diff --git "a/training/technical/codebert-xgb-ensemble.ipynb" "b/training/technical/codebert-xgb-ensemble.ipynb"
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+++ "b/training/technical/codebert-xgb-ensemble.ipynb"
@@ -0,0 +1,4776 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cb5f8ca7",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003855,
+     "end_time": "2026-02-15T15:41:19.443065",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:19.439210",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Paradigm Classification: XGBoost + CodeBERT Ensemble\n",
+    "\n",
+    "Binary ensemble: XGBoost (TF-IDF + features) + Fine-tuned CodeBERT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "316f6cff",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:41:19.450156Z",
+     "iopub.status.busy": "2026-02-15T15:41:19.449882Z",
+     "iopub.status.idle": "2026-02-15T15:41:23.827179Z",
+     "shell.execute_reply": "2026-02-15T15:41:23.826418Z"
+    },
+    "papermill": {
+     "duration": 4.382753,
+     "end_time": "2026-02-15T15:41:23.828864",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:19.446111",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q transformers datasets torch scikit-learn xgboost imbalanced-learn ipywidgets\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c3b6dc46",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:41:23.836625Z",
+     "iopub.status.busy": "2026-02-15T15:41:23.835978Z",
+     "iopub.status.idle": "2026-02-15T15:41:57.132006Z",
+     "shell.execute_reply": "2026-02-15T15:41:57.131323Z"
+    },
+    "papermill": {
+     "duration": 33.304703,
+     "end_time": "2026-02-15T15:41:57.136659",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:23.831956",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-15 15:41:40.990357: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1771170101.187712      24 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1771170101.239102      24 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "W0000 00:00:1771170101.712079      24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+      "W0000 00:00:1771170101.712121      24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+      "W0000 00:00:1771170101.712124      24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
+      "W0000 00:00:1771170101.712126      24 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.metrics import classification_report, accuracy_score\n",
+    "from sklearn.utils.class_weight import compute_class_weight\n",
+    "import xgboost as xgb\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
+    "from datasets import Dataset\n",
+    "from scipy.sparse import hstack\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "torch.manual_seed(42)\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Device: {device}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e0b4233",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002984,
+     "end_time": "2026-02-15T15:41:57.142722",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:57.139738",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 1. Load and Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "968009e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:41:57.149967Z",
+     "iopub.status.busy": "2026-02-15T15:41:57.149477Z",
+     "iopub.status.idle": "2026-02-15T15:41:59.659590Z",
+     "shell.execute_reply": "2026-02-15T15:41:59.658708Z"
+    },
+    "papermill": {
+     "duration": 2.515456,
+     "end_time": "2026-02-15T15:41:59.661083",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:57.145627",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train: 57,235 | Val: 12,265 | Test: 12,265\n",
+      "\n",
+      "After sampling: 19,643 train samples\n",
+      "\n",
+      "Class distribution:\n",
+      "label\n",
+      "Functional      5000\n",
+      "Non-Paradigm    5000\n",
+      "Oop             5000\n",
+      "Procedural      4643\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Labels: {'Functional': 0, 'Non-Paradigm': 1, 'Oop': 2, 'Procedural': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df = pd.read_csv('/kaggle/input/datasets/aryanprakhar/paradigm/data/train.csv')\n",
+    "val_df = pd.read_csv('/kaggle/input/datasets/aryanprakhar/paradigm/data/val.csv')\n",
+    "test_df = pd.read_csv('/kaggle/input/datasets/aryanprakhar/paradigm/data/test.csv')\n",
+    "\n",
+    "print(f\"Train: {len(train_df):,} | Val: {len(val_df):,} | Test: {len(test_df):,}\")\n",
+    "\n",
+    "# Combine title + body\n",
+    "def combine_text(row):\n",
+    "    title = str(row.get('title', '')) if pd.notna(row.get('title')) else ''\n",
+    "    body = str(row.get('question_body', '')) if pd.notna(row.get('question_body')) else ''\n",
+    "    return f\"{title} {body}\".strip()\n",
+    "\n",
+    "for df in [train_df, val_df, test_df]:\n",
+    "    if 'text' not in df.columns:\n",
+    "        df['text'] = df.apply(combine_text, axis=1)\n",
+    "    if 'paradigm_label' in df.columns:\n",
+    "        df['label'] = df['paradigm_label']\n",
+    "\n",
+    "# Remove Mixed class if present\n",
+    "for df in [train_df, val_df, test_df]:\n",
+    "    if 'Mixed' in df['label'].unique():\n",
+    "        df.drop(df[df['label'] == 'Mixed'].index, inplace=True)\n",
+    "\n",
+    "# Stratified sampling for balanced training\n",
+    "SAMPLES_PER_CLASS = 5000  # Adjust based on needs\n",
+    "\n",
+    "train_df = train_df.groupby('label', group_keys=False).apply(\n",
+    "    lambda x: x.sample(min(len(x), SAMPLES_PER_CLASS), random_state=42)\n",
+    ").reset_index(drop=True)\n",
+    "\n",
+    "print(f\"\\nAfter sampling: {len(train_df):,} train samples\")\n",
+    "print(\"\\nClass distribution:\")\n",
+    "print(train_df['label'].value_counts())\n",
+    "\n",
+    "label2id = {label: idx for idx, label in enumerate(sorted(train_df['label'].unique()))}\n",
+    "id2label = {idx: label for label, idx in label2id.items()}\n",
+    "print(f\"\\nLabels: {label2id}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fc0bcd4",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003083,
+     "end_time": "2026-02-15T15:41:59.667723",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:59.664640",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 2. Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d695f09a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:41:59.675370Z",
+     "iopub.status.busy": "2026-02-15T15:41:59.675061Z",
+     "iopub.status.idle": "2026-02-15T15:42:02.334217Z",
+     "shell.execute_reply": "2026-02-15T15:42:02.333391Z"
+    },
+    "papermill": {
+     "duration": 2.664696,
+     "end_time": "2026-02-15T15:42:02.335706",
+     "exception": false,
+     "start_time": "2026-02-15T15:41:59.671010",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features extracted: 10 features\n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "class FeatureExtractor:\n",
+    "    def __init__(self):\n",
+    "        self.oop_kw = ['class', 'object', 'this', 'self', 'extends', 'implements', 'interface',\n",
+    "                       'public', 'private', 'protected', 'static', 'virtual', 'override']\n",
+    "        self.fp_kw = ['map', 'filter', 'reduce', 'fold', 'lambda', 'closure', '=>',\n",
+    "                      'monad', 'functor', 'pure', 'immutable', 'const', 'let']\n",
+    "        self.proc_kw = ['void', 'int', 'char', 'float', 'struct', 'malloc', 'free',\n",
+    "                        'pointer', 'goto', 'scanf', 'printf']\n",
+    "    \n",
+    "    def extract(self, text):\n",
+    "        t = text.lower()\n",
+    "        return {\n",
+    "            'oop_score': sum(t.count(k) for k in self.oop_kw),\n",
+    "            'fp_score': sum(t.count(k) for k in self.fp_kw),\n",
+    "            'proc_score': sum(t.count(k) for k in self.proc_kw),\n",
+    "            'length': len(text),\n",
+    "            'num_lines': text.count('\\n') + 1,\n",
+    "            'has_class': 1 if re.search(r'\\bclass\\s+\\w+', t) else 0,\n",
+    "            'has_lambda': 1 if 'lambda' in t or '=>' in text else 0,\n",
+    "            'num_dots': text.count('.'),\n",
+    "            'num_arrows': text.count('->') + text.count('=>'),\n",
+    "            'num_braces': text.count('{') + text.count('}')\n",
+    "        }\n",
+    "    \n",
+    "    def extract_batch(self, texts):\n",
+    "        return pd.DataFrame([self.extract(t) for t in texts])\n",
+    "\n",
+    "feature_extractor = FeatureExtractor()\n",
+    "\n",
+    "train_features = feature_extractor.extract_batch(train_df['text'].values)\n",
+    "val_features = feature_extractor.extract_batch(val_df['text'].values)\n",
+    "test_features = feature_extractor.extract_batch(test_df['text'].values)\n",
+    "\n",
+    "print(f\"Features extracted: {train_features.shape[1]} features\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d501d2b8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003151,
+     "end_time": "2026-02-15T15:42:02.342259",
+     "exception": false,
+     "start_time": "2026-02-15T15:42:02.339108",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 3. XGBoost Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f5a061d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:42:02.349520Z",
+     "iopub.status.busy": "2026-02-15T15:42:02.349268Z",
+     "iopub.status.idle": "2026-02-15T15:42:15.967743Z",
+     "shell.execute_reply": "2026-02-15T15:42:15.966880Z"
+    },
+    "papermill": {
+     "duration": 13.6239,
+     "end_time": "2026-02-15T15:42:15.969262",
+     "exception": false,
+     "start_time": "2026-02-15T15:42:02.345362",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After SMOTE: 20,000 samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TF-IDF\n",
+    "tfidf = TfidfVectorizer(max_features=1000, ngram_range=(1, 2), min_df=2, max_df=0.95)\n",
+    "\n",
+    "tfidf_train = tfidf.fit_transform(train_df['text'])\n",
+    "tfidf_val = tfidf.transform(val_df['text'])\n",
+    "tfidf_test = tfidf.transform(test_df['text'])\n",
+    "\n",
+    "X_train = hstack([tfidf_train, train_features.values])\n",
+    "X_val = hstack([tfidf_val, val_features.values])\n",
+    "X_test = hstack([tfidf_test, test_features.values])\n",
+    "\n",
+    "y_train = train_df['label'].map(label2id).values\n",
+    "y_val = val_df['label'].map(label2id).values\n",
+    "y_test = test_df['label'].map(label2id).values\n",
+    "\n",
+    "# SMOTE\n",
+    "smote = SMOTE(random_state=42, k_neighbors=3)\n",
+    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
+    "\n",
+    "print(f\"After SMOTE: {X_train_balanced.shape[0]:,} samples\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7701b18d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:42:15.977275Z",
+     "iopub.status.busy": "2026-02-15T15:42:15.976976Z",
+     "iopub.status.idle": "2026-02-15T15:42:15.989126Z",
+     "shell.execute_reply": "2026-02-15T15:42:15.988614Z"
+    },
+    "papermill": {
+     "duration": 0.018139,
+     "end_time": "2026-02-15T15:42:15.990815",
+     "exception": false,
+     "start_time": "2026-02-15T15:42:15.972676",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
+    "sample_weights = np.array([class_weights[y] for y in y_train_balanced])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7e9494ac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:42:15.999038Z",
+     "iopub.status.busy": "2026-02-15T15:42:15.998799Z",
+     "iopub.status.idle": "2026-02-15T15:44:27.427666Z",
+     "shell.execute_reply": "2026-02-15T15:44:27.426660Z"
+    },
+    "papermill": {
+     "duration": 131.434731,
+     "end_time": "2026-02-15T15:44:27.429146",
+     "exception": false,
+     "start_time": "2026-02-15T15:42:15.994415",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\tvalidation_0-mlogloss:1.28819\n",
+      "[50]\tvalidation_0-mlogloss:0.39114\n",
+      "[100]\tvalidation_0-mlogloss:0.32716\n",
+      "[150]\tvalidation_0-mlogloss:0.30578\n",
+      "[199]\tvalidation_0-mlogloss:0.29511\n",
+      "\n",
+      "XGBoost Validation Accuracy: 0.9051\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "  Functional       0.80      0.90      0.85      1352\n",
+      "Non-Paradigm       0.90      0.91      0.90      4539\n",
+      "         Oop       0.94      0.89      0.92      5379\n",
+      "  Procedural       0.89      0.97      0.93       995\n",
+      "\n",
+      "    accuracy                           0.91     12265\n",
+      "   macro avg       0.88      0.92      0.90     12265\n",
+      "weighted avg       0.91      0.91      0.91     12265\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train XGBoost\n",
+    "class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
+    "sample_weights = np.array([class_weights[y] for y in y_train_balanced])\n",
+    "\n",
+    "xgb_model = xgb.XGBClassifier(\n",
+    "    n_estimators=200,\n",
+    "    max_depth=6,\n",
+    "    learning_rate=0.1,\n",
+    "    subsample=0.8,\n",
+    "    colsample_bytree=0.8,\n",
+    "    objective='multi:softprob',\n",
+    "    num_class=len(label2id),\n",
+    "    random_state=42,\n",
+    "    tree_method='hist'\n",
+    ")\n",
+    "\n",
+    "xgb_model.fit(X_train_balanced, y_train_balanced, sample_weight=sample_weights,\n",
+    "              eval_set=[(X_val, y_val)], verbose=50)\n",
+    "\n",
+    "xgb_val_preds = xgb_model.predict(X_val)\n",
+    "xgb_val_proba = xgb_model.predict_proba(X_val)\n",
+    "xgb_acc = accuracy_score(y_val, xgb_val_preds)\n",
+    "\n",
+    "print(f\"\\nXGBoost Validation Accuracy: {xgb_acc:.4f}\")\n",
+    "print(classification_report(y_val, xgb_val_preds, target_names=list(label2id.keys())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a54b788",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00343,
+     "end_time": "2026-02-15T15:44:27.436143",
+     "exception": false,
+     "start_time": "2026-02-15T15:44:27.432713",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 4. CodeBERT Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "50f1062f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:44:27.443936Z",
+     "iopub.status.busy": "2026-02-15T15:44:27.443695Z",
+     "iopub.status.idle": "2026-02-15T15:44:53.838922Z",
+     "shell.execute_reply": "2026-02-15T15:44:53.838324Z"
+    },
+    "papermill": {
+     "duration": 26.400859,
+     "end_time": "2026-02-15T15:44:53.840288",
+     "exception": false,
+     "start_time": "2026-02-15T15:44:27.439429",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cedf920aee4a495788cebf645ad3197e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a72f1db2e8234725b2f892727bcaa6d8",
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/498 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc21ce844a044791adca539392a65343",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "774350adbe334c7c8e39277cbc8427d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt: 0.00B [00:00, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c576aafe9a64f2691507c5be6c45f5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fa65ae2450a48f99e1369365855b5ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at microsoft/codebert-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f091577548414be79855753985855190",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/19643 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39f389cebf1b418a9bb9ed15a7ebe6a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9842976299d74d6c82ac619d5d3f3598",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/12265 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41246a78155941c79d30ecd7b5a1cf66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/12265 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def prepare_dataset(df, label2id):\n",
+    "    df = df.copy()\n",
+    "    df['label'] = df['label'].map(label2id)\n",
+    "    return Dataset.from_pandas(df[['text', 'label']])\n",
+    "\n",
+    "train_dataset = prepare_dataset(train_df, label2id)\n",
+    "val_dataset = prepare_dataset(val_df, label2id)\n",
+    "test_dataset = prepare_dataset(test_df, label2id)\n",
+    "\n",
+    "model_name = \"microsoft/codebert-base\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    model_name, num_labels=len(label2id), id2label=id2label, label2id=label2id\n",
+    ")\n",
+    "\n",
+    "def tokenize(examples):\n",
+    "    return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=256)\n",
+    "\n",
+    "tokenized_train = train_dataset.map(tokenize, batched=True, remove_columns=['text'])\n",
+    "tokenized_val = val_dataset.map(tokenize, batched=True, remove_columns=['text'])\n",
+    "tokenized_test = test_dataset.map(tokenize, batched=True, remove_columns=['text'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f47907c1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T15:44:53.850211Z",
+     "iopub.status.busy": "2026-02-15T15:44:53.849951Z",
+     "iopub.status.idle": "2026-02-15T16:11:34.821102Z",
+     "shell.execute_reply": "2026-02-15T16:11:34.820384Z"
+    },
+    "papermill": {
+     "duration": 1600.977769,
+     "end_time": "2026-02-15T16:11:34.822734",
+     "exception": false,
+     "start_time": "2026-02-15T15:44:53.844965",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='1842' max='1842' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [1842/1842 26:38, Epoch 3/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>F1 Macro</th>\n",
+       "      <th>F1 Weighted</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.188200</td>\n",
+       "      <td>0.228999</td>\n",
+       "      <td>0.926376</td>\n",
+       "      <td>0.921905</td>\n",
+       "      <td>0.927637</td>\n",
+       "      <td>0.904159</td>\n",
+       "      <td>0.948625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.084600</td>\n",
+       "      <td>0.264108</td>\n",
+       "      <td>0.936649</td>\n",
+       "      <td>0.932176</td>\n",
+       "      <td>0.936922</td>\n",
+       "      <td>0.916146</td>\n",
+       "      <td>0.951797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.060500</td>\n",
+       "      <td>0.231579</td>\n",
+       "      <td>0.947167</td>\n",
+       "      <td>0.945263</td>\n",
+       "      <td>0.947364</td>\n",
+       "      <td>0.936371</td>\n",
+       "      <td>0.955111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=1842, training_loss=0.13871806852689395, metrics={'train_runtime': 1600.323, 'train_samples_per_second': 36.823, 'train_steps_per_second': 1.151, 'total_flos': 7752574902638592.0, 'train_loss': 0.13871806852689395, 'epoch': 3.0})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_weights_tensor = torch.tensor(class_weights, dtype=torch.float32).to(device)\n",
+    "from sklearn.metrics import f1_score, precision_score, recall_score\n",
+    "\n",
+    "def compute_metrics(eval_pred):\n",
+    "    logits, labels = eval_pred\n",
+    "    preds = np.argmax(logits, axis=-1)\n",
+    "    \n",
+    "    return {\n",
+    "        \"accuracy\": accuracy_score(labels, preds),\n",
+    "        \"f1_macro\": f1_score(labels, preds, average='macro'),\n",
+    "        \"f1_weighted\": f1_score(labels, preds, average='weighted'),\n",
+    "        \"precision\": precision_score(labels, preds, average='macro'),\n",
+    "        \"recall\": recall_score(labels, preds, average='macro')\n",
+    "    }\n",
+    "\n",
+    "class WeightedTrainer(Trainer):\n",
+    "    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n",
+    "        labels = inputs.pop(\"labels\")\n",
+    "        outputs = model(**inputs)\n",
+    "        logits = outputs.logits\n",
+    "        loss_fct = torch.nn.CrossEntropyLoss(weight=class_weights_tensor)\n",
+    "        loss = loss_fct(logits, labels)\n",
+    "        return (loss, outputs) if return_outputs else loss\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"./results\",\n",
+    "    eval_strategy=\"epoch\",\n",
+    "    save_strategy=\"epoch\",\n",
+    "    learning_rate=3e-5,\n",
+    "    per_device_train_batch_size=16,\n",
+    "    per_device_eval_batch_size=32,\n",
+    "    num_train_epochs=3,\n",
+    "    weight_decay=0.01,\n",
+    "    load_best_model_at_end=True,\n",
+    "    metric_for_best_model=\"f1_macro\",\n",
+    "    fp16=torch.cuda.is_available(),\n",
+    "    gradient_accumulation_steps=2,\n",
+    "\n",
+    "    logging_strategy=\"steps\",\n",
+    "    logging_steps=10,\n",
+    "    disable_tqdm=False,\n",
+    "    report_to=\"none\" \n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "trainer = WeightedTrainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=tokenized_train,\n",
+    "    eval_dataset=tokenized_val,\n",
+    "    compute_metrics=compute_metrics\n",
+    ")\n",
+    "\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e428092b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T16:11:34.833452Z",
+     "iopub.status.busy": "2026-02-15T16:11:34.832900Z",
+     "iopub.status.idle": "2026-02-15T16:13:05.520893Z",
+     "shell.execute_reply": "2026-02-15T16:13:05.519939Z"
+    },
+    "papermill": {
+     "duration": 90.695098,
+     "end_time": "2026-02-15T16:13:05.522419",
+     "exception": false,
+     "start_time": "2026-02-15T16:11:34.827321",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Validation Accuracy: 0.9472\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "  Functional       0.87      0.94      0.90      1352\n",
+      "Non-Paradigm       0.95      0.95      0.95      4539\n",
+      "         Oop       0.96      0.94      0.95      5379\n",
+      "  Procedural       0.96      0.99      0.98       995\n",
+      "\n",
+      "    accuracy                           0.95     12265\n",
+      "   macro avg       0.94      0.96      0.95     12265\n",
+      "weighted avg       0.95      0.95      0.95     12265\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "codebert_val_results = trainer.predict(tokenized_val)\n",
+    "codebert_val_preds = np.argmax(codebert_val_results.predictions, axis=-1)\n",
+    "codebert_val_proba = torch.softmax(torch.tensor(codebert_val_results.predictions), dim=-1).numpy()\n",
+    "codebert_acc = accuracy_score(y_val, codebert_val_preds)\n",
+    "\n",
+    "print(f\"\\nValidation Accuracy: {codebert_acc:.4f}\")\n",
+    "print(classification_report(y_val, codebert_val_preds, target_names=list(label2id.keys())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb38ece9",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004888,
+     "end_time": "2026-02-15T16:13:05.532005",
+     "exception": false,
+     "start_time": "2026-02-15T16:13:05.527117",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5. Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0d1f3337",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T16:13:05.542314Z",
+     "iopub.status.busy": "2026-02-15T16:13:05.542033Z",
+     "iopub.status.idle": "2026-02-15T16:13:05.556713Z",
+     "shell.execute_reply": "2026-02-15T16:13:05.555968Z"
+    },
+    "papermill": {
+     "duration": 0.021499,
+     "end_time": "2026-02-15T16:13:05.558128",
+     "exception": false,
+     "start_time": "2026-02-15T16:13:05.536629",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Ensemble Validation Accuracy: 0.9491\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "  Functional       0.87      0.95      0.91      1352\n",
+      "Non-Paradigm       0.95      0.95      0.95      4539\n",
+      "         Oop       0.96      0.94      0.95      5379\n",
+      "  Procedural       0.97      0.99      0.98       995\n",
+      "\n",
+      "    accuracy                           0.95     12265\n",
+      "   macro avg       0.94      0.96      0.95     12265\n",
+      "weighted avg       0.95      0.95      0.95     12265\n",
+      "\n",
+      "\n",
+      "Model Comparison:\n",
+      "XGBoost:  0.9051\n",
+      "CodeBERT: 0.9472\n",
+      "Ensemble: 0.9491\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weighted average: CodeBERT (60%) + XGBoost (40%)\n",
+    "ensemble_val_proba = 0.6 * codebert_val_proba + 0.4 * xgb_val_proba\n",
+    "ensemble_val_preds = np.argmax(ensemble_val_proba, axis=1)\n",
+    "ensemble_acc = accuracy_score(y_val, ensemble_val_preds)\n",
+    "\n",
+    "print(f\"\\nEnsemble Validation Accuracy: {ensemble_acc:.4f}\")\n",
+    "print(classification_report(y_val, ensemble_val_preds, target_names=list(label2id.keys())))\n",
+    "\n",
+    "print(f\"\\nModel Comparison:\")\n",
+    "print(f\"XGBoost:  {xgb_acc:.4f}\")\n",
+    "print(f\"CodeBERT: {codebert_acc:.4f}\")\n",
+    "print(f\"Ensemble: {ensemble_acc:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4d3b6c1",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004365,
+     "end_time": "2026-02-15T16:13:05.567562",
+     "exception": false,
+     "start_time": "2026-02-15T16:13:05.563197",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 6. Test Set Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e2f5f2a0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T16:13:05.577419Z",
+     "iopub.status.busy": "2026-02-15T16:13:05.577152Z",
+     "iopub.status.idle": "2026-02-15T16:14:36.715691Z",
+     "shell.execute_reply": "2026-02-15T16:14:36.714952Z"
+    },
+    "papermill": {
+     "duration": 91.145215,
+     "end_time": "2026-02-15T16:14:36.717102",
+     "exception": false,
+     "start_time": "2026-02-15T16:13:05.571887",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Test Set Accuracy: 0.9525\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "  Functional       0.88      0.96      0.92      1352\n",
+      "Non-Paradigm       0.96      0.95      0.96      4539\n",
+      "         Oop       0.97      0.94      0.95      5379\n",
+      "  Procedural       0.97      0.99      0.98       995\n",
+      "\n",
+      "    accuracy                           0.95     12265\n",
+      "   macro avg       0.94      0.96      0.95     12265\n",
+      "weighted avg       0.95      0.95      0.95     12265\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgb_test_proba = xgb_model.predict_proba(X_test)\n",
+    "\n",
+    "codebert_test_results = trainer.predict(tokenized_test)\n",
+    "codebert_test_proba = torch.softmax(torch.tensor(codebert_test_results.predictions), dim=-1).numpy()\n",
+    "\n",
+    "ensemble_test_proba = 0.6 * codebert_test_proba + 0.4 * xgb_test_proba\n",
+    "ensemble_test_preds = np.argmax(ensemble_test_proba, axis=1)\n",
+    "test_acc = accuracy_score(y_test, ensemble_test_preds)\n",
+    "\n",
+    "print(f\"\\nTest Set Accuracy: {test_acc:.4f}\")\n",
+    "print(classification_report(y_test, ensemble_test_preds, target_names=list(label2id.keys())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f89a26c",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004672,
+     "end_time": "2026-02-15T16:14:36.726621",
+     "exception": false,
+     "start_time": "2026-02-15T16:14:36.721949",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 7. Save Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e7af5486",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-15T16:14:36.736589Z",
+     "iopub.status.busy": "2026-02-15T16:14:36.736360Z",
+     "iopub.status.idle": "2026-02-15T16:14:38.393837Z",
+     "shell.execute_reply": "2026-02-15T16:14:38.392936Z"
+    },
+    "papermill": {
+     "duration": 1.664389,
+     "end_time": "2026-02-15T16:14:38.395432",
+     "exception": false,
+     "start_time": "2026-02-15T16:14:36.731043",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Models saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "trainer.save_model(\"./codebert_model\")\n",
+    "tokenizer.save_pretrained(\"./codebert_model\")\n",
+    "\n",
+    "with open('xgboost_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(xgb_model, f)\n",
+    "with open('tfidf_vectorizer.pkl', 'wb') as f:\n",
+    "    pickle.dump(tfidf, f)\n",
+    "\n",
+    "results_df = test_df.copy()\n",
+    "results_df['predicted'] = [id2label[p] for p in ensemble_test_preds]\n",
+    "results_df['confidence'] = np.max(ensemble_test_proba, axis=1)\n",
+    "results_df.to_csv('predictions.csv', index=False)\n",
+    "\n",
+    "print(\"Models saved\")"
+   ]
+  }
+ ],
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