12-11T06:17:21.759883",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Inference function defined!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 12: INFERENCE FUNCTION\n",
-    "# ============================================================================\n",
-    "\n",
-    "def generate_argument(model, tokenizer, topic, stance, device, max_length=256, num_beams=8, temperature=0.85):\n",
-    "    \"\"\"\n",
-    "    Generate an argument for a given topic and stance with improved parameters\n",
-    "    \n",
-    "    Args:\n",
-    "        model: Fine-tuned T5 model\n",
-    "        tokenizer: T5 tokenizer\n",
-    "        topic: Topic string\n",
-    "        stance: 'positive' or 'negative'\n",
-    "        device: torch device\n",
-    "        max_length: Maximum length of generated text\n",
-    "        num_beams: Number of beams for beam search (higher = better quality)\n",
-    "        temperature: Sampling temperature (lower = more focused)\n",
-    "    \n",
-    "    Returns:\n",
-    "        Generated argument text\n",
-    "    \"\"\"\n",
-    "    model.eval()\n",
-    "    \n",
-    "    # Create input text - matching training format with stronger stance conditioning\n",
-    "    stance_instruction = \"support\" if stance == 'positive' else \"oppose\"\n",
-    "    input_text = f\"Task: {stance_instruction} the following position with a logical argument. Topic: {topic} | Generate argument:\"\n",
-    "    \n",
-    "    # Tokenize\n",
-    "    input_ids = tokenizer(\n",
-    "        input_text,\n",
-    "        return_tensors='pt',\n",
-    "        max_length=config.MAX_INPUT_LENGTH,\n",
-    "        truncation=True\n",
-    "    ).input_ids.to(device)\n",
-    "    \n",
-    "    # Generate with improved parameters for coherence\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model.generate(\n",
-    "            input_ids=input_ids,\n",
-    "            max_length=max_length,\n",
-    "            min_length=35,  # Minimum length for substance\n",
-    "            num_beams=num_beams,  # More beams for better quality\n",
-    "            temperature=temperature,  # Lower temperature for more coherent output\n",
-    "            top_k=40,  # Reduced top-k for better focus\n",
-    "            top_p=0.92,  # Slightly lower nucleus sampling\n",
-    "            do_sample=True,  # Enable sampling for diversity\n",
-    "            early_stopping=True,\n",
-    "            no_repeat_ngram_size=4,  # Prevent 4-gram repetition (stricter)\n",
-    "            length_penalty=1.5,  # Stronger encouragement for longer outputs\n",
-    "            repetition_penalty=2.0,  # Very strong repetition penalty\n",
-    "            encoder_repetition_penalty=1.5,  # Also penalize input repetition\n",
-    "        )\n",
-    "    \n",
-    "    # Decode\n",
-    "    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
-    "    \n",
-    "    return generated_text\n",
-    "\n",
-    "print(\"✅ Inference function defined!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "db3c5d44",
-   "metadata": {
-    "execution": {
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-     "iopub.status.idle": "2025-12-11T06:17:21.874293Z",
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-     "exception": false,
-     "start_time": "2025-12-11T06:17:21.829580",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Evaluator initialized!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 13: EVALUATION METRICS\n",
-    "# ============================================================================\n",
-    "\n",
-    "class ArgumentEvaluator:\n",
-    "    \"\"\"Comprehensive evaluation metrics for generated arguments\"\"\"\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        if ROUGE_AVAILABLE:\n",
-    "            self.rouge_scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n",
-    "    \n",
-    "    def calculate_rouge(self, reference, generated):\n",
-    "        \"\"\"Calculate ROUGE scores\"\"\"\n",
-    "        if not ROUGE_AVAILABLE:\n",
-    "            return None\n",
-    "        scores = self.rouge_scorer.score(reference, generated)\n",
-    "        return {\n",
-    "            'rouge1': scores['rouge1'].fmeasure,\n",
-    "            'rouge2': scores['rouge2'].fmeasure,\n",
-    "            'rougeL': scores['rougeL'].fmeasure\n",
-    "        }\n",
-    "    \n",
-    "    def calculate_bert_score(self, references, generated_list):\n",
-    "        \"\"\"Calculate BERTScore (batch processing for efficiency)\"\"\"\n",
-    "        if not BERT_SCORE_AVAILABLE:\n",
-    "            return None\n",
-    "        P, R, F1 = bert_score(generated_list, references, lang='en', verbose=False)\n",
-    "        return {\n",
-    "            'precision': P.mean().item(),\n",
-    "            'recall': R.mean().item(),\n",
-    "            'f1': F1.mean().item()\n",
-    "        }\n",
-    "    \n",
-    "    def calculate_diversity(self, generated_text):\n",
-    "        \"\"\"Calculate lexical diversity metrics\"\"\"\n",
-    "        words = generated_text.lower().split()\n",
-    "        if len(words) == 0:\n",
-    "            return 0.0\n",
-    "        unique_words = set(words)\n",
-    "        return len(unique_words) / len(words)\n",
-    "    \n",
-    "    def calculate_length_similarity(self, reference, generated):\n",
-    "        \"\"\"Calculate how similar the lengths are\"\"\"\n",
-    "        ref_len = len(reference.split())\n",
-    "        gen_len = len(generated.split())\n",
-    "        return 1 - abs(ref_len - gen_len) / max(ref_len, gen_len)\n",
-    "    \n",
-    "    def check_creativity(self, reference, generated):\n",
-    "        \"\"\"\n",
-    "        Check if generated text is creative (not just copying reference)\n",
-    "        Returns similarity score (lower = more creative)\n",
-    "        \"\"\"\n",
-    "        ref_words = set(reference.lower().split())\n",
-    "        gen_words = set(generated.lower().split())\n",
-    "        \n",
-    "        if len(gen_words) == 0:\n",
-    "            return 0.0\n",
-    "        \n",
-    "        # Calculate word overlap\n",
-    "        overlap = len(ref_words & gen_words) / len(gen_words)\n",
-    "        return 1 - overlap  # Return creativity score (higher = more creative)\n",
-    "    \n",
-    "    def evaluate_test_set(self, model, tokenizer, test_df, device, sample_size=None):\n",
-    "        \"\"\"Evaluate model on test set with comprehensive metrics\"\"\"\n",
-    "        print(\"\\n📊 Evaluating model on test set...\")\n",
-    "        \n",
-    "        if sample_size:\n",
-    "            test_sample = test_df.sample(n=min(sample_size, len(test_df)), random_state=42)\n",
-    "        else:\n",
-    "            test_sample = test_df\n",
-    "        \n",
-    "        results = []\n",
-    "        references = []\n",
-    "        generated_list = []\n",
-    "        \n",
-    "        for idx, row in tqdm(test_sample.iterrows(), total=len(test_sample), desc=\"Evaluating\"):\n",
-    "            # Generate argument\n",
-    "            generated = generate_argument(\n",
-    "                model, tokenizer, row['topic'], row['stance'], device\n",
-    "            )\n",
-    "            \n",
-    "            reference = row['argument']\n",
-    "            \n",
-    "            # Calculate metrics\n",
-    "            rouge_scores = self.calculate_rouge(reference, generated)\n",
-    "            diversity = self.calculate_diversity(generated)\n",
-    "            length_sim = self.calculate_length_similarity(reference, generated)\n",
-    "            creativity = self.check_creativity(reference, generated)\n",
-    "            \n",
-    "            results.append({\n",
-    "                'topic': row['topic'],\n",
-    "                'stance': row['stance'],\n",
-    "                'reference': reference,\n",
-    "                'generated': generated,\n",
-    "                'rouge1': rouge_scores['rouge1'] if rouge_scores else None,\n",
-    "                'rouge2': rouge_scores['rouge2'] if rouge_scores else None,\n",
-    "                'rougeL': rouge_scores['rougeL'] if rouge_scores else None,\n",
-    "                'diversity': diversity,\n",
-    "                'length_similarity': length_sim,\n",
-    "                'creativity': creativity\n",
-    "            })\n",
-    "            \n",
-    "            references.append(reference)\n",
-    "            generated_list.append(generated)\n",
-    "        \n",
-    "        # Calculate BERTScore for all samples at once (more efficient)\n",
-    "        if BERT_SCORE_AVAILABLE:\n",
-    "            bert_scores = self.calculate_bert_score(references, generated_list)\n",
-    "            print(f\"\\n📈 BERTScore (Overall):\")\n",
-    "            print(f\"   Precision: {bert_scores['precision']:.4f}\")\n",
-    "            print(f\"   Recall: {bert_scores['recall']:.4f}\")\n",
-    "            print(f\"   F1: {bert_scores['f1']:.4f}\")\n",
-    "        \n",
-    "        results_df = pd.DataFrame(results)\n",
-    "        \n",
-    "        # Print summary statistics\n",
-    "        print(f\"\\n📈 Evaluation Metrics Summary:\")\n",
-    "        if ROUGE_AVAILABLE:\n",
-    "            print(f\"   ROUGE-1: {results_df['rouge1'].mean():.4f} ± {results_df['rouge1'].std():.4f}\")\n",
-    "            print(f\"   ROUGE-2: {results_df['rouge2'].mean():.4f} ± {results_df['rouge2'].std():.4f}\")\n",
-    "            print(f\"   ROUGE-L: {results_df['rougeL'].mean():.4f} ± {results_df['rougeL'].std():.4f}\")\n",
-    "        print(f\"   Diversity: {results_df['diversity'].mean():.4f} ± {results_df['diversity'].std():.4f}\")\n",
-    "        print(f\"   Length Similarity: {results_df['length_similarity'].mean():.4f} ± {results_df['length_similarity'].std():.4f}\")\n",
-    "        print(f\"   Creativity Score: {results_df['creativity'].mean():.4f} ± {results_df['creativity'].std():.4f}\")\n",
-    "        \n",
-    "        return results_df\n",
-    "\n",
-    "evaluator = ArgumentEvaluator()\n",
-    "print(\"✅ Evaluator initialized!\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "4ad7cc5a",
-   "metadata": {
-    "execution": {
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-     "iopub.status.idle": "2025-12-11T06:18:41.994531Z",
-     "shell.execute_reply": "2025-12-11T06:18:41.993721Z"
-    },
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-     "duration": 80.090603,
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-     "exception": false,
-     "start_time": "2025-12-11T06:17:21.905198",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Evaluating model on test set...\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8ad7a9f1aaa4d29bcb7e7b3d69707a1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Evaluating:   0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
-      ]
-     },
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-    },
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-      "text/plain": [
-       "config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
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-      "text/plain": [
-       "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
-      ]
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-    },
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-      },
-      "text/plain": [
-       "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be39831b0d7a43c29d99ee8d67dae5f9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📈 BERTScore (Overall):\n",
-      "   Precision: 0.8745\n",
-      "   Recall: 0.8849\n",
-      "   F1: 0.8796\n",
-      "\n",
-      "📈 Evaluation Metrics Summary:\n",
-      "   ROUGE-1: 0.2740 ± 0.0900\n",
-      "   ROUGE-2: 0.0656 ± 0.0565\n",
-      "   ROUGE-L: 0.1995 ± 0.0668\n",
-      "   Diversity: 0.8859 ± 0.0546\n",
-      "   Length Similarity: 0.6121 ± 0.1967\n",
-      "   Creativity Score: 0.7837 ± 0.0847\n",
-      "\n",
-      "📋 Sample Evaluation Results:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>topic</th>\n",
-       "      <th>stance</th>\n",
-       "      <th>reference</th>\n",
-       "      <th>generated</th>\n",
-       "      <th>rouge1</th>\n",
-       "      <th>diversity</th>\n",
-       "      <th>creativity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Social media platforms should be regulated by ...</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>People should be able to express themselves fr...</td>\n",
-       "      <td>Social media platforms should not be regulated...</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.851852</td>\n",
-       "      <td>0.695652</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>We should end racial profiling</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>racial profiling does no one any good. it shou...</td>\n",
-       "      <td>racial profiling is discriminatory and should ...</td>\n",
-       "      <td>0.324324</td>\n",
-       "      <td>0.961538</td>\n",
-       "      <td>0.760000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>We should adopt atheism</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>People should be allowed to choose how to beli...</td>\n",
-       "      <td>we should not adopt atheism because it is a fo...</td>\n",
-       "      <td>0.447761</td>\n",
-       "      <td>0.896552</td>\n",
-       "      <td>0.615385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Homeschooling should be banned</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>absolutely wrong to ban homeschooling because ...</td>\n",
-       "      <td>homeschooling should not be banned because it ...</td>\n",
-       "      <td>0.274510</td>\n",
-       "      <td>0.861111</td>\n",
-       "      <td>0.838710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>We should subsidize space exploration</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>we should subsidize space exploration for rese...</td>\n",
-       "      <td>space exploration should be subsidized because...</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.766667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>We should ban cosmetic surgery</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>we should not ban cosmetic surgery because peo...</td>\n",
-       "      <td>cosmetic surgery can help people who have a se...</td>\n",
-       "      <td>0.307692</td>\n",
-       "      <td>0.967742</td>\n",
-       "      <td>0.733333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>We should prohibit flag burning</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>burning the flag is freedom of speech and it w...</td>\n",
-       "      <td>flag burning is a free speech right and should...</td>\n",
-       "      <td>0.423077</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>0.642857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>We should end racial profiling</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>racial profiling has been used to target minor...</td>\n",
-       "      <td>racial profiling is discriminatory and should ...</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.964286</td>\n",
-       "      <td>0.703704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>We should legalize cannabis</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>law enforcement are unable to effectively prev...</td>\n",
-       "      <td>we should legalize cannabis because it is a na...</td>\n",
-       "      <td>0.318841</td>\n",
-       "      <td>0.974359</td>\n",
-       "      <td>0.736842</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>We should introduce compulsory voting</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>everyone should be required to vote to make su...</td>\n",
-       "      <td>compulsory voting would ensure that all voters...</td>\n",
-       "      <td>0.307692</td>\n",
-       "      <td>0.818182</td>\n",
-       "      <td>0.777778</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               topic    stance  \\\n",
-       "0  Social media platforms should be regulated by ...  negative   \n",
-       "1                     We should end racial profiling  positive   \n",
-       "2                            We should adopt atheism  negative   \n",
-       "3                     Homeschooling should be banned  negative   \n",
-       "4              We should subsidize space exploration  positive   \n",
-       "5                     We should ban cosmetic surgery  negative   \n",
-       "6                    We should prohibit flag burning  negative   \n",
-       "7                     We should end racial profiling  positive   \n",
-       "8                        We should legalize cannabis  positive   \n",
-       "9              We should introduce compulsory voting  positive   \n",
-       "\n",
-       "                                           reference  \\\n",
-       "0  People should be able to express themselves fr...   \n",
-       "1  racial profiling does no one any good. it shou...   \n",
-       "2  People should be allowed to choose how to beli...   \n",
-       "3  absolutely wrong to ban homeschooling because ...   \n",
-       "4  we should subsidize space exploration for rese...   \n",
-       "5  we should not ban cosmetic surgery because peo...   \n",
-       "6  burning the flag is freedom of speech and it w...   \n",
-       "7  racial profiling has been used to target minor...   \n",
-       "8  law enforcement are unable to effectively prev...   \n",
-       "9  everyone should be required to vote to make su...   \n",
-       "\n",
-       "                                           generated    rouge1  diversity  \\\n",
-       "0  Social media platforms should not be regulated...  0.291667   0.851852   \n",
-       "1  racial profiling is discriminatory and should ...  0.324324   0.961538   \n",
-       "2  we should not adopt atheism because it is a fo...  0.447761   0.896552   \n",
-       "3  homeschooling should not be banned because it ...  0.274510   0.861111   \n",
-       "4  space exploration should be subsidized because...  0.400000   1.000000   \n",
-       "5  cosmetic surgery can help people who have a se...  0.307692   0.967742   \n",
-       "6  flag burning is a free speech right and should...  0.423077   0.800000   \n",
-       "7  racial profiling is discriminatory and should ...  0.347826   0.964286   \n",
-       "8  we should legalize cannabis because it is a na...  0.318841   0.974359   \n",
-       "9  compulsory voting would ensure that all voters...  0.307692   0.818182   \n",
-       "\n",
-       "   creativity  \n",
-       "0    0.695652  \n",
-       "1    0.760000  \n",
-       "2    0.615385  \n",
-       "3    0.838710  \n",
-       "4    0.766667  \n",
-       "5    0.733333  \n",
-       "6    0.642857  \n",
-       "7    0.703704  \n",
-       "8    0.736842  \n",
-       "9    0.777778  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 14: RUN TEST SET EVALUATION\n",
-    "# ============================================================================\n",
-    "\n",
-    "# Evaluate on test set (use sample_size=50 for faster evaluation)\n",
-    "test_results = evaluator.evaluate_test_set(\n",
-    "    model, \n",
-    "    tokenizer, \n",
-    "    test_df, \n",
-    "    device,\n",
-    "    sample_size=100  # Adjust based on your needs\n",
-    ")\n",
-    "\n",
-    "# Display sample results\n",
-    "print(\"\\n📋 Sample Evaluation Results:\")\n",
-    "display(test_results[['topic', 'stance', 'reference', 'generated', 'rouge1', 'diversity', 'creativity']].head(10))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "5798664b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T06:18:42.059176Z",
-     "iopub.status.busy": "2025-12-11T06:18:42.058941Z",
-     "iopub.status.idle": "2025-12-11T06:18:43.060165Z",
-     "shell.execute_reply": "2025-12-11T06:18:43.059452Z"
-    },
-    "papermill": {
-     "duration": 1.036006,
-     "end_time": "2025-12-11T06:18:43.063635",
-     "exception": false,
-     "start_time": "2025-12-11T06:18:42.027629",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x1000 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 4: DATA VISUALIZATION\n",
-    "# ============================================================================\n",
-    "\n",
-    "# Create visualizations\n",
-    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
-    "\n",
-    "# 1. ROUGE Scores Distribution\n",
-    "if ROUGE_AVAILABLE:\n",
-    "    rouge_data = test_results[['rouge1', 'rouge2', 'rougeL']].melt(var_name='Metric', value_name='Score')\n",
-    "    sns.boxplot(data=rouge_data, x='Metric', y='Score', ax=axes[0, 0], palette='Set2')\n",
-    "    axes[0, 0].set_title('ROUGE Scores Distribution', fontsize=14, fontweight='bold')\n",
-    "    axes[0, 0].set_ylim(0, 1)\n",
-    "\n",
-    "# 2. Diversity Score Distribution\n",
-    "axes[0, 1].hist(test_results['diversity'], bins=30, color='#95E1D3', edgecolor='black')\n",
-    "axes[0, 1].set_title('Diversity Score Distribution', fontsize=14, fontweight='bold')\n",
-    "axes[0, 1].set_xlabel('Diversity Score')\n",
-    "axes[0, 1].set_ylabel('Frequency')\n",
-    "axes[0, 1].axvline(test_results['diversity'].mean(), color='red', linestyle='--', \n",
-    "                    label=f'Mean: {test_results[\"diversity\"].mean():.3f}')\n",
-    "axes[0, 1].legend()\n",
-    "\n",
-    "# 3. Creativity Score Distribution\n",
-    "axes[0, 2].hist(test_results['creativity'], bins=30, color='#F38181', edgecolor='black')\n",
-    "axes[0, 2].set_title('Creativity Score Distribution', fontsize=14, fontweight='bold')\n",
-    "axes[0, 2].set_xlabel('Creativity Score')\n",
-    "axes[0, 2].set_ylabel('Frequency')\n",
-    "axes[0, 2].axvline(test_results['creativity'].mean(), color='red', linestyle='--',\n",
-    "                    label=f'Mean: {test_results[\"creativity\"].mean():.3f}')\n",
-    "axes[0, 2].legend()\n",
-    "\n",
-    "# 4. Metrics by Stance\n",
-    "stance_metrics = test_results.groupby('stance')[['diversity', 'creativity', 'length_similarity']].mean()\n",
-    "stance_metrics.plot(kind='bar', ax=axes[1, 0], rot=0)\n",
-    "axes[1, 0].set_title('Metrics by Stance', fontsize=14, fontweight='bold')\n",
-    "axes[1, 0].set_xlabel('Stance')\n",
-    "axes[1, 0].set_ylabel('Score')\n",
-    "axes[1, 0].legend(loc='best')\n",
-    "\n",
-    "# 5. Length Similarity\n",
-    "axes[1, 1].scatter(test_results.index, test_results['length_similarity'], \n",
-    "                   c=test_results['stance'].map({'positive': '#4ECDC4', 'negative': '#FF6B6B'}),\n",
-    "                   alpha=0.6)\n",
-    "axes[1, 1].set_title('Length Similarity Across Samples', fontsize=14, fontweight='bold')\n",
-    "axes[1, 1].set_xlabel('Sample Index')\n",
-    "axes[1, 1].set_ylabel('Length Similarity')\n",
-    "axes[1, 1].axhline(test_results['length_similarity'].mean(), color='black', linestyle='--', alpha=0.5)\n",
-    "\n",
-    "# 6. Creativity vs Diversity Scatter\n",
-    "scatter = axes[1, 2].scatter(test_results['diversity'], test_results['creativity'],\n",
-    "                             c=test_results['stance'].map({'positive': '#4ECDC4', 'negative': '#FF6B6B'}),\n",
-    "                             alpha=0.6, s=50)\n",
-    "axes[1, 2].set_title('Creativity vs Diversity', fontsize=14, fontweight='bold')\n",
-    "axes[1, 2].set_xlabel('Diversity Score')\n",
-    "axes[1, 2].set_ylabel('Creativity Score')\n",
-    "axes[1, 2].legend(handles=scatter.legend_elements()[0], labels=['Negative', 'Positive'])\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
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-    },
-    "tags": []
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-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "📝 QUALITATIVE ANALYSIS - Sample Generated Arguments\n",
-      "================================================================================\n",
-      "\n",
-      "🌟 Top 5 Most Creative & Diverse Arguments:\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: We should ban the use of child actors\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   movies and shows without children in them would feel incomplete and unrealistic.\n",
-      "\n",
-      "✨ Generated:\n",
-      "   we should not ban the use of child actors because it is important for them to be able to make their own decisions about what they want to do with their lives.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.966 | Diversity: 0.906 | ROUGE-1: 0.045\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: We should subsidize journalism\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   subsidizing journalism lowers the barrier to entry and can allow more fake news and junk reporting to proliferate so to speak\n",
-      "\n",
-      "✨ Generated:\n",
-      "   we should not subsidize journalism because it is a waste of money that would be better spent on other things such as education, health care, etc. the government should not subsidise journalism at all.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.935 | Diversity: 0.912 | ROUGE-1: 0.109\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: We should legalize prostitution\n",
-      "Stance: POSITIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   legalising prostitution could lead to better working conditions for women.\n",
-      "\n",
-      "✨ Generated:\n",
-      "   legalizing prostitution would make it safer for prostitutes and women to be able to do what they want with their bodies without fear of being harmed.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.880 | Diversity: 0.962 | ROUGE-1: 0.222\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: We should fight urbanization\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   we need to support urbanization to support growing populations.\n",
-      "\n",
-      "✨ Generated:\n",
-      "   urbanization should not be fought as it allows for more people to live and work in an environment that is conducive to health and well-being. the benefits of urbanization are endless.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.929 | Diversity: 0.903 | ROUGE-1: 0.146\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: The use of public defenders should be mandatory\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   public defenders are often overworked and should only support those who cannot afford another attorney.\n",
-      "\n",
-      "✨ Generated:\n",
-      "   the use of public defenders should not be mandatory because it is unfair to force someone to pay for a lawyer that they don't want to spend their money on.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.893 | Diversity: 0.933 | ROUGE-1: 0.130\n",
-      "\n",
-      "\n",
-      "================================================================================\n",
-      "⚠️  Samples Needing Improvement:\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: We should legalize sex selection\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   sex selection can lead to societies where there are more men than women. this can mean that a lot of men cannot find a wife and are unhappy.\n",
-      "\n",
-      "⚠️  Generated:\n",
-      "   sex selection can lead to an imbalance between the sexes, which can lead to a gender imbalance in the population. this could lead to more males being born than females\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.609 | Diversity: 0.767 | ROUGE-1: 0.345\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: We should prohibit flag burning\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   burning the flag is freedom of speech and it would be unconstitutional to ban it.\n",
-      "\n",
-      "⚠️  Generated:\n",
-      "   flag burning is a free speech right and should not be prohibited. it's a way of showing our country's commitment to freedom of expression. the only way to show this is by burning the flag.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.643 | Diversity: 0.800 | ROUGE-1: 0.423\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "Topic: The vow of celibacy should be abandoned\n",
-      "Stance: NEGATIVE\n",
-      "\n",
-      "📖 Reference:\n",
-      "   the vow of celibacy, and those who choose to make that vow, should be respected. it should be a matter of individual choice for the individual.\n",
-      "\n",
-      "⚠️  Generated:\n",
-      "   the vow of celibacy should not be abandoned because it is an important part of a person's life and should not be sacrificed in order to become a priest.\n",
-      "\n",
-      "📊 Scores:\n",
-      "   Creativity: 0.625 | Diversity: 0.828 | ROUGE-1: 0.464\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 16: QUALITATIVE ANALYSIS\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"📝 QUALITATIVE ANALYSIS - Sample Generated Arguments\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "# Show best performing samples (high creativity and diversity)\n",
-    "test_results['combined_score'] = (test_results['creativity'] + test_results['diversity']) / 2\n",
-    "best_samples = test_results.nlargest(5, 'combined_score')\n",
-    "\n",
-    "print(\"\\n🌟 Top 5 Most Creative & Diverse Arguments:\\n\")\n",
-    "for idx, row in best_samples.iterrows():\n",
-    "    print(f\"{'─'*80}\")\n",
-    "    print(f\"Topic: {row['topic']}\")\n",
-    "    print(f\"Stance: {row['stance'].upper()}\")\n",
-    "    print(f\"\\n📖 Reference:\")\n",
-    "    print(f\"   {row['reference']}\")\n",
-    "    print(f\"\\n✨ Generated:\")\n",
-    "    print(f\"   {row['generated']}\")\n",
-    "    print(f\"\\n📊 Scores:\")\n",
-    "    print(f\"   Creativity: {row['creativity']:.3f} | Diversity: {row['diversity']:.3f} | ROUGE-1: {row['rouge1']:.3f}\")\n",
-    "    print()\n",
-    "\n",
-    "# Show samples that need improvement (low scores)\n",
-    "worst_samples = test_results.nsmallest(3, 'combined_score')\n",
-    "\n",
-    "print(f\"\\n{'='*80}\")\n",
-    "print(\"⚠️  Samples Needing Improvement:\\n\")\n",
-    "for idx, row in worst_samples.iterrows():\n",
-    "    print(f\"{'─'*80}\")\n",
-    "    print(f\"Topic: {row['topic']}\")\n",
-    "    print(f\"Stance: {row['stance'].upper()}\")\n",
-    "    print(f\"\\n📖 Reference:\")\n",
-    "    print(f\"   {row['reference']}\")\n",
-    "    print(f\"\\n⚠️  Generated:\")\n",
-    "    print(f\"   {row['generated']}\")\n",
-    "    print(f\"\\n📊 Scores:\")\n",
-    "    print(f\"   Creativity: {row['creativity']:.3f} | Diversity: {row['diversity']:.3f} | ROUGE-1: {row['rouge1']:.3f}\")\n",
-    "    print()"
-   ]
-  },
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-    },
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-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "================================================================================\n",
-      "🧪 TESTING ON NEW UNSEEN TOPICS\n",
-      "================================================================================\n",
-      "\n",
-      "🔮 Generating arguments for new topics...\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Nuclear energy should be expanded\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   nuclear energy should be expanded because it is a renewable resource that can be used for many other purposes, such as agriculture and food production. there is no need to limit the use of nuclear energy.\n",
-      "   [Diversity: 0.917, Length: 36 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   nuclear energy is a waste of resources and should not be expanded. there is no need to increase the amount of nuclear waste that can be used for other purposes such as heating water or cooking food.\n",
-      "   [Diversity: 0.865, Length: 37 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Social media does more harm than good\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Social media does more harm than good because it encourages people to post things that they don't want to retweet or share with friends and family.\n",
-      "   [Diversity: 0.962, Length: 26 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Social media does more good than harm because it allows people to express themselves in a way that they would not otherwise be able to do without the use of social media.\n",
-      "   [Diversity: 0.938, Length: 32 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Universal basic income should be implemented\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Universal basic income should be implemented to ensure that all citizens have access to the basic services they need to live a normal life and not having to worry about what other people are doing\n",
-      "   [Diversity: 0.886, Length: 35 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Universal basic income should not be implemented because a lot of people have no idea what they are getting into when they start out and need help to pay for their basic needs.\n",
-      "   [Diversity: 0.939, Length: 33 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Space exploration is worth the cost\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   space exploration is worth the cost because it will allow us to learn more about the universe we live in, as well as a lot of new information that could help us improve our lives.\n",
-      "   [Diversity: 0.914, Length: 35 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   space exploration is not worth the cost because there are so many things that can be done on earth that it's impossible to know if it's worth the cost.\n",
-      "   [Diversity: 0.862, Length: 29 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Genetic engineering of humans should be allowed\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Genetic engineering of humans should be allowed because it allows for a better understanding of the human body and how it affects other parts of the body as well as the ability to learn new things.\n",
-      "   [Diversity: 0.806, Length: 36 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Genetic engineering of humans shouldn't be allowed because it could lead to the death of a lot of innocent people who have no idea what is going on in the world.\n",
-      "   [Diversity: 0.903, Length: 31 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Remote work should become the standard\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   remote work should become the standard because it allows for a better understanding of the world around us and can be used to improve our quality of life with less distractions.\n",
-      "   [Diversity: 0.935, Length: 31 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   remote work should not become the standard because a lot of people have a hard time finding a job that they can't do at home or on the go.\n",
-      "   [Diversity: 0.897, Length: 29 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Cryptocurrencies should replace traditional currency\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Cryptocurrencies should replace traditional currency because they can be used for a variety of purposes, such as the payment of bills and other financial transactions. They can also be used to pay for medical bills and other expenses.\n",
-      "   [Diversity: 0.763, Length: 38 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Cryptocurrencies should not replace traditional currency because a lot of people don't have the means to pay for everything they want with their wallets full of money.\n",
-      "   [Diversity: 0.963, Length: 27 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Artificial intelligence poses an existential threat\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Artificial intelligence poses an existential threat because it can be used to manipulate a person's thoughts and feelings, making it more likely that they will not be able to make a decision.\n",
-      "   [Diversity: 0.875, Length: 32 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Artificial intelligence is an existential threat to the human race because it can be used to a great extent to improve the lives of people who do not have access to it.\n",
-      "   [Diversity: 0.875, Length: 32 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: College education should be free for all\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   college education should be free for all because a lot of people do not have access to the same level of education as their parents or guardians. it's important that everyone has access to this level of education.\n",
-      "   [Diversity: 0.842, Length: 38 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   college education should not be free for all because it is a personal choice and the students should be able to choose how they want to go to school regardless of their income.\n",
-      "   [Diversity: 0.879, Length: 33 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Meat consumption should be banned\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Meat consumption should be banned because it is a waste of money that can be spent on other things such as drugs and alcohol. It is also a source of pollution for the government.\n",
-      "   [Diversity: 0.853, Length: 34 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Meat consumption should not be banned because it is a good source of protein for many people who are suffering from diseases such as cancer and heart disease. It is also a good way to reduce the number of illnesses that can be prevented with meat consumption.\n",
-      "   [Diversity: 0.851, Length: 47 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Surveillance is necessary for public safety\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Surveillance is necessary for public safety because it is a way to ensure that the public is safe from terrorists and other criminals, as well as to ensure the safety of citizens.\n",
-      "   [Diversity: 0.750, Length: 32 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   Surveillance is not necessary for public safety because it is a way of keeping tabs on what is going on in the world around us and should not be required.\n",
-      "   [Diversity: 0.867, Length: 30 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Climate change is the most pressing issue\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   climate change is the most pressing issue as it affects millions of people a year, making it impossible for them to continue to live in harmony with their natural environment.\n",
-      "   [Diversity: 0.933, Length: 30 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   climate change is the most pressing issue because it affects millions of people a year and causes more pollution than ever before. this could lead to an increase in global warming.\n",
-      "   [Diversity: 1.000, Length: 31 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Immigration policies should be more lenient\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   immigration policies should be more lenient in order to reduce the number of illegal immigrants entering the country and keep them out of the country for the rest of their lives.\n",
-      "   [Diversity: 0.806, Length: 31 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   immigration policies should not be more lenient because it is unfair to force people to come from countries that are not in the same country as the one you are applying for.\n",
-      "   [Diversity: 0.875, Length: 32 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Death penalty should be abolished\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   Death penalty should be abolished because it is a cruel punishment for a person who has committed a crime with no hope of regaining his or her life.\n",
-      "   [Diversity: 0.929, Length: 28 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   death penalty should not be abolished because a person has the right to live their life as long as they want to. it is a personal choice and should not be taken away from them.\n",
-      "   [Diversity: 0.857, Length: 35 words]\n",
-      "\n",
-      "────────────────────────────────────────────────────────────────────────────────\n",
-      "📌 Topic: Homework should be banned in schools\n",
-      "\n",
-      "   POSITIVE stance:\n",
-      "   homework should be banned in schools because it is a waste of time and money for students to spend at home. they do not learn how to read, write, or listen to music.\n",
-      "   [Diversity: 0.939, Length: 33 words]\n",
-      "\n",
-      "   NEGATIVE stance:\n",
-      "   homework should not be banned in schools because it is a way for students to learn at their own pace without having to worry about what they are doing at the school.\n",
-      "   [Diversity: 0.938, Length: 32 words]\n",
-      "\n",
-      "\n",
-      "================================================================================\n",
-      "✅ Generated arguments for all new topics!\n",
-      "================================================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 17: TEST ON NEW TOPICS\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"🧪 TESTING ON NEW UNSEEN TOPICS\")\n",
-    "print(\"=\"*80)\n",
-    "\n",
-    "test_topics = [\n",
-    "    \"Nuclear energy should be expanded\",\n",
-    "    \"Social media does more harm than good\",\n",
-    "    \"Universal basic income should be implemented\",\n",
-    "    \"Space exploration is worth the cost\",\n",
-    "    \"Genetic engineering of humans should be allowed\",\n",
-    "    \"Remote work should become the standard\",\n",
-    "    \"Cryptocurrencies should replace traditional currency\",\n",
-    "    \"Artificial intelligence poses an existential threat\",\n",
-    "    \"College education should be free for all\",\n",
-    "    \"Meat consumption should be banned\",\n",
-    "    \"Surveillance is necessary for public safety\",\n",
-    "    \"Climate change is the most pressing issue\",\n",
-    "    \"Immigration policies should be more lenient\",\n",
-    "    \"Death penalty should be abolished\",\n",
-    "    \"Homework should be banned in schools\"\n",
-    "]\n",
-    "\n",
-    "new_topic_results = []\n",
-    "\n",
-    "print(\"\\n🔮 Generating arguments for new topics...\\n\")\n",
-    "\n",
-    "for topic in test_topics:\n",
-    "    print(f\"{'─'*80}\")\n",
-    "    print(f\"📌 Topic: {topic}\")\n",
-    "    \n",
-    "    for stance in ['positive', 'negative']:\n",
-    "        argument = generate_argument(model, tokenizer, topic, stance, device)\n",
-    "        \n",
-    "        # Calculate diversity for this generation\n",
-    "        diversity = evaluator.calculate_diversity(argument)\n",
-    "        \n",
-    "        new_topic_results.append({\n",
-    "            'topic': topic,\n",
-    "            'stance': stance,\n",
-    "            'generated_argument': argument,\n",
-    "            'diversity': diversity,\n",
-    "            'length': len(argument.split())\n",
-    "        })\n",
-    "        \n",
-    "        print(f\"\\n   {stance.upper()} stance:\")\n",
-    "        print(f\"   {argument}\")\n",
-    "        print(f\"   [Diversity: {diversity:.3f}, Length: {len(argument.split())} words]\")\n",
-    "    \n",
-    "    print()\n",
-    "\n",
-    "# Convert to DataFrame\n",
-    "new_results_df = pd.DataFrame(new_topic_results)\n",
-    "\n",
-    "print(f\"\\n{'='*80}\")\n",
-    "print(\"✅ Generated arguments for all new topics!\")\n",
-    "print(f\"{'='*80}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "2679fc37",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T06:19:05.269880Z",
-     "iopub.status.busy": "2025-12-11T06:19:05.269630Z",
-     "iopub.status.idle": "2025-12-11T06:19:05.541621Z",
-     "shell.execute_reply": "2025-12-11T06:19:05.540898Z"
-    },
-    "papermill": {
-     "duration": 0.308871,
-     "end_time": "2025-12-11T06:19:05.542720",
-     "exception": false,
-     "start_time": "2025-12-11T06:19:05.233849",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "📊 Analysis of New Topic Generations:\n",
-      "\n",
-      "Average Diversity: 0.8873\n",
-      "Average Length: 32.8 words\n",
-      "\n",
-      "By Stance:\n",
-      "          diversity     length\n",
-      "stance                        \n",
-      "negative   0.900515  32.666667\n",
-      "positive   0.874013  33.000000\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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S4eHh5vCSJUsaL7zwgjFp0iSjSZMmVvOPiYkx68uqB17btm2NiRMnGkOGDDGWLFli1bO3WLFiRmRkpDF58mRj6NChxgMPPGB4e3vfMml79epVY/r06UavXr2sxk2fPt18bdmyxTAMw3jsscfM8Q899JDVukzfuJw+fbpNn1P6LxXpZiP1wQcfNCZOnGisXr06ywN9cnKyVVJRutmAGzNmjDF69GijRYsWRu3atc3yGzZssPpcypcvb7z00kvG0KFDrRoAQUFBOfbO8PT0NJ588knjtddeM/r06WMcOnTI+PPPPw0/Pz+zTHh4uDFu3Djj5Zdftlq2atWq2ZyQzviZ16tXzxg/frzx0EMPWQ1P/8vxvHnzzOHBwcFWv5IPHjzYHNelSxebYujXr5/VvPz8/IxWrVoZI0eONJYtW5ZlD4Hp06cbzz33nNV0L7/8srkNpf0aGxcXZ1SqVMno06ePMXr0aGPKlCnGpEmTjN69exuenp7mtMuWLTPrzuoEo0GDBsa4ceOMpk2bWg1funSpOd22bdusGkienp5G7969jUmTJhmDBg0yQkJCrBonzz77rFnW29vb6Nu3r/H6668bjz76qNU29Omnn9q0Hjds2GBOExAQkKl3umFk3s/fffdd8/9Ro0YZhpFz0nbp0qVW4zp06GC89tprxtChQ616pgwaNMic5ptvvjGHFypUyNw2M37uc+fONQzDMPbs2WMOc3NzM86fP2/T8qeXvt6MSdvXXnvNanzjxo2NCRMmGI888ki223zGOm3dV3JK2r755puZjkeDBw82Xn31VaNPnz5GQECAmbS199ibZvv27VbH/KSkJLvXJYC7Q6dOncz9vW7dutkOSy+rXmnh4eHGmDFjjJdeesl4+eWXDcMwMiXjOnfubIwfP96oW7dupunTc0TSNi2m8ePHGw0aNMg0vzp16hjjx4+3+nE9MDDQ6nh3O9+3Wa2bBx54wBg/fnymdmFaG/yzzz4zpk+fbjWuV69eZtskqytgMrI3aeuodr+7u7vRt29fY8yYMUaJEiWyXZf2bgv2tPsznjdIN9v/r7zyilVS2t3dPVMSMjsZ29dly5Y1XnrpJWPIkCGGj4+PObxo0aLGlStXDMO42TMw/Xaxf/9+s770P44EBQXZlCTKuFzu7u5G3bp1jWeffdb44IMPMl2lYxj2bUudOnUyunbtagwfPtx47bXXjMmTJxtDhgwxihcvbk47ePBgq/oz7mP33HOP8dJLL1ldoSPJePbZZ81pYmNjMyVs27RpY4wfP9544YUXjHr16lklbW+nzZiTpKQkw9fX15wuq6ssM+63b7/9ttneT0vA55S0vZ1zrcuXLxseHh6Z4sr4uT/66KOGYdzsnFC0aFFz+BdffGHT8qeXU9LWUeeituwrhpH9cd6e86LffvvNav/o37+/MWXKFCMyMtJ46KGHjGLFimWZtE1OTrbKlWzcuNHudQnHIWkLl2Zr0jZj7660ZER2SVvDMIz+/fubwx9++GFzePoT/mrVqpnD0//a5+HhYfz+++/muIwJyBEjRpjjMjbehg8fnin+mTNnZvklniYuLs44e/as+T6nS+ZyGpdmy5Yt5ngvLy+z7vS/qnl6elrNMycrVqzIsrdG2qtIkSLGpEmTrBKe6RNO0s0ezRkvFUvfCO7WrZtZtnDhwsa5c+fMcd9//32mhkSajF+UK1euzBR/+h6L9913n1Uvz9OnT1s1GL7++mub1kn6eYaHh1s1zAcNGmQ1Pq1RGRcXZ9XY+PLLLw3DuLl9pb81QtrwWzl+/LhVfRlfnp6exhNPPJHp0s6c9puMzp07Z3z99dfG3LlzjbfeesuYPn26VY/hp556yiyb8YSpYcOGZmL6xo0bRqlSpcxx6Xvm9OjRw6rBkbHhkJSUZF6OdOnSJauk8YIFC6zKDhkyxBxXp04dm9bjggULzGnuvffeLMtk3M+vXr1q/gDi5+dnnDp1KsekbfpbWfTt29eq7s8//9zqM7t06ZJhGDdP2NJvm2mXQlWqVMmQZJ5U9O7d2zAMw5g1a5ZZtmbNmjYte0bp40+ftE1JSbHqZRMREWG1v7/44otW06a/Lcnt7CvZJW1TUlKses6WK1fO6lhhGDcvP0xrFNt77E3z999/27yPALh7ZWwDpP2Y/fHHH1sdA9Innwwj8/do48aNM11Bcvr0aau2U/oT58TERKNq1arZtucckbQtXry4eQVO+tvqSDJKlSpl9tJbvXp1lst6u9+3GdfNQw89ZLb/Ll26ZLW+M/ZiTj9dducE2bE3aeuodv+sWbPMcStXrsxyXd7JtmBLuz9jmfTnIvv27bMa980339x6ZRrW7WsvLy+r78FPP/3Uqs4PP/zQHNeuXTtz+PPPP28Of/jhh7McnhOLxWLUr1/fal4ZX82aNTP27duXaVpbt6X4+Hjjxx9/ND744ANj5syZxvTp063OSSpVqmRVPv0+FhAQYJUE7969uzku/Y89//3vf63imTx5cqY40p8T3U6bMSd//vmn1fyzStxn3G+//fZb45lnnjHfr1ixIsek7e2ea6X/fNOOv0899ZR5DJNklClTxjAM623Zzc3N7lvbGUbOSVtHnIvas69kd5y357wofYeNatWqZTrXTk5ONo4fP57luqhSpcptH2/hWO4C8gHDMOyeZsCAAeb/q1at0rVr1yRJS5cuzbLMli1bzP9TUlJ03333yc3NTW5ubvL09LR64NPWrVuzne+4ceMyDWvatKnc3NwkSe+//77q1aunJ598Um+88YZWr14tT09PBQcH272M2WnSpInq1q0rSbJYLOYDNr744guzTOfOnW2e50MPPaSffvpJbdq0kbt75sPK1atXNXHiRL3++uvmsM2bN1uVef311811kKZSpUrm/+kfJNCxY0eVKlXKfN+pUyerB0Nl99CBGjVqqFu3bpmGp/9sf//9d/n5+ZmfbdmyZZWSkmKOz+mzzU6vXr3k7e1tvn/iiSesxu/evVvSzYe4PfXUU+bwDz/8UJK0ceNGnTt3TpJUokQJde3a1ab5VqhQQbt379YTTzwhf3//TOOTk5P1ySef6JFHHrF7H7p+/boGDBigMmXKqFu3bhoyZIhGjRql0aNH6+DBg2a5v//+O9s6Bg4cKC8vL0mSl5eXQkNDzXGXL182/0+/rXTo0EHNmze3qsfb29t8ENTPP/+s5ORkc9xTTz1lfpZubm6aO3euOW7fvn1KSEi45bKmPYBMkoKCgm5ZXpLc3d31xhtvSLq5rl577bVsyyYkJGjfvn3m+48//tgq5kcffdQcl5ycrB07dki6+aCItP1Yurmezpw5Yz5A4d///rckadOmTVZ/Jal169Y2LYetjhw5on/++cd8/8QTT8jDw8N8369fP6vy2e2jtu4rOcWR/vP697//bXWskKTixYurSJEikm7/2Fu8eHGr9+nnCSD/WLx4sdkGcHNzMx++2L17d/n6+prlFi5cmGM9o0aNsiov3Tyepf/u7du3r/m/j49Prj/4pWvXrubD0CpWrGg1rkuXLgoICJAkVa5c2Wpc2vezo75vBw8ebB6Hg4KCVKJEiUzzcgZHtPs9PDz07LPPmu+rVq1qNT5t+fJ6WxgyZMgtY7JH8+bNrbahXr16me07yfq7+/nnnzf//+STT5SYmKj4+Hh9//335vD051858fT01E8//aSxY8dme86yefNmtWvX7ra+p2fOnKng4GC1bdtWzzzzjCIjIzV69Gh9/fXXZpmc2rndunVT2bJlzffp13V27dzChQvrpZdeylRX2jnR7bYZc5Jx3dja1p0wYYJ5XBs3bpxSU1OzLXu751rp26tp6yntb1o798yZM4qJibFq59asWdPqWOIIjjgXtWdfyY4950XVqlUz26yHDh1SlSpV1LNnT7388sv67LPPdPnyZVWoUCHL+aRv69LOdS6StsgXfv/9d/N/X1/fTCfUWWnRooWqVKkiSUpMTNSKFSt0+PBh7d27V9LNhkD6RlP6hMStZHdgK1GiRJaxNWzYUDNnzjSfYLlnzx598sknGj9+vDp16qR77rlH0dHRNs/fFmlfdJI0f/58SdZJ2/TJQ1u0atVKUVFR+ueff/TDDz/o1VdfVf369a3KvP322+b/6denv79/psRKRunLZ9UwSz8suwZnWFjYLeu+ldv50sq4bBnjv3Llivn/sGHDzMT32rVrdfLkSX3++efm+CeeeMLqy/1WKlWqpMWLF+vy5cvaunWr3n77bXXp0sUqub5+/Xpzu7fV2LFjtWjRohwbaJKUlJSU7biMJ4k+Pj7m/+nrTf/5pE/sZsWez9IwDF26dMnm8vbq0aOHuQ8sWLBAMTExWZa7fPmyXUnz9NtgmzZtzP83bdpkNljLli1rJkr//vtvHTt2zKqRl346R8i43jNu4xnfZ7eP2rOv2BLHrbaX2z323s4PhQDuPmk/aks3f/AOCQmRdDOx0qVLF3Pcp59+apXAzCir9kfG41np0qVzfJ+djMejnL5300ufTEr/Y1nGcZ6enlbj0r6fHfV9a2tbIK85om0YHBxslaxPv2zS/y2fo7YFW6Vf59nFZI+M390eHh5W5zvpl69Lly5mAvLy5cv68ssv9d133+n69euSpNq1a6tOnTo2z7tw4cKaMmWKzpw5o4MHD2r+/Pnq16+fChcubJa5cOGCFi9ebNcyrVy5UiNHjlRcXFyO5W7cuJHtuNtp54aEhFj96J3RnbQZHa1cuXIaOnSoJOm3337TJ598km3Z292f0rdXN2/erLNnz5rn/Y899pi5jjdu3GiVtHV0O1dyzLmoPfuKLXHcqp3r6+urzz//XOXLl5ck/fnnn/ryyy81depU9e7dW+XKldPMmTOznJa2ruvwvHURwLXt2rVLv/zyi/m+ZcuWWfb2zEr//v3Nnq9Lly41e6hJN38xS3/wTf+ro6+vr1Wv0YzSenFllNZrISvDhw/XM888o+3bt+vXX3/VH3/8odWrV+uPP/7QxYsX1a9fP/311182LZctHnvsMY0ePVoXLlzQH3/8oXfffVeHDh2SdPMLJf3JiD2KFCmijh07qmPHjpo4caKefvppLViwQJIUGxurc+fOKTg42Gp9JiQk6Pz58zkmboOCgnT+/HlJMnudppd+WLFixbKsI7v1nz6W8PBw9e/fP9s4atSoke247KTFnSZj/EWLFjX/Dw0NVZcuXfTtt98qNTVVH374oVasWGGOt7X3QUbe3t6KiIhQRESEhg8frsWLF1v9KPHHH39Y9dq8lWXLlpn/16xZU0uXLlXVqlXl6empRx991OoHgOxkTD5n7GmdJv1nf+zYsRzrzNg7YMSIEVYnnxllt6+mdye9fqZMmaL27dvLYrFo2rRpWZZJ//lL0oMPPpjpV/P00n9OrVu31n/+8x9JNxuzacvarFkzVahQQSEhITp58qT+97//mdudu7u7WrZsaddy3ErG9Z5xG8/4Prt91J59xZY4brW9SLd37M148pG+dwWA/OHnn38220XSzZ5i2X1PnT9/Xt9//70efPDBLMdn1f7IeDzLePw7e/ZstrGlb+emJbvS/PHHH9lOl15OPwBnTNRmxVHft7a2BfKaI9r9ti7bnWwLtyN9XI5Y3xnjTUlJsUrSp18+d3d3DR06VCNHjpQk/e9//7NKWt1uO9fNzU3h4eEKDw/XU089pVdffVWVK1c2k6O27hdp0rdzCxUqpBUrVqh58+by9fXV3LlzzWRlTuxp56Y5efKkUlJSsk3c3kmbMTsZe6RevnxZZcqUueV00s1OHB9++KFiY2Nz3Edu91yrWbNm8vLyksVi0aVLl8yrEEuVKqX77rtPzZs31/Hjx606LkiOv6IsbRnu9FzUnn3Fljhsaee2adNGx44d0549e7Rv3z4dPXpUW7du1aZNm3Tjxg2NHj1aDz74oNmZLU36ti7tXOciaYu72pEjR/TYY49ZDYuMjLR5+n79+mnChAlKTU1VVFSUfvvtN3Ncxp6mTZo0Mf9PTExUeHi4OnXqlKnOn3/+OdOv1rdy+vRpeXh4KDg4WG3atDF/Hdy7d6/5ZXvixAldunTplr2IMzYQEhISsrw03sfHR4MGDdKUKVMkSaNHjzbHPfnkkzY12NP069dP//73v1WvXr1M49J6sEk3G2ppv3w3a9ZMb775pjlu4sSJmjt3rlWD5q+//jIv2WjSpIlWrlwpSVq9erVVkveHH36w+lU2/WdliyZNmpiXD505c8b85TG95ORkffvtt2rUqJFddUs3G35jxowxP5uMv0RnXG/PP/+8vv32W0nS9OnTlZiYaJa7//77bZ7vuHHj1LhxY3Xs2DHT55n+c5GsGwlZbUMZpW9gtG7dWuHh4ZJu/jru6F7hzZo1MxPXa9eu1ZYtW9S0aVNzfHJyss6dO6dy5cqpUaNG8vDwMC+z8vLy0qhRozLVefz4cR05csS8NDQn6W/TcerUKaWmptr8w1C7du3UqlUrRUdHZ3vSFRAQoNq1a5uXu126dEkvvPBCps/h6tWr+uGHH8x1LVk3Zs+fP6/PPvtMkswGfPPmzbVkyRLNmTPHnKZOnTo2NQrtUbVqVQUFBZkNvE8++UTPPvusedLx0UcfWZXPbh+1d1/JKo6SJUuax4N33nlHTz31VKbEu4eHhwIDA2/72Hvy5Enzf19f3xwTFQDuTul72dpaPrukbVbq1asnNzc3szfT0qVL1bFjR0k3e8umv11XRumP4Tt27JBhGHJzc9OBAwfM9kNuy43v21vx9PQ0ezTbcnujO5Gb7f6M7mRbsLXdn5s2bdqk48ePm70ely1bJovFYo7P+N391FNPacKECYqPj1d0dLS5/ry9vfX444/bPN+PPvpIiYmJ6t27d6btKyAgQO7u7mbSNmO751bbUvp2bqVKldSuXTtJN3vILl++3OYYbdGsWTPzqrpr165p+vTpGjNmjFWZtHOiO2kzZqdcuXLy9vY2ew2fPHnS5qRt8eLFFRkZqVdffTXHHxdu91yrUKFCatCggXnLhP/+97+SrNu5ixcv1ooVK3T16lVJN3uvOrpzQtoy3Om5qL37SlbsOS9KTEzUsWPHVK1aNdWvX9+8AtAwDBUrVkxXr15VamqqfvnlF6ukbUpKik6fPm2+T38uhLxH0hZ3ldWrV+vixYuKjY3V3r17tXr1aqvL0YYOHar27dvbXN8999yjdu3aac2aNUpOTjZPxLPqadqlSxdVq1bN7HXRvXt39ejRQ9WrV1dqaqpiYmK0ceNG/fXXX1q4cKFq165tcxwbN27U448/rmbNmqlatWrmvX3S97D09va2qRGW8QuwT58+atKkidzd3fXkk09a9R4ePHiw3nzzTSUnJ5uJQcn+X7k//vhjffzxx6pcubKaNWumSpUqyc3NTb/88ovVMrRo0cJchs6dO6tmzZrmPcHmzZunvXv3qk2bNjIMQ3v27NH58+fNy/ZHjBihr7/+WoZh6Nq1a2rQoIH69OmjuLg4syevdPPXx4z3z7yV559/XvPmzVNiYqL++ecf1a5dW4888ohCQkIUFxen3377TdHR0bpy5YqOHTuW7a+n2fn1118VERGhLl266ODBg1brpFWrVpl+2Wzbtq3CwsJ0+PDhO/pcNm/erMmTJ6t48eJq2bKlqlWrpoCAAB07dsxM7kk3742avnGRcRsaOnSoOnToIE9PTz344IO67777VLVqVfPetR9++KHc3d3l7++vxYsXO/xSrNGjR2vlypVKTU1VSkqKWrdurUcffVRVq1bV2bNntWbNGg0bNkzDhw9XUFCQnnrqKfOX+DfffFO7du1SkyZN5Ovrq1OnTmn79u3au3ev+vXrpw4dOtxy/g0bNjQTo/Hx8fr999+zvdVGVqZMmXLLHxJGjx5tnqhs2bJF999/v7p27apixYrp0qVL2rt3rzZv3qwyZcpY/VAVEBCghg0bmvcKu3jxoqTMSdu0hqyUO70P3N3dNWLECI0fP17SzXt5NWvWTO3bt9fhw4etbvHRunVr1apVK8t67N1Xsopj9OjRevHFFyXdvDVEtWrV9Oijjyo4OFjHjh3TypUrtX79etWuXfu2j727du0y/2/YsGGmS4sB3N0SExOtvidDQ0PVsGHDTOUOHDhg/uD/3Xff6eLFizbfR7FMmTLq0qWLvvvuO0k321JXr15VrVq19N133+nIkSPZTtugQQOzfbRhwwY1btxYZcuW1Y8//pjj5dqOlBvft7dSrlw588qHGTNm6NKlS/Lz81OdOnX0wAMP2FXXBx98YK77jHbt2pWr7f6M7mRbsKfdn1ssFouaNm2qJ598UteuXTNvuybd7IX8yCOPWJUvWrSonnjiCb3//vuS/u+WHg8++KBNt7hLc+zYMU2aNEnDhw9Xs2bNVLt2bQUFBenSpUtavny51TliWhI8za22papVq2rdunWSpP3796t3796qVq2afvjhB23fvt2OtXNr/fv31+TJk82emmPHjlVUVJQiIiKUkJCg7du3q0SJEmbC8HbbjNnx8fFR/fr1zcTonj17sjzeZScyMlLvvvuu2QbNyp2ca7Vu3dqMLat2riSrdm6dOnVsupLOXo44F7V3X8mKPedFV65cUfXq1RUeHq6GDRuqbNmy8vPz0+bNm63WWcYfNQ4dOmT+mOHt7W3X9oBckNdPPgPskfFJldm9PD09jddff91ISUnJcfqsnvC9bNmyTPWlf3J9ekeOHDEqVqx4y3jSP2Exp6f4plm6dOkt60wfU05Pik1MTDTKlCmTZR1pT5dPr2fPnlZlGjRokM2nkT1bPqOgoCDjwIEDVtPFxMRYPZky46tWrVpW5d9++23D3d092/JFihQxnyKfJv0TO9M/7T6jr776yggICLjlctj6lPj006SPIeM6OXToUJbTv/vuu1ZlfXx8jH/++cemeWe17Nm93N3djcWLF2eaNv2TadO/vvjiC8Mwst9my5QpY/Vk4JYtW5p1Znxysz2f1f/+9z/D29s72+VI/5TW+Ph4o23btrdc9py2h5zWZcYnZBtG5qdFX7t2zWp8165dM80/4/KPHTv2ljFndQwZP358pv0g7Vh48ODBTHV8//33Ni93Rjmtv+TkZOORRx7JMf5q1apleipx+vGdOnWyeoJ2dvtKTttSamqqMXDgwBzj2Lt3r2EY9h970/Tp08cc//rrr9/2+gTgmjIeGz755JMsy0VFRVmVmzVrlmEYtrU/DePmsax06dKZjjtubm5Gx44drd6n9+uvvxo+Pj6ZpvPz8zNatWqV7XdGdk8jNwzrY3H6cTkdb2/n+/ZW6yanGNM/gT79a+jQoVmu34zr+lZxpr3SOLrdn9O6vN1twZZ2f07nDYZhZLssOUnfLqpbt65RuHDhTPN3d3c3li5dmuX0WbVPVq1aZdO802Rse2X3GjRoUKZpb7Ut/fHHH1kuk6enp/H4449nuz5z2n5z2jZ27NhhBAcHZ7sM3bp1syp/u21GW9Zl3759M43PuN9+++23VuNnzJiRaf4Zl/92z7UyHmclGbt37zbHlyxZ0mrciy++aPNyZ5R+u85q/d3puWjjxo2NoKAgm/aVnLYlW8+Lzpw5c8v13bBhQ8NisVjV/8EHH5jjH3jggdten3AMHkSGu46Hh4cKFy6s0NBQPfDAA5o0aZKOHz+ucePG2XzJcnrdunXLdF+u7Ho03nfffdq/f7/efPNNNWnSRMWKFTPjuf/++zVw4EB99dVX6tOnj10xNGvWTJMnT1aXLl1UuXJlFS5cWJ6enipZsqQeeOABLVq0SDNmzLCpLh8fH33//fdq3769TZeipX8gmWT/A8ikm7/ITp8+3eyVULx4cXO91KlTRy+++KJ+/fXXTPeDrVSpkvbt26eZM2eqWbNmKlasmDw9PVWiRAk1bdpUAwcOtCo/fPhw/fzzz3ryySdVoUIFeXt7y8/PT9WqVdOIESN04MABtWrVyu74pZs9KA4ePKjIyEjVrFlThQoVMm8OHxERodGjR2vLli2ZHip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-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 18: ANALYZE NEW TOPIC RESULTS\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n📊 Analysis of New Topic Generations:\\n\")\n",
-    "print(f\"Average Diversity: {new_results_df['diversity'].mean():.4f}\")\n",
-    "print(f\"Average Length: {new_results_df['length'].mean():.1f} words\")\n",
-    "print(f\"\\nBy Stance:\")\n",
-    "print(new_results_df.groupby('stance')[['diversity', 'length']].mean())\n",
-    "\n",
-    "# Visualize new topic results\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "\n",
-    "# Diversity by stance\n",
-    "new_results_df.boxplot(column='diversity', by='stance', ax=axes[0])\n",
-    "axes[0].set_title('Diversity Score by Stance (New Topics)', fontsize=14, fontweight='bold')\n",
-    "axes[0].set_xlabel('Stance')\n",
-    "axes[0].set_ylabel('Diversity Score')\n",
-    "plt.suptitle('')\n",
-    "\n",
-    "# Length by stance\n",
-    "new_results_df.boxplot(column='length', by='stance', ax=axes[1])\n",
-    "axes[1].set_title('Argument Length by Stance (New Topics)', fontsize=14, fontweight='bold')\n",
-    "axes[1].set_xlabel('Stance')\n",
-    "axes[1].set_ylabel('Word Count')\n",
-    "plt.suptitle('')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "1f87df8b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-11T06:19:05.616935Z",
-     "iopub.status.busy": "2025-12-11T06:19:05.616700Z",
-     "iopub.status.idle": "2025-12-11T06:19:05.634548Z",
-     "shell.execute_reply": "2025-12-11T06:19:05.633972Z"
-    },
-    "papermill": {
-     "duration": 0.055557,
-     "end_time": "2025-12-11T06:19:05.635559",
-     "exception": false,
-     "start_time": "2025-12-11T06:19:05.580002",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "💾 Saving results to CSV files...\n",
-      "✅ Test set evaluation saved to: test_set_evaluation.csv\n",
-      "✅ New topic generations saved to: generated_arguments_new_topics.csv\n",
-      "✅ Evaluation summary saved to: evaluation_summary.csv\n",
-      "\n",
-      "================================================================================\n",
-      "📋 EVALUATION SUMMARY\n",
-      "================================================================================\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Metric</th>\n",
-       "      <th>Value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Train Loss (Final)</td>\n",
-       "      <td>2.169585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Val Loss (Final)</td>\n",
-       "      <td>2.005616</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Avg ROUGE-1</td>\n",
-       "      <td>0.273987</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Avg ROUGE-2</td>\n",
-       "      <td>0.065607</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Avg ROUGE-L</td>\n",
-       "      <td>0.199495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Avg Diversity</td>\n",
-       "      <td>0.885864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Avg Creativity</td>\n",
-       "      <td>0.783749</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Avg Length Similarity</td>\n",
-       "      <td>0.612062</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Metric     Value\n",
-       "0     Train Loss (Final)  2.169585\n",
-       "1       Val Loss (Final)  2.005616\n",
-       "2            Avg ROUGE-1  0.273987\n",
-       "3            Avg ROUGE-2  0.065607\n",
-       "4            Avg ROUGE-L  0.199495\n",
-       "5          Avg Diversity  0.885864\n",
-       "6         Avg Creativity  0.783749\n",
-       "7  Avg Length Similarity  0.612062"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 19: SAVE RESULTS TO CSV\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n💾 Saving results to CSV files...\")\n",
-    "\n",
-    "# Save test set evaluation results\n",
-    "test_results.to_csv('test_set_evaluation.csv', index=False)\n",
-    "print(f\"✅ Test set evaluation saved to: test_set_evaluation.csv\")\n",
-    "\n",
-    "# Save new topic generation results\n",
-    "new_results_df.to_csv('generated_arguments_new_topics.csv', index=False)\n",
-    "print(f\"✅ New topic generations saved to: generated_arguments_new_topics.csv\")\n",
-    "\n",
-    "# Create a summary report\n",
-    "summary_df = pd.DataFrame({\n",
-    "    'Metric': ['Train Loss (Final)', 'Val Loss (Final)', \n",
-    "               'Avg ROUGE-1', 'Avg ROUGE-2', 'Avg ROUGE-L',\n",
-    "               'Avg Diversity', 'Avg Creativity', 'Avg Length Similarity'],\n",
-    "    'Value': [\n",
-    "        history['train_loss'][-1],\n",
-    "        history['val_loss'][-1],\n",
-    "        test_results['rouge1'].mean() if ROUGE_AVAILABLE else 'N/A',\n",
-    "        test_results['rouge2'].mean() if ROUGE_AVAILABLE else 'N/A',\n",
-    "        test_results['rougeL'].mean() if ROUGE_AVAILABLE else 'N/A',\n",
-    "        test_results['diversity'].mean(),\n",
-    "        test_results['creativity'].mean(),\n",
-    "        test_results['length_similarity'].mean()\n",
-    "    ]\n",
-    "})\n",
-    "\n",
-    "summary_df.to_csv('evaluation_summary.csv', index=False)\n",
-    "print(f\"✅ Evaluation summary saved to: evaluation_summary.csv\")\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"📋 EVALUATION SUMMARY\")\n",
-    "print(\"=\"*80)\n",
-    "display(summary_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "8fd541ac",
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-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "💾 Saving trained model...\n",
-      "✅ Model and tokenizer saved to: ./argument_generator_model\n",
-      "✅ Training configuration saved to: training_config.csv\n",
-      "\n",
-      "================================================================================\n",
-      "🎉 PIPELINE COMPLETED SUCCESSFULLY!\n",
-      "================================================================================\n",
-      "\n",
-      "📁 Generated Files:\n",
-      "   1. test_set_evaluation.csv - Detailed evaluation on test set\n",
-      "   2. generated_arguments_new_topics.csv - Arguments for 15 new topics\n",
-      "   3. evaluation_summary.csv - Overall metrics summary\n",
-      "   4. training_config.csv - Training configuration and final losses\n",
-      "   5. ./argument_generator_model/ - Saved model and tokenizer\n",
-      "\n",
-      "✨ Your model is ready to generate creative arguments!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ============================================================================\n",
-    "# CELL 20: SAVE MODEL\n",
-    "# ============================================================================\n",
-    "\n",
-    "print(\"\\n💾 Saving trained model...\")\n",
-    "\n",
-    "model_save_path = './argument_generator_model'\n",
-    "model.save_pretrained(model_save_path)\n",
-    "tokenizer.save_pretrained(model_save_path)\n",
-    "\n",
-    "print(f\"✅ Model and tokenizer saved to: {model_save_path}\")\n",
-    "\n",
-    "# Save training configuration\n",
-    "config_dict = {\n",
-    "    'model_name': config.MODEL_NAME,\n",
-    "    'epochs': config.EPOCHS,\n",
-    "    'batch_size': config.BATCH_SIZE,\n",
-    "    'learning_rate': config.LEARNING_RATE,\n",
-    "    'max_input_length': config.MAX_INPUT_LENGTH,\n",
-    "    'max_target_length': config.MAX_TARGET_LENGTH,\n",
-    "    'train_samples': len(train_df),\n",
-    "    'val_samples': len(val_df),\n",
-    "    'test_samples': len(test_df),\n",
-    "    'final_train_loss': history['train_loss'][-1],\n",
-    "    'final_val_loss': history['val_loss'][-1]\n",
-    "}\n",
-    "\n",
-    "config_df = pd.DataFrame([config_dict])\n",
-    "config_df.to_csv('training_config.csv', index=False)\n",
-    "print(f\"✅ Training configuration saved to: training_config.csv\")\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"🎉 PIPELINE COMPLETED SUCCESSFULLY!\")\n",
-    "print(\"=\"*80)\n",
-    "print(\"\\n📁 Generated Files:\")\n",
-    "print(\"   1. test_set_evaluation.csv - Detailed evaluation on test set\")\n",
-    "print(\"   2. generated_arguments_new_topics.csv - Arguments for 15 new topics\")\n",
-    "print(\"   3. evaluation_summary.csv - Overall metrics summary\")\n",
-    "print(\"   4. training_config.csv - Training configuration and final losses\")\n",
-    "print(\"   5. ./argument_generator_model/ - Saved model and tokenizer\")\n",
-    "print(\"\\n✨ Your model is ready to generate creative arguments!\")\n"
-   ]
-  },
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-   "source": [
-    "# # ============================================================================\n",
-    "# # CELL 21: INTERACTIVE TESTING (OPTIONAL)\n",
-    "# # ============================================================================\n",
-    "\n",
-    "# print(\"\\n\" + \"=\"*80)\n",
-    "# print(\"🎮 INTERACTIVE TESTING\")\n",
-    "# print(\"=\"*80)\n",
-    "# print(\"\\nYou can now test the model with custom topics!\")\n",
-    "# print(\"Example usage:\\n\")\n",
-    "# print(\"topic = 'Your custom topic here'\")\n",
-    "# print(\"stance = 'positive'  # or 'negative'\")\n",
-    "# print(\"argument = generate_argument(model, tokenizer, topic, stance, device)\")\n",
-    "# print(\"print(argument)\")\n",
-    "\n",
-    "# # Example interactive test\n",
-    "# def test_custom_topic(topic, stance):\n",
-    "#     \"\"\"Helper function for quick testing\"\"\"\n",
-    "#     print(f\"\\n{'─'*60}\")\n",
-    "#     print(f\"Topic: {topic}\")\n",
-    "#     print(f\"Stance: {stance.upper()}\")\n",
-    "#     print(f\"{'─'*60}\")\n",
-    "    \n",
-    "#     argument = generate_argument(model, tokenizer, topic, stance, device)\n",
-    "#     diversity = evaluator.calculate_diversity(argument)\n",
-    "    \n",
-    "#     print(f\"\\nGenerated Argument:\")\n",
-    "#     print(f\"{argument}\")\n",
-    "#     print(f\"\\n📊 Diversity Score: {diversity:.3f}\")\n",
-    "#     print(f\"📏 Length: {len(argument.split())} words\")\n",
-    "    \n",
-    "#     return argument\n",
-    "\n",
-    "# # Example test\n",
-    "# print(\"\\n🔍 Example Test:\")\n",
-    "# test_custom_topic(\"Video games should be considered a sport\", \"positive\")2, 2, figsize=(15, 10))\n",
-    "\n",
-    "# # 1. Stance distribution\n",
-    "# stance_counts.plot(kind='bar', ax=axes[0, 0], color=['#FF6B6B', '#4ECDC4'])\n",
-    "# axes[0, 0].set_title('Stance Distribution', fontsize=14, fontweight='bold')\n",
-    "# axes[0, 0].set_xlabel('Stance')\n",
-    "# axes[0, 0].set_ylabel('Count')\n",
-    "# axes[0, 0].tick_params(axis='x', rotation=0)\n",
-    "\n",
-    "# # 2. Argument length distribution\n",
-    "# df['arg_length'] = df['argument'].str.len()\n",
-    "# axes[0, 1].hist(df['arg_length'], bins=50, color='#95E1D3', edgecolor='black')\n",
-    "# axes[0, 1].set_title('Argument Length Distribution', fontsize=14, fontweight='bold')\n",
-    "# axes[0, 1].set_xlabel('Character Count')\n",
-    "# axes[0, 1].set_ylabel('Frequency')\n",
-    "# axes[0, 1].axvline(df['arg_length'].mean(), color='red', linestyle='--', label=f'Mean: {df[\"arg_length\"].mean():.0f}')\n",
-    "# axes[0, 1].legend()\n",
-    "\n",
-    "# # 3. Top 10 topics\n",
-    "# top_topics = df['topic'].value_counts().head(10)\n",
-    "# axes[1, 0].barh(range(len(top_topics)), top_topics.values, color='#F38181')\n",
-    "# axes[1, 0].set_yticks(range(len(top_topics)))\n",
-    "# axes[1, 0].set_yticklabels([topic[:40] + '...' if len(topic) > 40 else topic for topic in top_topics.index])\n",
-    "# axes[1, 0].set_title('Top 10 Topics', fontsize=14, fontweight='bold')\n",
-    "# axes[1, 0].set_xlabel('Count')\n",
-    "# axes[1, 0].invert_yaxis()\n",
-    "\n",
-    "# # 4. Stance by argument length\n",
-    "# df.boxplot(column='arg_length', by='stance', ax=axes[1, 1])\n",
-    "# axes[1, 1].set_title('Argument Length by Stance', fontsize=14, fontweight='bold')\n",
-    "# axes[1, 1].set_xlabel('Stance')\n",
-    "# axes[1, 1].set_ylabel('Character Count')\n",
-    "# plt.suptitle('')  # Remove automatic title\n",
-    "\n",
-    "# plt.tight_layout()\n",
-    "# plt.show()\n"
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