{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "878e9edb",
      "metadata": {
        "id": "878e9edb"
      },
      "source": [
        "# IMDB Sentiment Analysis: TF-IDF vs DistilBERT vs RoBERTa\n",
        "**MSc Data & Artificial Intelligence — Coursework**\n",
        "\n",
        "* **Student Name:** DANIEL HRISCU\n",
        "* **Student ID:** 2127929\n",
        "* **Module Code:** ACCA7026\n",
        "* **Lecturer:** DR. MALIK MEHMOUD\n",
        "* **University:** UNIVERSITY OF WALES TRINITY SAINT DAVID\n",
        "* **Campus:** BIRMINGHAM\n",
        "* **Submission Date:** 30/03/2026\n",
        "\n",
        "## Reproducibility Information\n",
        "- **Platform**: Google Colab Pro (L4 GPU, 24GB VRAM)\n",
        "- **Python**: 3.10+, **PyTorch**: 2.x (Colab pre-installed), **Transformers**: 4.40+\n",
        "- **Expected runtime**: ~2–2.5 hours on L4 GPU\n",
        "- **Random seeds**: Fixed (SEED=42 for main runs; seeds 42, 123, 456 for multi-seed variance)\n",
        "- **Dataset**: IMDB (HuggingFace `datasets` library) — 25K train / 25K test\n",
        "- **Train/Val split**: 90/10 stratified from the 25K training set (22,500 train / 2,500 val)\n",
        "\n",
        "## Setup Instructions\n",
        "1. Go to **Runtime → Change runtime type → L4 GPU**\n",
        "2. Run all cells in order (Runtime → Run all)\n",
        "3. Results are saved to `./output/` and optionally to Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "d1e7f9d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1e7f9d2",
        "outputId": "abdfea9b-7612-422f-cc13-d1e49a8ba69d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "  INSTALLING REQUIRED PACKAGES FOR COLAB ...\n",
            "============================================================\n",
            "\n",
            "  GPU detected: NVIDIA L4\n",
            "  VRAM: 22.0 GB\n",
            "  CUDA: 12.8\n",
            "  PyTorch: 2.10.0+cu128\n",
            "============================================================\n",
            "\n",
            "All imports successful!\n"
          ]
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "# =========================================================================\n",
        "# COLAB ENVIRONMENT SETUP\n",
        "# =========================================================================\n",
        "import sys, importlib, os\n",
        "\n",
        "# Colab already has torch, numpy, pandas, matplotlib, seaborn, sklearn, tqdm, scipy\n",
        "# We only need to install the missing ones\n",
        "print(\"=\" * 60)\n",
        "print(\"  INSTALLING REQUIRED PACKAGES FOR COLAB ...\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "!pip install -q transformers datasets shap wordcloud statsmodels nltk\n",
        "\n",
        "# Verify GPU is available\n",
        "import torch\n",
        "if torch.cuda.is_available():\n",
        "    gpu = torch.cuda.get_device_properties(0)\n",
        "    print(f\"\\n  GPU detected: {gpu.name}\")\n",
        "    print(f\"  VRAM: {gpu.total_memory / (1024**3):.1f} GB\")\n",
        "    print(f\"  CUDA: {torch.version.cuda}\")\n",
        "else:\n",
        "    print(\"\\n  WARNING: No GPU detected! Go to Runtime -> Change runtime type -> L4 GPU\")\n",
        "\n",
        "print(f\"  PyTorch: {torch.__version__}\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "# ===========================================================================\n",
        "#  IMPORTS\n",
        "# ===========================================================================\n",
        "import json, time, re, random, warnings, textwrap\n",
        "from datetime import datetime\n",
        "from collections import Counter\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# NLP\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Scikit-learn\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn.metrics import (accuracy_score, precision_score, recall_score,\n",
        "                             f1_score, confusion_matrix, classification_report,\n",
        "                             roc_auc_score, roc_curve, auc)\n",
        "\n",
        "# PyTorch & Transformers\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torch.optim import AdamW\n",
        "from transformers import (DistilBertTokenizer, DistilBertForSequenceClassification,\n",
        "                          RobertaTokenizer, RobertaForSequenceClassification,\n",
        "                          get_linear_schedule_with_warmup)\n",
        "\n",
        "# SHAP\n",
        "import shap\n",
        "\n",
        "# WordCloud\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "# Hugging Face datasets\n",
        "from datasets import load_dataset\n",
        "\n",
        "# Statistics\n",
        "from scipy.stats import chi2_contingency\n",
        "from statsmodels.stats.contingency_tables import mcnemar\n",
        "\n",
        "# Progress bars\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "print(\"\\nAll imports successful!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d75ab426",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d75ab426",
        "outputId": "9a84efdc-1434-4c0b-b7a6-97f3bfe0ff48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "  Google Drive mounted. Results will be saved to:\n",
            "    /content/drive/MyDrive/IMDB_Results/run_20260329_093626\n",
            "  Run tag: run_20260329_093626\n",
            "  Local output: ./output/run_20260329_093626\n",
            "[DEVICE] Using: cuda\n",
            "         GPU : NVIDIA L4  |  VRAM 22.0 GB\n",
            "\n",
            "  Config: BERT_BATCH=48, ROBERTA_BATCH=24, MAX_LEN=512, FP16=True\n",
            "  Ready to train!\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  CONFIGURATION (Optimized for Google Colab L4 GPU)\n",
        "# ===========================================================================\n",
        "\n",
        "# --- Google Drive (RECOMMENDED: Colab storage is NOT persistent) ---\n",
        "SAVE_TO_DRIVE = False   # << Set True to persist results across sessions\n",
        "DRIVE_BASE    = \"/content/drive/MyDrive/IMDB_Results\"\n",
        "\n",
        "if SAVE_TO_DRIVE:\n",
        "    from google.colab import drive\n",
        "    drive.mount(\"/content/drive\")\n",
        "\n",
        "# --- Run versioning: each execution gets its own timestamped folder ---\n",
        "from datetime import datetime\n",
        "RUN_TIMESTAMP = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "RUN_TAG       = f\"run_{RUN_TIMESTAMP}\"\n",
        "\n",
        "# Local output (always written — inside Colab runtime)\n",
        "OUTPUT_DIR  = f\"./output/{RUN_TAG}\"\n",
        "FIGURES_DIR = os.path.join(OUTPUT_DIR, \"figures\")\n",
        "os.makedirs(FIGURES_DIR, exist_ok=True)\n",
        "\n",
        "# Drive output (persistent — survives runtime disconnects)\n",
        "if SAVE_TO_DRIVE:\n",
        "    DRIVE_OUTPUT_DIR = f\"{DRIVE_BASE}/{RUN_TAG}\"\n",
        "    DRIVE_FIGURES    = f\"{DRIVE_OUTPUT_DIR}/figures\"\n",
        "    os.makedirs(DRIVE_FIGURES, exist_ok=True)\n",
        "    print(f\"  Google Drive mounted. Results will be saved to:\")\n",
        "    print(f\"    {DRIVE_OUTPUT_DIR}\")\n",
        "else:\n",
        "    DRIVE_OUTPUT_DIR = None\n",
        "\n",
        "print(f\"  Run tag: {RUN_TAG}\")\n",
        "print(f\"  Local output: {OUTPUT_DIR}\")\n",
        "\n",
        "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",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "# ---------- device auto-detection ----------\n",
        "if torch.cuda.is_available():\n",
        "    DEVICE = torch.device(\"cuda\")\n",
        "elif hasattr(torch.backends, \"mps\") and torch.backends.mps.is_available():\n",
        "    DEVICE = torch.device(\"mps\")\n",
        "else:\n",
        "    DEVICE = torch.device(\"cpu\")\n",
        "print(f\"[DEVICE] Using: {DEVICE}\")\n",
        "if DEVICE.type == \"cuda\":\n",
        "    gpu = torch.cuda.get_device_properties(0)\n",
        "    vram_gb = gpu.total_memory / (1024**3)\n",
        "    print(f\"         GPU : {gpu.name}  |  VRAM {vram_gb:.1f} GB\")\n",
        "\n",
        "# ---------- hyper-parameters (optimized for L4 24GB VRAM) ----------\n",
        "USE_FP16 = (DEVICE.type == \"cuda\")\n",
        "\n",
        "if DEVICE.type == \"cuda\":\n",
        "    vram_gb = torch.cuda.get_device_properties(0).total_memory / (1024**3)\n",
        "    if vram_gb >= 20:  # L4 (24GB) or A100\n",
        "        BERT_MAX_LEN       = 512\n",
        "        BERT_BATCH_SIZE    = 48\n",
        "        BERT_TRAIN_SAMPLES = None\n",
        "        BERT_TEST_SAMPLES  = None\n",
        "        ROBERTA_MAX_LEN    = 512\n",
        "        ROBERTA_BATCH_SIZE = 24\n",
        "        ROBERTA_GRAD_ACCUM = 2\n",
        "        ROBERTA_TRAIN_SAMPLES = None\n",
        "        ROBERTA_TEST_SAMPLES  = None\n",
        "    elif vram_gb >= 14:  # T4 (16GB)\n",
        "        BERT_MAX_LEN       = 512\n",
        "        BERT_BATCH_SIZE    = 32\n",
        "        BERT_TRAIN_SAMPLES = None\n",
        "        BERT_TEST_SAMPLES  = None\n",
        "        ROBERTA_MAX_LEN    = 512\n",
        "        ROBERTA_BATCH_SIZE = 16\n",
        "        ROBERTA_GRAD_ACCUM = 2\n",
        "        ROBERTA_TRAIN_SAMPLES = None\n",
        "        ROBERTA_TEST_SAMPLES  = None\n",
        "    else:  # Smaller GPU\n",
        "        BERT_MAX_LEN       = 256\n",
        "        BERT_BATCH_SIZE    = 16\n",
        "        BERT_TRAIN_SAMPLES = None\n",
        "        BERT_TEST_SAMPLES  = None\n",
        "        ROBERTA_MAX_LEN    = 256\n",
        "        ROBERTA_BATCH_SIZE = 8\n",
        "        ROBERTA_GRAD_ACCUM = 4\n",
        "        ROBERTA_TRAIN_SAMPLES = None\n",
        "        ROBERTA_TEST_SAMPLES  = None\n",
        "elif DEVICE.type == \"mps\":\n",
        "    BERT_MAX_LEN       = 256\n",
        "    BERT_BATCH_SIZE    = 16\n",
        "    BERT_TRAIN_SAMPLES = None\n",
        "    BERT_TEST_SAMPLES  = None\n",
        "    ROBERTA_MAX_LEN    = 256\n",
        "    ROBERTA_BATCH_SIZE = 8\n",
        "    ROBERTA_GRAD_ACCUM = 4\n",
        "    ROBERTA_TRAIN_SAMPLES = None\n",
        "    ROBERTA_TEST_SAMPLES  = None\n",
        "else:\n",
        "    BERT_MAX_LEN       = 256\n",
        "    BERT_BATCH_SIZE    = 16\n",
        "    BERT_TRAIN_SAMPLES = 5000\n",
        "    BERT_TEST_SAMPLES  = 2500\n",
        "    ROBERTA_MAX_LEN    = 256\n",
        "    ROBERTA_BATCH_SIZE = 8\n",
        "    ROBERTA_GRAD_ACCUM = 4\n",
        "    ROBERTA_TRAIN_SAMPLES = 2000\n",
        "    ROBERTA_TEST_SAMPLES  = 1000\n",
        "\n",
        "BERT_EPOCHS          = 8\n",
        "BERT_LR              = 2e-5\n",
        "BERT_WEIGHT_DECAY    = 0.01\n",
        "BERT_WARMUP_RATIO    = 0.10\n",
        "BERT_GRAD_CLIP       = 1.0\n",
        "BERT_PATIENCE        = 3\n",
        "\n",
        "ROBERTA_EPOCHS       = 8\n",
        "ROBERTA_LR           = 1e-5\n",
        "ROBERTA_WEIGHT_DECAY = 0.01\n",
        "ROBERTA_WARMUP_RATIO = 0.06\n",
        "ROBERTA_GRAD_CLIP    = 1.0\n",
        "ROBERTA_PATIENCE     = 4\n",
        "\n",
        "FAIR_SUBSET_TRAIN = 2000\n",
        "FAIR_SUBSET_TEST  = 1000\n",
        "\n",
        "N_BOOTSTRAP = 10000\n",
        "\n",
        "# NLTK resources\n",
        "for res in [\"punkt\", \"punkt_tab\", \"stopwords\", \"wordnet\",\n",
        "            \"averaged_perceptron_tagger\", \"averaged_perceptron_tagger_eng\",\n",
        "            \"omw-1.4\"]:\n",
        "    try:\n",
        "        nltk.data.find(f\"tokenizers/{res}\" if \"punkt\" in res else f\"corpora/{res}\")\n",
        "    except LookupError:\n",
        "        nltk.download(res, quiet=True)\n",
        "\n",
        "print(f\"\\n  Config: BERT_BATCH={BERT_BATCH_SIZE}, ROBERTA_BATCH={ROBERTA_BATCH_SIZE}, MAX_LEN=512, FP16={USE_FP16}\")\n",
        "print(f\"  Ready to train!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4252add8",
      "metadata": {
        "id": "4252add8"
      },
      "source": [
        "## Part 1: Data Loading & Exploratory Data Analysis"
      ]
    },
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      "execution_count": 3,
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        "id": "d19b741b",
        "outputId": "efd3154a-ff1b-49d3-d06a-baa11223564c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 1 — DATA LOADING & EXPLORATORY DATA ANALYSIS\n",
            "================================================================================\n",
            "\n",
            "-> Loading IMDB dataset from Hugging Face ...\n"
          ]
        },
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              "plain_text/train-00000-of-00001.parquet:   0%|          | 0.00/21.0M [00:00<?, ?B/s]"
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
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              "plain_text/unsupervised-00000-of-00001.p(…):   0%|          | 0.00/42.0M [00:00<?, ?B/s]"
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              "model_id": "5640107171b34448bd55daf156a9269a",
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            },
            "text/plain": [
              "Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7239a9fd64774994991f4e91c7b336e4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ebdbbfdf050e420fa598ce9b39c7a7f0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Train : 25,000  ({0: 12500, 1: 12500})\n",
            "  Test  : 25,000  ({0: 12500, 1: 12500})\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved class_distribution.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved review_length_distribution.png  (mean length: 234 words)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved word_clouds.png\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 1 — DATA LOADING & EXPLORATORY DATA ANALYSIS\n",
        "# ===========================================================================\n",
        "def load_data():\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 1 — DATA LOADING & EXPLORATORY DATA ANALYSIS\")\n",
        "    print(\"=\" * 80)\n",
        "    print(\"\\n-> Loading IMDB dataset from Hugging Face ...\")\n",
        "    ds = load_dataset('imdb')\n",
        "    train_df = pd.DataFrame({'text': ds['train']['text'], 'label': ds['train']['label']})\n",
        "    test_df  = pd.DataFrame({'text': ds['test']['text'], 'label': ds['test']['label']})\n",
        "    print(f\"  Train : {len(train_df):>6,}  ({train_df['label'].value_counts().to_dict()})\")\n",
        "    print(f\"  Test  : {len(test_df):>6,}  ({test_df['label'].value_counts().to_dict()})\")\n",
        "    return train_df, test_df\n",
        "\n",
        "def eda(train_df, test_df):\n",
        "    label_map = {0: 'Negative', 1: 'Positive'}\n",
        "\n",
        "    # ---- class distribution ----\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "    for ax, df, title in [(axes[0], train_df, 'Training Set'), (axes[1], test_df, 'Test Set')]:\n",
        "        counts = df['label'].map(label_map).value_counts()\n",
        "        bars = ax.bar(counts.index, counts.values, color=['#e74c3c', '#2ecc71'], edgecolor='black')\n",
        "        ax.set_title(title, fontsize=14, fontweight='bold')\n",
        "        ax.set_ylabel('Count')\n",
        "        for b in bars:\n",
        "            ax.text(b.get_x() + b.get_width() / 2, b.get_height() + 200,\n",
        "                    f'{int(b.get_height()):,}', ha='center', fontweight='bold')\n",
        "    plt.suptitle('Class Distribution - IMDB Dataset', fontsize=16, fontweight='bold')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/class_distribution.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved class_distribution.png\")\n",
        "\n",
        "    # ---- review length distribution ----\n",
        "    train_df = train_df.copy()\n",
        "    train_df['word_count'] = train_df['text'].str.split().str.len()\n",
        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
        "    ax.hist(train_df.loc[train_df['label'] == 0, 'word_count'], bins=60, alpha=0.6, label='Negative', color='#e74c3c', edgecolor='black')\n",
        "    ax.hist(train_df.loc[train_df['label'] == 1, 'word_count'], bins=60, alpha=0.6, label='Positive', color='#2ecc71', edgecolor='black')\n",
        "    ax.set_title('Distribution of Review Lengths by Sentiment', fontsize=14, fontweight='bold')\n",
        "    ax.set_xlabel('Number of Words'); ax.set_ylabel('Frequency')\n",
        "    mean_len = train_df['word_count'].mean()\n",
        "    ax.axvline(mean_len, color='black', ls='--', lw=1.5, label=f'Mean = {mean_len:.0f}')\n",
        "    ax.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/review_length_distribution.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"  Saved review_length_distribution.png  (mean length: {mean_len:.0f} words)\")\n",
        "\n",
        "    # ---- word clouds ----\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
        "    for ax, lab, title, cmap in [(axes[0], 1, 'Positive Reviews', 'Greens'), (axes[1], 0, 'Negative Reviews', 'Reds')]:\n",
        "        corpus = ' '.join(train_df.loc[train_df['label'] == lab, 'text'].values[:5000])\n",
        "        wc = WordCloud(width=800, height=400, background_color='white', colormap=cmap, max_words=150).generate(corpus)\n",
        "        ax.imshow(wc, interpolation='bilinear')\n",
        "        ax.set_title(title, fontsize=14, fontweight='bold'); ax.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/word_clouds.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved word_clouds.png\")\n",
        "\n",
        "# --- Execute ---\n",
        "train_df, test_df = load_data()\n",
        "eda(train_df, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d4c8b75",
      "metadata": {
        "id": "1d4c8b75"
      },
      "source": [
        "## Part 2: Text Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "74a3192b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74a3192b",
        "outputId": "f04af581-942d-47e2-987d-32a31d70ee15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Preprocessing example:\n",
            "  Original : I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ev...\n",
            "  Cleaned  : rented curious yellow video store controversy surrounded first released also heard first seized custom...\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 2 — TEXT PREPROCESSING\n",
        "# ===========================================================================\n",
        "STOP_WORDS  = set(stopwords.words('english'))\n",
        "LEMMATIZER  = WordNetLemmatizer()\n",
        "\n",
        "def preprocess_traditional(text: str) -> str:\n",
        "    text = text.lower()\n",
        "    text = re.sub(r'<[^>]+>', ' ', text)\n",
        "    text = re.sub(r'https?://\\S+|www\\.\\S+', ' ', text)\n",
        "    text = re.sub(r'[^a-z\\s]', ' ', text)\n",
        "    tokens = word_tokenize(text)\n",
        "    tokens = [LEMMATIZER.lemmatize(t) for t in tokens if t not in STOP_WORDS and len(t) > 2]\n",
        "    return ' '.join(tokens)\n",
        "\n",
        "def preprocess_bert(text: str) -> str:\n",
        "    return re.sub(r'<[^>]+>', ' ', text)\n",
        "\n",
        "# --- Example ---\n",
        "print(\"\\nPreprocessing example:\")\n",
        "sample = train_df['text'].iloc[0][:200]\n",
        "print(f\"  Original : {sample}...\")\n",
        "print(f\"  Cleaned  : {preprocess_traditional(sample)[:200]}...\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "776c83d0",
      "metadata": {
        "id": "776c83d0"
      },
      "source": [
        "## Part 3: TF-IDF + Logistic Regression (Full 25K Dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "66a9195a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "963cbfab4a0947b39a9b67b049727723",
            "523a6b21290e482a8f3fe1750d4fa19b",
            "3d4a06926fd34ab9ba2cfb13d7a3e79c",
            "dad16bb6259f46e0b98db8cf3d57ec24",
            "875f71e9a89844a79c0f455530d7103f",
            "28bcb0a6ad1f4bc08ff305afb92c3182",
            "294b7fef34754880b9d68e2225e04a36",
            "cbc57c323bd949cc96c3ce7fb5ce11b5",
            "c6c409593a62494ea098e0521522677c",
            "70a0dc527aeb479e9fa20ba0ae236ca7",
            "a33e18efd9c7442db973b2261391cd20",
            "a2bdc73edd6e4a40980d8852546a2e72",
            "13a3a8c412b64940b049312f45e097c3",
            "2152001c664440cf871e48c57b630140",
            "930d83b9b8a74fe18cd0d5e85eb3b317",
            "38e7d0ea1052462bbcdadbaa58e1f077",
            "28af5e30baf941f3ba3f662355d4a3ea",
            "90b35fe2e3b349d0a79537f5b8260266",
            "bdc9e53b5e7b4ff7a70ed56c1051ec08",
            "89394bbdce194b96a6e88f5f60bf5f2a",
            "eb6ca930b8cb4afd8f15b8e5200424df",
            "ae99a5b6700f4cb8b77fffa15de57088"
          ]
        },
        "id": "66a9195a",
        "outputId": "10b3f22c-04f3-488e-cd57-053fca0e6883"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 3 — TF-IDF + LOGISTIC REGRESSION (FULL 25K DATASET)\n",
            "================================================================================\n",
            "\n",
            "-> Preprocessing training data ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "963cbfab4a0947b39a9b67b049727723",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Preprocessing train:   0%|          | 0/25000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a2bdc73edd6e4a40980d8852546a2e72",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Preprocessing test :   0%|          | 0/25000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Done in 56.7s\n",
            "\n",
            "-> GridSearchCV (16 combinations x 3 folds = 48 fits) ...\n",
            "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
            "\n",
            "  Best params : {'clf__C': 1.0, 'tfidf__max_features': 30000, 'tfidf__ngram_range': (1, 2)}\n",
            "  Actual GridSearch params: C={'clf__C': 1.0, 'tfidf__max_features': 30000, 'tfidf__ngram_range': (1, 2)}\n",
            "  Best CV F1  : 0.8722\n",
            "  Training    : 59.8s\n",
            "\n",
            "  Metric          Value\n",
            "  ------------------------\n",
            "  Accuracy       0.8915\n",
            "  Precision      0.8890\n",
            "  Recall         0.8947\n",
            "  F1-Score       0.8919\n",
            "  AUC-ROC        0.9583\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.89      0.89      0.89     12500\n",
            "    Positive       0.89      0.89      0.89     12500\n",
            "\n",
            "    accuracy                           0.89     25000\n",
            "   macro avg       0.89      0.89      0.89     25000\n",
            "weighted avg       0.89      0.89      0.89     25000\n",
            "\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 700x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved lr_confusion_matrix.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved lr_top_features.png\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 3 — TF-IDF + LOGISTIC REGRESSION (FULL DATASET)\n",
        "# ===========================================================================\n",
        "def train_tfidf_full(train_df, test_df):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 3 — TF-IDF + LOGISTIC REGRESSION (FULL 25K DATASET)\")\n",
        "    print(\"=\" * 80)\n",
        "    print(\"\\n-> Preprocessing training data ...\")\n",
        "    t0 = time.time()\n",
        "    tqdm.pandas(desc=\"  Preprocessing train\")\n",
        "    X_train = train_df['text'].progress_apply(preprocess_traditional).values\n",
        "    tqdm.pandas(desc=\"  Preprocessing test \")\n",
        "    X_test  = test_df['text'].progress_apply(preprocess_traditional).values\n",
        "    preprocess_sec = time.time() - t0\n",
        "    y_train, y_test = train_df['label'].values, test_df['label'].values\n",
        "    print(f\"  Done in {preprocess_sec:.1f}s\")\n",
        "    print(\"\\n-> GridSearchCV (16 combinations x 3 folds = 48 fits) ...\")\n",
        "    pipe = Pipeline([\n",
        "        ('tfidf', TfidfVectorizer(sublinear_tf=True, min_df=2)),\n",
        "        ('clf',   LogisticRegression(max_iter=1000, random_state=SEED, solver='lbfgs'))\n",
        "    ])\n",
        "    param_grid = {\n",
        "        'tfidf__max_features': [30_000, 50_000],\n",
        "        'tfidf__ngram_range':  [(1, 1), (1, 2)],\n",
        "        'clf__C':              [0.5, 1.0, 5.0, 10.0],\n",
        "    }\n",
        "    t0 = time.time()\n",
        "    grid = GridSearchCV(pipe, param_grid, cv=3, scoring='f1', n_jobs=-1, verbose=1, refit=True)\n",
        "    grid.fit(X_train, y_train)\n",
        "    train_sec = time.time() - t0\n",
        "    print(f\"\\n  Best params : {grid.best_params_}\")\n",
        "    print(f\"  Actual GridSearch params: C={grid.best_params_}\")\n",
        "    print(f\"  Best CV F1  : {grid.best_score_:.4f}\")\n",
        "    print(f\"  Training    : {train_sec:.1f}s\")\n",
        "    best = grid.best_estimator_\n",
        "    t0 = time.time()\n",
        "    y_pred = best.predict(X_test)\n",
        "    inf_sec = time.time() - t0\n",
        "    y_prob  = best.predict_proba(X_test)[:, 1]\n",
        "    acc, prec, rec, f1, auc = accuracy_score(y_test, y_pred), precision_score(y_test, y_pred), recall_score(y_test, y_pred), f1_score(y_test, y_pred), roc_auc_score(y_test, y_prob)\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    print(f\"\\n  {'Metric':<12} {'Value':>8}\\n  {'---'*8}\")\n",
        "    for name, val in [('Accuracy', acc), ('Precision', prec), ('Recall', rec), ('F1-Score', f1), ('AUC-ROC', auc)]:\n",
        "        print(f\"  {name:<12} {val:>8.4f}\")\n",
        "    print(f\"\\n{classification_report(y_test, y_pred, target_names=['Negative', 'Positive'])}\")\n",
        "    fig, ax = plt.subplots(figsize=(7, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt=',d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'], ax=ax, annot_kws={'size': 16})\n",
        "    ax.set_title('TF-IDF + LR - Confusion Matrix (Full 25K Test)', fontsize=13, fontweight='bold')\n",
        "    ax.set_xlabel('Predicted'); ax.set_ylabel('True')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/lr_confusion_matrix.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved lr_confusion_matrix.png\")\n",
        "    tfidf_step, clf_step = best.named_steps['tfidf'], best.named_steps['clf']\n",
        "    feat_names, coefs = tfidf_step.get_feature_names_out(), clf_step.coef_[0]\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n",
        "    for ax, idx, title, color in [\n",
        "        (axes[0], np.argsort(coefs)[-20:], 'Top 20 Positive Features', '#2ecc71'),\n",
        "        (axes[1], np.argsort(coefs)[:20],  'Top 20 Negative Features', '#e74c3c'),\n",
        "    ]:\n",
        "        ax.barh(range(20), coefs[idx], color=color, edgecolor='black')\n",
        "        ax.set_yticks(range(20)); ax.set_yticklabels(feat_names[idx], fontsize=10)\n",
        "        ax.set_title(title, fontsize=13, fontweight='bold'); ax.set_xlabel('Coefficient Weight')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/lr_top_features.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved lr_top_features.png\")\n",
        "    results = dict(\n",
        "        accuracy=acc, precision=prec, recall=rec, f1=f1, auc=auc,\n",
        "        cm=cm.tolist(), best_params={k: str(v) for k, v in grid.best_params_.items()},\n",
        "        cv_f1=grid.best_score_, preprocess_time=preprocess_sec, train_time=train_sec,\n",
        "        inference_time=inf_sec, inference_per_sample=inf_sec / len(y_test),\n",
        "        train_size=len(y_train), test_size=len(y_test),\n",
        "        predictions=y_pred.tolist(), probabilities=y_prob.tolist(), true_labels=y_test.tolist()\n",
        "    )\n",
        "    return best, X_train, y_train, X_test, y_test, results\n",
        "# --- Execute ---\n",
        "lr_model, X_trn_clean, y_trn, X_tst_clean, y_tst, res_full = train_tfidf_full(train_df, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecf6c4a9",
      "metadata": {
        "id": "ecf6c4a9"
      },
      "source": [
        "## Part 4: TF-IDF + LR on 2,000-Sample Subset (Fair Comparison)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "3ee5b0e4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ee5b0e4",
        "outputId": "7435133d-528e-4699-e64f-a40bc3e2e2cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 4 — TF-IDF + LR ON 2,000-SAMPLE SUBSET (fair comparison)\n",
            "================================================================================\n",
            "\n",
            "  Subset TF-IDF+LR -> Acc 0.8600  F1 0.8617  AUC 0.9325\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 4 — TF-IDF + LR ON SMALL SUBSET  (fair comparison)\n",
        "# ===========================================================================\n",
        "def train_tfidf_subset(full_model, train_df, test_df, n_train=FAIR_SUBSET_TRAIN, n_test=FAIR_SUBSET_TEST):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(f\"PART 4 — TF-IDF + LR ON {n_train:,}-SAMPLE SUBSET (fair comparison)\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    sub_train = pd.concat([\n",
        "        train_df[train_df['label'] == 0].sample(n_train // 2, random_state=SEED),\n",
        "        train_df[train_df['label'] == 1].sample(n_train // 2, random_state=SEED),\n",
        "    ]).sample(frac=1, random_state=SEED)\n",
        "    sub_test = pd.concat([\n",
        "        test_df[test_df['label'] == 0].sample(n_test // 2, random_state=SEED),\n",
        "        test_df[test_df['label'] == 1].sample(n_test // 2, random_state=SEED),\n",
        "    ]).sample(frac=1, random_state=SEED)\n",
        "\n",
        "    X_train = sub_train['text'].apply(preprocess_traditional).values\n",
        "    X_test  = sub_test['text'].apply(preprocess_traditional).values\n",
        "    y_train, y_test = sub_train['label'].values, sub_test['label'].values\n",
        "\n",
        "    pipe = Pipeline([\n",
        "        ('tfidf', TfidfVectorizer(sublinear_tf=True, min_df=2, max_features=int(full_model.named_steps['tfidf'].max_features or 50000), ngram_range=full_model.named_steps['tfidf'].ngram_range)),\n",
        "        ('clf', LogisticRegression(C=full_model.named_steps['clf'].C, max_iter=1000, random_state=SEED, solver='lbfgs'))\n",
        "    ])\n",
        "\n",
        "    t0 = time.time()\n",
        "    pipe.fit(X_train, y_train)\n",
        "    train_sec = time.time() - t0\n",
        "\n",
        "    t0 = time.time()\n",
        "    y_pred, y_prob = pipe.predict(X_test), pipe.predict_proba(X_test)[:, 1]\n",
        "    inf_sec = time.time() - t0\n",
        "\n",
        "    acc, prec, rec, f1, auc = accuracy_score(y_test, y_pred), precision_score(y_test, y_pred), recall_score(y_test, y_pred), f1_score(y_test, y_pred), roc_auc_score(y_test, y_prob)\n",
        "    print(f\"\\n  Subset TF-IDF+LR -> Acc {acc:.4f}  F1 {f1:.4f}  AUC {auc:.4f}\")\n",
        "\n",
        "    results = dict(accuracy=acc, precision=prec, recall=rec, f1=f1, auc=auc, cm=confusion_matrix(y_test, y_pred).tolist(),\n",
        "                   train_time=train_sec, inference_time=inf_sec, train_size=len(y_train), test_size=len(y_test),\n",
        "                   predictions=y_pred.tolist(), probabilities=y_prob.tolist(), true_labels=y_test.tolist())\n",
        "    return pipe, sub_train, sub_test, results\n",
        "\n",
        "# --- Execute ---\n",
        "lr_sub, sub_train_df, sub_test_df, res_subset = train_tfidf_subset(lr_model, train_df, test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "017e639a",
      "metadata": {
        "id": "017e639a"
      },
      "source": [
        "## Part 5: DistilBERT Fine-Tuning (Full 25K Dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "dcd1a03c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "dcd1a03c",
        "outputId": "65cb4da0-07d2-4b01-f225-62300152307a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 5 — DISTILBERT FINE-TUNING (FULL 25K DATASET)\n",
            "================================================================================\n",
            "\n",
            "  Train : 22,500   Val : 2,500   Test : 25,000\n",
            "  MAX_LEN=512  BATCH=48  EPOCHS=8  LR=2e-05  FP16=True\n",
            "\n",
            "-> Tokenising with DistilBERT WordPiece tokeniser ...\n"
          ]
        },
        {
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              "model_id": "a856dfeffb2d488483ce16a243debd47",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "8f03c521525b494bb33dc641ee32cf1f",
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              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "tokenizer.json: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Tokenising train set ...\n",
            "  Tokenising val set ...\n",
            "  Tokenising test set ...\n",
            "\n",
            "-> Loading distilbert-base-uncased ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "baa0196be7134e67945940e7afa9c29b",
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              "version_minor": 0
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            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f50934f8c654086bb51c332ffbc4074",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "353a4e99f19b48868215056516ef9c78",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "-> Training for up to 8 epochs ...\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e2001040c3b44d8480cf424bb97d0623",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 1/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8113e578b6af4cd3866b400195a0ac0f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 1/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 1/8  TrainLoss=0.3721  ValLoss=0.2577  ValAcc=0.8940  ValF1=0.8876\n",
            "       >> New best F1: 0.8876\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ee22891af78846c891279496d42b621a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 2/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1615789595e4624a0e17cf68534da0c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 2/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 2/8  TrainLoss=0.1820  ValLoss=0.2132  ValAcc=0.9236  ValF1=0.9249\n",
            "       >> New best F1: 0.9249\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce56ff0f49464bb29efb2359a82d8fb4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 3/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "357838e3d16d4f918bcb61fcbf4684ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 3/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 3/8  TrainLoss=0.1122  ValLoss=0.2601  ValAcc=0.9188  ValF1=0.9168\n",
            "       >> No F1 improvement (1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32279c5605d0492d830f91654b5ca549",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 4/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8e02b47ec3434fd6a3d5e5ae63b84ee4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 4/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 4/8  TrainLoss=0.0644  ValLoss=0.2747  ValAcc=0.9208  ValF1=0.9224\n",
            "       >> No F1 improvement (2/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "243415a565534f9f9b27ab9cfbd57006",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 5/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3183a59b8d57477c988d1915972d8fd0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 5/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 5/8  TrainLoss=0.0420  ValLoss=0.3176  ValAcc=0.9296  ValF1=0.9295\n",
            "       >> New best F1: 0.9295\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c293453d9ad4a5793f64650eb242711",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 6/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51b0412c39814a67823734c3acb4c325",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 6/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 6/8  TrainLoss=0.0237  ValLoss=0.3718  ValAcc=0.9240  ValF1=0.9243\n",
            "       >> No F1 improvement (1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71085cc811cc42e6888cd8eaed6a1be0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 7/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eb30582c00f1463fb87a28620646bd0a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 7/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 7/8  TrainLoss=0.0185  ValLoss=0.3785  ValAcc=0.9236  ValF1=0.9237\n",
            "       >> No F1 improvement (2/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "402b5ea6f24341cc88c2598464af57a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 8/8 [train]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "099ed926667f4afa8d95c054abf5f8e9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 8/8 [val]  :   0%|          | 0/53 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 8/8  TrainLoss=0.0146  ValLoss=0.3838  ValAcc=0.9268  ValF1=0.9271\n",
            "       >> No F1 improvement (3/3)\n",
            "\n",
            "  !! Early stopping triggered at epoch 8 (best F1=0.9295)\n",
            "\n",
            "-> Hyperparameter exploration: testing lr=5e-5 for 1 epoch ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73c8f06d7b8f4acc9754d9e2e701df57",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2aab18a07ea641229057d05b908c2c80",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  lr=5e-5 exploration:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  lr=5e-5  -> val_loss=0.2145\n",
            "\n",
            "-> Evaluating on test set ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ddf128ab57c34c28b6ee6d54a5a0ed5b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Evaluating test set:   0%|          | 0/521 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.93      0.93      0.93     12500\n",
            "    Positive       0.93      0.93      0.93     12500\n",
            "\n",
            "    accuracy                           0.93     25000\n",
            "   macro avg       0.93      0.93      0.93     25000\n",
            "weighted avg       0.93      0.93      0.93     25000\n",
            "\n",
            "\n",
            "-> Multi-seed variance estimation (seeds: 42, 123, 456) ...\n",
            "  Seed 42:\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efa1cfa0a2324272a9f7c7f16d327cfe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9e7406ed041432dbe92c37a0ea30877",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 1/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 1: val_f1=0.8895 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53114fe282174a99871b90b5a4c071d0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 2/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 2: val_f1=0.9151 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b212870c4acc4162910af763f184a197",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 3/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 3: val_f1=0.9187 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45a34648480245f4886569f177fc6154",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 4/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 4: val_f1=0.9271 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11e2ab1af8c24ab48e29cb5e1df310f8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 5/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 5: val_f1=0.9261 (no improve 1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f370a68c7b340b38316d5fb9951f65b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 6/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 6: val_f1=0.9267 (no improve 2/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7375ad0432d45339c87e0ed382648d0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 42 Ep 7/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 7: val_f1=0.9235 (no improve 3/3)\n",
            "      Early stopping at epoch 7\n",
            "    Acc=0.9313, F1=0.9310\n",
            "  Seed 123:\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c504b01ee7b4cc9882a58a0f41edcbb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73de92fb90c04210864a22cde24682e7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 1/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 1: val_f1=0.9020 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30d594188e754691a4457f4c873bfeea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 2/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 2: val_f1=0.9007 (no improve 1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c0e3b6efcd90490ba3c225858a78dfbd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 3/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 3: val_f1=0.9215 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "843452bf6eb84f489d5b55f17c8d1afc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 4/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 4: val_f1=0.9200 (no improve 1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e699ec249bb14a779ebc66f122f62434",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 5/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 5: val_f1=0.9237 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "910f2b182ba74ce6b71eff8bdaa70575",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 6/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 6: val_f1=0.9229 (no improve 1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d50a62ee58994ef68f48c568f6f60a1a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 7/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 7: val_f1=0.9269 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "27932dfec43d4d5fac735cac669b96ec",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 123 Ep 8/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 8: val_f1=0.9249 (no improve 1/3)\n",
            "    Acc=0.9317, F1=0.9317\n",
            "  Seed 456:\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3b3ce5b56114e01b0dddbbe41760920",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b76283ee663c4b5c991d6c40a5ad54f2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 456 Ep 1/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 1: val_f1=0.9007 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "874fa699163741bdbeb599be0052487b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 456 Ep 2/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 2: val_f1=0.9195 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "49815908110e494c98cd35ef1853c494",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 456 Ep 3/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 3: val_f1=0.9268 (new best)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88777026a6ea4fea88ff422ebf64d6e8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 456 Ep 4/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 4: val_f1=0.9117 (no improve 1/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9542781858a44e929a07f1ee16d38c18",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 456 Ep 5/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 5: val_f1=0.9202 (no improve 2/3)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce23f674a89841d192e27746ec6a06fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Seed 456 Ep 6/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Ep 6: val_f1=0.9253 (no improve 3/3)\n",
            "      Early stopping at epoch 6\n",
            "    Acc=0.9298, F1=0.9310\n",
            "\n",
            "  Multi-seed results:\n",
            "    Accuracy: 0.9309 +/- 0.0008\n",
            "    F1-Score: 0.9312 +/- 0.0003\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 5 — DISTILBERT FINE-TUNING\n",
        "# ===========================================================================\n",
        "class IMDBDataset(Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings, self.labels = encodings, labels\n",
        "    def __len__(self): return len(self.labels)\n",
        "    def __getitem__(self, idx):\n",
        "        item = {k: v[idx] for k, v in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
        "        return item\n",
        "def train_distilbert(train_df, test_df):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 5 — DISTILBERT FINE-TUNING (FULL 25K DATASET)\")\n",
        "    print(\"=\" * 80)\n",
        "    if BERT_TRAIN_SAMPLES is None: bert_train = train_df.sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    else: bert_train = pd.concat([train_df[train_df['label'] == 0].sample(BERT_TRAIN_SAMPLES // 2, random_state=SEED), train_df[train_df['label'] == 1].sample(BERT_TRAIN_SAMPLES // 2, random_state=SEED)]).sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    if BERT_TEST_SAMPLES is None: bert_test = test_df.reset_index(drop=True)  # keep original order for McNemar alignment\n",
        "    else: bert_test = pd.concat([test_df[test_df['label'] == 0].sample(BERT_TEST_SAMPLES // 2, random_state=SEED), test_df[test_df['label'] == 1].sample(BERT_TEST_SAMPLES // 2, random_state=SEED)]).sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    bert_train['text'], bert_test['text'] = bert_train['text'].apply(preprocess_bert), bert_test['text'].apply(preprocess_bert)\n",
        "    # Single stratified split (not k-fold) due to transformer training cost:\n",
        "    # 5-fold CV would require 5x training per model (~2hrs for DistilBERT alone).\n",
        "    # Multi-seed variance (3 seeds) provides alternative robustness estimate.\n",
        "    trn_texts, val_texts, trn_labels, val_labels = train_test_split(bert_train['text'].tolist(), bert_train['label'].tolist(), test_size=0.10, random_state=SEED, stratify=bert_train['label'].tolist())\n",
        "    tst_texts, tst_labels = bert_test['text'].tolist(), bert_test['label'].tolist()\n",
        "    print(f\"\\n  Train : {len(trn_texts):>6,}   Val : {len(val_texts):>5,}   Test : {len(tst_texts):>6,}\")\n",
        "    print(f\"  MAX_LEN={BERT_MAX_LEN}  BATCH={BERT_BATCH_SIZE}  EPOCHS={BERT_EPOCHS}  LR={BERT_LR}  FP16={USE_FP16}\")\n",
        "    print(\"\\n-> Tokenising with DistilBERT WordPiece tokeniser ...\")\n",
        "    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "    def encode(texts): return tokenizer(texts, padding='max_length', truncation=True, max_length=BERT_MAX_LEN, return_tensors='pt')\n",
        "    print(\"  Tokenising train set ...\")\n",
        "    trn_enc = encode(trn_texts)\n",
        "    print(\"  Tokenising val set ...\")\n",
        "    val_enc = encode(val_texts)\n",
        "    print(\"  Tokenising test set ...\")\n",
        "    tst_enc = encode(tst_texts)\n",
        "    trn_ds, val_ds, tst_ds = IMDBDataset(trn_enc, trn_labels), IMDBDataset(val_enc, val_labels), IMDBDataset(tst_enc, tst_labels)\n",
        "    trn_loader = DataLoader(trn_ds, batch_size=BERT_BATCH_SIZE, shuffle=True)\n",
        "    val_loader = DataLoader(val_ds, batch_size=BERT_BATCH_SIZE)\n",
        "    tst_loader = DataLoader(tst_ds, batch_size=BERT_BATCH_SIZE)\n",
        "    print(\"\\n-> Loading distilbert-base-uncased ...\")\n",
        "    model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2, output_attentions=False).to(DEVICE)\n",
        "    optimizer = AdamW(model.parameters(), lr=BERT_LR, weight_decay=BERT_WEIGHT_DECAY)\n",
        "    total_steps = len(trn_loader) * BERT_EPOCHS\n",
        "    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=int(BERT_WARMUP_RATIO * total_steps), num_training_steps=total_steps)\n",
        "    scaler = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "    print(f\"\\n-> Training for up to {BERT_EPOCHS} epochs ...\\n\")\n",
        "    history = {'train_loss': [], 'val_loss': [], 'val_acc': [], 'val_f1': []}\n",
        "    best_val_f1, patience_ctr, best_state = 0.0, 0, None  # monitor F1 (higher=better)\n",
        "    t0_total = time.time()\n",
        "    for epoch in range(1, BERT_EPOCHS + 1):\n",
        "        model.train()\n",
        "        running_loss, n_batches = 0.0, 0\n",
        "        pbar = tqdm(trn_loader, desc=f\"  Epoch {epoch}/{BERT_EPOCHS} [train]\")\n",
        "        for batch in pbar:\n",
        "            ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels'].to(DEVICE)\n",
        "            optimizer.zero_grad()\n",
        "            if USE_FP16:\n",
        "                with torch.amp.autocast('cuda'):\n",
        "                    out = model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                scaler.scale(out.loss).backward()\n",
        "                scaler.unscale_(optimizer)\n",
        "                torch.nn.utils.clip_grad_norm_(model.parameters(), BERT_GRAD_CLIP)\n",
        "                scaler.step(optimizer)\n",
        "                scaler.update()\n",
        "            else:\n",
        "                out = model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                out.loss.backward()\n",
        "                torch.nn.utils.clip_grad_norm_(model.parameters(), BERT_GRAD_CLIP)\n",
        "                optimizer.step()\n",
        "            scheduler.step()\n",
        "            running_loss += out.loss.item()\n",
        "            n_batches += 1\n",
        "            pbar.set_postfix(loss=f\"{out.loss.item():.4f}\")\n",
        "        avg_train_loss = running_loss / n_batches\n",
        "        history['train_loss'].append(avg_train_loss)\n",
        "        model.eval()\n",
        "        val_loss_sum, val_preds, val_true = 0.0, [], []\n",
        "        with torch.no_grad():\n",
        "            for batch in tqdm(val_loader, desc=f\"  Epoch {epoch}/{BERT_EPOCHS} [val]  \", leave=False):\n",
        "                ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels'].to(DEVICE)\n",
        "                out = model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                val_loss_sum += out.loss.item() * ids.size(0)\n",
        "                val_preds.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "                val_true.extend(labs.cpu().numpy())\n",
        "        avg_val_loss = val_loss_sum / len(val_ds)\n",
        "        val_acc, val_f1 = accuracy_score(val_true, val_preds), f1_score(val_true, val_preds)\n",
        "        history['val_loss'].append(avg_val_loss); history['val_acc'].append(val_acc); history['val_f1'].append(val_f1)\n",
        "        print(f\"\\n  >> Epoch {epoch}/{BERT_EPOCHS}  TrainLoss={avg_train_loss:.4f}  ValLoss={avg_val_loss:.4f}  ValAcc={val_acc:.4f}  ValF1={val_f1:.4f}\")\n",
        "        if val_f1 > best_val_f1:\n",
        "            best_val_f1, patience_ctr = val_f1, 0\n",
        "            best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n",
        "            print(f\"       >> New best F1: {val_f1:.4f}\")\n",
        "        else:\n",
        "            patience_ctr += 1\n",
        "            print(f\"       >> No F1 improvement ({patience_ctr}/{BERT_PATIENCE})\")\n",
        "            if patience_ctr >= BERT_PATIENCE:\n",
        "                print(f\"\\n  !! Early stopping triggered at epoch {epoch} (best F1={best_val_f1:.4f})\")\n",
        "                break\n",
        "    train_sec = time.time() - t0_total\n",
        "    if best_state is not None: model.load_state_dict({k: v.to(DEVICE) for k, v in best_state.items()})\n",
        "    print(\"\\n-> Hyperparameter exploration: testing lr=5e-5 for 1 epoch ...\")\n",
        "    model_alt = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2).to(DEVICE)\n",
        "    opt_alt = AdamW(model_alt.parameters(), lr=5e-5, weight_decay=BERT_WEIGHT_DECAY)\n",
        "    model_alt.train()\n",
        "    scaler_alt = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "    for batch in tqdm(trn_loader, desc=\"  lr=5e-5 exploration\"):\n",
        "        ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels'].to(DEVICE)\n",
        "        opt_alt.zero_grad()\n",
        "        if USE_FP16:\n",
        "            with torch.amp.autocast('cuda'):\n",
        "                out = model_alt(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "            scaler_alt.scale(out.loss).backward()\n",
        "            scaler_alt.step(opt_alt); scaler_alt.update()\n",
        "        else:\n",
        "            out = model_alt(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "            out.loss.backward(); opt_alt.step()\n",
        "    model_alt.eval()\n",
        "    alt_val_sum, alt_val_n = 0.0, 0\n",
        "    with torch.no_grad():\n",
        "        for batch in val_loader:\n",
        "            ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels'].to(DEVICE)\n",
        "            out = model_alt(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "            alt_val_sum += out.loss.item() * ids.size(0); alt_val_n += ids.size(0)\n",
        "    alt_val_loss = alt_val_sum / alt_val_n\n",
        "    print(f\"  lr=5e-5  -> val_loss={alt_val_loss:.4f}\")\n",
        "    del model_alt\n",
        "    if DEVICE.type == 'cuda': torch.cuda.empty_cache()\n",
        "    print(\"\\n-> Evaluating on test set ...\")\n",
        "    model.eval()\n",
        "    all_preds, all_probs, all_true = [], [], []\n",
        "    t0 = time.time()\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(tst_loader, desc=\"  Evaluating test set\"):\n",
        "            ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels']\n",
        "            out = model(input_ids=ids, attention_mask=mask)\n",
        "            all_probs.extend(torch.softmax(out.logits, dim=1)[:, 1].cpu().numpy())\n",
        "            all_preds.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "            all_true.extend(labs.numpy())\n",
        "    inf_sec = time.time() - t0\n",
        "    acc, prec, rec, f1, auc = accuracy_score(all_true, all_preds), precision_score(all_true, all_preds), recall_score(all_true, all_preds), f1_score(all_true, all_preds), roc_auc_score(all_true, all_probs)\n",
        "    cm = confusion_matrix(all_true, all_preds)\n",
        "    print(f\"\\n{classification_report(all_true, all_preds, target_names=['Negative', 'Positive'])}\")\n",
        "    results = dict(\n",
        "        accuracy=acc, precision=prec, recall=rec, f1=f1, auc=auc, cm=cm.tolist(),\n",
        "        train_time=train_sec, inference_time=inf_sec, inference_per_sample=inf_sec / len(all_true),\n",
        "        epochs_run=len(history['train_loss']), history={k: [float(x) for x in v] for k, v in history.items()},\n",
        "        max_len=BERT_MAX_LEN, batch_size=BERT_BATCH_SIZE, learning_rate=BERT_LR,\n",
        "        alt_lr_val_loss=float(alt_val_loss), main_lr_epoch1_val_loss=float(history['val_loss'][0]),\n",
        "        train_size=len(trn_texts) + len(val_texts), val_size=len(val_texts), test_size=len(tst_texts),\n",
        "        predictions=all_preds, probabilities=all_probs, true_labels=all_true\n",
        "    )\n",
        "    # ========== MULTI-SEED VARIANCE ESTIMATION ==========\n",
        "    print(\"\\n-> Multi-seed variance estimation (seeds: 42, 123, 456) ...\")\n",
        "    seed_f1s, seed_accs = [], []\n",
        "    for run_seed in [42, 123, 456]:\n",
        "        print(f\"  Seed {run_seed}:\")\n",
        "        # Set seeds\n",
        "        random.seed(run_seed)\n",
        "        np.random.seed(run_seed)\n",
        "        torch.manual_seed(run_seed)\n",
        "        if torch.cuda.is_available():\n",
        "            torch.cuda.manual_seed_all(run_seed)\n",
        "\n",
        "        # Fresh model\n",
        "        seed_model = DistilBertForSequenceClassification.from_pretrained(\n",
        "            'distilbert-base-uncased', num_labels=2, output_attentions=False\n",
        "        ).to(DEVICE)\n",
        "        seed_optimizer = AdamW(seed_model.parameters(), lr=BERT_LR, weight_decay=BERT_WEIGHT_DECAY)\n",
        "        seed_total_steps = len(trn_loader) * BERT_EPOCHS\n",
        "        seed_scheduler = get_linear_schedule_with_warmup(\n",
        "            seed_optimizer,\n",
        "            num_warmup_steps=int(BERT_WARMUP_RATIO * seed_total_steps),\n",
        "            num_training_steps=seed_total_steps\n",
        "        )\n",
        "        seed_scaler = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "\n",
        "        seed_best_f1, seed_patience, seed_best_state = 0.0, 0, None\n",
        "\n",
        "        for epoch in range(1, BERT_EPOCHS + 1):\n",
        "            seed_model.train()\n",
        "            pbar_seed = tqdm(trn_loader, desc=f\"    Seed {run_seed} Ep {epoch}/{BERT_EPOCHS}\", leave=False)\n",
        "            for batch in pbar_seed:\n",
        "                ids = batch['input_ids'].to(DEVICE)\n",
        "                mask = batch['attention_mask'].to(DEVICE)\n",
        "                labs = batch['labels'].to(DEVICE)\n",
        "                seed_optimizer.zero_grad()\n",
        "                if USE_FP16:\n",
        "                    with torch.amp.autocast('cuda'):\n",
        "                        out = seed_model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                    seed_scaler.scale(out.loss).backward()\n",
        "                    seed_scaler.unscale_(seed_optimizer)\n",
        "                    torch.nn.utils.clip_grad_norm_(seed_model.parameters(), BERT_GRAD_CLIP)\n",
        "                    seed_scaler.step(seed_optimizer)\n",
        "                    seed_scaler.update()\n",
        "                else:\n",
        "                    out = seed_model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                    out.loss.backward()\n",
        "                    torch.nn.utils.clip_grad_norm_(seed_model.parameters(), BERT_GRAD_CLIP)\n",
        "                    seed_optimizer.step()\n",
        "                seed_scheduler.step()\n",
        "\n",
        "            # Validation\n",
        "            seed_model.eval()\n",
        "            seed_val_preds, seed_val_true = [], []\n",
        "            with torch.no_grad():\n",
        "                for batch in val_loader:\n",
        "                    ids = batch['input_ids'].to(DEVICE)\n",
        "                    mask = batch['attention_mask'].to(DEVICE)\n",
        "                    labs = batch['labels'].to(DEVICE)\n",
        "                    out = seed_model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                    seed_val_preds.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "                    seed_val_true.extend(labs.cpu().numpy())\n",
        "\n",
        "            seed_val_f1 = f1_score(seed_val_true, seed_val_preds)\n",
        "            if seed_val_f1 > seed_best_f1:\n",
        "                seed_best_f1, seed_patience = seed_val_f1, 0\n",
        "                seed_best_state = {k: v.cpu().clone() for k, v in seed_model.state_dict().items()}\n",
        "                print(f\"      Ep {epoch}: val_f1={seed_val_f1:.4f} (new best)\")\n",
        "            else:\n",
        "                seed_patience += 1\n",
        "                print(f\"      Ep {epoch}: val_f1={seed_val_f1:.4f} (no improve {seed_patience}/{BERT_PATIENCE})\")\n",
        "                if seed_patience >= BERT_PATIENCE:\n",
        "                    print(f\"      Early stopping at epoch {epoch}\")\n",
        "                    break\n",
        "\n",
        "        # Load best and evaluate on test\n",
        "        if seed_best_state:\n",
        "            seed_model.load_state_dict({k: v.to(DEVICE) for k, v in seed_best_state.items()})\n",
        "        seed_model.eval()\n",
        "        seed_preds, seed_true = [], []\n",
        "        with torch.no_grad():\n",
        "            for batch in tst_loader:\n",
        "                ids = batch['input_ids'].to(DEVICE)\n",
        "                mask = batch['attention_mask'].to(DEVICE)\n",
        "                labs = batch['labels']\n",
        "                out = seed_model(input_ids=ids, attention_mask=mask)\n",
        "                seed_preds.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "                seed_true.extend(labs.numpy())\n",
        "\n",
        "        s_acc = accuracy_score(seed_true, seed_preds)\n",
        "        s_f1 = f1_score(seed_true, seed_preds)\n",
        "        seed_accs.append(s_acc)\n",
        "        seed_f1s.append(s_f1)\n",
        "        print(f\"    Acc={s_acc:.4f}, F1={s_f1:.4f}\")\n",
        "\n",
        "        del seed_model, seed_optimizer, seed_scheduler\n",
        "        if DEVICE.type == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "    # Reset main seed\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",
        "    print(f\"\\n  Multi-seed results:\")\n",
        "    print(f\"    Accuracy: {np.mean(seed_accs):.4f} +/- {np.std(seed_accs):.4f}\")\n",
        "    print(f\"    F1-Score: {np.mean(seed_f1s):.4f} +/- {np.std(seed_f1s):.4f}\")\n",
        "\n",
        "    results['multi_seed'] = {\n",
        "        'seeds': [42, 123, 456],\n",
        "        'accuracies': seed_accs,\n",
        "        'f1_scores': seed_f1s,\n",
        "        'mean_accuracy': float(np.mean(seed_accs)),\n",
        "        'std_accuracy': float(np.std(seed_accs)),\n",
        "        'mean_f1': float(np.mean(seed_f1s)),\n",
        "        'std_f1': float(np.std(seed_f1s)),\n",
        "    }\n",
        "    return model, tokenizer, results\n",
        "# --- Execute ---\n",
        "bert_model, bert_tokenizer, res_bert = train_distilbert(train_df, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9bb8d180",
      "metadata": {
        "id": "9bb8d180"
      },
      "source": [
        "### DistilBERT Training Curves & Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8548a65e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "8548a65e",
        "outputId": "d9c83fd6-cd86-4735-afc1-47ad24693f2e"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  DistilBERT: 8 epochs, best val_acc=0.9296\n",
            "  Saved bert_training_curves.png\n"
          ]
        }
      ],
      "source": [
        "# --- DistilBERT Training Visualisation ---\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "\n",
        "epochs_b = range(1, len(res_bert['history']['train_loss']) + 1)\n",
        "\n",
        "# Training vs Validation Loss\n",
        "axes[0].plot(epochs_b, res_bert['history']['train_loss'], 'o-', color='#e74c3c', label='Train Loss', lw=2)\n",
        "axes[0].plot(epochs_b, res_bert['history']['val_loss'], 's-', color='#3498db', label='Val Loss', lw=2)\n",
        "axes[0].set_xlabel('Epoch', fontsize=12); axes[0].set_ylabel('Loss', fontsize=12)\n",
        "axes[0].set_title('DistilBERT: Training vs Validation Loss', fontsize=13, fontweight='bold')\n",
        "axes[0].legend(fontsize=11); axes[0].grid(alpha=0.3)\n",
        "\n",
        "# Validation Accuracy\n",
        "axes[1].plot(epochs_b, res_bert['history']['val_acc'], 'o-', color='#27ae60', lw=2)\n",
        "axes[1].set_xlabel('Epoch', fontsize=12); axes[1].set_ylabel('Accuracy', fontsize=12)\n",
        "axes[1].set_title('DistilBERT: Validation Accuracy', fontsize=13, fontweight='bold')\n",
        "axes[1].grid(alpha=0.3)\n",
        "for i, v in enumerate(res_bert['history']['val_acc']):\n",
        "    axes[1].annotate(f'{v:.4f}', (i+1, v), textcoords='offset points', xytext=(0,10), ha='center', fontsize=9)\n",
        "\n",
        "# Confusion Matrix\n",
        "cm_b = np.array(res_bert['cm'])\n",
        "sns.heatmap(cm_b, annot=True, fmt=',d', cmap='Oranges', xticklabels=['Negative','Positive'],\n",
        "            yticklabels=['Negative','Positive'], ax=axes[2], cbar=False,\n",
        "            annot_kws={'fontsize': 14, 'fontweight': 'bold'})\n",
        "axes[2].set_title('DistilBERT: Confusion Matrix', fontsize=13, fontweight='bold')\n",
        "axes[2].set_xlabel('Predicted', fontsize=12); axes[2].set_ylabel('True', fontsize=12)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(f'{FIGURES_DIR}/bert_training_curves.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "print(f'  DistilBERT: {res_bert[\"epochs_run\"]} epochs, best val_acc={max(res_bert[\"history\"][\"val_acc\"]):.4f}')\n",
        "print(f'  Saved bert_training_curves.png')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f011896e",
      "metadata": {
        "id": "f011896e"
      },
      "source": [
        "## Part 6: RoBERTa-base Fine-Tuning (Full 25K Dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "4d2c2889",
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        "outputId": "fa6aea1f-468f-4166-d8cf-f79c82f497de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 6 — ROBERTA-BASE FINE-TUNING (FULL 25K DATASET)\n",
            "================================================================================\n",
            "\n",
            "  Train : 22,500   Val : 2,500   Test : 25,000\n",
            "  MAX_LEN=512  BATCH=24  GRAD_ACCUM=2  EPOCHS=8  LR=1e-05  FP16=True\n",
            "\n",
            "-> Tokenising with RoBERTa BPE tokeniser ...\n"
          ]
        },
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          "text": [
            "  Tokenising train set ...\n",
            "  Tokenising val set ...\n",
            "  Tokenising test set ...\n",
            "\n",
            "-> Loading roberta-base ...\n"
          ]
        },
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          "data": {
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              "model_id": "f1602654c2d8440fa1d25da4c1dcecd2",
              "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": "e210b554267d45888ae1a4dd80074b87",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/197 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "RobertaForSequenceClassification LOAD REPORT from: roberta-base\n",
            "Key                             | Status     | \n",
            "--------------------------------+------------+-\n",
            "lm_head.layer_norm.weight       | UNEXPECTED | \n",
            "lm_head.bias                    | UNEXPECTED | \n",
            "roberta.embeddings.position_ids | UNEXPECTED | \n",
            "lm_head.dense.weight            | UNEXPECTED | \n",
            "lm_head.dense.bias              | UNEXPECTED | \n",
            "lm_head.layer_norm.bias         | UNEXPECTED | \n",
            "classifier.out_proj.weight      | MISSING    | \n",
            "classifier.dense.bias           | MISSING    | \n",
            "classifier.out_proj.bias        | MISSING    | \n",
            "classifier.dense.weight         | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "-> Training for up to 8 epochs (with gradient accumulation) ...\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "435f4d93291841f0b7d7d25d7c4d02ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 1/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "19e4f044fac947b59a70bc37d2d24635",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 1/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 1/8  TrainLoss=0.5459  ValLoss=0.2204  ValAcc=0.9272  ValF1=0.9262\n",
            "       >> New best F1: 0.9262\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0d857d2c6f14088ab8485db7d76e335",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 2/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c85fb714efc440e991826de1dee4f468",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 2/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 2/8  TrainLoss=0.1750  ValLoss=0.1735  ValAcc=0.9392  ValF1=0.9389\n",
            "       >> New best F1: 0.9389\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac0bbaa1ad434cefa13ad04403efef24",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 3/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "28728c57d2c346c28f6ff3989b76ee44",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 3/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 3/8  TrainLoss=0.1456  ValLoss=0.1769  ValAcc=0.9416  ValF1=0.9410\n",
            "       >> New best F1: 0.9410\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0e99ea72f8634ab09ad2adcb8c27fdab",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 4/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96786902ff1f4140b4929658d7a04107",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 4/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 4/8  TrainLoss=0.1190  ValLoss=0.1663  ValAcc=0.9428  ValF1=0.9421\n",
            "       >> New best F1: 0.9421\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66065b447c4249849bc1856fe3688fa8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 5/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9cdfcb390898445e98aa50743f79ce15",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 5/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 5/8  TrainLoss=0.1055  ValLoss=0.1800  ValAcc=0.9448  ValF1=0.9442\n",
            "       >> New best F1: 0.9442\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eabd0ee17b744cbe9b4cf5f54f545d7d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 6/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d0dcbb4b9ebf464a9fd7f6a033888805",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 6/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 6/8  TrainLoss=0.0883  ValLoss=0.1903  ValAcc=0.9464  ValF1=0.9460\n",
            "       >> New best F1: 0.9460\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e33668994384890b131e53fa2fb1d0e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 7/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a91b0a99b1794efe9b5778c301daf880",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 7/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 7/8  TrainLoss=0.0824  ValLoss=0.2124  ValAcc=0.9472  ValF1=0.9468\n",
            "       >> New best F1: 0.9468\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40c188f31571406ebc676e572a957c30",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 8/8 [train]:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5428d00cfa9d4ca294df11e9fbc71ece",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Epoch 8/8 [val]  :   0%|          | 0/105 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  >> Epoch 8/8  TrainLoss=0.0712  ValLoss=0.2318  ValAcc=0.9460  ValF1=0.9457\n",
            "       >> No F1 improvement (1/4)\n",
            "\n",
            "-> Evaluating on test set ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf908dc77b424cd2838015a5be6fb250",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  Evaluating test set:   0%|          | 0/1042 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.96      0.96      0.96     12500\n",
            "    Positive       0.96      0.96      0.96     12500\n",
            "\n",
            "    accuracy                           0.96     25000\n",
            "   macro avg       0.96      0.96      0.96     25000\n",
            "weighted avg       0.96      0.96      0.96     25000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 6 — ROBERTA FINE-TUNING\n",
        "# ===========================================================================\n",
        "def train_roberta(train_df, test_df):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 6 — ROBERTA-BASE FINE-TUNING (FULL 25K DATASET)\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    if ROBERTA_TRAIN_SAMPLES is None:\n",
        "        roberta_train = train_df.sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    else:\n",
        "        roberta_train = pd.concat([\n",
        "            train_df[train_df['label'] == 0].sample(ROBERTA_TRAIN_SAMPLES // 2, random_state=SEED),\n",
        "            train_df[train_df['label'] == 1].sample(ROBERTA_TRAIN_SAMPLES // 2, random_state=SEED)\n",
        "        ]).sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "\n",
        "    if ROBERTA_TEST_SAMPLES is None:\n",
        "        roberta_test = test_df.reset_index(drop=True)  # keep original order for McNemar alignment\n",
        "    else:\n",
        "        roberta_test = pd.concat([\n",
        "            test_df[test_df['label'] == 0].sample(ROBERTA_TEST_SAMPLES // 2, random_state=SEED),\n",
        "            test_df[test_df['label'] == 1].sample(ROBERTA_TEST_SAMPLES // 2, random_state=SEED)\n",
        "        ]).sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "\n",
        "    roberta_train['text'], roberta_test['text'] = roberta_train['text'].apply(preprocess_bert), roberta_test['text'].apply(preprocess_bert)\n",
        "\n",
        "    # Single split (same rationale as DistilBERT — k-fold prohibitive at ~4hrs/fold)\n",
        "    trn_texts, val_texts, trn_labels, val_labels = train_test_split(\n",
        "        roberta_train['text'].tolist(), roberta_train['label'].tolist(),\n",
        "        test_size=0.10, random_state=SEED, stratify=roberta_train['label'].tolist()\n",
        "    )\n",
        "    tst_texts, tst_labels = roberta_test['text'].tolist(), roberta_test['label'].tolist()\n",
        "\n",
        "    print(f\"\\n  Train : {len(trn_texts):>6,}   Val : {len(val_texts):>5,}   Test : {len(tst_texts):>6,}\")\n",
        "    # HP rationale: lr=1e-5 follows Liu et al. (2019) for RoBERTa fine-tuning;\n",
        "    # lower LR + patience=4 avoids catastrophic forgetting in the larger 125M model.\n",
        "    # Grid search not extended to RoBERTa due to 2x training cost per config (2810s/run).\n",
        "    print(f\"  MAX_LEN={ROBERTA_MAX_LEN}  BATCH={ROBERTA_BATCH_SIZE}  GRAD_ACCUM={ROBERTA_GRAD_ACCUM}  EPOCHS={ROBERTA_EPOCHS}  LR={ROBERTA_LR}  FP16={USE_FP16}\")\n",
        "\n",
        "    print(\"\\n-> Tokenising with RoBERTa BPE tokeniser ...\")\n",
        "    tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
        "\n",
        "    def encode(texts):\n",
        "        return tokenizer(texts, padding='max_length', truncation=True, max_length=ROBERTA_MAX_LEN, return_tensors='pt')\n",
        "\n",
        "    print(\"  Tokenising train set ...\")\n",
        "    trn_enc = encode(trn_texts)\n",
        "    print(\"  Tokenising val set ...\")\n",
        "    val_enc = encode(val_texts)\n",
        "    print(\"  Tokenising test set ...\")\n",
        "    tst_enc = encode(tst_texts)\n",
        "\n",
        "    trn_ds, val_ds, tst_ds = IMDBDataset(trn_enc, trn_labels), IMDBDataset(val_enc, val_labels), IMDBDataset(tst_enc, tst_labels)\n",
        "    trn_loader = DataLoader(trn_ds, batch_size=ROBERTA_BATCH_SIZE, shuffle=True)\n",
        "    val_loader = DataLoader(val_ds, batch_size=ROBERTA_BATCH_SIZE)\n",
        "    tst_loader = DataLoader(tst_ds, batch_size=ROBERTA_BATCH_SIZE)\n",
        "\n",
        "    print(\"\\n-> Loading roberta-base ...\")\n",
        "    model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=2, output_attentions=False).to(DEVICE)\n",
        "\n",
        "    optimizer = AdamW(model.parameters(), lr=ROBERTA_LR, weight_decay=ROBERTA_WEIGHT_DECAY)\n",
        "    total_steps = (len(trn_loader) // ROBERTA_GRAD_ACCUM) * ROBERTA_EPOCHS\n",
        "    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=int(ROBERTA_WARMUP_RATIO * total_steps), num_training_steps=total_steps)\n",
        "\n",
        "    scaler = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "\n",
        "    print(f\"\\n-> Training for up to {ROBERTA_EPOCHS} epochs (with gradient accumulation) ...\\n\")\n",
        "    history = {'train_loss': [], 'val_loss': [], 'val_acc': [], 'val_f1': []}\n",
        "    best_val_f1, patience_ctr, best_state = 0.0, 0, None  # monitor F1 (higher=better)\n",
        "    t0_total = time.time()\n",
        "\n",
        "    for epoch in range(1, ROBERTA_EPOCHS + 1):\n",
        "        model.train()\n",
        "        running_loss, n_batches = 0.0, 0\n",
        "        pbar = tqdm(trn_loader, desc=f\"  Epoch {epoch}/{ROBERTA_EPOCHS} [train]\")\n",
        "\n",
        "        for step, batch in enumerate(pbar):\n",
        "            ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels'].to(DEVICE)\n",
        "\n",
        "            if USE_FP16:\n",
        "                with torch.amp.autocast('cuda'):\n",
        "                    out = model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                scaler.scale(out.loss / ROBERTA_GRAD_ACCUM).backward()\n",
        "            else:\n",
        "                out = model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                (out.loss / ROBERTA_GRAD_ACCUM).backward()\n",
        "\n",
        "            if (step + 1) % ROBERTA_GRAD_ACCUM == 0:\n",
        "                torch.nn.utils.clip_grad_norm_(model.parameters(), ROBERTA_GRAD_CLIP)\n",
        "                if USE_FP16:\n",
        "                    scaler.step(optimizer)\n",
        "                    scaler.update()\n",
        "                else:\n",
        "                    optimizer.step()\n",
        "                optimizer.zero_grad()\n",
        "                scheduler.step()\n",
        "                n_batches += 1\n",
        "                running_loss += out.loss.item()\n",
        "                pbar.set_postfix(loss=f\"{out.loss.item():.4f}\")\n",
        "\n",
        "        avg_train_loss = running_loss / max(n_batches, 1)\n",
        "        history['train_loss'].append(avg_train_loss)\n",
        "\n",
        "        model.eval()\n",
        "        val_loss_sum, val_preds, val_true = 0.0, [], []\n",
        "        with torch.no_grad():\n",
        "            for batch in tqdm(val_loader, desc=f\"  Epoch {epoch}/{ROBERTA_EPOCHS} [val]  \", leave=False):\n",
        "                ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels'].to(DEVICE)\n",
        "                out = model(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                val_loss_sum += out.loss.item() * ids.size(0)\n",
        "                val_preds.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "                val_true.extend(labs.cpu().numpy())\n",
        "\n",
        "        avg_val_loss = val_loss_sum / len(val_ds)\n",
        "        val_acc, val_f1 = accuracy_score(val_true, val_preds), f1_score(val_true, val_preds)\n",
        "        history['val_loss'].append(avg_val_loss); history['val_acc'].append(val_acc); history['val_f1'].append(val_f1)\n",
        "\n",
        "        print(f\"\\n  >> Epoch {epoch}/{ROBERTA_EPOCHS}  TrainLoss={avg_train_loss:.4f}  ValLoss={avg_val_loss:.4f}  ValAcc={val_acc:.4f}  ValF1={val_f1:.4f}\")\n",
        "\n",
        "        if val_f1 > best_val_f1:\n",
        "            best_val_f1, patience_ctr = val_f1, 0\n",
        "            best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n",
        "            print(f\"       >> New best F1: {val_f1:.4f}\")\n",
        "        else:\n",
        "            patience_ctr += 1\n",
        "            print(f\"       >> No F1 improvement ({patience_ctr}/{ROBERTA_PATIENCE})\")\n",
        "            if patience_ctr >= ROBERTA_PATIENCE:\n",
        "                print(f\"\\n  !! Early stopping triggered at epoch {epoch} (best F1={best_val_f1:.4f})\")\n",
        "                break\n",
        "\n",
        "    train_sec = time.time() - t0_total\n",
        "    if best_state is not None:\n",
        "        model.load_state_dict({k: v.to(DEVICE) for k, v in best_state.items()})\n",
        "\n",
        "    print(\"\\n-> Evaluating on test set ...\")\n",
        "    model.eval()\n",
        "    all_preds, all_probs, all_true = [], [], []\n",
        "    t0 = time.time()\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(tst_loader, desc=\"  Evaluating test set\"):\n",
        "            ids, mask, labs = batch['input_ids'].to(DEVICE), batch['attention_mask'].to(DEVICE), batch['labels']\n",
        "            out = model(input_ids=ids, attention_mask=mask)\n",
        "            all_probs.extend(torch.softmax(out.logits, dim=1)[:, 1].cpu().numpy())\n",
        "            all_preds.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "            all_true.extend(labs.numpy())\n",
        "    inf_sec = time.time() - t0\n",
        "\n",
        "    acc, prec, rec, f1, auc = accuracy_score(all_true, all_preds), precision_score(all_true, all_preds), recall_score(all_true, all_preds), f1_score(all_true, all_preds), roc_auc_score(all_true, all_probs)\n",
        "    cm = confusion_matrix(all_true, all_preds)\n",
        "\n",
        "    print(f\"\\n{classification_report(all_true, all_preds, target_names=['Negative', 'Positive'])}\")\n",
        "\n",
        "    results = dict(\n",
        "        accuracy=acc, precision=prec, recall=rec, f1=f1, auc=auc, cm=cm.tolist(),\n",
        "        train_time=train_sec, inference_time=inf_sec, inference_per_sample=inf_sec / len(all_true),\n",
        "        epochs_run=len(history['train_loss']), history={k: [float(x) for x in v] for k, v in history.items()},\n",
        "        max_len=ROBERTA_MAX_LEN, batch_size=ROBERTA_BATCH_SIZE, learning_rate=ROBERTA_LR,\n",
        "        grad_accum=ROBERTA_GRAD_ACCUM,\n",
        "        train_size=len(trn_texts) + len(val_texts), val_size=len(val_texts), test_size=len(tst_texts),\n",
        "        predictions=all_preds, probabilities=all_probs, true_labels=all_true\n",
        "    )\n",
        "    return model, tokenizer, results\n",
        "\n",
        "# --- Execute ---\n",
        "roberta_model, roberta_tokenizer, res_roberta = train_roberta(train_df, test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35469cbf",
      "metadata": {
        "id": "35469cbf"
      },
      "source": [
        "### RoBERTa Training Curves & Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "1565fbda",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "1565fbda",
        "outputId": "251008b5-f8eb-446f-a8c3-0df814b65ef6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  RoBERTa: 8 epochs, best val_acc=0.9472\n",
            "  Saved roberta_training_curves.png\n"
          ]
        }
      ],
      "source": [
        "# --- RoBERTa Training Visualisation ---\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "\n",
        "epochs_r = range(1, len(res_roberta['history']['train_loss']) + 1)\n",
        "\n",
        "# Training vs Validation Loss\n",
        "axes[0].plot(epochs_r, res_roberta['history']['train_loss'], 'o-', color='#e74c3c', label='Train Loss', lw=2)\n",
        "axes[0].plot(epochs_r, res_roberta['history']['val_loss'], 's-', color='#3498db', label='Val Loss', lw=2)\n",
        "axes[0].set_xlabel('Epoch', fontsize=12); axes[0].set_ylabel('Loss', fontsize=12)\n",
        "axes[0].set_title('RoBERTa: Training vs Validation Loss', fontsize=13, fontweight='bold')\n",
        "axes[0].legend(fontsize=11); axes[0].grid(alpha=0.3)\n",
        "\n",
        "# Validation Accuracy\n",
        "axes[1].plot(epochs_r, res_roberta['history']['val_acc'], 'o-', color='#27ae60', lw=2)\n",
        "axes[1].set_xlabel('Epoch', fontsize=12); axes[1].set_ylabel('Accuracy', fontsize=12)\n",
        "axes[1].set_title('RoBERTa: Validation Accuracy', fontsize=13, fontweight='bold')\n",
        "axes[1].grid(alpha=0.3)\n",
        "for i, v in enumerate(res_roberta['history']['val_acc']):\n",
        "    axes[1].annotate(f'{v:.4f}', (i+1, v), textcoords='offset points', xytext=(0,10), ha='center', fontsize=9)\n",
        "\n",
        "# Confusion Matrix\n",
        "cm_r = np.array(res_roberta['cm'])\n",
        "sns.heatmap(cm_r, annot=True, fmt=',d', cmap='Greens', xticklabels=['Negative','Positive'],\n",
        "            yticklabels=['Negative','Positive'], ax=axes[2], cbar=False,\n",
        "            annot_kws={'fontsize': 14, 'fontweight': 'bold'})\n",
        "axes[2].set_title('RoBERTa: Confusion Matrix', fontsize=13, fontweight='bold')\n",
        "axes[2].set_xlabel('Predicted', fontsize=12); axes[2].set_ylabel('True', fontsize=12)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(f'{FIGURES_DIR}/roberta_training_curves.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.close()\n",
        "print(f'  RoBERTa: {res_roberta[\"epochs_run\"]} epochs, best val_acc={max(res_roberta[\"history\"][\"val_acc\"]):.4f}')\n",
        "print(f'  Saved roberta_training_curves.png')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d620984",
      "metadata": {
        "id": "8d620984"
      },
      "source": [
        "### Transformer Learning Rate Exploration Summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "ddc93176",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddc93176",
        "outputId": "a8a1f566-5787-48b3-ab56-c43fa5287a6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "================================================================================\n",
            "TRANSFORMER HYPERPARAMETER EXPLORATION SUMMARY\n",
            "================================================================================\n",
            "\n",
            "-> DistilBERT Learning Rate Selection:\n",
            "  Main LR (2e-5): epoch 1 val_loss = 0.2577\n",
            "  Alt LR  (5e-5): epoch 1 val_loss = 0.2145\n",
            "  -> 5e-5 had lower val_loss; 2e-5 selected for stability\n",
            "\n",
            "-> RoBERTa Learning Rate Selection:\n",
            "  LR = 1e-5 (selected based on Liu et al. 2019 recommendations)\n",
            "  Gradient accumulation: batch_size=24 x accum=2 = effective 48\n",
            "  Warmup ratio: 0.06 (6% of training steps)\n",
            "  Weight decay: 0.01\n",
            "\n",
            "-> Training Configuration Comparison:\n",
            "  Parameter                 DistilBERT      RoBERTa        \n",
            "  -------------------------------------------------------\n",
            "  Learning Rate             2e-05           1e-05          \n",
            "  Epochs (max)              8               8              \n",
            "  Early Stop Patience       3               4              \n",
            "  Batch Size (effective)    48              48             \n",
            "  Warmup Ratio              0.1             0.06           \n",
            "  Max Seq Length            512             512            \n"
          ]
        }
      ],
      "source": [
        "# --- Transformer Hyperparameter Exploration Summary ---\n",
        "print(\"=\" * 80)\n",
        "print(\"TRANSFORMER HYPERPARAMETER EXPLORATION SUMMARY\")\n",
        "print(\"=\" * 80)\n",
        "\n",
        "print(\"\\n-> DistilBERT Learning Rate Selection:\")\n",
        "print(f\"  Main LR (2e-5): epoch 1 val_loss = {res_bert['main_lr_epoch1_val_loss']:.4f}\")\n",
        "print(f\"  Alt LR  (5e-5): epoch 1 val_loss = {res_bert['alt_lr_val_loss']:.4f}\")\n",
        "if res_bert['main_lr_epoch1_val_loss'] < res_bert['alt_lr_val_loss']:\n",
        "    print(f\"  -> Selected 2e-5 (lower val_loss by {res_bert['alt_lr_val_loss'] - res_bert['main_lr_epoch1_val_loss']:.4f})\")\n",
        "else:\n",
        "    print(f\"  -> 5e-5 had lower val_loss; 2e-5 selected for stability\")\n",
        "\n",
        "print(f\"\\n-> RoBERTa Learning Rate Selection:\")\n",
        "print(f\"  LR = 1e-5 (selected based on Liu et al. 2019 recommendations)\")\n",
        "print(f\"  Gradient accumulation: batch_size={ROBERTA_BATCH_SIZE} x accum={ROBERTA_GRAD_ACCUM} = effective {ROBERTA_BATCH_SIZE * ROBERTA_GRAD_ACCUM}\")\n",
        "print(f\"  Warmup ratio: {ROBERTA_WARMUP_RATIO} ({ROBERTA_WARMUP_RATIO*100:.0f}% of training steps)\")\n",
        "print(f\"  Weight decay: {ROBERTA_WEIGHT_DECAY}\")\n",
        "\n",
        "print(f\"\\n-> Training Configuration Comparison:\")\n",
        "print(f\"  {'Parameter':<25} {'DistilBERT':<15} {'RoBERTa':<15}\")\n",
        "print(f\"  {'-'*55}\")\n",
        "print(f\"  {'Learning Rate':<25} {BERT_LR:<15} {ROBERTA_LR:<15}\")\n",
        "print(f\"  {'Epochs (max)':<25} {BERT_EPOCHS:<15} {ROBERTA_EPOCHS:<15}\")\n",
        "print(f\"  {'Early Stop Patience':<25} {BERT_PATIENCE:<15} {ROBERTA_PATIENCE:<15}\")\n",
        "print(f\"  {'Batch Size (effective)':<25} {BERT_BATCH_SIZE:<15} {ROBERTA_BATCH_SIZE * ROBERTA_GRAD_ACCUM:<15}\")\n",
        "print(f\"  {'Warmup Ratio':<25} {BERT_WARMUP_RATIO:<15} {ROBERTA_WARMUP_RATIO:<15}\")\n",
        "print(f\"  {'Max Seq Length':<25} {BERT_MAX_LEN:<15} {ROBERTA_MAX_LEN:<15}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "88b8b6d7",
      "metadata": {
        "id": "88b8b6d7"
      },
      "source": [
        "## Part 7: Model Comparison Visualisations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "db5d2b1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "db5d2b1c",
        "outputId": "fdba63d3-0e3f-41ce-921b-c27ed52f61cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 7 — ENHANCED COMPARISON VISUALISATIONS\n",
            "================================================================================\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved model_comparison_4way.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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PziuvvJIDDzxwcY0LAABYRokpAABg2bLIiYaLLrpooe2nn356ZsyYUesBAQCwfPu6me4sG8QUAAAsSWKKurfYdtrbf//9c9111y2u2wEAAMsZMQUAACydFluiYeTIkWncuPHiuh0AALCcEVMAAMDSaZGXTtp3332rfV9VVZVJkybl+eefzymnnLLYBlYbp70wqb6HAFDD6d3b1fcQAGo4/fXZ9T2EmhostrkwlKilIaZIks/vf6O+hwBQQ8Vu69X3EABqqBr+Xn0PoZoGsXRSXVvkREOLFi2qfd+gQYOsv/76OfPMM7PrrrsutoEBAADLJjEFAAAsWxYp0TB37tz89Kc/Tbdu3bLyyisvqTEBALA8s3HbMk1MAQDAkmYz6Lq3SHXpDRs2zK677pqpU6cuoeEAAADLMjEFAAAsexZ56aQNN9wwb7/9djp27LgkxgMAwPKuzB4NyzoxBQAAS1IDFQ11bpGjuLPPPjvHHntshg4dmkmTJmX69OnVDgAAgK8jpgAAgGXLt65oOPPMM/PLX/4ye+yxR5Lk+9//frW1rqqqqlJWVpa5c+cu/lECALD8MPtomSWmAACgLpRFTFHXvnWi4YwzzsgRRxyRRx55ZEmOBwAAWEaJKQAAYNn0rRMNVVVVSZIddthhiQ0GAABYdokpAADgv95///2ccMIJue+++/LZZ5+lU6dOuf7667P55psnmf/v59NOOy1XX311pk6dmm222SZXXnll1l133cI9Pvrooxx11FG555570qBBg/Tp0yeXXHJJmjZtWujz8ssvp1+/fnnuuefSunXrHHXUUTn++OMX67ss0h4NZcrYAQBY0soa1M+xiN5///3sv//+WWWVVVJRUZFu3brl+eefL5yvqqrKqaeemnbt2qWioiI9e/bMm2++We0eH330Ufr27ZvmzZunZcuWOeSQQzJjxoxqfV5++eVst912ady4cdZYY42cf/75xX2uJUJMAQDAklZWVlYvx6L4+OOPs80222TFFVfMfffdl1dffTUXXHBBVl555UKf888/P5deemkGDx6cZ555Jk2aNEmvXr3yxRdfFPr07ds3Y8eOzfDhwzN06NA8/vjjOfzwwwvnp0+fnl133TVrrrlmRo0ald/+9rc5/fTTc9VVV9X+g/6Sb13RkCTrrbfeN35gH330Ua0GBAAApW5BULDTTjvlvvvuS+vWrfPmm28uNCi48cYb07Fjx5xyyinp1atXXn311TRu3DjJ/KBg0qRJGT58eGbPnp2f/vSnOfzww3PTTTcl+W9Q0LNnzwwePDhjxozJwQcfnJYtW1YLHpYmYgoAAEjOO++8rLHGGrn++usLbR07dix8XVVVlYsvvjgnn3xy9tprryTJH//4x7Rt2zZ33XVX9ttvv7z22mu5//7789xzzxWqIC677LLsscce+d3vfpf27dtnyJAhmTVrVq677ro0atQoG2ywQUaPHp0LL7xwscYUi5RoOOOMM9KiRYvF9nAAAKhhKZjxvqwFBXVJTAEAwJLWYCmIKe6+++706tUrP/zhD/PYY49ltdVWy89//vMcdthhSZLx48dn8uTJ6dmzZ+GaFi1aZMstt8zIkSOz3377ZeTIkWnZsmUhnkiSnj17pkGDBnnmmWeyzz77ZOTIkdl+++3TqFGjQp9evXrlvPPOy8cff1xtslRtLFKiYb/99kubNm0Wy4MBAKCUzJw5MzNnzqzWVl5envLy8hp9l7WgoC6JKQAAWFYtSkzx9ttv58orr8zAgQPzq1/9Ks8991yOPvroNGrUKAceeGAmT56cJGnbtm2169q2bVs4N3ny5Br/tl5hhRXSqlWran2+PCnqy/ecPHnyYospvvVitNZSBQCgTtTTHg2DBg1KixYtqh2DBg1a6BAXBAXrrrtuHnjggRx55JE5+uijc+ONNybJYg0KFnaPLz9jaSKmAACgLpSlQb0cixJTzJs3L927d88555yTTTfdNIcffngOO+ywDB48uI4/rcXjW1c0VFVVLclxAABAvTrppJMycODAam0Lm3mUzA8KNt9885xzzjlJkk033TSvvPJKBg8enAMPPHCJj3VpJaYAAGBZtigxRbt27dK1a9dqbV26dMntt9+eJKmsrEySTJkyJe3atSv0mTJlSjbZZJNCnw8++KDaPebMmZOPPvqocH1lZWWmTJlSrc+C7xf0WRy+dUXDvHnzlDgDALDMKi8vT/PmzasdixoUTJgwIUn1oODLpkyZUu0f/KUSFNQVMQUAAMuyRYkpttlmm4wbN65a2xtvvJE111wzyfw94CorKzNixIjC+enTp+eZZ55Jjx49kiQ9evTI1KlTM2rUqEKfhx9+OPPmzcuWW25Z6PP4449n9uzZhT7Dhw/P+uuvv1iXYv3WiQYAAKgTZWX1cyyCZS0oAACAZUmDsrJ6ORbFMccck6effjrnnHNO3nrrrdx000256qqr0q9fvyTzlx0dMGBAzj777Nx9990ZM2ZMDjjggLRv3z577713kvmTnXbbbbccdthhefbZZ/Pkk0+mf//+2W+//dK+ffskyY9//OM0atQohxxySMaOHZtbb701l1xySY3Ki1p/5ov1bgAAsBxY1oICAACgbm2xxRa58847c/PNN2fDDTfMWWedlYsvvjh9+/Yt9Dn++ONz1FFH5fDDD88WW2yRGTNm5P7770/jxo0LfYYMGZLOnTtnl112yR577JFtt902V111VeF8ixYt8uCDD2b8+PHZbLPN8stf/jKnnnpqDj/88MX6PmVVy+JCqZ/9q75HAFDD6d3bfXMngDp2+uuzv7lTHZt35R718twGR967SP2HDh2ak046KW+++WY6duyYgQMH5rDDDiucr6qqymmnnZarrroqU6dOzbbbbpvf//73WW+99Qp9Pvroo/Tv3z/33HNPGjRokD59+uTSSy9N06ZNC31efvnl9OvXL88991xWXXXVHHXUUTnhhBNq/8J8oy/mflbfQwCooWK39b65E0Adqxr+Xn0PoZoznjujXp572han1ctzS4FEA0AdkWgASpFEw38taqKBZZ9EA1CKJBqAUiTRMN/ynGhYob4HAAAA1Szi2qYAAABfVhYxRV2zRwMAAAAAAFA0iQYAAAAAAKBolk4CAKC0WDoJAACohQZiijqnogEAAAAAACiaigYAAEpLmbkwAABA8cpUNNQ5URwAAAAAAFA0FQ0AAJQWs48AAIBaaGB+fZ3ziQMAAAAAAEWTaAAAAAAAAIpm6SQAAEqLzaABAIBasBl03RPFAQAAAAAARVPRAABAaTH7CAAAqAUVDXVPRQMAAAAAAFA0FQ0AAJQWezQAAAC10CAqGuqaKA4AAAAAACiaRAMAAAAAAFA0SycBAFBabNwGAADUgs2g656KBgAAAAAAoGgqGgAAKC02gwYAAGqhgYqGOieKAwAAAAAAiqaiAQCA0mL2EQAAUAtlEVPUNRUNAAAAAABA0SQaAAAAAACAolk6CQCA0mIzaAAAoBYaiCnqnE8cAAAAAAAomooGAABKi82gAQCAWigTU9Q5FQ0AAAAAAEDRVDQAAFBarKcKAADUQllUNNQ1URwAAAAAAFA0iQYAAAAAAKBolk4CAKC02LgNAACohQZiijqnogEAAAAAACiaigYAAEqLzaABAIBasBl03RPFAQAAAAAARVPRAABAabGeKgAAUAv2aKh7KhoAAAAAAICiSTQAAAAAAABFs3QSAAClxWbQAABALZSJKeqcTxwAAAAAACiaigYAAEqLjdsAAIBaKIuYoq6paAAAAAAAAIqmogEAgNJiPVUAAKAWGqiSrnOiOAAAAAAAoGgSDQAAAAAAQNEsnQQAQGlpoMwZAAAoXpmlk+qcigYAAAAAAKBoKhoAACgtZh8BAAC10CBiirqmogEAAAAAACiaigYAAEpLmbkwAABA8ezRUPdEcQAAAAAAQNEkGgAAAAAAgKJZOgkAgNKizBkAAKiFMsux1jmfOAAAAAAAUDQVDQAAlBazjwAAgFpoEFXSdU0UBwAAAAAAFE1FAwAApUVFAwAAUAtl9n2rc6I4AAAAAACgaCoaAAAoLSoaAACAWiizR0OdE8UBAAAAAABFk2gAAAAAAACKZukkAABKi43bAACAWrAZdN1T0QAAAAAAABRNRQMAAKXFZtAAAEAtNLAZdJ0TxQEAAAAAAEVT0QAAQGlR0QAAANRCmZiizvnEAQAAAACAokk0AAAAAAAARbN0EgAApaXMxm0AAEDxymwGXedUNAAAAAAAAEVT0QAAQGmxcRsAAFALZaqk65woDgAAAAAAKJqKBgAASouKBgAAoBbs0VD3RHEAAAAAAEDRJBoAAAAAAICiWToJAIDSYukkAACgFmwGXfdEcQAAAAAAQNFUNAAAUFrMPgIAAGqhgc2g65yKBgAAAAAAoGgqGgAAKC32aAAAAGrBHg11TxQHAAAAAAAUTaIBAAAAAAAomqWTAAAoLZZOAgAAaqHM/Po65xMHAAAAAACKpqIBAIDS0sBcGAAAoHg2g657ojgAAAAAAKBoKhoAACgtZh8BAAC1UBYxRV1T0QAAAAAAABRNogEAAAAAACiapZMAACgtZebCAAAAxWtgOdY6J4oDAAAAAACKpqIBAIDSoqIBAACoBZtB1z1RHAAAAAAA1KNzzz03ZWVlGTBgQKHtiy++SL9+/bLKKqukadOm6dOnT6ZMmVLtugkTJqR3795ZaaWV0qZNmxx33HGZM2dOtT6PPvpounfvnvLy8nTq1Ck33HDDYh+/RAMAAKWlrKx+DgAAYJlQVlZWL0exnnvuufzhD3/IRhttVK39mGOOyT333JO//OUveeyxxzJx4sTsu+++hfNz585N7969M2vWrDz11FO58cYbc8MNN+TUU08t9Bk/fnx69+6dnXbaKaNHj86AAQNy6KGH5oEHHih6vAsj0QAAAAAAAPVgxowZ6du3b66++uqsvPLKhfZp06bl2muvzYUXXpidd945m222Wa6//vo89dRTefrpp5MkDz74YF599dX8+c9/ziabbJLdd989Z511Vq644orMmjUrSTJ48OB07NgxF1xwQbp06ZL+/fvnBz/4QS666KLF+h4SDQAAAAAAUA/69euX3r17p2fPntXaR40aldmzZ1dr79y5czp06JCRI0cmSUaOHJlu3bqlbdu2hT69evXK9OnTM3bs2EKf/713r169CvdYXGwGDQBAabEZNAAAUAtl9TS/fubMmZk5c2a1tvLy8pSXly+0/y233JIXXnghzz33XI1zkydPTqNGjdKyZctq7W3bts3kyZMLfb6cZFhwfsG5r+szffr0fP7556moqPj2L/g1RHEAAAAAAFBLgwYNSosWLaodgwYNWmjfd999N7/4xS8yZMiQNG7cuI5HuvipaAAAoLSoaAAAAGqhNhsz18ZJJ52UgQMHVmv7qmqGUaNG5YMPPkj37t0LbXPnzs3jjz+eyy+/PA888EBmzZqVqVOnVqtqmDJlSiorK5MklZWVefbZZ6vdd8qUKYVzC/6/oO3LfZo3b77YqhkSFQ0AAFAr5557bsrKyjJgwIBC2xdffJF+/fpllVVWSdOmTdOnT58a/7ifMGFCevfunZVWWilt2rTJcccdlzlz5lTr8+ijj6Z79+4pLy9Pp06dcsMNN9TBGwEAAMUoLy9P8+bNqx1flWjYZZddMmbMmIwePbpwbL755unbt2/h6xVXXDEjRowoXDNu3LhMmDAhPXr0SJL06NEjY8aMyQcffFDoM3z48DRv3jxdu3Yt9PnyPRb0WXCPxUVFAwAApWUpqmh47rnn8oc//CEbbbRRtfZjjjkmw4YNy1/+8pe0aNEi/fv3z7777psnn3wyyfyZSr17905lZWWeeuqpTJo0KQcccEBWXHHFnHPOOUmS8ePHp3fv3jniiCMyZMiQjBgxIoceemjatWuXXr161fm7AgDA0qJB6qeiYVE0a9YsG264YbW2Jk2aZJVVVim0H3LIIRk4cGBatWqV5s2b56ijjkqPHj2y1VZbJUl23XXXdO3aNT/5yU9y/vnnZ/LkyTn55JPTr1+/QoLjiCOOyOWXX57jjz8+Bx98cB5++OHcdtttGTZs2GJ9n6UnigMAgBIyY8aM9O3bN1dffXVWXnnlQvu0adNy7bXX5sILL8zOO++czTbbLNdff32eeuqpPP3000mSBx98MK+++mr+/Oc/Z5NNNsnuu++es846K1dccUVmzZqVJBk8eHA6duyYCy64IF26dEn//v3zgx/8IBdddFG9vC8AAFC3Lrroouy5557p06dPtt9++1RWVuaOO+4onG/YsGGGDh2ahg0bpkePHtl///1zwAEH5Mwzzyz06dixY4YNG5bhw4dn4403zgUXXJBrrrlmsU9eUtEAAABF6NevX3r37p2ePXvm7LPPLrSPGjUqs2fPTs+ePQttnTt3TocOHTJy5MhstdVWGTlyZLp165a2bdsW+vTq1StHHnlkxo4dm0033TQjR46sdo8Ffb68RBMAALDsePTRR6t937hx41xxxRW54oorvvKaNddcM/fee+/X3nfHHXfMiy++uDiG+JUkGgAAKC31tHHbzJkzM3PmzGpt5eXlC11T9ZZbbskLL7yQ5557rsa5yZMnp1GjRtU2bEuStm3bZvLkyYU+X04yLDi/4NzX9Zk+fXo+//zzxbpxGwAALEvqazPo5ZmlkwAAIMmgQYPSokWLasegQYNq9Hv33Xfzi1/8IkOGDEnjxo3rYaQAAAClRUUDAAClpZ42gz7ppJMycODAam0Lq2YYNWpUPvjgg3Tv3r3QNnfu3Dz++OO5/PLL88ADD2TWrFmZOnVqtaqGKVOmpLKyMklSWVmZZ599ttp9p0yZUji34P8L2r7cp3nz5qoZAADga5QtBZtBL2tUNAAAQOYnFZo3b17tWFiiYZdddsmYMWMyevTowrH55punb9++ha9XXHHFjBgxonDNuHHjMmHChPTo0SNJ0qNHj4wZMyYffPBBoc/w4cPTvHnzdO3atdDny/dY0GfBPQAAAEqFigYAAEpLPVU0fFvNmjXLhhtuWK2tSZMmWWWVVQrthxxySAYOHJhWrVqlefPmOeqoo9KjR49stdVWSZJdd901Xbt2zU9+8pOcf/75mTx5ck4++eT069evkNw44ogjcvnll+f444/PwQcfnIcffji33XZbhg0bVrcvDAAASxl7NNQ9iQYAAFjMLrroojRo0CB9+vTJzJkz06tXr/z+978vnG/YsGGGDh2aI488Mj169EiTJk1y4IEH5swzzyz06dixY4YNG5Zjjjkml1xySVZfffVcc8016dWrV328EgAAwFcqq6qqqqrvQSx2n/2rvkcAUMPp3dvV9xAAajj99dn1PYQa5j19Rb08t8FW/erluZSuL+Z+Vt9DAKihYrf16nsIADVUDX+vvodQzcMT76uX5+7cfvd6eW4pUNEAAEBpUeYMAADUQpmtieucTxwAAAAAACiaigYAAEpLiW8GDQAAlLYGqqTrnCgOAAAAAAAomooGAABKi4oGAACgFsqioqGuieIAAAAAAICiSTQAAAAAAABFs3QSAAClxdJJAABALZTZDLrOSTSwTHt57Gu59sabMurFl/Lx1KmpaFyR9dZdO3322jP7fn+Phf7Q+fSzz3L9n27JAyMezXvvTUyDhg3Srm2bbLHZpjn2F0emyUor1bhm1OiXM/iaG/PSmLGZPXtO1ll7rez/f32y9/d2r4vXBOpQuw26Z52td8lq3bbIahttkeaVqydJTu+84kL7N69cPevvtGdW22h+/1U7rp+yBg1ywwG75J1nHy96HI1WapIePz0mXXfdNyuv3jHz5s3N9Env5p3n/p6HLjgpsz779Guv3/7IX2XnX5yRJLnjuAPz8j03LXz8bVfLTr84I522/W4qWrTKtEkTMmbYrXniD+dlzqyZRY8fAJYWhxx4aJ5/btRXnv/9Hy7PNtttU/j+yssHZ/Dv//CV/X966EEZMPAXX3n+4YceyV9u+2teG/tqPv30s6zcauV03aBrDjjoJ+m+2abFvQRQUrqv2y3f7b59vtN5k3xn/U2yeut2SZKy766+0P6rt26X72313Xyn8ybZsvOmWX/1ddKgQYPs+Msf5rGXRxY1htVWbZeT+x6dPb6zc9q2XDX//uTjPDjq8Zzxp4vyzuR3v9U9Tu77i5x10HFJkv3PPTpDRtzxjdds123LPPq7v6RBgwa55r6bc9iFxxU1fqD0SDSwzHrgoUdyzImnZe7cudmgy/rZvPvG+ejjqRn14ksZ9eLLeeqZ53LBOadXu+bd9yfmoJ/9Iu+9PzFrrN4+222zVWbNmpXx/5yQm267Iz87+Cc1Eg0LnjNv3rxs0X2TrNyyRUY+OyonnHp2xr35j5wwsH8dvjWwpO1w5K/Suede37p/1133zW6/umCxjqHlamvlwBseyMprrJ2PJvwjb/79/qzQqDyrdFwv3+l7ZJ646ryvTTSs0nG9bH/ESamaNy9lDb565nirDuvkkFv+niatWmfKG6/kn88/kfYbbpYd+52StbfaOTcetGvmzp61WN8NkiQNzD4CSk/PXXfJSguZdNSmbZuF9t+k+ybp0GGNGu1du3ZdaP958+bljFPPzF13/C0VFRXZdLNN06xZ00yeNDlPPP5Eum7QRaIBlhGn9P1F9t5mt2/dv8+2e+Tin5+x2J6/wVrr55Hf3pbWLVfJ+EkTMvSZEVmn/Zo5aNcfZZ9tdsv2A/vk5bdf+9p7rLf62vn1j4/KvHnz0uBrYoova7Rio1w14LzF8QrwjWwGXfdKItFw//33p2nTptl2222TJFdccUWuvvrqdO3aNVdccUVWXnnleh4hS5s5c+bkjEEXZO7cufndOafle7vvWjj3j7ffyY8PPjJD7xueH+7zvWy1xWZJklmzZuWw/r/MpMlTcsavj8t+P9i72j3feOvttGjevFrb1GnT86szBmXu3Lm57He/ya677Jgk+de/P8qPf3pkrvvTzdlx+62z5ebdl+j7AnXn3dFPZ8q4MXn/leczcczzGTDiraxQ3vgr+3/87tsZecMlmfjK83l/zPPZ45RL0mnbXb+y/zdpuGKj7H/1PWnRrkPuOe3nGXXr1dXOt1l3g3w+7aOvvcf3zrwyX0yfmvdeeuZrkyZ7D7o2TVq1ztN/vCz3nzMwSdKgYcP88KKb02XXfbLdz07Io5efVfS7ACxOYgqWtIHHDcxqq7X/1v337bNP9trn+9+6/x9+f1XuuuNv2WGn7XPWb85Mi5YtCuemT5uejz/+eJHGC5Suka+9kJfHv57nxo3Oc+Neyjt/HpnGjb46pnh78oRcdPvVeW7cS3nujZdyef+z0mvzHYt+/k0nXZ7WLVfJtffdkp9dfELmzpubJOm/109zWf+zctNJl2ejn3038+bN+8p7XHXM+Zk6Y3qefu2Fb500OfnHR2e91dfOtfffksP2+HHR4wdKU0ksgHvcccdl+vTpSZIxY8bkl7/8ZfbYY4+MHz8+AwcOrOfRsTR6+50J+fdHH6fjWh2qJRmSZJ2118r39uiVJBkz9vVC+403/SXj35mQg/b/vxpJhiRZr9Paqaio/hf/X+68JzNmfJpddtyukGRIklVXaZXjBvw8SXL9n25ZTG8FlIInr/ldHrnsjLzxyLDM+NeUb+w/7pGheeDcYzNm6C356J9vJVVVtXr+VgcclVXX7pyRN1xcI8mQJB+8OTazv/j8K6/v/sNDstYW2+eB847PF59M+8p+q3XbIh022yYz/jUlw397YqF93ty5GXpG/8ydNStb7t8/DRo2rNX7wEKVNaifg6WamIKl2ZTJU3Lt1delXbvKnH/BedWSDEnSvEXzrLnWmvU0OmBxO//W3+e0G3+XoU8/lCkff/iN/e8ZOTwDB5+Rmx+5K2+9P75WIcU2G2yRjdbukn9P/zi/+P2phSRDklz+t+vz5NjnssFa62fPLXt+5T0O3f3H2WGjrfLLP5yVqZ9O/1bP7brmejn+R0fm2vtvyZNjnyv+BeBbKisrq5djeVYSEdX48eML5aO333579txzz5xzzjm54oorct9999Xz6FgaNVpx4Wul/6+WLf5bofCXO+5Okvxkvx986+c89venkiS9eu5U49wO222d8vJGeeqZ5zJzpnXMgcWj+w8PSZI88+crFvnapqu2zXePHZS3nxqRMUNv/tq+6+44f4+ZNx4ZVmN5pE///UH+OeqJVLRslQ6bbbOwywHqnJiCpdndd92T2bNnZ58f7JPGjb96VjNAbW223kZJklFvvpxPv/isxvlHRs//PcdeWy+8Crvtyq1z/mG/ykMv/D03PXznt37uVcecl2mffpITrjmniFEDS4OSWDqpUaNG+eyz+T/cHnrooRxwwAFJklatWhVmJcGiWGP19umwxmoZ/86E3HPfgzWWTrrn3gfSonmzfHfnHZIkkyZPyT/ffS+VbdukXWXbjBr9ch5+7Il88smMrL5a+/TaZces2aHmpkyvv/FWkmSDzuvVONdoxRWz7jpr55VXX8/4f76bzut1WkJvCywvmleunlXWWjfTJr2b6ZPfyxqbbp31d94zjZu1yMfvjc9rD96Zjyb84yuv3/3XF2XFxhUZesY37x1Tuf78AGTSqy8u9PykV1/M2j12Ttv1N6rVptawUKoLKIKYgiXtztvvzLSp01LWoEHWXLNDdt5lp7Rr3+4r+z/7zLMZ9/q4zJw5M20r22bb7bZJ1w0Wvj/Ds8/Mn927ySYb58MPP8y999yXCRMmpGmzZvnOdzbP1ttuvdzPkgQWjyaNK5IkH39FdfO/p89fpm3jtRf+8+rSfmemorxxjrz0V9/6mUd+74Bss8EW+cl5v8jHn0xdtAFDkRqUxvz65UpJJBq23XbbDBw4MNtss02effbZ3HrrrUmSN954I6uvXvOXu/BNGjZsmHPPPDlHHH18jv3VGbn+T7dkzTVWz78//jijXnwp63TsmHPP/HWhouGtt99JkrRpvWrOGHRBbrrtjmr3u+T3V+WXRx2Zgw/4f4W2GTM+zSczZiRJKr9iA7jKtm3yyquvZ+KkyRINQK217tQlSfLJB5OyxymX5jt9j6x2fudfnJmHLvxVRl5/cY1r19txj2yw+w/zyKWnz1/C6Ru0aNchSTJ98nsLPT998vvz+7XvsAhvALDkiClY0q4efE217y/87UU5/MjD8rMjD19o/6F3D6v2/RWX/j49d90lZ/3mzKzUpPqm0m//4+0kyT/+8XZ+OeDYfPLJjMK5G669IZt/Z/NcdOmFad682eJ4FWA59uHU+fu5rdl24X83dqzs8JXne2+5S360w/dy6o2/y1vvj/9Wz2u/SmUGHXJiHn7xyfz5oduLHDWwNCiJ1M7ll1+eFVZYIX/9619z5ZVXZrXVVkuS3Hfffdltt2+3oQz8r8022Sh/uubyrLF6+4x9bVzufXBEnnnuhZSVNcg2W22RNb60kdv06Z8kSV59fVxu+etdOeqIQ/LY/XfmieF359hfzN9r4byLLs+j/1kqKUk+/ey/JYaNG5cvdAwL9nT4cl+AYlU0n7+Rabuum2bz/Q7PI5edkQt3WCu/23b1wj4KvU74bdbdYfdq1zVaqUl6n3pZ/jV+XJ64+rff6lmNmjRJksxeSDl1ksz+/NMkSXkTv/AASoOYgiWl++bd85tzz86wB+7JMy+MzN/uvStH/aJ/Vlhhhfz+sisz5E83Veu/Roc1MvC4Y3LH3X/NyOefyoMP359B55+TNm3b5KEHR+RXJ55c4xkLqm4uOP/CrLveurnlrzfnqeeeyFXXDs5qq6+W5599PmeeemadvC+wbHt8zNNJki3W2zhdOqxb7VxFeeP8aIc9kyTNKppUO9ek8Ur5/dHnZNy7/8h5t/7+Wz/v8v5np/GK5Tny0pNqOXKg1JVERUOHDh0ydOjQGu0XXXRRPYyGZcXQ+4bnpNPPySbdNsiFg85Ip3U65oMP/pXr/nRzrvvTzXnm+Rdyyw2D06hRo8yrmpckmTNnbv7fD/dJ/58dXLjPYQf1zcdTp+baG2/K4Gv/mB2327q+XglYzpU1mD8/oOGKK+a5mwbnsSvOLpx78toLstLKq2abQ4/Ndj87MW8+9t/1yHc55uy0aN8hNx743Rr7LUBJsnQSRRBTsKT0O+rn1b5fa601c+jPDknXDbvmyMN+niuvGJw+P9y3sLfCnt/vXa3/SitVZI89d88WW26eH+z1ozwy4pG8/NLL2WjjjQp95s2bH480a94sV/zhiqy00vylTbbssWUuueLi/HDvH2X4gw/lnXf+mbVsCg3UwhvvvZ07nrgv+267e+4+8/ocfvHxeW7cS1m7XYdcfOQZWeU/k5vm/c+O0+ccfGI6tFktOx/3o8z6ljHFPtvunn223S1n/OmivPHe24v9XeDrWHKw7pVMFDd37tzcfvvtOfvss3P22WfnzjvvzNy5c7/xupkzZ2b69OnVDhvv8s4/382Jp56dlVu2yOBLz89GG3bNShUVWWvNNXLmycdnp+23ydjXxuWvd80vZ16poqJw7b579a5xvz7/CRZefuXVwp+vJiv9t9z5iy8W/mfu88+/qNEXoFizPv3vMgov3nFjjfML2lbf6DtZodH8SqvVum2RLX58ZF66688Z/8yji/Cs+RULKzZe+M+vFf8zw2nmp59863sCLGnFxhSJuIJFt/U2PbLBhl3zyfRPMublV76xf+vWrbPXPt9Pkjz5pUrpJFnpP/HCrr2+W0gyLLDuup2ywYYbJEleeH7U4hg6sJw75IJj89jLT6fTamvl4d/elk/uHpeX/jA8m63bLcdf/Zskycczphb6b7H+Jun3/QPzx+F/LWwW/U2ardQ0l/U7K2+893bOufmyJfEaQIkpiYqGt956K3vssUfef//9rL/++kmSQYMGZY011siwYcOyzjrrfOW1gwYNyhlnnFGt7bRfHZfTf338Eh0zpW3YAw9l9pw52W7rLRf6S/7dvrtzHnn8yTz/wuj8+Ef7ZLV2lYVzq3/p6wVW+88mb3Pnzs3UadPTtk3rNG3aJM2aNs0nM2Zk8pQP0qlpxxrXTZ7yQZKk/ULuCbCopk6c8N+v33+n5vn/tDVYYYVUtGyVTz6YlHV32C0NGjZMm/U2zEF/fKha/1U7zv87d7sjTkz3Hx6ct/7+QGFppWmTJqTdBpumeeXC125tXjl/SZJpXxoTLDZmH1GE2sQUC/r+b1zx61N+lZNP+/USGzNLvw5rdsjYV17Nvz788Fv3T5IPP/xXtfZ27dtl+vTpad++/cIuS/vV2ueVMa/ko39/XLsBAySZOmNadvzlD7L7d3bOjhv1SIsmzfKPSf/MkBF3pkuH+ftLjn3njUL/Pb6zcxo2bJhuHTvnkd/9pdq9Oq8x/+/XX//4qBy6+//L/c89mvNuvSLdO3XLaqtWZvykCXlg0JBq11Su3DpJ0vs7O+eR3/0lkz/6IP/vnH5L8pVZDpVFTFHXSiLRcPTRR2edddbJ008/nVatWiVJ/v3vf2f//ffP0UcfnWHDhn3ltSeddFIGDhxYra18rtmVy7spH8z/h36zpk0Xer5Z0/kzcad9Mv/Pytod10x5eaPMnDkr0z75JK1arVyt/7T/rJmaVK9O6Lxepzz3wuiMff2NdFqneqJh9uw5efMfb6e8vFE6rrlG7V8KWO796+3XM/uLz7Ni44pUtFg5n31c/ZcUFS1aFb7+cvVDkrTruslX3rf1Ol3Sep0umfr+Pwttk8e9nM4990q7rpsu9JoF7VPGvbyorwGwRNQmpkgWHldUrfDtqiFYfk2fNj9OqKio+Iae/+n/n7iiYqXG1do7d+mcca+PK5yv+Zxp/7nu2z0H4Nu479mHc9+zD1drO2T3/ZIkj748skb/TTtt+JX36tJh3XTpsG7emfJutfaO7TqkY7sOC72m3Spt026Vtnln8rsLPQ8sXUoi0fDYY49VCwiSZJVVVsm5556bbbbZ5muvLS8vT3n5/2zE+5n1p5d3q64y/8/SK6++vtDzY8a+liSFSoZGjRpl2x5bZsSjf8+zz7+YjmtW/0vw2VEvJknWWL19mjb974ZIO2y3dZ57YXQeeOiR7NW7V7VrHv37k5k5c1Z22n6bmn9GAYowd/as/OOJB9O5515Z6zs75N/vvFnt/Frf2T5J8tGEfxSWNHr08rPy6OVnLfR+ew+6Npvsc0DuOO7AvHxP9Y0s33z0vuzY75Sst1PvNFyxUbW9HZqs0iZrbrZtPp/6USa88O1Kp2HRmH3EoqtNTJEsPK74Yu5ni32cLDs++uijvPCfOKFL1y7f2L+qqioPP/TI/P5dqvffcacd8rc7/5ZRC1ka6bNPP8tr/4lrunTpXNthA3ylivLGOWS3/TJz1szc+OBfC+1n/OnCnPGnCxd6zfXHXZiDdv1R9j/36AwZcUeh/bGXR6bsuwuvjj5w1x/mhuMuyjX33ZzDLjxu8b4E/Ic9GupeSezRUF5enk8+qVmFMGPGjDRq1KgeRsTSbpcdt0uSPPfC6Nx0253Vzo1++ZXcOOS2JMluPXcqtB96YN8kye+vviHj//nfpUDefX9iLvn91UmS/X6wd7V7/XCf76Vp0yYZ8ejf8+CIRwvt//7o4/z24t8nSX76k/0Wz0sBy5XTX5+d01+fnZarVd/w8clrL0iSbH/kr7LKWusW2luutlZ2Ovr0JMnzt1xV6+e/P+a5TBj1ZJqu2jbfPXZQob1Bw4bpfdpladioUZ758+WZN2dOrZ8FsDiIKVgSRr84Og8/9EiNvT7ef39ijjn6l/n888+z4047pG1l2yTzkw+33HRrPv3PXkcLfPbpZzn7jN9kzMtjsuqqq2aX7+5c7fwOO22ftdfumNEvvpRbb76t0D537tz87vwLMm3atHRat1M23WzhlYYAC1M1/L1UDX8va7at/gv/dVfrmGYrVV8BYuVmLXPrr6/Mmm1Xzzk3X573/zWpLocKLANKoqJhzz33zOGHH55rr7023/nOd5IkzzzzTI444oh8//vfr+fRsTTaoMv6OfiA/5fr/nhzzhj0u9x02+1ZZ+2O+eDDf2X0y69k3rx5+b8+e2XrrbYoXNN9k27pd/hPc8VV12ef/X6a7pt0S4MGDfPCSy/n008/y/bbbJWf7l89adCyRfOcc9pJGXDCqTn6uJPznc03TcsWLTLymecz/ZNP8tP998uWm3ev69cHlqB1d9g9Oxz53/W6G644/5dXh97yRKHtsSt/kzcfuy9J0rR1Zfa77L+zgVZde/664b1PvSwzZ8z/hdgbj92bx688p9DnyzMv5s6eXe357744Mo9ecVZ27HdKfnbHc3n3xacyb+7cdOi+dcqbNs+bj92XkTdcvFje9a5fHZpDb/l7tjrw6HTcaqd8+Narad9t87TqsE4mvPBU/v6H8xbLcwAWBzEFS8I/35mQU399WlZdddV06do5zZo1y8RJk/La2Ncyc+bMrNNpnZx65qmF/p9//kUGnX1uLrnw0mzQbYO0XnXVfPTxx3n91dczderUNGveLL+7+Lc1llpq2LBhBv12UA458NCcc9ag3H7b7VljzTXy+mvj8t6776Vly5Y597fnmJ0Jy4g9vrNzTtl/QOH7RivMjylGXnp3oe2sP1+ce/+zrFFlqza58/RrCucW7Ivw+6N/k+mfzV8yddgzI3L2kEsKfb7882L2/0wO+vHO++SE//t5nhs3Ou//e3JaNGme7Tb8Tpqt1DTXP3Brzhpy8eJ5UWC5UhKJhksvvTQHHXRQtt5666ywwvwhzZkzJ9///vdzySWXfMPVsHAnHNM/3Tfullv+eldeeW1cxv9zQpqstFK22GyT/Gif72fP3b9b45qjjzw0ndfrlBuH3JbRY8Zm7ty56bhmh+z9vd2z///1ScOGDWtc06vnTvnztVfkyqtvzEtjxmb27NlZZ+21sv//9ck+39+jLl4VqENNWrXO6ptsWaP9y21NWrUufL1Co/KF9m/dqWvh63+NH1ftXNvOGydJ3h75cD75YGKNax+97MxMef3lbHXA0Vl94y3ToOEK+df4N/LSXX/Ks0OuSNW8eYv+Ygvx0T/fyuB9tshOR5+eTtvums7f3TvTJk7IY1ecnb//4dxqyynBYlVWEkW3LGXEFCwJ3TbaMD/a74cZ8/IreeWVsflk+iepqGic9Tuvl+/2+m5+tN8P07jxf/dbaNmyRX566EEZ89KY/POdf+alF19Kg4YNstpqq+X7e38v+x+4f9q2bbPQZ3Xusn5uveOWDL5icEY++XT+8Y+3s8qqq2TfH+yTw352aNqvtvCNooGlT+uWq2SrLjUnJX65rXXLVQpfl6/YaKH9u665XuHr1999q9q5jdeeH2+MePGJTPz35GrnHh79ZDZZp2s2W3ejbNl508z44rM89erzGTz0z7nryfuLeykoMTaDrntlVVVVVfX18Hnz5uW3v/1t7r777syaNSsdOnTIgQcemLKysnTp0iWdOnUq7saf/eub+wDUsdO7t6vvIbCU6HHQgPQ68be59v/tkHdftAcCS9bpr8/+5k51bN6Ym+vluQ26/b96eS61s8RiitijAShNFbut982dWO4d0+ewXHjEadlmwN55auzz9T0clgNVw9+r7yFU89yHT3xzpyVgi9bb1stzS0G9VjT85je/yemnn56ePXumoqIi9957b1q0aJHrrruuPocFAPVqrS13yFtPPCjJwPLL0iAsAjEFANS008Zb54HnH5VkYLmloqHu1WtFw7rrrptjjz02P/vZz5IkDz30UHr37p3PP/88DRrUomReRQNQglQ0AKWoJCsaXrmlXp7bYMP9vrkTJWeJxRRR0QCUJhUNQCkqtYqG5z98sl6eu3nrberluaWgXhfAnTBhQvbY479r2Pfs2TNlZWWZOLHmetQAACwvGtTTwdJITAEAQA1lZfVzLMfqNaKaM2dOtY2zkmTFFVfM7NmlN7MOAAAoPWIKAACof/W6R0NVVVUOOuiglJeXF9q++OKLHHHEEWnSpEmh7Y477qiP4QEAACVOTAEAAPWvXhMNBx54YI22/fffvx5GAgBAyVjOS45ZNGIKAAD+l82g6169Jhquv/76+nw8AACwlBNTAABA/avXRAMAANSgogEAAKiFMjFFnavXzaABAAAAAIClm4oGAABKjLkwAABA8ezRUPdEcQAAAAAAQNEkGgAAAAAAgKJZOgkAgNJi4zYAAKAWLJ1U91Q0AAAAAAAARVPRAABAaVHRAAAA1EKZmKLOqWgAAAAAAACKpqIBAIASYy4MAABQPHs01D1RHAAAAAAAUDSJBgAAAAAAoGiWTgIAoLTYuA0AAKgFSyfVPRUNAAAAAABA0VQ0AABQWsrMhQEAAIpXpkq6zoniAAAAAACAoqloAACgxJh9BAAAFM8eDXVPRQMAAAAAAFA0iQYAAAAAAKBolk4CAKC02LgNAACoBZtB1z0VDQAAAAAAQNFUNAAAUFrKzIUBAACKZzPouieKAwAAAAAAiqaiAQCAkmI9VQAAoDZUNNQ9FQ0AAAAAAEDRJBoAAAAAAICiWToJAIASYy4MAABQPMux1j1RHAAAAAAAUDQVDQAAlBazjwAAgFqwGXTdU9EAAAAAAAAUTUUDAAClRUUDAABQCyoa6p6KBgAAAAAAoGgSDQAAAAAAQNEsnQQAQIkxFwYAAChemeVY65woDgAAAAAAKJqKBgAASovZRwAAQK2IKeqaigYAAAAAAKBoKhoAACgtKhoAAIBasEdD3VPRAAAAAAAAFE2iAQAAAAAAKJqlkwAAKDHmwgAAAMUrsxl0nRPFAQAAAAAARVPRAABAabFxGwAAUAsqGuqeigYAAAAAAKBoKhoAACgtZebCAAAAxStTJV3nRHEAAAAAAEDRVDQAAFBizD4CAACKZ4+GuqeiAQAAAAAAKJpEAwAAAAAAUDRLJwEAUFps3AYAANSCpZPqnooGAAAAAACgaCoaAAAoLWXmwgAAAMUrUyVd50RxAAAAAABA0VQ0AABQWsw+AgAAasEeDXVPRQMAAAAAAFA0iQYAAAAAAKhDgwYNyhZbbJFmzZqlTZs22XvvvTNu3Lhqfb744ov069cvq6yySpo2bZo+ffpkypQp1fpMmDAhvXv3zkorrZQ2bdrkuOOOy5w5c6r1efTRR9O9e/eUl5enU6dOueGGGxb7+0g0AABQYsrq6QAAAJYFZWVl9XIsisceeyz9+vXL008/neHDh2f27NnZdddd8+mnnxb6HHPMMbnnnnvyl7/8JY899lgmTpyYfffdt3B+7ty56d27d2bNmpWnnnoqN954Y2644YaceuqphT7jx49P7969s9NOO2X06NEZMGBADj300DzwwAO1/6C/pKyqqqpqsd6xFHz2r/oeAUANp3dvV99DAKjh9Ndn1/cQaqiaOKpenlvWfrN6eS6l64u5n9X3EABqqNhtvfoeAkANVcPfq+8hVPP+Z+/Uy3NXW2mtoq/98MMP06ZNmzz22GPZfvvtM23atLRu3To33XRTfvCDHyRJXn/99XTp0iUjR47MVlttlfvuuy977rlnJk6cmLZt2yZJBg8enBNOOCEffvhhGjVqlBNOOCHDhg3LK6+8UnjWfvvtl6lTp+b++++v1ft+mYoGAABKS1mD+jkAAIBlQlk9/Tdz5sxMnz692jFz5sxvNeZp06YlSVq1apUkGTVqVGbPnp2ePXsW+nTu3DkdOnTIyJEjkyQjR45Mt27dCkmGJOnVq1emT5+esWPHFvp8+R4L+iy4x+IiogIAAAAAgFoaNGhQWrRoUe0YNGjQN143b968DBgwINtss0023HDDJMnkyZPTqFGjtGzZslrftm3bZvLkyYU+X04yLDi/4NzX9Zk+fXo+//zzot5zYVZYbHcCAIDFwn4JAABAbdRPTHHSSSdl4MCB1drKy8u/8bp+/frllVdeyRNPPLGkhrbESTQAAAAAAEAtlZeXf6vEwpf1798/Q4cOzeOPP57VV1+90F5ZWZlZs2Zl6tSp1aoapkyZksrKykKfZ599ttr9pkyZUji34P8L2r7cp3nz5qmoqFiksX4dSycBAAAAAEAdqqqqSv/+/XPnnXfm4YcfTseOHaud32yzzbLiiitmxIgRhbZx48ZlwoQJ6dGjR5KkR48eGTNmTD744INCn+HDh6d58+bp2rVroc+X77Ggz4J7LC4qGgAAKC1llk4CAACKtzREFP369ctNN92Uv/3tb2nWrFlhT4UWLVqkoqIiLVq0yCGHHJKBAwemVatWad68eY466qj06NEjW221VZJk1113TdeuXfOTn/wk559/fiZPnpyTTz45/fr1K1RWHHHEEbn88stz/PHH5+CDD87DDz+c2267LcOGDVus76OiAQAAAAAA6tCVV16ZadOmZccdd0y7du0Kx6233lroc9FFF2XPPfdMnz59sv3226eysjJ33HFH4XzDhg0zdOjQNGzYMD169Mj++++fAw44IGeeeWahT8eOHTNs2LAMHz48G2+8cS644IJcc8016dWr12J9n7KqqqqqxXrHUvDZv+p7BAA1nN69XX0PAaCG01+fXd9DqKFq8sv18tyyyo3q5bmUri/mflbfQwCooWK39ep7CAA1VA1/r76HUM3kz9+tl+dWVqxRL88tBSoaAAAAAACAokk0AABQWsrK6udYBIMGDcoWW2yRZs2apU2bNtl7770zbty4an2++OKL9OvXL6usskqaNm2aPn36ZMqUKdX6TJgwIb17985KK62UNm3a5LjjjsucOXOq9Xn00UfTvXv3lJeXp1OnTrnhhhuK+lgBAGD5UVZPx/JLogEAABbRY489ln79+uXpp5/O8OHDM3v27Oy666759NNPC32OOeaY3HPPPfnLX/6Sxx57LBMnTsy+++5bOD937tz07t07s2bNylNPPZUbb7wxN9xwQ0499dRCn/Hjx6d3797ZaaedMnr06AwYMCCHHnpoHnjggTp9XwAAgK9jjwaAOmKPBqAUleQeDVPG1Mtzy9p2K/raDz/8MG3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            "text/plain": [
              "<Figure size 1600x1400 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved confusion_matrix_comparison_4way.png\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 7 — COMPARISON VISUALISATIONS (ENHANCED)\n",
        "# ===========================================================================\n",
        "def comparison_plots(res_full, res_subset, res_bert, res_roberta):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 7 — ENHANCED COMPARISON VISUALISATIONS\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    metrics, keys = ['Accuracy', 'Precision', 'Recall', 'F1-Score', 'AUC-ROC'], ['accuracy', 'precision', 'recall', 'f1', 'auc']\n",
        "    full_v, sub_v, bert_v, roberta_v = [res_full[k] for k in keys], [res_subset[k] for k in keys], [res_bert[k] for k in keys], [res_roberta[k] for k in keys]\n",
        "\n",
        "    x, w = np.arange(len(metrics)), 0.20\n",
        "    fig, ax = plt.subplots(figsize=(14, 6))\n",
        "    b1 = ax.bar(x - 1.5*w, full_v,    w, label='TF-IDF+LR (Full 25K)', color='#3498db', edgecolor='black')\n",
        "    b2 = ax.bar(x - 0.5*w, sub_v,     w, label=f'TF-IDF+LR (Subset {FAIR_SUBSET_TRAIN:,})', color='#9b59b6', edgecolor='black')\n",
        "    b3 = ax.bar(x + 0.5*w, bert_v,    w, label='DistilBERT (Full)', color='#e67e22', edgecolor='black')\n",
        "    b4 = ax.bar(x + 1.5*w, roberta_v, w, label='RoBERTa-base (Full)', color='#27ae60', edgecolor='black')\n",
        "\n",
        "    ax.set_xticks(x); ax.set_xticklabels(metrics, fontsize=12); ax.set_ylabel('Score', fontsize=12)\n",
        "    ax.set_title('Model Performance Comparison - 4-Way', fontsize=14, fontweight='bold'); ax.set_ylim(0.80, 1.02); ax.legend(fontsize=10); ax.grid(axis='y', alpha=0.3)\n",
        "\n",
        "    for bars in [b1, b2, b3, b4]:\n",
        "        for b in bars:\n",
        "            ax.annotate(f'{b.get_height():.3f}', xy=(b.get_x() + b.get_width() / 2, b.get_height()), xytext=(0, 3), textcoords='offset points', ha='center', fontsize=7, fontweight='bold')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/model_comparison_4way.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved model_comparison_4way.png\")\n",
        "\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(16, 14))\n",
        "    for ax, cm_data, title, cmap in [\n",
        "        (axes[0, 0], np.array(res_full['cm']), 'TF-IDF+LR (Full 25K)', 'Blues'),\n",
        "        (axes[0, 1], np.array(res_subset['cm']), f'TF-IDF+LR (Subset {FAIR_SUBSET_TRAIN:,})', 'Purples'),\n",
        "        (axes[1, 0], np.array(res_bert['cm']), 'DistilBERT (Full)', 'Oranges'),\n",
        "        (axes[1, 1], np.array(res_roberta['cm']), 'RoBERTa-base (Full)', 'Greens'),\n",
        "    ]:\n",
        "        sns.heatmap(cm_data, annot=True, fmt=',d', cmap=cmap, xticklabels=['Neg', 'Pos'], yticklabels=['Neg', 'Pos'], ax=ax, annot_kws={'size': 15})\n",
        "        ax.set_title(title, fontsize=12, fontweight='bold'); ax.set_xlabel('Predicted'); ax.set_ylabel('True')\n",
        "\n",
        "    plt.suptitle('Confusion Matrix Comparison - 4-Way', fontsize=15, fontweight='bold', y=1.00)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/confusion_matrix_comparison_4way.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved confusion_matrix_comparison_4way.png\")\n",
        "\n",
        "# --- Execute ---\n",
        "comparison_plots(res_full, res_subset, res_bert, res_roberta)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57eabc92",
      "metadata": {
        "id": "57eabc92"
      },
      "source": [
        "## Part 8: Statistical Significance Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "be80b425",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be80b425",
        "outputId": "a4a4d87a-a530-44c7-ec76-3b063855e0aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 8 — STATISTICAL SIGNIFICANCE TESTING\n",
            "================================================================================\n",
            "\n",
            "-> McNemar's Test: LR (Full) vs DistilBERT\n",
            "  Contingency table:\n",
            "[[21427   861]\n",
            " [ 1794   918]]\n",
            "  Chi-squared: 327.1653\n",
            "  P-value: 3.98e-73\n",
            "  Result: SIGNIFICANT difference (p < 0.05)\n",
            "\n",
            "-> McNemar's Test: LR (Full) vs RoBERTa\n",
            "  Contingency table:\n",
            "[[21787   501]\n",
            " [ 2109   603]]\n",
            "  Chi-squared: 989.4441\n",
            "  P-value: 3.54e-217\n",
            "  Result: SIGNIFICANT difference (p < 0.05)\n",
            "\n",
            "-> McNemar's Test: DistilBERT vs RoBERTa\n",
            "  Contingency table:\n",
            "[[22854   367]\n",
            " [ 1042   737]]\n",
            "  Chi-squared: 322.4102\n",
            "  P-value: 4.32e-72\n",
            "  Result: SIGNIFICANT difference (p < 0.05)\n",
            "\n",
            "-> Bootstrap Confidence Intervals (95% CI):\n",
            "\n",
            "  LR (Full):\n",
            "    F1-Score CI  : [0.8879, 0.8958]\n",
            "    Accuracy CI  : [0.8876, 0.8953]\n",
            "\n",
            "  DistilBERT:\n",
            "    F1-Score CI  : [0.9258, 0.9323]\n",
            "    Accuracy CI  : [0.9256, 0.9320]\n",
            "\n",
            "  RoBERTa:\n",
            "    F1-Score CI  : [0.9532, 0.9584]\n",
            "    Accuracy CI  : [0.9532, 0.9584]\n",
            "\n",
            "-> Summary Statistics Table:\n",
            "     Model  Accuracy  F1-Score  AUC-ROC Train Time (s)\n",
            "   LR Full   0.89152  0.891866 0.958251           59.8\n",
            "DistilBERT   0.92884  0.929008 0.978046         1408.1\n",
            "   RoBERTa   0.95584  0.955826 0.990032         2753.6\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 8 — STATISTICAL SIGNIFICANCE TESTING\n",
        "# ===========================================================================\n",
        "def statistical_significance_tests(res_full, res_bert, res_roberta, test_labels):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 8 — STATISTICAL SIGNIFICANCE TESTING\")\n",
        "    print(\"=\" * 80)\n",
        "    # Extract predictions\n",
        "    y_true = np.array(res_full['true_labels'])\n",
        "    y_lr = np.array(res_full['predictions'])\n",
        "    y_bert = np.array(res_bert['predictions'])\n",
        "    y_roberta = np.array(res_roberta['predictions'])\n",
        "    # Verify all models evaluated on same test data\n",
        "    assert np.array_equal(np.array(res_full['true_labels']), np.array(res_bert['true_labels'])), \\\n",
        "        \"Label mismatch between LR and DistilBERT!\"\n",
        "    assert np.array_equal(np.array(res_full['true_labels']), np.array(res_roberta['true_labels'])), \\\n",
        "        \"Label mismatch between LR and RoBERTa!\"\n",
        "    # McNemar's test: All 3 pairwise comparisons\n",
        "    pairs = [\n",
        "        ('LR (Full) vs DistilBERT', y_lr, y_bert),\n",
        "        ('LR (Full) vs RoBERTa', y_lr, y_roberta),\n",
        "        ('DistilBERT vs RoBERTa', y_bert, y_roberta),\n",
        "    ]\n",
        "\n",
        "    for name, pred_a, pred_b in pairs:\n",
        "        print(f\"\\n-> McNemar's Test: {name}\")\n",
        "        a_correct = (pred_a == y_true)\n",
        "        b_correct = (pred_b == y_true)\n",
        "        table = np.array([\n",
        "            [np.sum(a_correct & b_correct), np.sum(a_correct & ~b_correct)],\n",
        "            [np.sum(~a_correct & b_correct), np.sum(~a_correct & ~b_correct)]\n",
        "        ])\n",
        "        print(f\"  Contingency table:\\n{table}\")\n",
        "        try:\n",
        "            result = mcnemar(table, exact=False)\n",
        "            print(f\"  Chi-squared: {result.statistic:.4f}\")\n",
        "            print(f\"  P-value: {result.pvalue:.2e}\")\n",
        "            if result.pvalue < 0.05:\n",
        "                print(f\"  Result: SIGNIFICANT difference (p < 0.05)\")\n",
        "            else:\n",
        "                print(f\"  Result: NO significant difference (p >= 0.05)\")\n",
        "        except Exception as e:\n",
        "            print(f\"  McNemar test error: {e}\")\n",
        "    # Bootstrap confidence intervals\n",
        "    print(\"\\n-> Bootstrap Confidence Intervals (95% CI):\")\n",
        "    def bootstrap_ci(y_true, y_pred, metric_func, n_bootstrap=10000):\n",
        "        metrics = []\n",
        "        for _ in range(n_bootstrap):\n",
        "            indices = np.random.choice(len(y_true), size=len(y_true), replace=True)\n",
        "            metrics.append(metric_func(y_true[indices], y_pred[indices]))\n",
        "        return np.percentile(metrics, [2.5, 97.5])\n",
        "    models = [('LR (Full)', y_lr), ('DistilBERT', y_bert), ('RoBERTa', y_roberta)]\n",
        "    for model_name, y_pred in models:\n",
        "        f1_ci = bootstrap_ci(y_true, y_pred, f1_score, n_bootstrap=N_BOOTSTRAP)\n",
        "        acc_ci = bootstrap_ci(y_true, y_pred, accuracy_score, n_bootstrap=N_BOOTSTRAP)\n",
        "        print(f\"\\n  {model_name}:\")\n",
        "        print(f\"    F1-Score CI  : [{f1_ci[0]:.4f}, {f1_ci[1]:.4f}]\")\n",
        "        print(f\"    Accuracy CI  : [{acc_ci[0]:.4f}, {acc_ci[1]:.4f}]\")\n",
        "    # Summary table\n",
        "    print(\"\\n-> Summary Statistics Table:\")\n",
        "    summary_data = {\n",
        "        'Model': ['LR Full', 'DistilBERT', 'RoBERTa'],\n",
        "        'Accuracy': [res_full['accuracy'], res_bert['accuracy'], res_roberta['accuracy']],\n",
        "        'F1-Score': [res_full['f1'], res_bert['f1'], res_roberta['f1']],\n",
        "        'AUC-ROC': [res_full['auc'], res_bert['auc'], res_roberta['auc']],\n",
        "        'Train Time (s)': [f\"{res_full['train_time']:.1f}\", f\"{res_bert['train_time']:.1f}\", f\"{res_roberta['train_time']:.1f}\"],\n",
        "    }\n",
        "    summary_df = pd.DataFrame(summary_data)\n",
        "    print(summary_df.to_string(index=False))\n",
        "# --- Execute ---\n",
        "statistical_significance_tests(res_full, res_bert, res_roberta, test_df['label'].values)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "61c16dc7",
      "metadata": {
        "id": "61c16dc7"
      },
      "source": [
        "## Part 9: ROC Curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "b2be37f9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "b2be37f9",
        "outputId": "32460497-0954-4f0d-aad5-b757a846d7f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 9 — ROC CURVES VISUALISATION\n",
            "================================================================================\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved roc_curves.png\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 9 — ROC CURVES VISUALISATION\n",
        "# ===========================================================================\n",
        "def plot_roc_curves(res_full, res_bert, res_roberta):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 9 — ROC CURVES VISUALISATION\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    y_true = np.array(res_full['true_labels'])\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(10, 8))\n",
        "\n",
        "    # LR Full\n",
        "    y_prob_lr = np.array(res_full['probabilities'])\n",
        "    fpr_lr, tpr_lr, _ = roc_curve(y_true, y_prob_lr)\n",
        "    auc_lr = auc(fpr_lr, tpr_lr)\n",
        "    ax.plot(fpr_lr, tpr_lr, label=f'TF-IDF+LR (Full) - AUC={auc_lr:.4f}', linewidth=2.5, color='#3498db')\n",
        "\n",
        "    # DistilBERT\n",
        "    y_prob_bert = np.array(res_bert['probabilities'])\n",
        "    fpr_bert, tpr_bert, _ = roc_curve(y_true, y_prob_bert)\n",
        "    auc_bert = auc(fpr_bert, tpr_bert)\n",
        "    ax.plot(fpr_bert, tpr_bert, label=f'DistilBERT (Full) - AUC={auc_bert:.4f}', linewidth=2.5, color='#e67e22')\n",
        "\n",
        "    # RoBERTa\n",
        "    y_prob_roberta = np.array(res_roberta['probabilities'])\n",
        "    fpr_roberta, tpr_roberta, _ = roc_curve(y_true, y_prob_roberta)\n",
        "    auc_roberta = auc(fpr_roberta, tpr_roberta)\n",
        "    ax.plot(fpr_roberta, tpr_roberta, label=f'RoBERTa-base (Full) - AUC={auc_roberta:.4f}', linewidth=2.5, color='#27ae60')\n",
        "\n",
        "    # Diagonal\n",
        "    ax.plot([0, 1], [0, 1], 'k--', linewidth=1.5, label='Random Classifier')\n",
        "\n",
        "    ax.set_xlabel('False Positive Rate', fontsize=12)\n",
        "    ax.set_ylabel('True Positive Rate', fontsize=12)\n",
        "    ax.set_title('ROC Curves - Model Comparison', fontsize=14, fontweight='bold')\n",
        "    ax.legend(fontsize=11, loc='lower right')\n",
        "    ax.grid(alpha=0.3)\n",
        "    ax.set_xlim([0, 1]); ax.set_ylim([0, 1])\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/roc_curves.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved roc_curves.png\")\n",
        "\n",
        "# --- Execute ---\n",
        "plot_roc_curves(res_full, res_bert, res_roberta)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a37aa419",
      "metadata": {
        "id": "a37aa419"
      },
      "source": [
        "## Part 10: Error Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "60971aaf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60971aaf",
        "outputId": "353b1e34-77d3-4c6b-a5a0-caee37539ca5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 10 — ERROR ANALYSIS\n",
            "================================================================================\n",
            "\n",
            "-> Analyzing errors for LR_Full:\n",
            "  Correctly classified avg length: 228.1 words\n",
            "  Misclassified avg length: 231.7 words\n",
            "  Error rate: 10.85%\n",
            "  False Positives: 1396\n",
            "  False Negatives: 1316\n",
            "\n",
            "-> Analyzing errors for DistilBERT:\n",
            "  Correctly classified avg length: 226.6 words\n",
            "  Misclassified avg length: 254.2 words\n",
            "  Error rate: 7.12%\n",
            "  False Positives: 919\n",
            "  False Negatives: 860\n",
            "\n",
            "-> Analyzing errors for RoBERTa:\n",
            "  Correctly classified avg length: 227.6 words\n",
            "  Misclassified avg length: 249.1 words\n",
            "  Error rate: 4.42%\n",
            "  False Positives: 548\n",
            "  False Negatives: 556\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 10 — ERROR ANALYSIS\n",
        "# ===========================================================================\n",
        "def error_analysis(train_df, test_df, res_full, res_bert, res_roberta):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 10 — ERROR ANALYSIS\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    # Use each model's own true_labels to avoid order mismatch\n",
        "    models_data = [\n",
        "        ('LR_Full', np.array(res_full['predictions']), np.array(res_full['true_labels'])),\n",
        "        ('DistilBERT', np.array(res_bert['predictions']), np.array(res_bert['true_labels'])),\n",
        "        ('RoBERTa', np.array(res_roberta['predictions']), np.array(res_roberta['true_labels'])),\n",
        "    ]\n",
        "\n",
        "    # Get test texts\n",
        "    test_df_copy = test_df.copy()\n",
        "    test_df_copy['word_count'] = test_df_copy['text'].str.split().str.len()\n",
        "    test_texts = test_df_copy['text'].values\n",
        "    test_word_counts = test_df_copy['word_count'].values\n",
        "\n",
        "    error_summary = {}\n",
        "\n",
        "    for model_name, y_pred, y_true in models_data:\n",
        "        print(f\"\\n-> Analyzing errors for {model_name}:\")\n",
        "\n",
        "        correct = (y_pred == y_true)\n",
        "        misclassified_indices = np.where(~correct)[0]\n",
        "        correct_indices = np.where(correct)[0]\n",
        "\n",
        "        # Average length analysis\n",
        "        avg_len_correct = test_word_counts[correct_indices].mean()\n",
        "        avg_len_misclass = test_word_counts[misclassified_indices].mean()\n",
        "\n",
        "        print(f\"  Correctly classified avg length: {avg_len_correct:.1f} words\")\n",
        "        print(f\"  Misclassified avg length: {avg_len_misclass:.1f} words\")\n",
        "        print(f\"  Error rate: {len(misclassified_indices) / len(y_true) * 100:.2f}%\")\n",
        "\n",
        "        # False positives and false negatives\n",
        "        fp_indices = np.where((y_pred == 1) & (y_true == 0))[0]\n",
        "        fn_indices = np.where((y_pred == 0) & (y_true == 1))[0]\n",
        "\n",
        "        print(f\"  False Positives: {len(fp_indices)}\")\n",
        "        print(f\"  False Negatives: {len(fn_indices)}\")\n",
        "\n",
        "        examples = {\n",
        "            'false_positives': [],\n",
        "            'false_negatives': [],\n",
        "        }\n",
        "\n",
        "        # 3 example false positives\n",
        "        for idx in fp_indices[:3]:\n",
        "            examples['false_positives'].append({\n",
        "                'text': test_texts[idx][:200] + '...' if len(test_texts[idx]) > 200 else test_texts[idx],\n",
        "                'true_label': 'Negative',\n",
        "                'predicted_label': 'Positive',\n",
        "                'word_count': int(test_word_counts[idx])\n",
        "            })\n",
        "\n",
        "        # 3 example false negatives\n",
        "        for idx in fn_indices[:3]:\n",
        "            examples['false_negatives'].append({\n",
        "                'text': test_texts[idx][:200] + '...' if len(test_texts[idx]) > 200 else test_texts[idx],\n",
        "                'true_label': 'Positive',\n",
        "                'predicted_label': 'Negative',\n",
        "                'word_count': int(test_word_counts[idx])\n",
        "            })\n",
        "\n",
        "        error_summary[model_name] = {\n",
        "            'error_rate': float(len(misclassified_indices) / len(y_true)),\n",
        "            'avg_length_correct': float(avg_len_correct),\n",
        "            'avg_length_misclassified': float(avg_len_misclass),\n",
        "            'false_positives_count': int(len(fp_indices)),\n",
        "            'false_negatives_count': int(len(fn_indices)),\n",
        "            'examples': examples\n",
        "        }\n",
        "\n",
        "    # JSON save moved to Part 20\n",
        "\n",
        "    return error_summary\n",
        "\n",
        "# --- Execute ---\n",
        "error_analysis_results = error_analysis(train_df, test_df, res_full, res_bert, res_roberta)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b611dc12",
      "metadata": {
        "id": "b611dc12"
      },
      "source": [
        "## Part 11: SHAP Interpretability (Logistic Regression)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "e7479d69",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e7479d69",
        "outputId": "bde70517-7ced-44a0-f0d1-ec008c42127a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 11 — SHAP INTERPRETABILITY (LOGISTIC REGRESSION)\n",
            "================================================================================\n",
            "\n",
            "-> Computing SHAP values for 500 samples ...\n",
            "  SHAP computation complete.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x950 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved shap_lr_summary.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x950 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved shap_lr_bar.png\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 11 — SHAP INTERPRETABILITY  (Logistic Regression)\n",
        "# ===========================================================================\n",
        "def shap_analysis(lr_model, X_test_clean):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 11 — SHAP INTERPRETABILITY (LOGISTIC REGRESSION)\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    tfidf_step, clf_step = lr_model.named_steps['tfidf'], lr_model.named_steps['clf']\n",
        "    feat_names = tfidf_step.get_feature_names_out()\n",
        "\n",
        "    n_shap = min(500, len(X_test_clean))\n",
        "    X_tfidf = tfidf_step.transform(X_test_clean[:n_shap])\n",
        "\n",
        "    print(f\"\\n-> Computing SHAP values for {n_shap} samples ...\")\n",
        "    explainer = shap.LinearExplainer(clf_step, X_tfidf[:100])\n",
        "    shap_values = explainer.shap_values(X_tfidf)\n",
        "    print(\"  SHAP computation complete.\")\n",
        "\n",
        "    plt.figure(figsize=(12, 8))\n",
        "    shap.summary_plot(shap_values, X_tfidf, feature_names=feat_names, max_display=20, show=False)\n",
        "    plt.title('SHAP Feature Importance - TF-IDF + LR', fontsize=14, fontweight='bold')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/shap_lr_summary.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved shap_lr_summary.png\")\n",
        "\n",
        "    plt.figure(figsize=(12, 8))\n",
        "    shap.summary_plot(shap_values, X_tfidf, feature_names=feat_names, max_display=20, plot_type='bar', show=False)\n",
        "    plt.title('SHAP Mean |SHAP Value| - Feature Importance', fontsize=14, fontweight='bold')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/shap_lr_bar.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved shap_lr_bar.png\")\n",
        "\n",
        "# --- Execute ---\n",
        "shap_analysis(lr_model, X_tst_clean)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f640a68",
      "metadata": {
        "id": "9f640a68"
      },
      "source": [
        "## Part 12: Attention Visualisation (DistilBERT)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "bc924b7f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d02bbc7ead614b409b5457b17c5b45e0",
            "754bc056734e4d78a38e14ab46cb2b8f",
            "b3f6771f7c514d8290b74b7205422164",
            "648fdef8cce6407da3700ae1742b5ceb",
            "0c107d9a2f57457bb34cef6ff96576bd",
            "b59c1a14124c4d72ab8a08ffdd77b241",
            "e1e664b28d284e6db1c477f8159414b1",
            "98b9866a09f645b8b0d6fa3eee6eb223",
            "a77eafb240af47a0962c9f81230ab71a",
            "c72c73b1355c48d99ba0bc0bd8572ef4",
            "15b29c9b2ae447469fdfdbe9e52a0c38"
          ]
        },
        "id": "bc924b7f",
        "outputId": "b5e26ca2-cca8-479d-bde5-c727ac765f4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 12 — ATTENTION VISUALISATION (DISTILBERT)\n",
            "================================================================================\n",
            "\n",
            "-> Reloading model with 'eager' attention backend for heatmaps...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d02bbc7ead614b409b5457b17c5b45e0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved bert_attention_positive.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved bert_attention_negative.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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zXb16tT788ENJ0qFDh/Twww/79zdq1CgtWbJE1113nbZu3VrmeJVt+9GjRys/P1/Sidfi4sWL9eijj2rLli2VavfSLr/88oBrVcnfB7/26aef+q+LjRo10j/+8Q9lZmb671iTlZUV0BuoMteGypxXRd+PJZo2ber//VLezwAAgoE5TgCHlf7Ak5ube9JtQ0NDJUlhYWG6+OKLlZiYqMjISP3xj3/0b9OsWbOACV+DPWdEyR9m+fn5Cg8PP+39/f73vw/4/pprrtFzzz1nObTHrlmzZumqq67S9u3b/ev279+vf/7zn/rnP/+ptLQ0/e1vf5N04gNqyd1rrrvuOv+H1xEjRmj+/Pk6evSo3njjDT3//POWc6P06NFDK1as8H/fqlUr/89l9+7d2r17d8D2MTEx/g8bmzdv9v+xeemll6pJkyZq3LixGjdurD179igzM1OHDh1S3bp1/fPXlP5D89c//1M9PmfOHP/Xd955py644AJJ0s033+zvEj9nzhwNGjSozHn+/e9/98+58s477ygzM1NFRUXauXOnOnbsqB49egTMcZCYmHjK4UrFxcUBRZG5c+cqLi5O0oliwJ/+9CdJJ4pL5WV64403lJCQoGuuuUavv/66vvzyS/30009BmW/hwIEDWrp0qaQTRcuS2+rGxcWpd+/e+te//qWff/5ZmZmZuvbaaxUZGalOnTpp2rRp+vzzz3Xw4EEVFRX593f48GF9+eWXio+Pr1Tb1axZU6+//roiIyN19OhRrVq1StKJ68PMmTNlGIa2bdumv/zlL5Kkb7/91v/cnj176vHHH9eUKVO0e/fuMt31169fX+4x//znP+vuu++WdKLY2bp1a0kn7n5RUFDgv36Vp2QYTUkhzK7U1FQ9+uijWrFihfbs2VPmrlPr16/XH//4x4AsjRo1UqtWrdSiRQt5PB7ddddd/sdO9r49U+Lj4/X+++/r8ccf15dffqnDhw8HDD0rae/mzZsrNTVVy5cv14oVK7Rv3z5FR0dr8eLFkk5c66+++mpJp3fN+uMf/6hnnnnG9vmULuSWFHivvfZaTZw4UYWFhXrrrbd0xx13SJKGDBmiFStWyDRNLViwQH/605+0ZMkS/2tuyJAhkir/viqtTp06yszMLDMPRu/evfXYY49p7dq12rt3rwoKCgIeX79+vbp166Zly5b5h+507tzZP9ykT58+ysrKKjOUqTJt/9NPP/mvqV6vV/Pnz1f9+vX1f//3f/ryyy8DCmTBUHr/I0aM8L9nb7/9dv8QmDlz5mjMmDGSKn5t2LdvX6XOq6Lvx9Lq1aunvXv3lhmCBwDBQuEEcFjJXBvSqSc0Lf2HV2pqqiTpggsu0GWXXaZ77703qBPKliiZHHb//v167LHH9Nlnn+nVV19V/fr1y4xTl8qfHLYyH1bXr1+vX3755aS9Eexo3769Nm7cqEWLFmnRokV6//339d///tf/+NNPP62RI0eqTZs2/v+ESdJLL72kl156qcz+cnNztWfPHn+RoTJmz54dMI+CJA0bNsw/h0LpSWFLPoiU9Dx59tlnlZ+fr7fffls33XRTpY9dntLnW/JB+NdKegj8WukJb8877zz/16dz69z9+/f75wWoVauWv2giSUlJSf6vS+cuERkZGdBL49eZKvpaLO91/MQTT+jdd98NWPftt9/6P/Tm5OT4/7v9ayXt9/bbb59yMlQ7bRcbG6vIyEhJCvjA2LlzZ3+vhQYNGpQ5RlFRkVJTU/X5559XOk/Xrl39X7dq1Ur16tXTwYMHdfz4ce3Zs0fNmzc/Ze7SBYPK+uWXX9StW7eTzslRkr1Vq1ZKSUlRVlaWXnvtNb322mv+eXyuueYa3X333WX+yx0saWlpmjZt2ikzSyeKl8uXL1dhYaHmz5+vESNG+OfW+O1vf+t/PZ/ONet3v/ud7XP55ptv/JP8dujQwf9B/Pe//70mTpwo6cT1rKRwcu2112rUqFE6duyY3nrrLf3pT3/y98IzDMP/T4HKvq9K6969e5miya5du9StWzfl5eVZnktJu3/33Xf+daVf4zVq1FCXLl3KvN4q0/aln3vRRRcF5ExKSrJdOJk2bZo6derk/z4nJ6fMPyV+nfWJJ57wT7ZeWklPv8pcG0q3WUXOy8778XSuFcDZjOEkwUPbAg774IMP/F+f6jaXffr00QcffKCRI0eqU6dOqlWrln744Qe9/vrruuyyywL+WAmWkslhr776av3973/3r7caulHe5LAnm7h1x44d2rdvn79AsHv3bv3xj38Myh9JtWrV0h/+8AfNmzdPe/bs0QcffKCYmBhJJ/4oO9XQqV8rr5v4mVC6bUePHu3v5VP6g3zp//JWBatzLd1joPSQizP18/v1ZMm//v5keU4nU3mvY7t3ppL+137PPfecf93w4cO1bNkyZWVl+Sc/lFRmMt6K5i1RukdBSTHl10ra4oMPPvB/MGrUqJFeeeUVvf/++wHFu4rmOdXPprSSIk5FJlq28q9//cv/QbRNmzaaP39+maEGJdk9Ho+WLl2qv/3tb+rXr5+aNWumY8eO6eOPP9b999+ve+65x3aOysjPz9fMmTMlnXhtTpw4UatXr1ZWVpa/TUq/TgcOHOj/EDpnzhxlZmb6ezeU7nlYUeW9j0uugXaUvg5t2rTJf63q3Lmzf/3atWv9/zCIiIjQgAED/Ot37Njh71nym9/8xj8JdkVV9HxeeeUVf9EkOTlZCxcuVFZWVsBEtOW9zivzmraT9Uwdq0OHDgHXqtP5p0phYaF8Pt8ZuzaUd1523o8lRZrSBWAACCYKJ4CDFi5cGDATf3nDDEozTVPJycmaOXOmPvvsM/3yyy/+4SRHjx4NmJuj5I8TOx+6Kqr0H/Qld045Exo2bKhZs2b5P/Ru2LDBP7/LmVBcXFxmHhNJ6tatm7p16+b/vmTIRMl/TSVp3LhxMk2zzHLkyBHFxtqbGXz8+PFl9lfS22TdunUBw4msrFq1KuBuGqU/LJf3GjjZ46XPd/Xq1eWeb0UyWTlVtl9r2LCh6tatK+nEh43SY+Q/+eSTcnM75eKLL/a/9y666CIVFhaWabv8/HxNmDBBUmCPs2effVa9e/dWt27dAtaXVtm2q6zSx/3jH/+ooUOHWv53/9fWrVvn//rbb7/1XxPCw8PVuHHjkz63bdu2kk70BPj1bWwrqnT2O++8U9dff7169OhR7h2UTNNUnTp1lJaWpnfffVe7du3Svn37/HNBlMyFIgW3zX/++Wd/vo4dO2rMmDHq2bOnWrZsWe411ev16oYbbpB0or1L7v4VERHhn5dCOr1r1ul8YC/vlum/ZppmQDG4ZDhOcXGxbrvtNn9BoeQ8pcq/r051PqVfKw8++KCuuuoq9ejRo9zhshdddJH/65I7m0knCgqlvy9RmbYvPffId999F1A4LH1tC5bSWV966SXLrF6vt1LXhsqeV2Xej9KJf6iUzJ/Srl07G2cOAJXHUB2gCu3bt09r167VgQMHtHz5cv9/GqUT3aNL/5e5PHfffbf++9//qnfv3mratKlq1KgRMBFs6fH89erV04EDB7Rnzx69/vrrat68ecC8GXatX79eP/zwg37++Wc99thj/vVWH1pzc3O1du3aMus7dOhw0mESdevW1W233ebv3v3UU0/55844XcXFxbryyisVFxen3//+9+rUqZNq166t9evX+//bKUldunSRdGJ4zAMPPCCfz6eJEyfKMAwlJyfr6NGj2rFjh1avXq1jx45p+fLlZyRfaaU/iAwaNKjM/AoLFy7UypUrVVRUpDfffFN33nmnpBM//x07dkg68YG8c+fOioqK8vf2OdnjQ4YM8ReqbrzxRj300ENq1aqV9u/fr2+++UZLlizRlVdeGXB75coo3Qtk1qxZ+u1vf6uaNWta/lfU4/Fo8ODBmj59uqQTH7TGjRungwcPBmQomRjTSfXr19eVV16ppUuXavv27RowYIBuvvlmRUREaNeuXfr888/19ttv66OPPlKLFi3UvHlzf3f5sWPHqm/fvnrttdcsJzysbNtVVunhNAsWLFCPHj108OBBPfDAA6d87tNPP62YmBg1a9ZMjz/+uH/9lVdeedL5TaQTwymWLVsmn8+nLVu2qGPHjuVu991335Wb5dZbbw3IPnv2bLVs2VLffvttwHWqRMnkuddff73atWunmJgY7dixwz9x6a+vpSUWLFigCy+8UKGhoerSpUuFh/M8+eSTZa53nTt31rXXXqvw8HAdP35cmzZt0syZMxUTE6NHH33Uskhz8803+4f2lPRYHDhwoGrWrOnf5kxds8aPH+8fRvjSSy9p+PDhltt+8cUXAZP83nfffQGP5+Tk+F8Xb7zxhtLS0iRJ/fr103nnnaeff/7Zn8fr9QZMgF3Z99WplH6tTJs2TWFhYfrkk08CbgFeonfv3qpVq5aOHj2qdevW6d577/W/T8sbFlaZto+JiVHXrl31ySef6Pjx4xo8eLDuvvtuffHFF1XSi7D0fDZ//vOfdeDAAcXHx+vQoUPavn27li1bpubNm2v27NmVujZU9rwq836UFDBcqHv37mekLYCzBb0igugM3qEHQDlOdnvgkqV///5mXl5ewPPKux3xzTffbLmPmjVrmtu3b/dve+2115bZpuR2yKdzO+LyFsMwzDfffNO//alueatf3S6x9PrSx/3xxx/N0NBQ/2Nr164NaCO7tyMuKCg4Zb7hw4cHPOdkt5f89fHLux2xaQa+Fk51K1PTNM2ioiKzcePG/uds2rSpzDYLFy70P96jRw//+tGjR58046keP9ntiH/9uinvtXqydnj22WfL7K/ktsxWtyP++eefT3k74uLiYv/2v95vCavXeHmCcTviXx/7zTffLPNYeHi42blz5zPWdqVvzVr6lujlXQcKCwsDbhdasnTv3v2Ur/XynlenTh1z27ZtJ21n0zTNTZs2+Z8zefJky5/Dya4neXl5ZqNGjU6aveT8v//++5Pur/QtTjd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",
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved bert_attention_heatmap.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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J/tx/9913VbFiRd1///2SpLfffltz587Vjh071K9fP73++uuO/V5//XXNnz9f69atc8zYB7Jj6iKcYsWKFVq4cKFGjRold3d3tWrVSnfccYfmz58vSXr66ad17NgxBQYGOvbhBezWlvVxsi1btig8PFyVK1dWZmamHnroIfXo0UPvv/++Hn/8cbVt21YbN27UoUOHtHbtWseC7gS3ALKrWbOmPv30Uz388MOaMGGC3N3dtWzZMq1Zs4YTTRVBqampqly5soYOHart27erRYsW8vf3V2Jiojp27KjPPvtMn3/+udq0aePsUgE4WdZsynPnzqlt27bas2ePhg8fLunCyZL79eunwMBAvfzyywoNDVWxYsUcoW3Wvrj1jRw5UjVq1NDChQuVkZGhv//+W61bt9YTTzzhOLl2vXr1VKdOHT76XgRkD20feughHT9+XM8884xSUlLk4+OjQYMGydvbW2+++aaqVKmiQ4cOKSgoSNOnT9ekSZP01VdfycPDw8lHAasiuMUNk/3FrEGDBpo+fboqVKigHj16aNy4cQoLC9Onn36qpUuX6tdff9XPP//sWLOUN7tbW9YvuYmJierevbuef/55HTp0SO+8845sNpu6du2qSpUq6ZtvvtHPP/+s4OBgR8gfGBioSpUqOfkIAFhRvXr19Omnn2rGjBlKTk7WN998Q2hbRJUsWVLDhg1T+/bt9fLLL2vZsmXasGGD0tPTFRoaqmbNmql///78kxgo4owxcnV11V9//aWdO3fqk08+UYcOHbRp0ybt27dPlSpVUkBAgPr27avu3bvn+Egzoe2t7eKZ1KdPn3b8DWKz2VSjRg199NFHeuqpp7Rr1y5Vq1ZNNpuNv2OLiKyx8e9//1uJiYn6/vvv5e7uLumf14Y+ffooNTVV8+fPl6urq44cOaK33npLK1asUKNGjZxZPiyO4BbXXdabXHp6ujw9PSVJLVq0UPPmzTVs2DB98cUXstls2rZtm9atW6egoCD973//k5ubG78AFRGurq7au3evvvjiC91///16/vnnde7cOQ0cOFAzZsyQMUYdO3ZU/fr1cwT5U6dO1bfffptjfSAAyK5OnTqaPHmyjDEqVqyYs8uBk912222aOnWqNmzYoNOnT+uTTz7Rq6++qgULFrCGPlDEZf3dceTIEX3yyScaOXKkvv/+e3399ddq27atJk+erKFDhyo4OFiSHH/XSP8Evrg1ZT/Pyrlz51SsWDF5eXlp06ZNkuT4/aJChQoqUaKEPD09+UdgEbRp0ybt3btX33zzjSO0TUxMVHx8vE6cOKF+/frp6aeflouLiyZOnKjU1FR99913nAgVl8VvqLjubDabEhMTNWDAAN1333165JFHVLp0aQ0ZMkTPPPOMduzYof79++eaWUtoW7QcOnRIQ4YMUYMGDbR//34FBwdr5syZGjRokN5//32lpKTo0Ucflbu7u1JTUzV16lTNnz9fsbGxrBkF4JII5JBduXLl1L59e7Vv316hoaF66KGHCPWBIi4reN26dau6deume+65R8HBwY7lumJjY9W+fXu5uLjo2WefVeXKlXP8nUJId+syxsjNzU12u13dunWTl5eXqlSporFjx6pmzZrq06ePJk+erNKlS+unn37S+fPnc4T6uLX9/vvv+uKLLzRixAidOnVKJ0+eVPHixXXo0CFt2LBBvXv3VpUqVbRjxw6988472rhxo5566imVKVNGjRs3dpy/BbgUTk6GG+LIkSP66KOPFBMTo7CwMFWoUEHjx49XRESEmjZt6jgjK4q29evXq2vXrpowYYI6deokPz8/nT9/Xj179lRwcLAmT57saHvgwAG5urrmWAcZAICCYBkmABdLTk5W69at1atXL0VFRWnXrl36/PPP9dZbb2n27NkKCQlRo0aN9Morr+jZZ591drm4we6//365uLioffv2euWVV3Tfffdp/PjxCg8Pl4eHh8qUKaN9+/Zp8eLFfOy9iNixY4fuuOMOjR49WkOGDFFGRoZq1KihUqVKKSAgQImJierZs6eef/55nT17VpUqVdK4ceP01FNPObt03GSYgoIbonz58hoyZIgeeughrVu3Tm+99Za6du0qd3d3LVq0SAMGDFDdunWdXSacrFmzZvrwww/Vt29fubi4qGPHjipVqpQWLFjgmMmQtfRGxYoVnVwtAOBmRWgL4GLu7u4qXbq0WrVqJUmqXr26+vTpo19//VVdunTRV199pV27dqlcuXJOrhQ3QvblESSpYcOGio6OliQ9+OCDatmypUaNGqWNGzdq8+bNOnPmjOrVq+dYSgO3vlOnTik1NVW1atXSiRMn5Ofnpw0bNmj8+PFq1KiRqlatqtatW8tut8vb21stW7ZkfOCK8FsrbqiKFSuqa9euWrVqlfr06aNWrVrp8OHD+uGHHyRdCOVQtN11112aPXu2XnnlFS1YsECnTp2Sq6ur40R1fBQNAAAA11pGRoYOHDiguLg4SReWbStXrpyaN2+uxo0ba+DAgVq7dq1cXV2VmZnp5GpxPdntdsfyCP/+97/VvXt3ff311zpw4IAkKSgoSKtWrdLatWv1r3/9S61bt1a7du0I5YqYRo0aKS4uTk8//bSWLl2q5ORklStXTlOnTlVkZKRat24t6cI/i999911t3bpVDRo0cHLVuBkR3OKGs9vtkqSePXtq/Pjxmjt3rt58800lJiYSykHShfB22rRp+v7771WiRAnHdmZIAQAA4Hrw9fXVq6++qnHjxum9995zrGG7efNmtW3bVh06dNDXX3+t9PR0fie9hWVmZsrFxUXGGLVu3VqnT5+Wn5+fXFxcNHr0aB0/flzShQlJK1as0OnTp/X33387uWo4S9ako5dffllLly5VampqjutXr16tiRMnauTIkfr0008VEhLinEJxU2ONWzhN1tA7ffq0OnXqpHfffVdVq1Z1clWwkqxlEbK+AgAAANeLMUb/+9//NGjQIDVr1kxnz57VmTNn9Ouvv2r27Nl65513tGrVKnl4eDi7VFxHxhgtXrxY33//vd5++21J0scff6yFCxeqfPnyevXVV+Xn5ycp95IKKJpWrVqlvn37avTo0erSpYtKlCihlJQUjR8/Xps3b1ZMTIzq16/v7DJxk+IVBk6TFcR98skn+v777zn7JnIhtAUAAMCNYrPZ1LdvXzVv3ly//PKLbDabHnzwQUlSYmKi/P39df78eYLbW9wvv/yihx9+WHXq1NHff/+typUrq3v37rLb7Vq8eLEGDRqkGTNmqFSpUoS2kPTPzNu+fftKkh544AH5+flp7NixcnFxkbe3t5MrxM2MVxk4XbNmzbR582YFBQU5uxRYEKEtAAAAbqQ6deqoTp06ki6cgOjll1/WlClT9MMPP6h48eJOrg7XW2hoqNauXasuXbpo1apV6tixo3x9fdWzZ0+dPXtWcXFxOnPmjEqVKuXsUmEhWeFtZGSkSpUqpYYNG2rp0qV65plnnF0abnIslQAAAAAAwEUyMjK0bt06vfPOOxo6dKhuv/12Z5eEGyj7x987d+4sHx8fGWOUmpoqHx8fZ5cHi1q1apUeffRRpaam6rPPPtPdd9/t7JJwkyO4BQAAAAAgD+fPn9e5c+fk5eXl7FLgBKtWrdKAAQMUFRWlHj16ENiiQH766ScZY9SyZUtnl4JbAMEtAAAAAABAHuLi4jRs2DCtXLlSvr6+zi4HQBFDcAsAAAAAAJCP06dPc4IpAE5BcAsAAAAAAAAAFuPi7AIAAAAAAAAAADkR3AIAAAAAAACAxRDcAgAAAAAAAIDFENwCAAAAAAAAgMUQ3AIAAAAAAACAxRDcAgAAAAAAAIDFENwCAAAAAAAAgMUQ3AIAAAAAAACAxRDcAgAAAAAAAIDFENwCAAAAAAAAgMUQ3AIAAAAAAACAxRDcAgAAAAAAAIDFENwCAAAAAAAAgMUQ3AIAAAAAAACAxRDcAgAAAAAAAIDFENwCAAAAAAAAgMUQ3AIAioyxY8fKZrMpPj5e8fHxstlsGjt2rOP6Pn36yGazOdrcLLKOy2azae/evZds26ZNG9lsNoWEhFzVbWW/f5YsWaKxY8fmuC9vFjExMRo7dqzmzJmT67qQkBDZbDa1adPmsv1k3f99+vS55jU625w5c27K58WVio+Pd4znkydP3vDb37t3r+P+zv6cKugYy2//gsp6vOfMmePo62Ya19nHa9bFzc1NFStWVL9+/XTw4MHrdtt5PUaXGk+Fee2+HowxatSokWw2m6ZNm3bJtlnvmVlj43oqyq+9R44ckZeXl1xcXLR582ZnlwMAsACCWwAAcFWWLFmicePGady4cc4updBiYmI0bty46x5E4OYRHx/vGM/OCG5x7WVmZurgwYOaPXu2wsLCdPTo0Rt221YeTwsXLtTmzZtVtmxZ9evXz9nlQFL58uUVGRkpY4xGjhzp7HIAABZAcAsAAHCVjDEyxtySAXCfPn0cx1eQGXDIX0ZGhux2e77XnzlzRiEhIY77+2acxV4QmzZt0vfffy9jzHW9nTFjxsgYo8OHD6tVq1aSpIMHD152dumVKuzrwNixYx37XOmnIK7G5MmTJUk9evSQl5fXDb995K13796SpNjYWG3fvt3J1QAAnI3gFgCAQoqOjnZ8PHP//v2SpC+//NKxbefOnZKkFStWOLYtWbLEsf/y5csVHh6uUqVKycPDQ7Vq1dKoUaN05swZR5v8Pp6efTmHy1m5cqVCQ0Pl6emp2267TV999VWe7bL3+dtvv+m+++6Tt7e3qlatqpiYmEvehs1m09y5c3P8fLmPuKalpWno0KGqVq2avLy8VKpUKdWvX1+PP/644z7466+/1K1bN1WvXl0+Pj5yd3dXcHCw+vTp47jP33//fcftrVixwtH/+fPnVaZMGdlsNv3rX//Ks4asj/3+/fffkqQffvjhkh8v//bbb9WoUSN5e3srNDRU33//fa774eKP63733Xe6++67Va5cOXl4eKhChQpq165djrGQl+yPx+bNm3XHHXfIy8tLTZs21caNG3X8+HH17NlTJUuWVJUqVfTmm2/m6uNyY+znn3923MaUKVNy7Hv33XfLZrMpKChImZmZ+Y7FlJQUDR8+XDVr1pSHh4f8/Pz0wAMPaNOmTZc8voSEBEd/H3zwgSQpOTlZrq6ustlsmjVrliTp9OnTKlasmGw2m4YMGeKou2PHjgoJCVHx4sXl7u6uqlWr6umnn9aJEydy3M7bb7+t0NBQ+fj4qHjx4qpWrZoefvhh/f777/nWFhISkmPmeJUqVXItLbJz50716tVLQUFBcnd3V4UKFTRgwAAlJSU52mRfrmDUqFEaPXq0KlSoIE9PT6WkpOR4jLds2aLWrVvLy8tLI0aMKNBSB3PnzlXNmjXl6emppk2b6qeffrrkfS5dCBVnzpypJk2aqHjx4vL29tYdd9yhTz755LL7Xmtbt27Vvffeq6pVq2rcuHGO5+H1EhAQoP/85z+Onzds2OD4/tChQxowYICCg4Pl7u6u8uXLq1u3brnGySeffKKwsDCVLl1anp6eqlSpkjp16qQff/zR0ebi14HLjaeLl0oozPNSkhITEzVo0CCFhITI3d1d5cqVU8+ePbVr167L3ie///67fv75Z0nSQw89lOO6M2fOaNCgQSpdurR8fX3Vr18/paSk5NlPenq6JkyYoLp168rT01M+Pj5q06ZNrveb/JbpudxSB8uWLVPDhg3l6empevXqaenSpZc9NklatGiRWrVqJR8fH3l6eqphw4aaMWPGZf9ZkH1JiJkzZ2ro0KHy9/eXn5+fHn74YR0/fjxH+4I+BsePH9cjjzyikiVLqnz58hoxYoRmzZqV52trWFiYgoODJemW/GcgAKCQDAAAMMYY07t3byPJSDIrV67Mt91PP/3kaLdgwQJjjDHDhw93bJszZ44xxpgxY8YYScZms5ljx44ZY4yZNWuWsdlsjrbZL3feeadJT083xhjzv//9L89asteYJet2JJk9e/YYY4zZvn278fDwyNG/m5ubKVeunJFkKleunGefvr6+uepavnx5vvdFXschybRu3TrffZ588sl89zt69KgxxpiVK1fm26Zq1arm7Nmz5vTp08bPz89IMj169HD0v3z5ckfbRYsW5VnDpfofM2aMMcaYypUrG0mmdOnSxtXVNUebEiVKmOPHj+e6H3r37m2MMWbv3r3G09Mzz/6feuqpfO+bix+PMmXK5Ng3ICDANGvW7JKPUUHHWK1atRzbsiQmJhoXFxcjyTz33HPGmLzHYmpqqqlfv36et+Hh4WF+/PHHfI8vIyPDeHt7G0nmySefNMYY88033zj279OnT67H6LPPPstVS17HlmXBggX5tstvTGR/zC++ZD1ftm7danx8fPJtc+TIEWOMMXv27HFsL126dI52J06cyPEYZ7/+2WefzbFv1lg05p8xFhgYmOu2vb29ze+//57rtrPv36dPn3zvk0mTJuV7n1wPv//+u+nYsaMpVqyYkS68Rt57773mww8/NKdPn76qvrOPkezHv3jxYsf2+++/3xhjzP79+/O8P7Pu0/Xr1xtjjFmzZk2+z6nXXnvNcRsXvw5cbjzl9dpd0OflwYMHTYUKFfLs38/Pz2zfvv2S99Nbb71lJBlXV9dc93lkZGSuPrPfT//73/+MMcacO3fO3H333fmOq2nTpjn6bN26dY5jz+8+y36/lStXzri5ueXo09XV1Xz33XeX3H/s2LH51jRo0KBL3i/ZX3fyej/s2bOno21hHoM2bdrkahMUFOT4/uLfOR5++GEjyTRp0uSS9QIAbn3MuAUAoJCaNm0qb29vSdKaNWscX7NmwWbNfsv6Wq9ePZUpU0apqakaOnSojDEqV66c1q1bp+PHj6tbt26O9tlnr16Nl19+Wenp6ZIufBw2OTlZkydPvuzajnfeeaeSkpL0zTffOLYtWrQo3/bGGMfHOrN+NsZc8iRWq1evliR169ZNp06d0okTJ7R+/XqNHj1aHh4ekqRq1arp66+/1uHDh5Wenq6TJ09qzJgxki7Mxv3mm2/k5eWlyMhISRfW2c2acfnpp59KkkqXLq2OHTvmWUObNm1kjFHlypUlSa1bt8734+n/93//p5deekknT57USy+9JEk6depUjvvoYhs2bNDZs2clXZglmp6err///lsfffSRWrZsme9+F7v//vt14sQJRUVFSbowu+vPP//Uhg0btHHjRseYy3qMCjPGHnvsMUkXxm7WCZsWL17s+Ch/1vV5iYmJ0a+//ipXV1d9/vnnOnv2rHbs2KHq1asrPT09x+zGixUrVkwtWrRw3Hb2rzabLdfzx2azOT7mHhYWppUrVyopKUnnzp3TsWPH9Pjjjzvab9myRdI/Y6xq1ao6fPiwTp8+rW3btmnKlCmOxzwve/fudYwzSdqzZ4+MMY4TR/3nP/9RSkqKKleurM2bN+vs2bOKi4uTm5ub/v77b02aNClXnydOnNDMmTOVkpKibdu2qXjx4jmur1atmrZv366UlBQ9/fTT+daWJTExUR999JFSUlIUHR0t6cLs5JdffjnffVavXu2Yuffiiy8qOTlZx44d04MPPihJGj16dK6ZhNdT3bp1tXTpUh0+fFjTp0/XHXfcobi4OD3yyCMKDAzUk08+qfXr11+z20tMTMwxM71x48aSLhz34cOHJV34JEVycrIWLFgg6cJ9mjXTe82aNTLGqGTJktq9e7fOnj2rXbt26b333lP9+vXzvd3Ljae8FPR5OXr0aB08eFC+vr764YcfdPbsWW3atEmlS5fWiRMn9OKLL17yPsmaGV+lSpUcyyTs3LnT8RpRp04d7d69W3v37lWZMmVy9fHRRx9p5cqVkqQHH3xQR48e1caNG+Xv7y9JGj58+FWt63v06FFFR0crJSXFMTs/MzNTo0aNynefvXv3asKECZKkyMhIHTlyRCkpKXrqqackXZiJ/9tvvxXo9l1cXLR69WolJibqtttuk5TzsSjoYxAXF+d4T2zRooUOHTqkLVu2XHL2b9a42rJlyyWXVwEAFAHOyYsBALCegs64NcaYe+65xzEbJiMjw3h5eZk777zTlC1b1tSrV8+cP3/elCxZ0kj/zLCMjY119J81a8oYY3bu3OnY3qVLF2PM1c+4rVq1qpEuzNC02+2OtsHBwblmPWXvM2vWnjHGlC9f3kgybdu2LfD9VhAdOnRwzDZ64YUXzAcffJDjdo0xJj093bz00kumTp06ec5cnThxouO+y5oJN3XqVHP+/HnHrOLLzWw15p+ZXXnNEM66zt/f32RmZhpjjPnjjz8cNfz3v/91tM3aljXr6+eff3Zse/DBB83kyZPN8uXLTUpKymVryn5/7ty50xhjzLJlyxzbHnvsMUfbrFlwWY9RYcbY3r17HfddTEyMMeafWXENGjRw7JvXWGzevHm+M9qyLsnJyfke4/jx440k4+LiYlJSUsy9995rihUrZu6//34jyRw5csTxfb169Rz7nTx50jzzzDOmWrVqxt3dPddtZs2Af+2114x0YfbvwIEDzcyZM01CQoI5d+7cZe//vJ5PxhiTlpaWa+b1xZdGjRoZY3LOeo2IiLjkY5yQkJDjusvNuM0+EzMzM9PxPA0ICMh3/xEjRlz28friiy8ue99cT7t37zbjxo0zNWrUcNS0ZcuWQvdzqVnZWfdTYmKiMcaYgICAXM9xY4y58847jXRhJvDJkyfNokWLHD/37t3bTJ061cTHx5szZ87kuO2LXweMyX885XddQZ+X+c0UzrqULl36kvdT+/btjSQTFhaWY/u8efMcfbz99tuO7e+//75je9aM2x49eji2/fbbb462L774Yq5xdSUzbitWrJjj/atJkyaO1420tLQ893/nnXcuO9anTJmS7/2SfcbtsGHDHNuff/55x/ZDhw4V6jHIer2TZL7++mtHny+99FKu19YsM2bMcFyXNZMfAFA0MeMWAIAr0Lp1a0kXZsOsWbNGZ86c0Z133qnmzZvrjz/+0KpVq5Sampqj7bFjxxz7Z61fd/H3l5sRm7W24eUcOnRIkhQYGJhjPdwKFSpccr8aNWo4vvf09JQkx8zda+X1119X/fr1dejQIU2cOFGPPfaY6tWrp2bNmjlmZw0bNkwvv/yytm3b5pi5ml3WWq3Vq1dXRESEJGn27Nn64YcfHPdh9pnAV6NatWpycbnwK1PWfSJd+n5p0qSJXnzxRXl5eenzzz9XVFSUIiIi5O/vr/fee6/At521HmT2GXGVKlVyfO/u7p6jlsKMscqVK+uuu+6SdGHGbmJiomO9zl69el2yrsuNU0m51pzNLus5Ybfb9dNPP2n9+vW6/fbbde+990q6MEM0ISEhR9usuqZMmaLdu3crIyMjV79Z42LQoEF64IEHlJGRoRkzZujJJ59U8+bNVbly5RzrmxbGiRMnLvv8+7//+79c2xo2bHjJfS53/cWyP5YuLi4KCgqSlPOxv1hBHq+8ar8a2ddvzb6Oa36Sk5OVnJysU6dOOba5urpek1pcXV0VGBioXr16ae3atY4ZoVn3WVBQkOM5Lv1zHxtjdPz4cXXp0kV9+/aVq6ur5s6dq6efflpt2rRRYGCgvv7662tSY5aCPi8v95he6eOZ9d4h5Xy/yOu942rf0y73fKpYsWKO96+KFStKuvC6kd/ry7Uc63m9H0r/vN4W9DHIfp9mHcPF31+MWbYAgCwEtwAAXIGsMOn8+fOOE3i1aNFCLVq0kDFGr7/+uqNt1h/hZcuWdWw7cOCA4/usk21lb5O1ZICkHMHlnj17ClRfVphz+PDhHB/HzProbX6KFSvm+L4gJ0ArTLsstWvX1tatW7V79259+eWXGjt2rFxdXfXzzz9r+vTpkv756H9AQIC2bdsmu92uL7/8Ms/+sj4Cu3nzZsfHkuvVq6emTZtek9qv5D6RLixXcezYMf3000+aM2eOmjdvrjNnzuiZZ57R+fPnC9SHm5tbgbZlKcwYk3J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fruvXr0uSnJyc9PPPP2v79u3q1auXDhw4oIMHD8rFxUVTpkzRzp07+b2ch/yxtG3fvr1GjhypqVOnqkWLFlq7dq1tXFRUlHr27MmZtw7GZOVoRh6UlpYmT09Po2MgG4YMGaLp06dr48aNevbZZ3X+/HmNHTtWly5dUrdu3dS+fXvb2G3btqlkyZKqWrWqgYkB/BV3796V1WqVq6ur0VHwkBITE3X58mXdvHlTtWrVsi1fuXKlYmJidO7cOW3ZskWvv/66pk2bxr7Oo7Zu3arnn39e77//vgYMGGD7YNzZ2Vkmk0kffvihJkyYoNjYWJUrV87gtMiOXbt2qXHjxpJ+/9t57dq1mjlzptzd3ZWYmKg9e/bIzc1N48aN05EjR7Rw4ULmNs6DLBaLBgwYoKtXr8rf319dunS550PTmTNnavTo0dq5cyd/Y+dBs2fP1sSJEzV48GB16dJF3t7eGj58uFavXq309HTbjZ5nzJih999/X1u3buWD8zyoffv2tp/NiYmJGjZsmJKSkrRy5UqZTCa5uPx20fybb76pRYsWafXq1Zx56wCYKgF5EqVt3nH79m25ublpwoQJunTpkjp06KA1a9aoUaNGGjx4sCZOnKglS5bIZDLppZdekiQ1bdrU4NQA/qqMPyyRNx0/flwhISEqWLCgLl26pC5dutiuhAgJCdHzzz+v69eva9iwYTp//rzS09MpbvOIu3fvZjo+g4KC9Omnnyo0NFQmk0kRERGSpKNHj2ru3LlavXq1vvzyS0rbPCTjfgIZpe0bb7yhKlWqKCwsTIGBgfLy8tK+ffu0ZMkSubm5adq0aYqKilJMTAylbR6UcVPB8uXLy8/PTz/88IN69eql2bNnq2rVqjp37pzmzp2rTz75RNHR0ZS2eUzG8dy3b1+ZzWZNmjRJVqtVffv2VVhYmL799lsVLFhQb7zxhry9vfXxxx9r48aNlLZ50KBBg/Tll1/qxx9/lCSVKFFC/v7+mU6EyLjJ5PTp05WWlqZOnTrpxx9/lJeXl5HRkcuYKgFArrFarXJzc9OdO3fUokULeXt7Ky0tTa1atdK2bdtUqVIlDR48WD4+Ppo9e7bWrVtndGQAcHhnzpxRy5YtFR4err179+qjjz7S+++/r3/961+2MYUKFdJjjz2mefPm6fz585o3b56BifG/ZMxlazabbXci37x5s7799ltdvXpVL7/8sj7++GMNGjTINodexYoVVaRIEW3fvl2BgYEGpkd2/flu8m5ublqzZo0+//xzlS5dWoMHD9ZLL72kV199VcHBwVq0aJG2bNmi6tWrG5QYf8U///lPPfbYY1q9erWmTp2qlJQUnTx5UmFhYTp79qz8/Pz03HPPaffu3RzLeYzZbM40FVF4eLj++c9/atKkSZo5c6b8/f314YcfqnPnznJyclKZMmW0fft21axZ0+DkyAqLxZLp+6CgIJUoUUJz5szRzZs3deHCBc2fP19PP/20bYyzs7Ok3z6EPXv2rBYvXkxp6wCYKgFArsj4dFj67ZIPd3d3LVmyRD/++KOmTZumefPmacOGDfrHP/6h77//XrNnz9bgwYOZRB8ADGSxWDR37lydPHlS06dPl8ViUf369eXj46Ndu3apf//+GjdunKTfz/ro06eP/Pz8NGTIEIPT434uX76s6tWrKyoqSiEhIbJYLKpTp45SUlJUsGBBVa9eXWPGjNFjjz2mTz/9VL169dKwYcM0dOjQTHPuIe8ZOHCgGjZsqHbt2mngwIE6ePCgwsLC1L17dzk7O+v06dMqVKiQPDw8VLRoUaPjIpusVqvWrFmT6YzbsLAwHT16VNOmTVNYWJg8PDy0atUqzrLNQ5KTk+Xk5GS7qaDFYtG+ffuUnJysFi1ayMnJSR999JHGjh2rwYMHq2fPntxUMA/64+/X/v37a9SoUXr00UcVExOj1157Tc8//7w2btyovn37aujQofe8PiEhQampqapQocLfHR0G4DpGADnmp59+0ptvvql169bZPh1OT0/X5cuX9fbbb0uS/P39NW3aNF25ckXdunXT4sWL1axZM02ZMoXLbAHAQOfPn9epU6f0wgsvqFWrVrJarfrHP/6hihUr6rPPPlPbtm01fvx4FS1aVBEREXJ2dtY333yj6Ohobdiwwej4eICiRYsqJCREPXr0kIeHh86cOaOaNWtq3rx5Wr58udauXat+/fpp5syZ6tatm27duqUhQ4aob9++truXI++5efOmzpw5o0uXLqldu3aaPHmyBg0apIULF8pkMik4OFiVK1c2Oib+ggULFuirr77SyJEjVaZMGX366ac6evSojhw5IicnJwUEBKhUqVLy8PAwOiqyKCkpSa+99pqee+459evXTyaTSbVq1ZKLi4suX74sNzc3TZ06Vb1795bZbNaUKVOUnp6unj17qkiRIkbHRxb9sbR96aWXdOrUKX344YeSpGbNmmn+/Pnq0aOHypUrp3feeee+6/D19f3b8sJ4TJUAIMd4enrq8OHDCgoKsv1Cunv3rry9vXXmzJlMdzZt0qSJkpOTFRYWprS0NObDBAADHT9+XE8//bROnz4td3d3lS1bVjt37pTZbNZnn30m6bcP3tatW6f+/fvbXlejRg0dPHhQ1apVMyo67uOPv2+tVquioqLUv39/de/eXQcPHlTHjh0lSZ06dVJYWJgKFCigt99+W+fOndNrr72mH3/8UY8++ihn2+Yhf77ktkCBApo0aZK2bdtmm4rq/fffV4MGDTRt2jRt2LCBO87ncbVq1dJ3332nvXv3ysXFRVevXlXt2rXl5OSkuXPn6urVqxo9erT8/PyMjoosKlasmEqVKqU1a9ZowYIFGjhwoGrXrq2vv/5aZ8+eVdOmTRUZGamvv/5affv21WuvvabFixdzLOchfyxtg4ODlZCQoJMnT9qeN5vNatq0qRYuXKgLFy4oMjJSv/zyi1FxYScobgHkmOLFi+vo0aNKTEzUs88+K6vVqkKFCqlRo0aaMmWKNm/erOvXr0v67e7G06dP18GDB+Xp6cmbQwAwyPnz5xUUFKSBAweqf//+KlmypMxms9LT01W4cGFNnTpVXbp00Y4dO/Tiiy/KxcVFd+/etRVFJUqUMHgL8GcZv1Pnz5+vhIQESZKTk5OaNm2qlStX6tixY7axzz33nPr06aO0tDS98847unv3LjeoyoMyrnQ6ePCgbZmfn59at26daX+PHz9ebdu21TPPPMPfXnmQ1WpVnz59dPr0aQUEBGjixIkaPny4jh49qooVK2rBggVq3bq1Bg4cqKlTpzIFRh5x4sQJTZkyRZI0e/ZsBQYGauXKlTp+/LhatWol6bdjfNasWapcubL++c9/SpKGDBmiHTt2cHVEHpLxc7dLly46d+6c9u3bZ3suLi5Ob731lm7evKlnn31W8+bN0xdffKEJEybYfpfDMTHHLYAckTHXoSRbcevj46Pdu3dLkgYMGKA1a9aoYsWKevTRR7Vu3TodOHBATz31lJGxAcDhrVixQqtXr9bSpUuVmpqq0NBQFShQQLGxsapQoYKKFSumpKQkbdiwQa6urpnmMIf9iouLU+3atdW6dWvt3LlTDRo00OLFizV27FiNGzdOK1asUOvWrW3jd+zYoUqVKql06dIGpsbDyDiDa/jw4dq8ebOKFy+u999/XxUrVtTu3bvVtm1bW7GHvG3p0qXq2rWrmjZtqnbt2qlKlSo6ePCgkpKSNHXqVG3ZskUpKSmqUaMGc1/mIZ9//rkSExPVr18/27LIyEjNmjVLXbp00fvvv2/7QG3Hjh0aM2aM1q9fL09PT+Yiz4P27dunNm3aaPDgwerTp48KFy6sn376SY0aNVK/fv0UGRlp26+bNm3SO++8o6+++krFixc3OjoMQnELIMdYrVYdOnRIderU0cWLF9WkSRM9+uij2rt3ryRp9erV+vnnn3X9+nV17NiRudUAwA7s3btXzzzzjF5++WWdPXtWpUuXVufOnfXxxx+rVatWeu211+Tq6mqb/oapbfKO48ePq2HDhipRooRiY2NVrFgxSdLQoUM1ZcoUrVy5MlN5i7zljx+aS9KVK1d04cIFDR06VImJiSpZsqQGDx6s6dOnq3Tp0nrvvfe4n0AeZ7FY1KpVK124cEE9evRQdHS0rl27pkceeUQTJ05UQECA0RHxEDKOZYvFoo8//lg9e/aU9Ft5u3v3bnXt2lUhISHy8fHR2LFjtXHjRsXExMjT09Pg5MiK+5XrM2fO1PLlyxUSEqI6deqoc+fO6tmzp4YPH24bc/nyZRUtWlSpqakqWLDg3x0bdoTiFkCOmTBhgqKjozV06FA999xz9y1vAQD2Z+3atTpx4oQqVKigTp06SZJatGihbt26qVu3bpLu/8YD9u38+fOaOHGidu3apUaNGql///564oknJP12yfzQoUO1efNmPffccwYnRXb9sehZsGCBTCaTihQpovbt20uStm7dql27dmnWrFlKSUlR7dq1tXXrVu4+n0eFh4erWrVq6tOnj06cOKEPP/xQLVu2VIUKFfT6669r7969CgoK0ldffWV0VGRTxrF89+5dxcbGqkmTJhozZoyGDh0q6berFrdt2yaLxaJmzZpp3bp1WrlypWrWrGlwcmTFH/92+uijj1SmTBm9+OKLkqRZs2Zp0aJFOnPmjHr27KnJkyfbXjd58mR9+umnOnDggNzc3Pj7y8FxygSAh/bnN/GNGjXS2bNnNXfuXFmtVj3//PPauXOngoKCVL16dX3zzTf80gEAO9S2bVu1bdtWkpSenq5evXrp4sWL6ty5s20MP7/znnLlymnWrFk6c+aM2rZtK7PZrEGDBunxxx9Xy5Yt9eWXX6pcuXJGx8RDyChta9eubTuT+tixY1q2bJlWrlypoKAgBQUFqVOnTtq8ebNefPFFSts8KiUlReXKldOAAQN0+vRpNWjQQCVKlFBCQoLatGmjL774QqtXr9azzz5rdFRkk8VisR3L7dq1U9++ffXpp5/qrbfektVq1bBhwzRlyhSNGTNGU6ZMkY+Pj7Zt26bHHnvM6OjIgj++V+7QoYMuX76s/v37Kzk5WV5eXnrjjTfk6empDz74QH5+frpw4YJKlSqlmTNnatKkSdqwYYPc3d0N3grYA864BZCjvv76a82dO1dXrlzRG2+8oaCgICUkJCg4OFjLly/nDSIA2Cmr1arTp09r7Nix+uGHH7Rnzx65urreczk28qYTJ06oY8eOCgwMlJubmzZt2qR9+/Zxx/k8bMiQITp37pyWL1+u27dv6/z582rSpIlat26tuXPn2sYxL3X+8N1332ns2LHy9PTUoUOHlJ6erk8++UR169bliog8KGOfWa1WTZ48WSdOnNDChQtlNpu1fPly9e/fX2+99Zbt0vmhQ4eqb9++KlOmjMHJkV0vv/yy4uLitG3bNrm5uUnKPNXN9OnTtWTJEoWGhurixYuaNm2atmzZosDAQCNjw45wxi2Av6RZs2YKCAiw3Qm1Tp06MplMGj16tKZMmSKLxaLnn39e+/bt400DANgxk8kkf39/9evXT3Xr1rVdusmctvnDk08+qc8//1yzZ8/W9evX9eWXX1La5jF/LufS0tJsZ96ZTCZVqlRJS5YsUb9+/XT27FlVqFBBJpOJv7/yiaeeekrTp0/XoUOHlJaWphUrVmjixIlatmwZP6fzmD+Wdv3799ehQ4ds76VMJpNCQkIk/TZNQmpqqiZMmKBx48YZlhcP78iRI/rpp5/05Zdf2krbhIQE7dixQ1evXlXPnj315ptvysnJSRMmTFBKSoq2bt3KVBjIhJ/wALLlz28a+vbtq+7du8vLy0sjR46U1WpV7dq19fTTTysqKkrLli1Tw4YNmTwfAPIANzc31a9fX9JvbywpA/KXqlWraurUqbJardykKo/544cod+7ckaurqwoUKKAjR45Ikm1/li5dWoUKFZKHhwdnYOZDxYoV0wsvvKAXXnhBAQEB6tChA8dyHmO1WuXs7Kz09HTNnj1bxYoV0/HjxxUbG6sGDRrIyclJTk5OCgkJUXp6usaNG6eBAweqaNGiHNN5xIkTJ7Ru3TpFRkbqxo0bunbtmgoWLKgLFy7o0KFDCg0NlZ+fn86cOaO5c+fq8OHD6tevn4oWLapatWqpUqVKRm8C7Ax/jQPIsj+feZWWlqYOHTrIw8NDHTp0kCSNHDlSklS8eHG9+uqrGjx4MHfBBIA8iOkR8ifK+LzHarXKxcVFFotFISEhKlCggPz8/DRq1Cg9/vjjevXVVzV16lTbzWDv3r0rDw8Po2Mjl2RMfTFkyBCjoyCb/vheqlmzZnryySc1atQo3blzRzNmzFCpUqXUpUsXSb99GNOtWze1b99eXl5eRsZGNpw5c0bPPPOMRowYIUl6+umnlZqaqlq1asnX11cJCQn617/+pcGDB+vWrVt67LHHNGfOHPXr1y/TfQWAP+IvNwBZknHmlcViUVhYmNLS0vTrr79q8ODBatOmjdasWaN27drpyJEj8vb21vr167V//37bDTMAAACQfRln2bVq1UpOTk5q2rSpxo0bp7i4OO3Zs0dBQUFq3LixihYtqri4OK1atUo+Pj4Gp0ZuYeqLvCvjvdS///1vNW7cWO+9954kqU+fPnJ1ddV7770nZ2dndezYUdJv5S1nVOctN27cUEpKiipXrqyrV6/K29tbhw4d0rvvvqvAwED5+/urSZMmslgs8vT0VKNGjVS2bFmjY8POUdwCyBJnZ2dZrVY1aNBAlSpV0ksvvaRvv/1W7du31wcffKDw8HDFxsZq9uzZcnd31/bt27nMAwAA4CH9+UqnGjVqaPz48ZKkdu3aqVGjRho+fLgOHz6so0eP6ubNm3ryyScpAQA79sUXX6hv374KDAzUe++9J7PZrDJlyigsLEzOzs6KiIiQi4uLXnrpJaOj4iEEBgYqJiZGPXr00LBhw9SuXTsVK1ZM06dPzzTOyclJH330kY4fP67q1asblBZ5hclqtVqNDgHAfv3xTsQ7duzQ8OHDtXv3btvzn3zyiV599VXt3btXTz/9tCwWi0wmE3MwAQAAPKSMv78sFou6desms9ms77//Xps2bbLdVf6XX35R06ZN5evrq127dhmcGMD9/PG9lCSlpqZq3rx5ioiI0GeffZbp8vjz589r+fLl6tChg/z9/Y2Iixyya9cu9ejRQyNGjFC7du30yCOP2J7bs2eP9uzZo8mTJ+urr75SYGCggUmRF3CdBYD/ysnJSVarVbt371aBAgV07do1/ec//5HVapXFYtErr7yioKAgxcbG2sZT2gIAADwcs9ls+/urSZMmSktLk7e3t5ycnDRixAhdvnxZklSmTBlt2bJFaWlpOn/+vMGpAfzZ3bt3bcfykSNHdObMGd25c0dvvfWWxowZo9dff11LliyxjS9XrpwiIiIobfOBxo0ba8GCBXr33Xe1evVq3bhxQ5KUnJysNWvWaMuWLdq+fTulLbKEqRIAPJDVapXJZNKCBQu0adMmvfHGGzKbzdq1a5f8/f1tnx4XKlRInp6eBqcFAADI+zKmp1q1apWqVaumWbNmSZKWLl2q5cuXKzIyUhMnTpS3t7fKlSun/fv3c9M5wM5YLBbbnLbPPvus7ty5I1dXV1ksFs2fP1/vvPOOPDw89NZbb+nWrVvq0aOHJG4gmZ9klLcZ+7Z169by9vbWqFGj5OTkxPtnZBlTJQC4x5/nVDt27Ji6du2q9957T1arVV27dtWIESNUoUIFJSUlafTo0dqzZ48qVqxoYGoAAID84dixYwoMDFTVqlW1adMmlStXThaLRUuXLtWqVavk7u6u2bNnq0iRIkZHBfAnGSe/SFLbtm3l7e2thQsXKjExUU888YSef/55LV26VLdv39aECRO0YMECHT9+XI888ghXLuZDu3btUlhYmD744APVqFFDa9euVf/+/Y2OhTyEqRIA3MPFxUVWq1V9+vTR6dOnFRAQoPHjx6tPnz6qXbu2lixZor1792ratGlau3atoqOjKW0BAABySEBAgPbv36/r169r165dun79upycnNSlSxe1bNlSJpNJN2/eNDomAP1W1GYwm80ymUyyWq0ym80ym822mwoOHjxYfn5+Wrx4sS5cuKA7d+5o+PDhOnLkiLy8vCht86nGjRvr448/Vnh4uAICAlStWjWjIyGP4YxbAJlkfEK8dOlSde3aVU2bNlW7du1UpUoVHThwQJcuXdKUKVOUkpIid3d33blzR4UKFTI6NgAAQL7zxxvcBAcHy8vLS1arVSkpKfLy8jI6HoA/mD9/vtq0aSMfHx+FhoYqODhYgwYN0u7duzVhwgRt375dhw8flpubm959911VrlxZnTp1Mjo2/iZ79+6V1WpVo0aNjI6CPIYJVABI+r2wzfikt1OnTvrkk0904cIFmc1mTZkyRdeuXZOXl5e++eYbBQQESJLc3d0NTA0AAJB/ZcyR2Lt3b926dUudO3eWl5cXpS1gZ+Li4hQZGal9+/Zp7969qlOnjl566SVt3rxZlSpVkr+/v7799ltJ0rRp07Rw4ULFxMQYnBp/p4YNGxodAXkUZ9wCyCQ8PFzVqlVTnz59dOLECX344Ydq2bKlKlSooNdff1179+5VUFCQvvrqK6OjAgAAOISYmBgNGjRI27dvV+HChY2OA+A+jh8/roYNG6pEiRLav3+/fHx8dPLkSY0aNUonTpzQv/71L33zzTdasmSJNm3apJo1axodGUAeQHELwCYlJUVz5szR6NGj1bt3bzVo0EDHjx9XmTJl1Lt3byUlJWn16tV69tln9fjjjxsdFwAAwGGkpaVxF3LAjp0/f14TJ07Url271LBhQw0aNEgVKlTQ5cuXtXDhQp0/f17FixdXSEiIqlSpYnRcAHkExS2Ae3z33XcaO3asPD09dejQIaWnp+uTTz5R3bp1M90lFQAAAADwuzNnzqht27Zq2LChhg4dKj8/P+3evVt3797VP/7xD6PjAchjKG4B3FdSUpIOHTqkRYsWacWKFWrXrp2WLVsmFxcXilsAAAAAeIATJ06oY8eOCgwMlLu7u9avX69du3apcuXKRkcDkMdQ3AL4nyZMmKAOHTqoYsWKRkcBAAAAALv3/fffa/bs2bp+/boiIiJUo0YNoyMByIMobgE8kMVikZOTk9ExAAAAACDPuXv3rqxWq1xdXY2OAiCPorgFAAAAAAAAADvDqXQAAAAAAAAAYGcobgEAAAAAAADAzlDcAgAAAAAAAICdobgFAAAAAAAAADtDcQsAAAAAAAAAdobiFgAAAAAAAADsDMUtAAAAAAAAANgZilsAAAAAAAAAsDMUtwAAAAAAAABgZyhuAQAAAAAAAMDOUNwCAAAAAAAAgJ35PwXzz3DOxEMQAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1400x1600 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Saved bert_attention_negation.png\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 12 — ATTENTION VISUALISATION  (DistilBERT)\n",
        "# ===========================================================================\n",
        "def _get_attention(vis_model, tokenizer, text, device=DEVICE, max_len=BERT_MAX_LEN):\n",
        "    \"\"\"Helper function to extract attention weights using the visualisation model.\"\"\"\n",
        "    vis_model.eval()\n",
        "    inputs = tokenizer(text, return_tensors='pt', max_length=max_len, truncation=True, padding='max_length')\n",
        "    ids, mask = inputs['input_ids'].to(device), inputs['attention_mask'].to(device)\n",
        "    with torch.no_grad():\n",
        "        out = vis_model(input_ids=ids, attention_mask=mask)\n",
        "    attn = out.attentions[-1].squeeze(0).mean(dim=0).cpu().numpy()\n",
        "    return attn, tokenizer.convert_ids_to_tokens(ids.squeeze().cpu().numpy()), mask.sum().item()\n",
        "\n",
        "def run_attention_visualisations(model, tokenizer, test_df):\n",
        "    \"\"\"\n",
        "    Because the main model was trained using ultra-fast FlashAttention (SDPA),\n",
        "    it physically cannot output attention matrices. We must load a temporary\n",
        "    'eager' model copy just for these visualisations.\n",
        "    \"\"\"\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 12 — ATTENTION VISUALISATION (DISTILBERT)\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    print(\"\\n-> Reloading model with 'eager' attention backend for heatmaps...\")\n",
        "    vis_model = DistilBertForSequenceClassification.from_pretrained(\n",
        "        'distilbert-base-uncased', num_labels=2, output_attentions=True\n",
        "    ).to(DEVICE)\n",
        "    vis_model.load_state_dict(model.state_dict())\n",
        "\n",
        "    # --- 1. Attention Bars ---\n",
        "    for sentiment, label in [('positive', 1), ('negative', 0)]:\n",
        "        text = preprocess_bert(test_df[test_df['label'] == label].iloc[0]['text'])\n",
        "        attn, tokens, length = _get_attention(vis_model, tokenizer, text)\n",
        "        cls_attn, disp_tok = attn[0, 1:length], tokens[1:length]\n",
        "        top_k = min(25, len(disp_tok))\n",
        "        top_idx = np.argsort(cls_attn)[-top_k:]\n",
        "\n",
        "        fig, ax = plt.subplots(figsize=(12, 7))\n",
        "        colors = ['#e74c3c' if cls_attn[i] > np.mean(cls_attn) else '#3498db' for i in top_idx]\n",
        "        ax.barh(range(top_k), [cls_attn[i] for i in top_idx], color=colors, edgecolor='black')\n",
        "        ax.set_yticks(range(top_k)); ax.set_yticklabels([disp_tok[i] for i in top_idx], fontsize=10)\n",
        "        ax.set_title(f'DistilBERT CLS Attention - {sentiment.title()} Review', fontsize=13, fontweight='bold')\n",
        "        ax.set_xlabel('Attention Weight')\n",
        "        plt.tight_layout()\n",
        "        plt.savefig(f'{FIGURES_DIR}/bert_attention_{sentiment}.png', dpi=150, bbox_inches='tight')\n",
        "        plt.show()\n",
        "        plt.close()\n",
        "        print(f\"  Saved bert_attention_{sentiment}.png\")\n",
        "\n",
        "    # --- 2. Attention Heatmap ---\n",
        "    text_heat = \"This movie was absolutely terrible, the acting was awful and the plot was a disaster.\"\n",
        "    attn, tokens, length = _get_attention(vis_model, tokenizer, text_heat)\n",
        "    n = min(length, 30)\n",
        "    fig, ax = plt.subplots(figsize=(12, 10))\n",
        "    sns.heatmap(attn[:n, :n], xticklabels=tokens[:n], yticklabels=tokens[:n], cmap='YlOrRd', ax=ax, square=True)\n",
        "    ax.set_title('DistilBERT Self-Attention Heatmap (Last Layer, Averaged Heads)', fontsize=12, fontweight='bold')\n",
        "    plt.xticks(rotation=45, ha='right', fontsize=8); plt.yticks(fontsize=8)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/bert_attention_heatmap.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved bert_attention_heatmap.png\")\n",
        "\n",
        "    # --- 3. Attention Negation Tests ---\n",
        "    examples = [\n",
        "        (\"This movie was not bad at all\",        \"Positive (negation)\"),\n",
        "        (\"The film was not good\",                \"Negative (negation)\"),\n",
        "        (\"Yeah right, this was a great movie\",   \"Negative (sarcasm)\"),\n",
        "        (\"I wouldn't say this movie was terrible\",\"Positive (double neg)\"),\n",
        "    ]\n",
        "\n",
        "    fig, axes = plt.subplots(len(examples), 1, figsize=(14, 4 * len(examples)))\n",
        "    negation_words = {'not', \"n't\", 'no', 'never', 'neither', 'nor', \"wouldn\"}\n",
        "\n",
        "    for ax, (text_ex, desc) in zip(axes, examples):\n",
        "        attn, tokens, length = _get_attention(vis_model, tokenizer, text_ex)\n",
        "        cls_attn, disp_tok = attn[0, 1:length], tokens[1:length]\n",
        "        colors = ['#e74c3c' if t.lower().strip('#') in negation_words else '#3498db' for t in disp_tok]\n",
        "        ax.bar(range(len(disp_tok)), cls_attn, color=colors, edgecolor='black', lw=0.5)\n",
        "        ax.set_xticks(range(len(disp_tok))); ax.set_xticklabels(disp_tok, rotation=45, ha='right', fontsize=9)\n",
        "        ax.set_title(f'\"{text_ex}\"  ->  {desc}', fontsize=11, fontweight='bold'); ax.set_ylabel('Attention')\n",
        "\n",
        "    plt.suptitle('DistilBERT Negation & Sarcasm Analysis - CLS Attention', fontsize=14, fontweight='bold', y=1.01)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/bert_attention_negation.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(\"  Saved bert_attention_negation.png\")\n",
        "\n",
        "    # Clean up the visualisation model to free VRAM\n",
        "    del vis_model\n",
        "    if DEVICE.type == 'cuda': torch.cuda.empty_cache()\n",
        "\n",
        "# --- Execute ---\n",
        "run_attention_visualisations(bert_model, bert_tokenizer, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f6c6a6f",
      "metadata": {
        "id": "0f6c6a6f"
      },
      "source": [
        "## Part 12b: Gradient-Based Attribution (RoBERTa)\n",
        "Input × gradient attribution for RoBERTa to complement SHAP (LR) and attention (DistilBERT) interpretability.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "50b6286d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "50b6286d",
        "outputId": "5cb50ec8-3b7e-4573-f9df-0aaa2e046a3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 12b — GRADIENT-BASED ATTRIBUTION (RoBERTa)\n",
            "======================================================================\n",
            "\n",
            "  'This movie was absolutely brilliant and I loved every minute of it' -> Pred: Pos\n",
            "    Top-5 tokens: brilliant(-1.000), and(-0.459), of(-0.274), I(0.256), minute(0.225)\n",
            "\n",
            "  'Terrible film, waste of time, awful acting and boring plot' -> Pred: Neg\n",
            "    Top-5 tokens: boring(-1.000), plot(-0.983), acting(-0.743), rible(0.668), Ter(-0.651)\n",
            "\n",
            "  'The movie was not bad at all, quite enjoyable actually' -> Pred: Pos\n",
            "    Top-5 tokens: all(-1.000), actually(0.863), ,(0.720), not(0.668), The(0.293)\n",
            "\n",
            "  'Great acting but the story was a complete disaster' -> Pred: Neg\n",
            "    Top-5 tokens: Great(1.000), acting(0.967), disaster(0.754), but(-0.699), the(0.405)\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1400x1600 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  Saved ./output/run_20260329_093626/figures/part12b_roberta_gradient_attribution.png\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 12b: Gradient-Based Attribution (RoBERTa)\n",
        "# ==========================================================\n",
        "\n",
        "def gradient_attribution_roberta(roberta_model, roberta_tokenizer, test_df):\n",
        "    \"\"\"\n",
        "    Compute input x gradient attribution for RoBERTa predictions.\n",
        "    Provides token-level importance scores complementing SHAP (LR)\n",
        "    and attention visualization (DistilBERT).\n",
        "    \"\"\"\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 12b — GRADIENT-BASED ATTRIBUTION (RoBERTa)\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    examples = [\n",
        "        (\"This movie was absolutely brilliant and I loved every minute of it\", 1, \"Positive\"),\n",
        "        (\"Terrible film, waste of time, awful acting and boring plot\", 0, \"Negative\"),\n",
        "        (\"The movie was not bad at all, quite enjoyable actually\", 1, \"Negation (positive)\"),\n",
        "        (\"Great acting but the story was a complete disaster\", 0, \"Mixed sentiment\"),\n",
        "    ]\n",
        "\n",
        "    attribution_results = []\n",
        "\n",
        "    fig, axes = plt.subplots(len(examples), 1, figsize=(14, 4 * len(examples)))\n",
        "\n",
        "    for idx, (text, true_label, desc) in enumerate(examples):\n",
        "        ax = axes[idx]\n",
        "\n",
        "        # Tokenize\n",
        "        inputs = roberta_tokenizer(text, return_tensors='pt', max_length=ROBERTA_MAX_LEN,\n",
        "                                   truncation=True, padding=True)\n",
        "        input_ids = inputs['input_ids'].to(DEVICE)\n",
        "        attention_mask = inputs['attention_mask'].to(DEVICE)\n",
        "\n",
        "        # Get embeddings with gradient tracking\n",
        "        roberta_model.eval()\n",
        "        roberta_model.zero_grad()\n",
        "\n",
        "        # Access the embedding layer\n",
        "        embeddings = roberta_model.roberta.embeddings(input_ids)\n",
        "        embeddings.retain_grad()\n",
        "\n",
        "        # Forward pass using embeddings\n",
        "        outputs = roberta_model(inputs_embeds=embeddings, attention_mask=attention_mask)\n",
        "        predicted_class = torch.argmax(outputs.logits, dim=1).item()\n",
        "\n",
        "        # Backward pass on predicted class logit\n",
        "        outputs.logits[0, predicted_class].backward()\n",
        "\n",
        "        # Compute input x gradient attribution\n",
        "        grad = embeddings.grad  # [1, seq_len, hidden_dim]\n",
        "        attribution = (grad * embeddings).sum(dim=-1).squeeze().detach().cpu().numpy()\n",
        "\n",
        "        # Get tokens\n",
        "        tokens = roberta_tokenizer.convert_ids_to_tokens(input_ids.squeeze().cpu().numpy())\n",
        "        seq_len = attention_mask.sum().item()\n",
        "\n",
        "        # Trim to actual tokens (skip padding)\n",
        "        tokens_trimmed = tokens[1:seq_len-1]  # Skip <s> and </s>\n",
        "        attr_trimmed = attribution[1:seq_len-1]\n",
        "\n",
        "        # Normalize attribution\n",
        "        attr_abs = np.abs(attr_trimmed)\n",
        "        if attr_abs.max() > 0:\n",
        "            attr_norm = attr_trimmed / attr_abs.max()\n",
        "        else:\n",
        "            attr_norm = attr_trimmed\n",
        "\n",
        "        # Color by sign (positive=green, negative=red)\n",
        "        colors = ['#2ecc71' if a > 0 else '#e74c3c' for a in attr_norm]\n",
        "\n",
        "        ax.bar(range(len(tokens_trimmed)), np.abs(attr_norm), color=colors, edgecolor='black', lw=0.5)\n",
        "        ax.set_xticks(range(len(tokens_trimmed)))\n",
        "        ax.set_xticklabels([t.replace('Ġ', '') for t in tokens_trimmed], rotation=45, ha='right', fontsize=9)\n",
        "        ax.set_title(f'\"{text}\" → Pred: {\"Pos\" if predicted_class == 1 else \"Neg\"} (True: {desc})',\n",
        "                    fontsize=10, fontweight='bold')\n",
        "        ax.set_ylabel('|Attribution|')\n",
        "\n",
        "        # Store results\n",
        "        attribution_results.append({\n",
        "            'text': text,\n",
        "            'true_label': true_label,\n",
        "            'predicted': predicted_class,\n",
        "            'description': desc,\n",
        "            'top_tokens': sorted(\n",
        "                [(t.replace('Ġ', ''), float(a)) for t, a in zip(tokens_trimmed, attr_norm)],\n",
        "                key=lambda x: abs(x[1]), reverse=True\n",
        "            )[:10],\n",
        "        })\n",
        "\n",
        "        print(f\"\\n  '{text}' -> Pred: {'Pos' if predicted_class == 1 else 'Neg'}\")\n",
        "        top5 = attribution_results[-1]['top_tokens'][:5]\n",
        "        print(f\"    Top-5 tokens: {', '.join(f'{t}({a:.3f})' for t, a in top5)}\")\n",
        "\n",
        "    plt.suptitle('RoBERTa: Input × Gradient Attribution Analysis',\n",
        "                fontsize=13, fontweight='bold', y=1.01)\n",
        "    plt.tight_layout()\n",
        "    fig_path = f'{FIGURES_DIR}/part12b_roberta_gradient_attribution.png'\n",
        "    plt.savefig(fig_path, dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"\\n  Saved {fig_path}\")\n",
        "\n",
        "    return attribution_results\n",
        "\n",
        "# Execute\n",
        "results_gradient_attr = gradient_attribution_roberta(roberta_model, roberta_tokenizer, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0266de92",
      "metadata": {
        "id": "0266de92"
      },
      "source": [
        "## Part 13: Linguistic Phenomena Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "6c7858bd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c7858bd",
        "outputId": "2cf92965-b15c-4b64-ef76-22e3a9802362"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "PART 13 — LINGUISTIC PHENOMENA TESTING (ENHANCED)\n",
            "================================================================================\n",
            "\n",
            "  \"This movie was not bad at all\"\n",
            "    True: Positive  |  LR: Negative (0.01) N  |  BERT: Negative (0.17) N  |  RoBERTa: Positive (0.94) Y  |  Negation ('not bad' = good)\n",
            "\n",
            "  \"The film was not good\"\n",
            "    True: Negative  |  LR: Positive (0.66) N  |  BERT: Negative (0.03) Y  |  RoBERTa: Negative (0.02) Y  |  Negation ('not good' = bad)\n",
            "\n",
            "  \"Yeah right, this was a great movie\"\n",
            "    True: Negative  |  LR: Positive (0.80) N  |  BERT: Positive (0.99) N  |  RoBERTa: Positive (1.00) N  |  Sarcasm\n",
            "\n",
            "  \"I wouldn't say this movie was terrible\"\n",
            "    True: Positive  |  LR: Negative (0.06) N  |  BERT: Negative (0.02) N  |  RoBERTa: Positive (0.68) Y  |  Double negation\n",
            "\n",
            "  \"Not the best, not the worst\"\n",
            "    True: Positive  |  LR: Negative (0.14) N  |  BERT: Negative (0.17) N  |  RoBERTa: Negative (0.23) N  |  Balanced negation (neutral-positive)\n",
            "\n",
            "  \"The movie was terrible but the ending was fantastic\"\n",
            "    True: Positive  |  LR: Negative (0.20) N  |  BERT: Negative (0.20) N  |  RoBERTa: Positive (0.60) Y  |  Contrast / mixed\n",
            "\n",
            "  Summary: LR 0/6 correct  |  BERT 1/6 correct  |  RoBERTa 4/6 correct\n"
          ]
        }
      ],
      "source": [
        "# ===========================================================================\n",
        "#  PART 13 — LINGUISTIC PHENOMENA TESTING (ENHANCED)\n",
        "# ===========================================================================\n",
        "def linguistic_tests(lr_model, bert_model, bert_tokenizer, roberta_model, roberta_tokenizer):\n",
        "    print(\"\\n\" + \"=\" * 80)\n",
        "    print(\"PART 13 — LINGUISTIC PHENOMENA TESTING (ENHANCED)\")\n",
        "    print(\"=\" * 80)\n",
        "\n",
        "    tests = [\n",
        "        (\"This movie was not bad at all\",           1, \"Negation ('not bad' = good)\"),\n",
        "        (\"The film was not good\",                    0, \"Negation ('not good' = bad)\"),\n",
        "        (\"Yeah right, this was a great movie\",       0, \"Sarcasm\"),\n",
        "        (\"I wouldn't say this movie was terrible\",   1, \"Double negation\"),\n",
        "        (\"Not the best, not the worst\",              1, \"Balanced negation (neutral-positive)\"),\n",
        "        (\"The movie was terrible but the ending was fantastic\", 1, \"Contrast / mixed\"),\n",
        "    ]\n",
        "\n",
        "    label_map, results = {0: 'Negative', 1: 'Positive'}, []\n",
        "\n",
        "    for text, true_label, desc in tests:\n",
        "        clean = preprocess_traditional(text)\n",
        "        lr_pred  = int(lr_model.predict([clean])[0])\n",
        "        lr_prob  = float(lr_model.predict_proba([clean])[0][1])\n",
        "\n",
        "        bert_model.eval()\n",
        "        enc = bert_tokenizer(text, return_tensors='pt', max_length=BERT_MAX_LEN, truncation=True, padding='max_length')\n",
        "        with torch.no_grad(): out = bert_model(input_ids=enc['input_ids'].to(DEVICE), attention_mask=enc['attention_mask'].to(DEVICE))\n",
        "        bert_probs = torch.softmax(out.logits, dim=1).cpu().numpy()[0]\n",
        "        bert_pred, bert_prob = int(np.argmax(bert_probs)), float(bert_probs[1])\n",
        "\n",
        "        roberta_model.eval()\n",
        "        enc_rob = roberta_tokenizer(text, return_tensors='pt', max_length=ROBERTA_MAX_LEN, truncation=True, padding='max_length')\n",
        "        with torch.no_grad(): out_rob = roberta_model(input_ids=enc_rob['input_ids'].to(DEVICE), attention_mask=enc_rob['attention_mask'].to(DEVICE))\n",
        "        roberta_probs = torch.softmax(out_rob.logits, dim=1).cpu().numpy()[0]\n",
        "        roberta_pred, roberta_prob = int(np.argmax(roberta_probs)), float(roberta_probs[1])\n",
        "\n",
        "        lr_correct   = 'Y' if lr_pred == true_label else 'N'\n",
        "        bert_correct = 'Y' if bert_pred == true_label else 'N'\n",
        "        roberta_correct = 'Y' if roberta_pred == true_label else 'N'\n",
        "\n",
        "        results.append(dict(\n",
        "            text=text, true_label=true_label, true_sentiment=label_map[true_label], description=desc,\n",
        "            lr_pred=lr_pred, lr_sentiment=label_map[lr_pred], lr_prob_positive=round(lr_prob, 4), lr_correct=(lr_pred == true_label),\n",
        "            bert_pred=bert_pred, bert_sentiment=label_map[bert_pred], bert_prob_positive=round(bert_prob, 4), bert_correct=(bert_pred == true_label),\n",
        "            roberta_pred=roberta_pred, roberta_sentiment=label_map[roberta_pred], roberta_prob_positive=round(roberta_prob, 4), roberta_correct=(roberta_pred == true_label),\n",
        "        ))\n",
        "\n",
        "        print(f\"\\n  \\\"{text}\\\"\")\n",
        "        print(f\"    True: {label_map[true_label]:>8}  |  LR: {label_map[lr_pred]:>8} ({lr_prob:.2f}) {lr_correct}  |  BERT: {label_map[bert_pred]:>8} ({bert_prob:.2f}) {bert_correct}  |  RoBERTa: {label_map[roberta_pred]:>8} ({roberta_prob:.2f}) {roberta_correct}  |  {desc}\")\n",
        "\n",
        "    print(f\"\\n  Summary: LR {sum(r['lr_correct'] for r in results)}/{len(results)} correct  |  BERT {sum(r['bert_correct'] for r in results)}/{len(results)} correct  |  RoBERTa {sum(r['roberta_correct'] for r in results)}/{len(results)} correct\")\n",
        "    return results\n",
        "\n",
        "# --- Execute ---\n",
        "linguistic = linguistic_tests(lr_model, bert_model, bert_tokenizer, roberta_model, roberta_tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a96f67e",
      "metadata": {
        "id": "8a96f67e"
      },
      "source": [
        "## Part 15: Expanded Linguistic & Robustness Testing (96 Test Suite)\n",
        "\n",
        "Comprehensive CheckList-inspired test suite covering 8 linguistic phenomena with ~6-8 test cases each for systematic evaluation of model robustness."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "880321b5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "880321b5",
        "outputId": "4a207bd1-90b9-4979-c8a9-3c03bca73e2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 15 — EXPANDED LINGUISTIC & ROBUSTNESS TESTING (96-case suite)\n",
            "======================================================================\n",
            "\n",
            "Overall Accuracy:\n",
            "  Logistic Regression: 60.4%\n",
            "  DistilBERT:          68.8%\n",
            "  RoBERTa:             70.8%\n",
            "\n",
            "Per-Category Accuracy:\n",
            "----------------------------------------------------------------------\n",
            "Category                               LR   DistilBERT      RoBERTa\n",
            "----------------------------------------------------------------------\n",
            "Simple Negation                     12.5%       100.0%        87.5%\n",
            "Double Negation                      0.0%         0.0%        37.5%\n",
            "Sarcasm/Irony                       37.5%        25.0%        50.0%\n",
            "Contrastive Conjunctions            25.0%        50.0%        50.0%\n",
            "Hedging/Uncertainty                 75.0%        75.0%        75.0%\n",
            "Intensifiers                       100.0%       100.0%        87.5%\n",
            "Mixed Sentiment                     50.0%        50.0%        62.5%\n",
            "Implicit Sentiment                  87.5%        75.0%        62.5%\n",
            "Comparative                         75.0%        87.5%        75.0%\n",
            "Temporal/Narrative Arc              87.5%        87.5%       100.0%\n",
            "Hypothetical/Conditional           100.0%        87.5%        62.5%\n",
            "Domain-Specific Jargon              75.0%        87.5%       100.0%\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Visualization saved to ./output/run_20260329_093626/figures/part15_linguistic_heatmap.png\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 15: Expanded Linguistic & Robustness Testing (96-case suite)\n",
        "# ==========================================================\n",
        "def test_expanded_linguistic_phenomena(lr_model, bert_model, bert_tokenizer,\n",
        "                                       roberta_model, roberta_tokenizer):\n",
        "    \"\"\"\n",
        "    Comprehensive 96-case test suite covering 12 linguistic phenomena.\n",
        "    Tests:\n",
        "    1. Simple negation\n",
        "    2. Double negation\n",
        "    3. Sarcasm/irony\n",
        "    4. Contrastive conjunctions\n",
        "    5. Hedging/uncertainty\n",
        "    6. Intensifiers\n",
        "    7. Mixed sentiment\n",
        "    8. Implicit sentiment\n",
        "    Returns:\n",
        "        dict: Results with per-category accuracy for each model\n",
        "    \"\"\"\n",
        "    test_cases = {\n",
        "        'Simple Negation': [\n",
        "            (\"This movie is not good\", 0),\n",
        "            (\"I did not enjoy this film\", 0),\n",
        "            (\"The plot wasn't great\", 0),\n",
        "            (\"Not worth watching\", 0),\n",
        "            (\"I was not impressed\", 0),\n",
        "            (\"The acting is not impressive\", 0),\n",
        "            (\"This film is not entertaining\", 0),\n",
        "            (\"Not a masterpiece\", 0),\n",
        "        ],\n",
        "        'Double Negation': [\n",
        "            (\"This is not unenjoyable\", 1),\n",
        "            (\"I wouldn't say it was terrible\", 1),\n",
        "            (\"Not a bad movie at all\", 1),\n",
        "            (\"The acting is not terrible\", 1),\n",
        "            (\"Not the worst film I've seen\", 1),\n",
        "            (\"The story wasn't uninteresting\", 1),\n",
        "            (\"Not an unsuccessful attempt\", 1),\n",
        "            (\"I wouldn't call it unwatchable\", 1),\n",
        "        ],\n",
        "        'Sarcasm/Irony': [\n",
        "            (\"Oh great, another masterpiece\", 0),\n",
        "            (\"Yeah, totally worth watching\", 0),\n",
        "            (\"Wow, such brilliant acting\", 0),\n",
        "            (\"Fantastic, I'll never get those hours back\", 0),\n",
        "            (\"Honestly, I can't believe how good this was\", 1),\n",
        "            (\"Seriously, this blew me away\", 1),\n",
        "            (\"I mean, wow, just incredible talent\", 1),\n",
        "            (\"Sure, go ahead and watch it, you won't regret it\", 1),\n",
        "        ],\n",
        "        'Contrastive Conjunctions': [\n",
        "            (\"Terrible plot but ending was great\", 1),\n",
        "            (\"Great acting but awful script\", 1),\n",
        "            (\"Brilliant visuals although the story was boring\", 1),\n",
        "            (\"Bad beginning yet fantastic climax\", 1),\n",
        "            (\"Awful first half, excellent second half\", 1),\n",
        "            (\"Decent acting but predictable storyline\", 1),\n",
        "            (\"Impressive cinematography despite weak dialogue\", 1),\n",
        "            (\"Good premise, unfortunately poor execution\", 1),\n",
        "        ],\n",
        "        'Hedging/Uncertainty': [\n",
        "            (\"This movie is somewhat enjoyable\", 1),\n",
        "            (\"Might be worth watching\", 1),\n",
        "            (\"Arguably one of the better films\", 1),\n",
        "            (\"Possibly the most entertaining movie\", 1),\n",
        "            (\"It was kind of entertaining\", 1),\n",
        "            (\"Sort of a good movie\", 1),\n",
        "            (\"Perhaps one of the best\", 1),\n",
        "            (\"Arguably not the worst I've seen\", 1),\n",
        "        ],\n",
        "        'Intensifiers': [\n",
        "            (\"Absolutely terrible film\", 0),\n",
        "            (\"Incredibly brilliant performance\", 1),\n",
        "            (\"Extremely boring and predictable\", 0),\n",
        "            (\"Absolutely fantastic movie\", 1),\n",
        "            (\"Utterly dreadful experience\", 0),\n",
        "            (\"Remarkably good acting\", 1),\n",
        "            (\"Horrendously bad screenplay\", 0),\n",
        "            (\"Stunningly beautiful cinematography\", 1),\n",
        "        ],\n",
        "        'Mixed Sentiment': [\n",
        "            (\"Good acting, bad script, decent ending\", 1),\n",
        "            (\"Brilliant cinematography but terrible dialogue\", 0),\n",
        "            (\"Enjoyable plot, forgettable characters\", 1),\n",
        "            (\"Great premise, awful execution, interesting twist\", 1),\n",
        "            (\"Amazing cast, weak writing, decent pacing\", 1),\n",
        "            (\"Excellent direction, boring story, poor acting\", 0),\n",
        "            (\"Wonderful visuals, mediocre plot, great sound\", 1),\n",
        "            (\"Fantastic opening, confusing middle, satisfying conclusion\", 1),\n",
        "        ],\n",
        "        'Implicit Sentiment': [\n",
        "            (\"I fell asleep twice during this movie\", 0),\n",
        "            (\"I watched it three times\", 1),\n",
        "            (\"I walked out of the cinema\", 0),\n",
        "            (\"I immediately ordered the DVD\", 1),\n",
        "            (\"I couldn't stop watching\", 1),\n",
        "            (\"I couldn't wait for it to end\", 0),\n",
        "            (\"I sat in stunned silence\", 0),  # ambiguous but neg context\n",
        "            (\"I laughed throughout the entire film\", 1),\n",
        "        ],\n",
        "        'Comparative': [\n",
        "            (\"Better than most films this year\", 1),\n",
        "            (\"Worse than the original in every way\", 0),\n",
        "            (\"Not as good as the book\", 0),\n",
        "            (\"Far superior to the sequel\", 1),\n",
        "            (\"One of the weakest films in the franchise\", 0),\n",
        "            (\"Among the best performances of the decade\", 1),\n",
        "            (\"Pales in comparison to the director's earlier work\", 0),\n",
        "            (\"Rivals the greatest films ever made\", 1),\n",
        "        ],\n",
        "        'Temporal/Narrative Arc': [\n",
        "            (\"Started slow but built to a powerful climax\", 1),\n",
        "            (\"Promising beginning that gradually fell apart\", 0),\n",
        "            (\"Gets better with each viewing\", 1),\n",
        "            (\"Initially charming but ultimately disappointing\", 0),\n",
        "            (\"The first hour was painful, the second was brilliant\", 1),\n",
        "            (\"Engaging at first, then completely lost the plot\", 0),\n",
        "            (\"A slow burn that rewards patient viewers\", 1),\n",
        "            (\"What started as genius ended as nonsense\", 0),\n",
        "        ],\n",
        "        'Hypothetical/Conditional': [\n",
        "            (\"If you enjoy thrillers, this is for you\", 1),\n",
        "            (\"Unless you like pretentious films, skip this\", 0),\n",
        "            (\"Would have been great with better editing\", 0),\n",
        "            (\"Could have been a masterpiece with a stronger script\", 0),\n",
        "            (\"If you can overlook the pacing, it's rewarding\", 1),\n",
        "            (\"Would recommend to anyone who loves cinema\", 1),\n",
        "            (\"Might have worked with a different director\", 0),\n",
        "            (\"Should satisfy fans of the genre\", 1),\n",
        "        ],\n",
        "        'Domain-Specific Jargon': [\n",
        "            (\"The mise-en-scene was breathtaking throughout\", 1),\n",
        "            (\"Wooden performances and ham-fisted dialogue\", 0),\n",
        "            (\"A tour de force of method acting\", 1),\n",
        "            (\"The pacing was glacial and the editing jarring\", 0),\n",
        "            (\"Oscar-worthy cinematography and score\", 1),\n",
        "            (\"A paint-by-numbers blockbuster with no soul\", 0),\n",
        "            (\"Masterclass in narrative structure and character development\", 1),\n",
        "            (\"B-movie schlock dressed up as serious drama\", 0),\n",
        "        ],\n",
        "    }\n",
        "    results = {\n",
        "        'lr': {'correct': 0, 'total': 0, 'per_category': {}},\n",
        "        'bert': {'correct': 0, 'total': 0, 'per_category': {}},\n",
        "        'roberta': {'correct': 0, 'total': 0, 'per_category': {}},\n",
        "    }\n",
        "    for category, cases in test_cases.items():\n",
        "        lr_cat_correct = 0\n",
        "        bert_cat_correct = 0\n",
        "        roberta_cat_correct = 0\n",
        "        for text, true_label in cases:\n",
        "            # LR prediction\n",
        "            clean = preprocess_traditional(text)\n",
        "            lr_pred = int(lr_model.predict([clean])[0])\n",
        "            if lr_pred == true_label:\n",
        "                lr_cat_correct += 1\n",
        "                results['lr']['correct'] += 1\n",
        "            results['lr']['total'] += 1\n",
        "            # DistilBERT prediction\n",
        "            bert_model.eval()\n",
        "            enc = bert_tokenizer(text, return_tensors='pt',\n",
        "                                max_length=BERT_MAX_LEN, truncation=True,\n",
        "                                padding='max_length')\n",
        "            with torch.no_grad():\n",
        "                out = bert_model(\n",
        "                    input_ids=enc['input_ids'].to(DEVICE),\n",
        "                    attention_mask=enc['attention_mask'].to(DEVICE)\n",
        "                )\n",
        "            bert_probs = torch.softmax(out.logits, dim=1).cpu().numpy()[0]\n",
        "            bert_pred = int(np.argmax(bert_probs))\n",
        "            if bert_pred == true_label:\n",
        "                bert_cat_correct += 1\n",
        "                results['bert']['correct'] += 1\n",
        "            results['bert']['total'] += 1\n",
        "            # RoBERTa prediction\n",
        "            roberta_model.eval()\n",
        "            enc_r = roberta_tokenizer(text, return_tensors='pt',\n",
        "                                     max_length=ROBERTA_MAX_LEN, truncation=True,\n",
        "                                     padding='max_length')\n",
        "            with torch.no_grad():\n",
        "                out_r = roberta_model(\n",
        "                    input_ids=enc_r['input_ids'].to(DEVICE),\n",
        "                    attention_mask=enc_r['attention_mask'].to(DEVICE)\n",
        "                )\n",
        "            roberta_probs = torch.softmax(out_r.logits, dim=1).cpu().numpy()[0]\n",
        "            roberta_pred = int(np.argmax(roberta_probs))\n",
        "            if roberta_pred == true_label:\n",
        "                roberta_cat_correct += 1\n",
        "                results['roberta']['correct'] += 1\n",
        "            results['roberta']['total'] += 1\n",
        "        # Store per-category accuracy\n",
        "        results['lr']['per_category'][category] = lr_cat_correct / len(cases)\n",
        "        results['bert']['per_category'][category] = bert_cat_correct / len(cases)\n",
        "        results['roberta']['per_category'][category] = roberta_cat_correct / len(cases)\n",
        "    # Print results\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 15 — EXPANDED LINGUISTIC & ROBUSTNESS TESTING (96-case suite)\")\n",
        "    print(\"=\"*70)\n",
        "    print(f\"\\nOverall Accuracy:\")\n",
        "    print(f\"  Logistic Regression: {results['lr']['correct'] / results['lr']['total']:.1%}\")\n",
        "    print(f\"  DistilBERT:          {results['bert']['correct'] / results['bert']['total']:.1%}\")\n",
        "    print(f\"  RoBERTa:             {results['roberta']['correct'] / results['roberta']['total']:.1%}\")\n",
        "    print(f\"\\nPer-Category Accuracy:\")\n",
        "    print(\"-\" * 70)\n",
        "    print(f\"{'Category':<30} {'LR':>10} {'DistilBERT':>12} {'RoBERTa':>12}\")\n",
        "    print(\"-\" * 70)\n",
        "    categories = list(test_cases.keys())\n",
        "    for cat in categories:\n",
        "        lr_acc = results['lr']['per_category'][cat]\n",
        "        bert_acc = results['bert']['per_category'][cat]\n",
        "        roberta_acc = results['roberta']['per_category'][cat]\n",
        "        print(f\"{cat:<30} {lr_acc:>10.1%} {bert_acc:>12.1%} {roberta_acc:>12.1%}\")\n",
        "    # Create heatmap visualization\n",
        "    import pandas as pd\n",
        "    import seaborn as sns\n",
        "    import matplotlib.pyplot as plt\n",
        "    heatmap_data = pd.DataFrame({\n",
        "        'LR': [results['lr']['per_category'][cat] for cat in categories],\n",
        "        'DistilBERT': [results['bert']['per_category'][cat] for cat in categories],\n",
        "        'RoBERTa': [results['roberta']['per_category'][cat] for cat in categories],\n",
        "    }, index=categories)\n",
        "    fig, ax = plt.subplots(figsize=(10, 7))\n",
        "    sns.heatmap(heatmap_data, annot=True, fmt='.1%', cmap='RdYlGn',\n",
        "                vmin=0, vmax=1, cbar_kws={'label': 'Accuracy'}, ax=ax)\n",
        "    ax.set_title('Per-Category Accuracy on Linguistic Phenomena Tests', fontsize=14, fontweight='bold')\n",
        "    ax.set_ylabel('Linguistic Phenomenon', fontsize=11)\n",
        "    ax.set_xlabel('Model', fontsize=11)\n",
        "    plt.tight_layout()\n",
        "    fig_path = f\"{FIGURES_DIR}/part15_linguistic_heatmap.png\"\n",
        "    plt.savefig(fig_path, dpi=300, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"\\nVisualization saved to {fig_path}\")\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    return results\n",
        "# Execute Part 15\n",
        "results_linguistic = test_expanded_linguistic_phenomena(\n",
        "    lr_model, bert_model, bert_tokenizer, roberta_model, roberta_tokenizer\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff0669cf",
      "metadata": {
        "id": "ff0669cf"
      },
      "source": [
        "## Part 16: Ablation Study\n",
        "\n",
        "Measure the impact of DistilBERT training components (warmup, learning rate, max_length) by training for 1 epoch with different configurations.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "cb122b8f",
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        },
        "id": "cb122b8f",
        "outputId": "ff114424-dc85-4ada-bc99-d0cb0e5b364e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 16 — ABLATION STUDY: DISTILBERT TRAINING COMPONENTS\n",
            "======================================================================\n",
            "  Baseline (epoch-1 of main run): val_loss=0.2577  val_acc=0.8940  val_f1=0.8876\n",
            "\n",
            "  -> No Warmup: lr=2e-05, warmup=0, max_len=512\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2090594ba0fe4d558956adf76bd733bb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2c5be72654848cd92ba21defa20e6e1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Training [No Warmup]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     val_loss=0.2212  val_acc=0.9128  val_f1=0.9126\n",
            "\n",
            "  -> High LR (5e-5): lr=5e-05, warmup=0.1, max_len=512\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b4a1f9a514b6483782fd4eb31fef21c3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a7b9e0001f944b5b58949aec4735e67",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Training [High LR (5e-5)]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     val_loss=0.2062  val_acc=0.9212  val_f1=0.9216\n",
            "\n",
            "  -> Low LR (5e-6): lr=5e-06, warmup=0.1, max_len=512\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d28c73d483c4b31a941bc149e75cbd7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45b2936471564b43b834b77d17a7cb73",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Training [Low LR (5e-6)]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     val_loss=0.2710  val_acc=0.8948  val_f1=0.8953\n",
            "\n",
            "  -> MaxLen=256: lr=2e-05, warmup=0.1, max_len=256\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4ca8a14dce7486f8c49bc15006b2f40",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "03a358cffea44688bb81ec676552f253",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Training [MaxLen=256]:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     val_loss=0.2530  val_acc=0.9000  val_f1=0.8991\n",
            "\n",
            "----------------------------------------------------------------------\n",
            "  BASELINE (epoch-1): val_loss=0.2577  val_acc=0.8940  val_f1=0.8876\n",
            "----------------------------------------------------------------------\n",
            "  Config                   Val Loss    Val Acc     Val F1      ΔLoss\n",
            "  ----------------------------------------------------------------\n",
            "  No Warmup                  0.2212     0.9128     0.9126    -0.0365\n",
            "  High LR (5e-5)             0.2062     0.9212     0.9216    -0.0515\n",
            "  Low LR (5e-6)              0.2710     0.8948     0.8953    +0.0133\n",
            "  MaxLen=256                 0.2530     0.9000     0.8991    -0.0048\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "  Saved ./output/run_20260329_093626/figures/part16_ablation_study.png\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 16: Ablation Study\n",
        "# ==========================================================\n",
        "def run_ablation_study(train_df, test_df, res_bert):\n",
        "    \"\"\"\n",
        "    Ablation study: trains DistilBERT for 1 epoch under 4 modified configs\n",
        "    and compares val loss/accuracy against the main model's epoch-1 performance.\n",
        "    Ablations:\n",
        "    1. No warmup (warmup_ratio=0)\n",
        "    2. Higher LR (5e-5)\n",
        "    3. Lower LR (5e-6)\n",
        "    4. Smaller max_len (256)\n",
        "    Parameters:\n",
        "        train_df  : Full training dataframe\n",
        "        test_df   : Full test dataframe\n",
        "        res_bert  : DistilBERT results dict (keys: history, accuracy, f1, ...)\n",
        "    Returns:\n",
        "        dict: Ablation results for all configurations\n",
        "    \"\"\"\n",
        "    # ---- baseline = epoch 1 from the main training run ----\n",
        "    baseline = {\n",
        "        'val_loss': res_bert['history']['val_loss'][0],\n",
        "        'val_acc':  res_bert['history']['val_acc'][0],\n",
        "        'val_f1':   res_bert['history']['val_f1'][0],\n",
        "    }\n",
        "    ablation_configs = [\n",
        "        {'name': 'No Warmup',      'warmup_ratio': 0,                  'lr': BERT_LR,  'max_len': BERT_MAX_LEN},\n",
        "        {'name': 'High LR (5e-5)', 'warmup_ratio': BERT_WARMUP_RATIO,  'lr': 5e-5,     'max_len': BERT_MAX_LEN},\n",
        "        {'name': 'Low LR (5e-6)',  'warmup_ratio': BERT_WARMUP_RATIO,  'lr': 5e-6,     'max_len': BERT_MAX_LEN},\n",
        "        {'name': 'MaxLen=256',     'warmup_ratio': BERT_WARMUP_RATIO,  'lr': BERT_LR,  'max_len': 256},\n",
        "    ]\n",
        "    ablation_results = {}\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 16 — ABLATION STUDY: DISTILBERT TRAINING COMPONENTS\")\n",
        "    print(\"=\"*70)\n",
        "    print(f\"  Baseline (epoch-1 of main run): \"\n",
        "          f\"val_loss={baseline['val_loss']:.4f}  \"\n",
        "          f\"val_acc={baseline['val_acc']:.4f}  \"\n",
        "          f\"val_f1={baseline['val_f1']:.4f}\")\n",
        "    # ---- shared tokeniser (already downloaded) ----\n",
        "    tokenizer_abl = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "    # ---- 90/10 train/val split (same seed as main run) ----\n",
        "    bert_train_abl = train_df.sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    trn_texts_abl, val_texts_abl, trn_labels_abl, val_labels_abl = train_test_split(\n",
        "        bert_train_abl['text'].apply(preprocess_bert).tolist(),\n",
        "        bert_train_abl['label'].tolist(),\n",
        "        test_size=0.10, random_state=SEED, stratify=bert_train_abl['label'].tolist()\n",
        "    )\n",
        "    for cfg in ablation_configs:\n",
        "        name = cfg['name']\n",
        "        max_len_abl    = cfg['max_len']\n",
        "        lr_abl         = cfg['lr']\n",
        "        warmup_abl     = cfg['warmup_ratio']\n",
        "        print(f\"\\n  -> {name}: lr={lr_abl}, warmup={warmup_abl}, max_len={max_len_abl}\")\n",
        "        # ---- tokenise ----\n",
        "        def encode_abl(texts, ml):\n",
        "            return tokenizer_abl(texts, padding='max_length', truncation=True,\n",
        "                                  max_length=ml, return_tensors='pt')\n",
        "        trn_enc = encode_abl(trn_texts_abl, max_len_abl)\n",
        "        val_enc = encode_abl(val_texts_abl, max_len_abl)\n",
        "        trn_ds_abl = IMDBDataset(trn_enc, trn_labels_abl)\n",
        "        val_ds_abl = IMDBDataset(val_enc, val_labels_abl)\n",
        "        trn_loader_abl = DataLoader(trn_ds_abl, batch_size=BERT_BATCH_SIZE, shuffle=True)\n",
        "        val_loader_abl = DataLoader(val_ds_abl, batch_size=BERT_BATCH_SIZE)\n",
        "        # ---- fresh model ----\n",
        "        model_abl = DistilBertForSequenceClassification.from_pretrained(\n",
        "            'distilbert-base-uncased', num_labels=2\n",
        "        ).to(DEVICE)\n",
        "        opt_abl = AdamW(model_abl.parameters(), lr=lr_abl,\n",
        "                        weight_decay=BERT_WEIGHT_DECAY)\n",
        "        total_steps_abl = len(trn_loader_abl)   # 1 epoch\n",
        "        sched_abl = get_linear_schedule_with_warmup(\n",
        "            opt_abl,\n",
        "            num_warmup_steps=int(total_steps_abl * warmup_abl),\n",
        "            num_training_steps=total_steps_abl\n",
        "        )\n",
        "        # ---- 1-epoch training ----\n",
        "        model_abl.train()\n",
        "        scaler_abl = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "        for batch in tqdm(trn_loader_abl, desc=f\"    Training [{name}]\", leave=False):\n",
        "            ids  = batch['input_ids'].to(DEVICE)\n",
        "            mask = batch['attention_mask'].to(DEVICE)\n",
        "            labs = batch['labels'].to(DEVICE)\n",
        "            opt_abl.zero_grad()\n",
        "            with torch.amp.autocast('cuda', enabled=USE_FP16):\n",
        "                out  = model_abl(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                loss = out.loss\n",
        "            scaler_abl.scale(loss).backward()\n",
        "            scaler_abl.unscale_(opt_abl)\n",
        "            torch.nn.utils.clip_grad_norm_(model_abl.parameters(), BERT_GRAD_CLIP)\n",
        "            scaler_abl.step(opt_abl)\n",
        "            scaler_abl.update()\n",
        "            sched_abl.step()\n",
        "        # ---- validation ----\n",
        "        model_abl.eval()\n",
        "        val_loss_sum, all_preds_abl, all_true_abl = 0.0, [], []\n",
        "        with torch.no_grad():\n",
        "            for batch in val_loader_abl:\n",
        "                ids  = batch['input_ids'].to(DEVICE)\n",
        "                mask = batch['attention_mask'].to(DEVICE)\n",
        "                labs = batch['labels'].to(DEVICE)\n",
        "                with torch.amp.autocast('cuda', enabled=USE_FP16):\n",
        "                    out = model_abl(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                val_loss_sum += out.loss.item()\n",
        "                preds_abl = torch.argmax(out.logits, dim=1).cpu().tolist()\n",
        "                all_preds_abl.extend(preds_abl)\n",
        "                all_true_abl.extend(labs.cpu().tolist())\n",
        "        val_loss_abl = val_loss_sum / len(val_loader_abl)\n",
        "        val_acc_abl  = accuracy_score(all_true_abl, all_preds_abl)\n",
        "        val_f1_abl   = f1_score(all_true_abl, all_preds_abl)\n",
        "        ablation_results[name] = {\n",
        "            'val_loss': val_loss_abl,\n",
        "            'val_acc':  val_acc_abl,\n",
        "            'val_f1':   val_f1_abl,\n",
        "            'config':   cfg,\n",
        "        }\n",
        "        print(f\"     val_loss={val_loss_abl:.4f}  val_acc={val_acc_abl:.4f}  val_f1={val_f1_abl:.4f}\")\n",
        "        # ---- cleanup GPU memory ----\n",
        "        del model_abl, trn_enc, val_enc, trn_ds_abl, val_ds_abl\n",
        "        del trn_loader_abl, val_loader_abl, opt_abl, sched_abl\n",
        "        if DEVICE.type == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "    # ---- comparison table ----\n",
        "    print(\"\\n\" + \"-\"*70)\n",
        "    print(f\"  BASELINE (epoch-1): val_loss={baseline['val_loss']:.4f}  \"\n",
        "          f\"val_acc={baseline['val_acc']:.4f}  val_f1={baseline['val_f1']:.4f}\")\n",
        "    print(\"-\"*70)\n",
        "    print(f\"  {'Config':<22} {'Val Loss':>10} {'Val Acc':>10} {'Val F1':>10} {'ΔLoss':>10}\")\n",
        "    print(f\"  {'-'*64}\")\n",
        "    for name, res in ablation_results.items():\n",
        "        delta = res['val_loss'] - baseline['val_loss']\n",
        "        print(f\"  {name:<22} {res['val_loss']:>10.4f} {res['val_acc']:>10.4f} \"\n",
        "              f\"{res['val_f1']:>10.4f} {delta:>+10.4f}\")\n",
        "    # ---- visualisation ----\n",
        "    configs_names = ['Baseline (Ep.1)'] + list(ablation_results.keys())\n",
        "    losses = [baseline['val_loss']] + [ablation_results[k]['val_loss'] for k in ablation_results]\n",
        "    accs   = [baseline['val_acc']]  + [ablation_results[k]['val_acc']  for k in ablation_results]\n",
        "    f1s    = [baseline['val_f1']]   + [ablation_results[k]['val_f1']   for k in ablation_results]\n",
        "    colours = ['#2ecc71'] + ['#e74c3c' if l > baseline['val_loss'] else '#3498db' for l in losses[1:]]\n",
        "    x = np.arange(len(configs_names))\n",
        "    fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "    for ax, vals, ylabel, title in zip(\n",
        "        axes,\n",
        "        [losses, accs, f1s],\n",
        "        ['Validation Loss', 'Validation Accuracy', 'Validation F1'],\n",
        "        ['Val Loss (lower=better)', 'Val Accuracy (higher=better)', 'Val F1 (higher=better)']\n",
        "    ):\n",
        "        bars = ax.bar(x, vals, color=colours, edgecolor='black', width=0.6)\n",
        "        ax.axhline(vals[0], color='green', linestyle='--', linewidth=1.5, label='Baseline')\n",
        "        ax.set_xticks(x)\n",
        "        ax.set_xticklabels(configs_names, rotation=30, ha='right', fontsize=9)\n",
        "        ax.set_ylabel(ylabel, fontsize=11)\n",
        "        ax.set_title(title, fontsize=12, fontweight='bold')\n",
        "        ax.legend(fontsize=9)\n",
        "        ax.grid(axis='y', alpha=0.3)\n",
        "        for bar, val in zip(bars, vals):\n",
        "            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.001,\n",
        "                    f'{val:.3f}', ha='center', va='bottom', fontsize=8)\n",
        "    plt.suptitle('Part 16: Ablation Study — Impact of Training Hyperparameters',\n",
        "                  fontsize=13, fontweight='bold')\n",
        "    plt.tight_layout()\n",
        "    path_fig = f'{FIGURES_DIR}/part16_ablation_study.png'\n",
        "    plt.savefig(path_fig, dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"\\n  Saved {path_fig}\")\n",
        "    ablation_results['baseline'] = baseline\n",
        "    return ablation_results\n",
        "# Execute Part 16\n",
        "results_ablation = run_ablation_study(train_df, test_df, res_bert)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b159b710",
      "metadata": {
        "id": "b159b710"
      },
      "source": [
        "## Part 16b: DistilBERT Hyperparameter Grid Search\n",
        "Systematic grid search over learning rates, training each configuration to full convergence with early stopping.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "74f2b89d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 16b — DISTILBERT HYPERPARAMETER GRID SEARCH (FULL TRAINING)\n",
            "======================================================================\n",
            "\n",
            "  -> Training: LR=1e-5\n"
          ]
        },
        {
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              "model_id": "a0e26793f0e84c5e8fcf6ceeee22a538",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
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              "model_id": "9641753a428d48e59ce82f8435aa632c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 1/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 1: val_f1=0.9056, val_acc=0.9032\n"
          ]
        },
        {
          "data": {
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              "model_id": "8d3e0e945e234bf7bf9c4c33c2eede9f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 2/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 2: val_f1=0.9139, val_acc=0.9124\n"
          ]
        },
        {
          "data": {
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              "model_id": "b835307fa10b46a99cf5173dff555618",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 3/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 3: val_f1=0.9115, val_acc=0.9076\n"
          ]
        },
        {
          "data": {
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              "model_id": "0c6a0df506a2456cb1d4b9f7b8556b9f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 4/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 4: val_f1=0.9194, val_acc=0.9188\n"
          ]
        },
        {
          "data": {
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              "model_id": "463103b1019e4502ae7ef79e043e6a4f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 5/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 5: val_f1=0.9214, val_acc=0.9208\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f2aa310504d4baaad164acf018ca548",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 6/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 6: val_f1=0.9230, val_acc=0.9232\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d5bd6c153ab45bdbfe813410cb131ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 7/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 7: val_f1=0.9207, val_acc=0.9192\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5eee7f8c97944e56ac40d92459b43c93",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 8/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 8: val_f1=0.9216, val_acc=0.9208\n",
            "\n",
            "  -> Training: LR=2e-5 (baseline)\n"
          ]
        },
        {
          "data": {
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              "model_id": "266e886f0698472fabad12d0fe3bc731",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5138056739a64990b5a723a2df74c461",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 1/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 1: val_f1=0.9041, val_acc=0.9072\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c1b3fb8746f1452e8a88fb75a026ce1f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 2/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 2: val_f1=0.9087, val_acc=0.9044\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7095b599dfb64a91a67ac95c34a252ee",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 3/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 3: val_f1=0.9235, val_acc=0.9216\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41be56778c20458d8988fd2ce1dfb161",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 4/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 4: val_f1=0.9261, val_acc=0.9260\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd7a5dbf230e423c831060f48f809692",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 5/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 5: val_f1=0.9210, val_acc=0.9188\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4b37bae5f80644f7ac85e83bb62233c2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 6/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 6: val_f1=0.9317, val_acc=0.9308\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "148f614cbc7041fd8d194151f9902fd9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 7/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 7: val_f1=0.9299, val_acc=0.9304\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "073fde0f3386497abe6f63a31eca1977",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 8/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 8: val_f1=0.9314, val_acc=0.9308\n",
            "\n",
            "  -> Training: LR=3e-5\n"
          ]
        },
        {
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              "model_id": "71d06d4518404e26b5d4b7770ba268d5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "classifier.bias         | MISSING    | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1ca1b0af3d604623a57b68e2cd27d427",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 1/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 1: val_f1=0.9028, val_acc=0.9068\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a7ab4c00aa94c51ac2837b798763a5e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 2/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 2: val_f1=0.9176, val_acc=0.9148\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "655472c84856493cb2064a764f9cacea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 3/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 3: val_f1=0.9270, val_acc=0.9260\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c03fe52207934db6a609009abbe61c1c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 4/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 4: val_f1=0.9233, val_acc=0.9240\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "15a8011fa4ff46af87bd80af563a8f71",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 5/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 5: val_f1=0.9285, val_acc=0.9276\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa72b28ba37940249c556a96b336c2ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 6/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 6: val_f1=0.9266, val_acc=0.9276\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "130569054c8343a8b3c0bc4ad2d83b0e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 7/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 7: val_f1=0.9267, val_acc=0.9272\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8d84043feac4e5287bd8a472f551a68",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 8/8:   0%|          | 0/469 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 8: val_f1=0.9317, val_acc=0.9312\n",
            "\n",
            "----------------------------------------------------------------------\n",
            "  Config                     Best Val F1   Epochs\n",
            "  -----------------------------------------------\n",
            "  LR=1e-5                         0.9230        8\n",
            "  LR=2e-5 (baseline)              0.9317        8\n",
            "  LR=3e-5                         0.9317        8\n",
            "\n",
            "  Best config: LR=2e-5 (baseline) (F1=0.9317)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 16b: DistilBERT Hyperparameter Grid Search (Full Training)\n",
        "# ==========================================================\n",
        "\n",
        "def distilbert_hp_grid_search(train_df, test_df):\n",
        "    \"\"\"\n",
        "    Systematic hyperparameter grid search for DistilBERT.\n",
        "    Trains 3 learning rate configurations to full convergence with early stopping.\n",
        "    Reports best validation F1 for each to justify final LR selection.\n",
        "    \"\"\"\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 16b — DISTILBERT HYPERPARAMETER GRID SEARCH (FULL TRAINING)\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    tokenizer_hp = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "\n",
        "    # Use same train/val split as main run\n",
        "    bert_train_hp = train_df.sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    trn_texts_hp, val_texts_hp, trn_labels_hp, val_labels_hp = train_test_split(\n",
        "        bert_train_hp['text'].apply(preprocess_bert).tolist(),\n",
        "        bert_train_hp['label'].tolist(),\n",
        "        test_size=0.10, random_state=SEED, stratify=bert_train_hp['label'].tolist()\n",
        "    )\n",
        "\n",
        "    def encode_hp(texts):\n",
        "        return tokenizer_hp(texts, padding='max_length', truncation=True,\n",
        "                           max_length=BERT_MAX_LEN, return_tensors='pt')\n",
        "\n",
        "    trn_enc_hp = encode_hp(trn_texts_hp)\n",
        "    val_enc_hp = encode_hp(val_texts_hp)\n",
        "    trn_ds_hp = IMDBDataset(trn_enc_hp, trn_labels_hp)\n",
        "    val_ds_hp = IMDBDataset(val_enc_hp, val_labels_hp)\n",
        "    trn_loader_hp = DataLoader(trn_ds_hp, batch_size=BERT_BATCH_SIZE, shuffle=True)\n",
        "    val_loader_hp = DataLoader(val_ds_hp, batch_size=BERT_BATCH_SIZE)\n",
        "\n",
        "    hp_configs = [\n",
        "        {'lr': 1e-5, 'warmup': 0.10, 'name': 'LR=1e-5'},\n",
        "        {'lr': 2e-5, 'warmup': 0.10, 'name': 'LR=2e-5 (baseline)'},\n",
        "        {'lr': 3e-5, 'warmup': 0.06, 'name': 'LR=3e-5'},\n",
        "    ]\n",
        "\n",
        "    hp_results = {}\n",
        "\n",
        "    for cfg in hp_configs:\n",
        "        name = cfg['name']\n",
        "        print(f\"\\n  -> Training: {name}\")\n",
        "\n",
        "        model_hp = DistilBertForSequenceClassification.from_pretrained(\n",
        "            'distilbert-base-uncased', num_labels=2\n",
        "        ).to(DEVICE)\n",
        "\n",
        "        opt_hp = AdamW(model_hp.parameters(), lr=cfg['lr'], weight_decay=BERT_WEIGHT_DECAY)\n",
        "        total_steps_hp = len(trn_loader_hp) * BERT_EPOCHS\n",
        "        sched_hp = get_linear_schedule_with_warmup(\n",
        "            opt_hp,\n",
        "            num_warmup_steps=int(total_steps_hp * cfg['warmup']),\n",
        "            num_training_steps=total_steps_hp\n",
        "        )\n",
        "        scaler_hp = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "\n",
        "        best_f1_hp, patience_hp = 0.0, 0\n",
        "        epoch_f1s = []\n",
        "\n",
        "        for epoch in range(1, BERT_EPOCHS + 1):\n",
        "            model_hp.train()\n",
        "            for batch in tqdm(trn_loader_hp, desc=f\"    Ep {epoch}/{BERT_EPOCHS}\", leave=False):\n",
        "                ids = batch['input_ids'].to(DEVICE)\n",
        "                mask = batch['attention_mask'].to(DEVICE)\n",
        "                labs = batch['labels'].to(DEVICE)\n",
        "                opt_hp.zero_grad()\n",
        "                with torch.amp.autocast('cuda', enabled=USE_FP16):\n",
        "                    out = model_hp(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                scaler_hp.scale(out.loss).backward()\n",
        "                scaler_hp.unscale_(opt_hp)\n",
        "                torch.nn.utils.clip_grad_norm_(model_hp.parameters(), BERT_GRAD_CLIP)\n",
        "                scaler_hp.step(opt_hp)\n",
        "                scaler_hp.update()\n",
        "                sched_hp.step()\n",
        "\n",
        "            # Validate\n",
        "            model_hp.eval()\n",
        "            vp, vt = [], []\n",
        "            with torch.no_grad():\n",
        "                for batch in val_loader_hp:\n",
        "                    ids = batch['input_ids'].to(DEVICE)\n",
        "                    mask = batch['attention_mask'].to(DEVICE)\n",
        "                    labs = batch['labels'].to(DEVICE)\n",
        "                    out = model_hp(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                    vp.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "                    vt.extend(labs.cpu().numpy())\n",
        "\n",
        "            vf1 = f1_score(vt, vp)\n",
        "            vacc = accuracy_score(vt, vp)\n",
        "            epoch_f1s.append(vf1)\n",
        "            print(f\"     Epoch {epoch}: val_f1={vf1:.4f}, val_acc={vacc:.4f}\")\n",
        "\n",
        "            if vf1 > best_f1_hp:\n",
        "                best_f1_hp, patience_hp = vf1, 0\n",
        "            else:\n",
        "                patience_hp += 1\n",
        "                if patience_hp >= BERT_PATIENCE:\n",
        "                    print(f\"     Early stopping at epoch {epoch}\")\n",
        "                    break\n",
        "\n",
        "        hp_results[name] = {\n",
        "            'lr': cfg['lr'],\n",
        "            'warmup': cfg['warmup'],\n",
        "            'best_val_f1': best_f1_hp,\n",
        "            'epochs_run': len(epoch_f1s),\n",
        "            'epoch_f1s': epoch_f1s,\n",
        "        }\n",
        "\n",
        "        del model_hp, opt_hp, sched_hp\n",
        "        if DEVICE.type == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "    # Summary\n",
        "    print(\"\\n\" + \"-\"*70)\n",
        "    print(f\"  {'Config':<25} {'Best Val F1':>12} {'Epochs':>8}\")\n",
        "    print(f\"  {'-'*47}\")\n",
        "    for name, res in hp_results.items():\n",
        "        print(f\"  {name:<25} {res['best_val_f1']:>12.4f} {res['epochs_run']:>8}\")\n",
        "\n",
        "    best_config = max(hp_results.items(), key=lambda x: x[1]['best_val_f1'])\n",
        "    print(f\"\\n  Best config: {best_config[0]} (F1={best_config[1]['best_val_f1']:.4f})\")\n",
        "\n",
        "    # Visualization\n",
        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
        "    for name, res in hp_results.items():\n",
        "        ax.plot(range(1, len(res['epoch_f1s'])+1), res['epoch_f1s'], 'o-', label=name, lw=2)\n",
        "    ax.set_xlabel('Epoch', fontsize=11)\n",
        "    ax.set_ylabel('Validation F1', fontsize=11)\n",
        "    ax.set_title('DistilBERT HP Grid Search: Validation F1 by Epoch', fontsize=12, fontweight='bold')\n",
        "    ax.legend(fontsize=10)\n",
        "    ax.grid(alpha=0.3)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/part16b_hp_grid_search.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "    return hp_results\n",
        "\n",
        "# Execute\n",
        "results_hp_grid = distilbert_hp_grid_search(train_df, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "uiRPLBot8liF",
      "metadata": {
        "id": "uiRPLBot8liF"
      },
      "source": [
        "## Part 16c: RoBERTa Hyperparameter Grid Search\n",
        "Systematic comparison of 2 learning rate configurations for RoBERTa-base.\n",
        "Trains each to full convergence with F1-based early stopping to justify the final lr=1e-5 selection.\n",
        "Limited to 2 configs (vs 3 for DistilBERT) due to RoBERTa's 2x longer per-epoch training time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "i06Mt4Xx8liF",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "8f748e26-f9e4-412d-cce5-33ca7a7bbba3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 16c — ROBERTA HYPERPARAMETER GRID SEARCH (FULL TRAINING)\n",
            "======================================================================\n",
            "\n",
            "  -> Training: LR=1e-5 (baseline)\n"
          ]
        },
        {
          "data": {
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              "model_id": "e05e6f2d63104c01be40b229fb359574",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/197 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "RobertaForSequenceClassification LOAD REPORT from: roberta-base\n",
            "Key                             | Status     | \n",
            "--------------------------------+------------+-\n",
            "lm_head.layer_norm.weight       | UNEXPECTED | \n",
            "lm_head.bias                    | UNEXPECTED | \n",
            "roberta.embeddings.position_ids | UNEXPECTED | \n",
            "lm_head.dense.weight            | UNEXPECTED | \n",
            "lm_head.dense.bias              | UNEXPECTED | \n",
            "lm_head.layer_norm.bias         | UNEXPECTED | \n",
            "classifier.out_proj.weight      | MISSING    | \n",
            "classifier.dense.bias           | MISSING    | \n",
            "classifier.out_proj.bias        | MISSING    | \n",
            "classifier.dense.weight         | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9109c17926194fc09fd3bd5ab8f7ee45",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 1/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 1: val_f1=0.9397, val_acc=0.9392\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c731298f543046f1a5b4d0d02f1d9d7b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 2/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 2: val_f1=0.9446, val_acc=0.9448\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a8cee564b33406490522957cff76b34",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 3/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 3: val_f1=0.9370, val_acc=0.9384\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55f4f7bbb16f49c693f6b1847c197bf9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 4/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 4: val_f1=0.9468, val_acc=0.9472\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b4e5101183ce45d6ad19ad50a6ead766",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 5/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 5: val_f1=0.9465, val_acc=0.9464\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d147fa84e7049258a0a9500b3b0ee13",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 6/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 6: val_f1=0.9493, val_acc=0.9492\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0ec1a1decd9405a8cd97a8332c882a4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 7/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 7: val_f1=0.9455, val_acc=0.9452\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13a00e4281cb403dbdc84384a6efd322",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 8/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 8: val_f1=0.9447, val_acc=0.9456\n",
            "\n",
            "  -> Training: LR=2e-5\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d54a10b0228444e284ca910f1ac4e0aa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/197 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "RobertaForSequenceClassification LOAD REPORT from: roberta-base\n",
            "Key                             | Status     | \n",
            "--------------------------------+------------+-\n",
            "lm_head.layer_norm.weight       | UNEXPECTED | \n",
            "lm_head.bias                    | UNEXPECTED | \n",
            "roberta.embeddings.position_ids | UNEXPECTED | \n",
            "lm_head.dense.weight            | UNEXPECTED | \n",
            "lm_head.dense.bias              | UNEXPECTED | \n",
            "lm_head.layer_norm.bias         | UNEXPECTED | \n",
            "classifier.out_proj.weight      | MISSING    | \n",
            "classifier.dense.bias           | MISSING    | \n",
            "classifier.out_proj.bias        | MISSING    | \n",
            "classifier.dense.weight         | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b72aacb0b01141dd8bde8833436dfbe9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 1/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 1: val_f1=0.9396, val_acc=0.9384\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a048db9a996d42b4a099d105ce4274ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 2/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 2: val_f1=0.9438, val_acc=0.9436\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1379e253ff194b7fa90451240da8c298",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 3/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 3: val_f1=0.9433, val_acc=0.9436\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e8f5a6bd05b84a98acca3c50c43c337c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 4/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 4: val_f1=0.9404, val_acc=0.9420\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4073ac4b9cb742acbbe2e6d172a77ab8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 5/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 5: val_f1=0.9376, val_acc=0.9352\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c371bf093a2647d4952e38ee27f63211",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 6/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 6: val_f1=0.9463, val_acc=0.9464\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a03ebf8f4c3f40428169ccedf6d43da5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 7/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 7: val_f1=0.9490, val_acc=0.9488\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a2d971f77b1445c682bbc864634ba139",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "    Ep 8/8:   0%|          | 0/938 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     Epoch 8: val_f1=0.9468, val_acc=0.9464\n",
            "\n",
            "----------------------------------------------------------------------\n",
            "  Config                     Best Val F1   Epochs\n",
            "  -----------------------------------------------\n",
            "  LR=1e-5 (baseline)              0.9493        8\n",
            "  LR=2e-5                         0.9490        8\n",
            "\n",
            "  Best RoBERTa config: LR=1e-5 (baseline) (F1=0.9493)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 16c: RoBERTa Hyperparameter Grid Search (Full Training)\n",
        "# ==========================================================\n",
        "# Rationale: Addresses the limitation that only DistilBERT received HP tuning.\n",
        "# 2 configs (not 3) due to RoBERTa's ~2810s/run training cost on L4 GPU.\n",
        "\n",
        "def roberta_hp_grid_search(train_df, test_df):\n",
        "    \"\"\"\n",
        "    Systematic HP comparison for RoBERTa-base.\n",
        "    Trains 2 learning rate configs {1e-5 (baseline), 2e-5} to full convergence.\n",
        "    Uses same train/val split, F1-based early stopping (patience=4).\n",
        "    \"\"\"\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 16c — ROBERTA HYPERPARAMETER GRID SEARCH (FULL TRAINING)\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    tokenizer_rhp = RobertaTokenizer.from_pretrained('roberta-base')\n",
        "\n",
        "    # Same train/val split as main RoBERTa run\n",
        "    rob_train_hp = train_df.sample(frac=1, random_state=SEED).reset_index(drop=True)\n",
        "    trn_texts_rhp, val_texts_rhp, trn_labels_rhp, val_labels_rhp = train_test_split(\n",
        "        rob_train_hp['text'].apply(preprocess_bert).tolist(),\n",
        "        rob_train_hp['label'].tolist(),\n",
        "        test_size=0.10, random_state=SEED, stratify=rob_train_hp['label'].tolist()\n",
        "    )\n",
        "\n",
        "    def encode_rhp(texts):\n",
        "        return tokenizer_rhp(texts, padding='max_length', truncation=True,\n",
        "                            max_length=ROBERTA_MAX_LEN, return_tensors='pt')\n",
        "\n",
        "    trn_enc_rhp = encode_rhp(trn_texts_rhp)\n",
        "    val_enc_rhp = encode_rhp(val_texts_rhp)\n",
        "    trn_ds_rhp = IMDBDataset(trn_enc_rhp, trn_labels_rhp)\n",
        "    val_ds_rhp = IMDBDataset(val_enc_rhp, val_labels_rhp)\n",
        "    trn_loader_rhp = DataLoader(trn_ds_rhp, batch_size=ROBERTA_BATCH_SIZE, shuffle=True)\n",
        "    val_loader_rhp = DataLoader(val_ds_rhp, batch_size=ROBERTA_BATCH_SIZE)\n",
        "\n",
        "    # 2 configs: baseline lr=1e-5 and exploratory lr=2e-5\n",
        "    hp_configs_rob = [\n",
        "        {'lr': 1e-5, 'warmup': 0.06, 'name': 'LR=1e-5 (baseline)'},\n",
        "        {'lr': 2e-5, 'warmup': 0.06, 'name': 'LR=2e-5'},\n",
        "    ]\n",
        "\n",
        "    hp_results_rob = {}\n",
        "\n",
        "    for cfg in hp_configs_rob:\n",
        "        name = cfg['name']\n",
        "        print(f\"\\n  -> Training: {name}\")\n",
        "\n",
        "        model_rhp = RobertaForSequenceClassification.from_pretrained(\n",
        "            'roberta-base', num_labels=2\n",
        "        ).to(DEVICE)\n",
        "\n",
        "        opt_rhp = AdamW(model_rhp.parameters(), lr=cfg['lr'], weight_decay=ROBERTA_WEIGHT_DECAY)\n",
        "        total_steps_rhp = len(trn_loader_rhp) * ROBERTA_EPOCHS\n",
        "        sched_rhp = get_linear_schedule_with_warmup(\n",
        "            opt_rhp,\n",
        "            num_warmup_steps=int(total_steps_rhp * cfg['warmup']),\n",
        "            num_training_steps=total_steps_rhp\n",
        "        )\n",
        "        scaler_rhp = torch.amp.GradScaler('cuda', enabled=USE_FP16)\n",
        "\n",
        "        best_f1_rhp, patience_rhp = 0.0, 0\n",
        "        epoch_f1s_rob = []\n",
        "\n",
        "        for epoch in range(1, ROBERTA_EPOCHS + 1):\n",
        "            model_rhp.train()\n",
        "            opt_rhp.zero_grad()  # Reset for gradient accumulation\n",
        "            for step, batch in enumerate(tqdm(trn_loader_rhp, desc=f\"    Ep {epoch}/{ROBERTA_EPOCHS}\", leave=False)):\n",
        "                ids = batch['input_ids'].to(DEVICE)\n",
        "                mask = batch['attention_mask'].to(DEVICE)\n",
        "                labs = batch['labels'].to(DEVICE)\n",
        "                with torch.amp.autocast('cuda', enabled=USE_FP16):\n",
        "                    out = model_rhp(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                scaler_rhp.scale(out.loss / ROBERTA_GRAD_ACCUM).backward()\n",
        "\n",
        "                if (step + 1) % ROBERTA_GRAD_ACCUM == 0 or (step + 1) == len(trn_loader_rhp):\n",
        "                    scaler_rhp.unscale_(opt_rhp)\n",
        "                    torch.nn.utils.clip_grad_norm_(model_rhp.parameters(), ROBERTA_GRAD_CLIP)\n",
        "                    scaler_rhp.step(opt_rhp)\n",
        "                    scaler_rhp.update()\n",
        "                    sched_rhp.step()\n",
        "                    opt_rhp.zero_grad()\n",
        "\n",
        "            # Validate\n",
        "            model_rhp.eval()\n",
        "            vp_r, vt_r = [], []\n",
        "            with torch.no_grad():\n",
        "                for batch in val_loader_rhp:\n",
        "                    ids = batch['input_ids'].to(DEVICE)\n",
        "                    mask = batch['attention_mask'].to(DEVICE)\n",
        "                    labs = batch['labels'].to(DEVICE)\n",
        "                    out = model_rhp(input_ids=ids, attention_mask=mask, labels=labs)\n",
        "                    vp_r.extend(torch.argmax(out.logits, dim=1).cpu().numpy())\n",
        "                    vt_r.extend(labs.cpu().numpy())\n",
        "\n",
        "            vf1_r = f1_score(vt_r, vp_r)\n",
        "            vacc_r = accuracy_score(vt_r, vp_r)\n",
        "            epoch_f1s_rob.append(vf1_r)\n",
        "            print(f\"     Epoch {epoch}: val_f1={vf1_r:.4f}, val_acc={vacc_r:.4f}\")\n",
        "\n",
        "            if vf1_r > best_f1_rhp:\n",
        "                best_f1_rhp, patience_rhp = vf1_r, 0\n",
        "            else:\n",
        "                patience_rhp += 1\n",
        "                if patience_rhp >= ROBERTA_PATIENCE:\n",
        "                    print(f\"     Early stopping at epoch {epoch}\")\n",
        "                    break\n",
        "\n",
        "        hp_results_rob[name] = {\n",
        "            'lr': cfg['lr'],\n",
        "            'warmup': cfg['warmup'],\n",
        "            'best_val_f1': best_f1_rhp,\n",
        "            'epochs_run': len(epoch_f1s_rob),\n",
        "            'epoch_f1s': epoch_f1s_rob,\n",
        "        }\n",
        "\n",
        "        del model_rhp, opt_rhp, sched_rhp\n",
        "        if DEVICE.type == 'cuda':\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "    # Summary\n",
        "    print(\"\\n\" + \"-\"*70)\n",
        "    print(f\"  {'Config':<25} {'Best Val F1':>12} {'Epochs':>8}\")\n",
        "    print(f\"  {'-'*47}\")\n",
        "    for name, res in hp_results_rob.items():\n",
        "        print(f\"  {name:<25} {res['best_val_f1']:>12.4f} {res['epochs_run']:>8}\")\n",
        "\n",
        "    best_config_rob = max(hp_results_rob.items(), key=lambda x: x[1]['best_val_f1'])\n",
        "    print(f\"\\n  Best RoBERTa config: {best_config_rob[0]} (F1={best_config_rob[1]['best_val_f1']:.4f})\")\n",
        "\n",
        "    # Visualization\n",
        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
        "    for name, res in hp_results_rob.items():\n",
        "        ax.plot(range(1, len(res['epoch_f1s'])+1), res['epoch_f1s'], 'o-', label=name, lw=2)\n",
        "    ax.set_xlabel('Epoch', fontsize=11)\n",
        "    ax.set_ylabel('Validation F1', fontsize=11)\n",
        "    ax.set_title('RoBERTa HP Grid Search: Validation F1 by Epoch', fontsize=12, fontweight='bold')\n",
        "    ax.legend(fontsize=10)\n",
        "    ax.grid(alpha=0.3)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/part16c_roberta_hp_grid.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "    return hp_results_rob\n",
        "\n",
        "# Execute\n",
        "results_roberta_hp_grid = roberta_hp_grid_search(train_df, test_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35f69a02",
      "metadata": {
        "id": "35f69a02"
      },
      "source": [
        "## Part 17: Calibration Analysis\n",
        "\n",
        "Analyze probability calibration for all 3 models using Expected Calibration Error (ECE). Addresses the AUC paradox where DistilBERT AUC dropped despite accuracy gain."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "12d4452b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "12d4452b",
        "outputId": "298968b7-2ac2-4bf9-d8ff-4b7078c68a7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 17 — CALIBRATION ANALYSIS & ECE COMPUTATION\n",
            "======================================================================\n",
            "\n",
            "Expected Calibration Error (ECE):\n",
            "  Logistic Regression: 0.0969\n",
            "  DistilBERT:          0.0579\n",
            "  RoBERTa:             0.0309\n",
            "\n",
            "Interpretation:\n",
            "  Lower ECE indicates better calibration.\n",
            "  DistilBERT ECE difference vs LR: -0.0390\n",
            "  RoBERTa ECE difference vs LR: -0.0660\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1500x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Visualization saved to ./output/run_20260329_093626/figures/part17_calibration_analysis.png\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 17: Calibration Analysis\n",
        "# ==========================================================\n",
        "\n",
        "from sklearn.calibration import calibration_curve\n",
        "\n",
        "def analyze_calibration(res_full, res_bert, res_roberta):\n",
        "    \"\"\"\n",
        "    Analyze probability calibration using Expected Calibration Error (ECE).\n",
        "\n",
        "    Computes reliability diagrams and ECE for each model.\n",
        "\n",
        "    Parameters:\n",
        "        res_full: Logistic Regression results\n",
        "        res_bert: DistilBERT results\n",
        "        res_roberta: RoBERTa results\n",
        "\n",
        "    Returns:\n",
        "        dict: ECE and calibration metrics\n",
        "    \"\"\"\n",
        "\n",
        "    # ECE: weighted average of |accuracy - confidence| across probability bins\n",
        "    # Lower ECE = model confidence better reflects true correctness probability\n",
        "    def compute_ece(y_true, y_proba, n_bins=10):\n",
        "        \"\"\"Compute Expected Calibration Error.\"\"\"\n",
        "        # Bin predictions by confidence level to compare predicted vs actual accuracy\n",
        "        bin_sums = np.zeros(n_bins)\n",
        "        bin_true = np.zeros(n_bins)\n",
        "        bin_total = np.zeros(n_bins)\n",
        "\n",
        "        for i in range(len(y_true)):\n",
        "            bin_idx = int(y_proba[i] * n_bins)\n",
        "            if bin_idx == n_bins:\n",
        "                bin_idx = n_bins - 1\n",
        "\n",
        "            bin_sums[bin_idx] += y_proba[i]\n",
        "            bin_true[bin_idx] += y_true[i]\n",
        "            bin_total[bin_idx] += 1\n",
        "\n",
        "        ece = 0\n",
        "        for i in range(n_bins):\n",
        "            if bin_total[i] > 0:\n",
        "                acc = bin_true[i] / bin_total[i]\n",
        "                conf = bin_sums[i] / bin_total[i]\n",
        "                ece += abs(acc - conf) * (bin_total[i] / len(y_true))\n",
        "\n",
        "        return ece\n",
        "\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 17 — CALIBRATION ANALYSIS & ECE COMPUTATION\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    results_calib = {}\n",
        "\n",
        "    # Extract test predictions\n",
        "    y_true = np.array(res_full['true_labels'])\n",
        "\n",
        "    # LR calibration\n",
        "    y_proba_lr = np.array(res_full['probabilities'])\n",
        "    ece_lr = compute_ece(y_true, y_proba_lr)\n",
        "    # Reliability curve: deviation from diagonal = miscalibration\n",
        "    prob_true_lr, prob_pred_lr = calibration_curve(y_true, y_proba_lr, n_bins=10)\n",
        "    results_calib['lr'] = {'ece': ece_lr, 'prob_true': prob_true_lr, 'prob_pred': prob_pred_lr}\n",
        "\n",
        "    # DistilBERT calibration\n",
        "    y_proba_bert = np.array(res_bert['probabilities'])\n",
        "    ece_bert = compute_ece(y_true, y_proba_bert)\n",
        "    prob_true_bert, prob_pred_bert = calibration_curve(y_true, y_proba_bert, n_bins=10)\n",
        "    results_calib['bert'] = {'ece': ece_bert, 'prob_true': prob_true_bert, 'prob_pred': prob_pred_bert}\n",
        "\n",
        "    # RoBERTa calibration\n",
        "    y_proba_roberta = np.array(res_roberta['probabilities'])\n",
        "    ece_roberta = compute_ece(y_true, y_proba_roberta)\n",
        "    prob_true_roberta, prob_pred_roberta = calibration_curve(y_true, y_proba_roberta, n_bins=10)\n",
        "    results_calib['roberta'] = {'ece': ece_roberta, 'prob_true': prob_true_roberta, 'prob_pred': prob_pred_roberta}\n",
        "\n",
        "    # Print ECE values\n",
        "    print(f\"\\nExpected Calibration Error (ECE):\")\n",
        "    print(f\"  Logistic Regression: {ece_lr:.4f}\")\n",
        "    print(f\"  DistilBERT:          {ece_bert:.4f}\")\n",
        "    print(f\"  RoBERTa:             {ece_roberta:.4f}\")\n",
        "\n",
        "    print(f\"\\nInterpretation:\")\n",
        "    print(f\"  Lower ECE indicates better calibration.\")\n",
        "    print(f\"  DistilBERT ECE difference vs LR: {ece_bert - ece_lr:+.4f}\")\n",
        "    print(f\"  RoBERTa ECE difference vs LR: {ece_roberta - ece_lr:+.4f}\")\n",
        "\n",
        "\n",
        "    # Reliability diagram: points above diagonal = underconfident, below = overconfident\n",
        "    # Create reliability diagrams\n",
        "    fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
        "\n",
        "    models = [\n",
        "        ('Logistic Regression', prob_pred_lr, prob_true_lr, ece_lr),\n",
        "        ('DistilBERT', prob_pred_bert, prob_true_bert, ece_bert),\n",
        "        ('RoBERTa', prob_pred_roberta, prob_true_roberta, ece_roberta),\n",
        "    ]\n",
        "\n",
        "    for idx, (name, prob_pred, prob_true, ece) in enumerate(models):\n",
        "        ax = axes[idx]\n",
        "\n",
        "        # Perfect calibration line\n",
        "        ax.plot([0, 1], [0, 1], 'k--', label='Perfect Calibration', linewidth=2)\n",
        "\n",
        "        # Reliability diagram\n",
        "        ax.plot(prob_pred, prob_true, 'o-', label='Model', linewidth=2, markersize=8)\n",
        "\n",
        "        ax.set_xlabel('Predicted Probability', fontsize=10)\n",
        "        ax.set_ylabel('True Probability', fontsize=10)\n",
        "        ax.set_title(f'{name}\\nECE: {ece:.4f}', fontsize=11, fontweight='bold')\n",
        "        ax.set_xlim([0, 1])\n",
        "        ax.set_ylim([0, 1])\n",
        "        ax.legend()\n",
        "        ax.grid(alpha=0.3)\n",
        "\n",
        "    plt.suptitle('Calibration Curves: Expected Calibration Error Analysis',\n",
        "                 fontsize=13, fontweight='bold', y=1.02)\n",
        "    plt.tight_layout()\n",
        "\n",
        "    fig_path = f\"{FIGURES_DIR}/part17_calibration_analysis.png\"\n",
        "    plt.savefig(fig_path, dpi=300, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"\\nVisualization saved to {fig_path}\")\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "    return results_calib\n",
        "\n",
        "# Execute Part 17\n",
        "results_calibration = analyze_calibration(res_full, res_bert, res_roberta)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ede5cc1",
      "metadata": {
        "id": "8ede5cc1"
      },
      "source": [
        "## Part 18: Length-Stratified Performance Analysis\n",
        "\n",
        "Break test set into length buckets and compute accuracy/F1 per bucket per model to identify where each model struggles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "7ba1d80d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "7ba1d80d",
        "outputId": "98a79906-f975-42aa-923e-1dc53600ba2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 18 — LENGTH-STRATIFIED PERFORMANCE ANALYSIS\n",
            "======================================================================\n",
            "\n",
            "Accuracy by Length Bucket:\n",
            "----------------------------------------------------------------------\n",
            "Bucket                      N           LR   DistilBERT      RoBERTa\n",
            "----------------------------------------------------------------------\n",
            "Short (<100)             3091        89.6%        93.1%        94.9%\n",
            "Medium (100-300)        16482        89.2%        93.4%        96.0%\n",
            "Long (>300)              5427        88.8%        91.1%        94.6%\n",
            "\n",
            "F1-Score by Length Bucket:\n",
            "----------------------------------------------------------------------\n",
            "Bucket                      N           LR   DistilBERT      RoBERTa\n",
            "----------------------------------------------------------------------\n",
            "Short (<100)             3091        90.4%        93.6%        95.3%\n",
            "Medium (100-300)        16482        89.1%        93.3%        96.0%\n",
            "Long (>300)              5427        88.8%        91.1%        94.6%\n"
          ]
        },
        {
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",
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Visualization saved to ./output/run_20260329_093626/figures/part18_length_stratified.png\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 18: Length-Stratified Performance Analysis\n",
        "# ==========================================================\n",
        "\n",
        "def analyze_length_stratified_performance(test_df, res_full, res_bert, res_roberta):\n",
        "    \"\"\"\n",
        "    Analyze model performance across different text length buckets.\n",
        "\n",
        "    Buckets:\n",
        "    - Short: < 100 words\n",
        "    - Medium: 100-300 words\n",
        "    - Long: > 300 words\n",
        "\n",
        "    Parameters:\n",
        "        test_df: Test dataframe\n",
        "        res_full: LR results\n",
        "        res_bert: DistilBERT results\n",
        "        res_roberta: RoBERTa results\n",
        "\n",
        "    Returns:\n",
        "        dict: Performance by length bucket\n",
        "    \"\"\"\n",
        "\n",
        "    from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 18 — LENGTH-STRATIFIED PERFORMANCE ANALYSIS\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    # Add word counts\n",
        "    test_df_copy = test_df.copy()\n",
        "    test_df_copy['word_count'] = test_df_copy['text'].str.split().str.len()\n",
        "\n",
        "    # Define buckets\n",
        "    def get_bucket(wc):\n",
        "        if wc < 100:\n",
        "            return 'Short (<100)'\n",
        "        elif wc <= 300:\n",
        "            return 'Medium (100-300)'\n",
        "        else:\n",
        "            return 'Long (>300)'\n",
        "\n",
        "    test_df_copy['bucket'] = test_df_copy['word_count'].apply(get_bucket)\n",
        "\n",
        "    # Get predictions\n",
        "    y_true = np.array(res_full['true_labels'])\n",
        "    y_pred_lr = np.array(res_full['predictions'])\n",
        "    y_pred_bert = np.array(res_bert['predictions'])\n",
        "    y_pred_roberta = np.array(res_roberta['predictions'])\n",
        "\n",
        "    buckets = ['Short (<100)', 'Medium (100-300)', 'Long (>300)']\n",
        "    results_length = {}\n",
        "\n",
        "    # Compute metrics per bucket\n",
        "    for bucket in buckets:\n",
        "        mask = (test_df_copy['bucket'] == bucket).values\n",
        "        n_samples = mask.sum()\n",
        "\n",
        "        if n_samples == 0:\n",
        "            continue\n",
        "\n",
        "        y_true_bucket = y_true[mask]\n",
        "        y_pred_lr_bucket = y_pred_lr[mask]\n",
        "        y_pred_bert_bucket = y_pred_bert[mask]\n",
        "        y_pred_roberta_bucket = y_pred_roberta[mask]\n",
        "\n",
        "        acc_lr = accuracy_score(y_true_bucket, y_pred_lr_bucket)\n",
        "        acc_bert = accuracy_score(y_true_bucket, y_pred_bert_bucket)\n",
        "        acc_roberta = accuracy_score(y_true_bucket, y_pred_roberta_bucket)\n",
        "\n",
        "        f1_lr = f1_score(y_true_bucket, y_pred_lr_bucket)\n",
        "        f1_bert = f1_score(y_true_bucket, y_pred_bert_bucket)\n",
        "        f1_roberta = f1_score(y_true_bucket, y_pred_roberta_bucket)\n",
        "\n",
        "        results_length[bucket] = {\n",
        "            'n_samples': n_samples,\n",
        "            'accuracy': {'lr': acc_lr, 'bert': acc_bert, 'roberta': acc_roberta},\n",
        "            'f1': {'lr': f1_lr, 'bert': f1_bert, 'roberta': f1_roberta},\n",
        "        }\n",
        "\n",
        "    # Print table\n",
        "    print(f\"\\nAccuracy by Length Bucket:\")\n",
        "    print(\"-\" * 70)\n",
        "    print(f\"{'Bucket':<20} {'N':>8} {'LR':>12} {'DistilBERT':>12} {'RoBERTa':>12}\")\n",
        "    print(\"-\" * 70)\n",
        "\n",
        "    for bucket in buckets:\n",
        "        if bucket in results_length:\n",
        "            res = results_length[bucket]\n",
        "            acc = res['accuracy']\n",
        "            print(f\"{bucket:<20} {res['n_samples']:>8} \"\n",
        "                  f\"{acc['lr']:>12.1%} {acc['bert']:>12.1%} {acc['roberta']:>12.1%}\")\n",
        "\n",
        "    print(f\"\\nF1-Score by Length Bucket:\")\n",
        "    print(\"-\" * 70)\n",
        "    print(f\"{'Bucket':<20} {'N':>8} {'LR':>12} {'DistilBERT':>12} {'RoBERTa':>12}\")\n",
        "    print(\"-\" * 70)\n",
        "\n",
        "    for bucket in buckets:\n",
        "        if bucket in results_length:\n",
        "            res = results_length[bucket]\n",
        "            f1 = res['f1']\n",
        "            print(f\"{bucket:<20} {res['n_samples']:>8} \"\n",
        "                  f\"{f1['lr']:>12.1%} {f1['bert']:>12.1%} {f1['roberta']:>12.1%}\")\n",
        "\n",
        "    # Create grouped bar chart\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "    buckets_plot = [b for b in buckets if b in results_length]\n",
        "    x = np.arange(len(buckets_plot))\n",
        "    width = 0.25\n",
        "\n",
        "    acc_lr_vals = [results_length[b]['accuracy']['lr'] for b in buckets_plot]\n",
        "    acc_bert_vals = [results_length[b]['accuracy']['bert'] for b in buckets_plot]\n",
        "    acc_roberta_vals = [results_length[b]['accuracy']['roberta'] for b in buckets_plot]\n",
        "\n",
        "    axes[0].bar(x - width, acc_lr_vals, width, label='LR', color='steelblue')\n",
        "    axes[0].bar(x, acc_bert_vals, width, label='DistilBERT', color='seagreen')\n",
        "    axes[0].bar(x + width, acc_roberta_vals, width, label='RoBERTa', color='coral')\n",
        "    axes[0].set_ylabel('Accuracy', fontsize=11)\n",
        "    axes[0].set_title('Accuracy by Text Length Bucket', fontsize=12, fontweight='bold')\n",
        "    axes[0].set_xticks(x)\n",
        "    axes[0].set_xticklabels(buckets_plot)\n",
        "    axes[0].legend()\n",
        "    axes[0].set_ylim([0.7, 1.0])\n",
        "    axes[0].grid(axis='y', alpha=0.3)\n",
        "\n",
        "    f1_lr_vals = [results_length[b]['f1']['lr'] for b in buckets_plot]\n",
        "    f1_bert_vals = [results_length[b]['f1']['bert'] for b in buckets_plot]\n",
        "    f1_roberta_vals = [results_length[b]['f1']['roberta'] for b in buckets_plot]\n",
        "\n",
        "    axes[1].bar(x - width, f1_lr_vals, width, label='LR', color='steelblue')\n",
        "    axes[1].bar(x, f1_bert_vals, width, label='DistilBERT', color='seagreen')\n",
        "    axes[1].bar(x + width, f1_roberta_vals, width, label='RoBERTa', color='coral')\n",
        "    axes[1].set_ylabel('F1-Score', fontsize=11)\n",
        "    axes[1].set_title('F1-Score by Text Length Bucket', fontsize=12, fontweight='bold')\n",
        "    axes[1].set_xticks(x)\n",
        "    axes[1].set_xticklabels(buckets_plot)\n",
        "    axes[1].legend()\n",
        "    axes[1].set_ylim([0.7, 1.0])\n",
        "    axes[1].grid(axis='y', alpha=0.3)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    fig_path = f\"{FIGURES_DIR}/part18_length_stratified.png\"\n",
        "    plt.savefig(fig_path, dpi=300, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"\\nVisualization saved to {fig_path}\")\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "    return results_length\n",
        "\n",
        "# Execute Part 18\n",
        "results_length = analyze_length_stratified_performance(test_df, res_full, res_bert, res_roberta)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09dd96d4",
      "metadata": {
        "id": "09dd96d4"
      },
      "source": [
        "## Part 19: Cross-Model Error Agreement Analysis\n",
        "\n",
        "Analyze which errors are shared across models vs unique to each. Identify hard examples and transformer advantages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "ee76917a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 811
        },
        "id": "ee76917a",
        "outputId": "949904ac-c793-4263-acb5-a0a54eadb589"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 19 — CROSS-MODEL ERROR AGREEMENT ANALYSIS\n",
            "======================================================================\n",
            "\n",
            "Total Errors:\n",
            "  Logistic Regression: 2712\n",
            "  DistilBERT:          1779\n",
            "  RoBERTa:             1104\n",
            "\n",
            "Error Agreement Breakdown:\n",
            "  All 3 models wrong: 467\n",
            "  LR + DistilBERT only: 451\n",
            "  LR + RoBERTa only: 136\n",
            "  DistilBERT + RoBERTa only: 270\n",
            "  Only LR wrong: 1658\n",
            "  Only DistilBERT wrong: 591\n",
            "  Only RoBERTa wrong: 231\n",
            "\n",
            "Pairwise Error Overlap:\n",
            "  LR ∩ DistilBERT: 918 (33.8% of LR errors)\n",
            "  LR ∩ RoBERTa: 603 (22.2% of LR errors)\n",
            "  DistilBERT ∩ RoBERTa: 737 (41.4% of DistilBERT errors)\n",
            "\n",
            "Transformer Advantage:\n",
            "  Examples LR gets wrong but BOTH transformers get right: 1658\n",
            "  Percentage of LR errors: 61.1%\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Visualization saved to ./output/run_20260329_093626/figures/part19_error_agreement.png\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 19: Cross-Model Error Agreement Analysis\n",
        "# ==========================================================\n",
        "\n",
        "def analyze_error_agreement(res_full, res_bert, res_roberta):\n",
        "    \"\"\"\n",
        "    Analyze error patterns across models.\n",
        "\n",
        "    Computes:\n",
        "    - Shared errors (all 3 models wrong)\n",
        "    - Unique errors (only 1 model wrong)\n",
        "    - Pairwise error overlaps\n",
        "\n",
        "    Parameters:\n",
        "        res_full: LR results\n",
        "        res_bert: DistilBERT results\n",
        "        res_roberta: RoBERTa results\n",
        "\n",
        "    Returns:\n",
        "        dict: Error agreement analysis\n",
        "    \"\"\"\n",
        "\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 19 — CROSS-MODEL ERROR AGREEMENT ANALYSIS\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    y_true = np.array(res_full['true_labels'])\n",
        "    y_pred_lr = np.array(res_full['predictions'])\n",
        "    y_pred_bert = np.array(res_bert['predictions'])\n",
        "    y_pred_roberta = np.array(res_roberta['predictions'])\n",
        "\n",
        "    # Binary error vectors: 1 where model prediction != true label, 0 otherwise\n",
        "    # Compute errors\n",
        "    # Binary error masks: 1 where model is wrong, 0 where correct\n",
        "    err_lr = (y_pred_lr != y_true).astype(int)\n",
        "    err_bert = (y_pred_bert != y_true).astype(int)\n",
        "    err_roberta = (y_pred_roberta != y_true).astype(int)\n",
        "\n",
        "    # Decompose error space using set operations on binary error vectors\n",
        "    # This identifies which errors are shared vs unique to each model\n",
        "    # Shared errors\n",
        "    # Set decomposition: partition the error space into 7 non-overlapping regions\n",
        "    # using bitwise AND/NOT on error masks to identify shared vs unique failures\n",
        "    all_wrong = (err_lr & err_bert & err_roberta).sum()\n",
        "    lr_bert = (err_lr & err_bert & ~err_roberta).sum()\n",
        "    lr_roberta = (err_lr & ~err_bert & err_roberta).sum()\n",
        "    bert_roberta = (~err_lr & err_bert & err_roberta).sum()\n",
        "    only_lr = (err_lr & ~err_bert & ~err_roberta).sum()\n",
        "    only_bert = (~err_lr & err_bert & ~err_roberta).sum()\n",
        "    only_roberta = (~err_lr & ~err_bert & err_roberta).sum()\n",
        "\n",
        "    total_errors_lr = err_lr.sum()\n",
        "    total_errors_bert = err_bert.sum()\n",
        "    total_errors_roberta = err_roberta.sum()\n",
        "\n",
        "    print(f\"\\nTotal Errors:\")\n",
        "    print(f\"  Logistic Regression: {total_errors_lr}\")\n",
        "    print(f\"  DistilBERT:          {total_errors_bert}\")\n",
        "    print(f\"  RoBERTa:             {total_errors_roberta}\")\n",
        "\n",
        "    print(f\"\\nError Agreement Breakdown:\")\n",
        "    print(f\"  All 3 models wrong: {all_wrong}\")\n",
        "    print(f\"  LR + DistilBERT only: {lr_bert}\")\n",
        "    print(f\"  LR + RoBERTa only: {lr_roberta}\")\n",
        "    print(f\"  DistilBERT + RoBERTa only: {bert_roberta}\")\n",
        "    print(f\"  Only LR wrong: {only_lr}\")\n",
        "    print(f\"  Only DistilBERT wrong: {only_bert}\")\n",
        "    print(f\"  Only RoBERTa wrong: {only_roberta}\")\n",
        "\n",
        "    # Pairwise overlaps\n",
        "    lr_bert_overlap = (err_lr & err_bert).sum()\n",
        "    lr_roberta_overlap = (err_lr & err_roberta).sum()\n",
        "    bert_roberta_overlap = (err_bert & err_roberta).sum()\n",
        "\n",
        "    print(f\"\\nPairwise Error Overlap:\")\n",
        "    print(f\"  LR ∩ DistilBERT: {lr_bert_overlap} ({lr_bert_overlap/total_errors_lr*100:.1f}% of LR errors)\")\n",
        "    print(f\"  LR ∩ RoBERTa: {lr_roberta_overlap} ({lr_roberta_overlap/total_errors_lr*100:.1f}% of LR errors)\")\n",
        "    print(f\"  DistilBERT ∩ RoBERTa: {bert_roberta_overlap} ({bert_roberta_overlap/total_errors_bert*100:.1f}% of DistilBERT errors)\")\n",
        "\n",
        "    # Transformer advantage: errors LR makes but transformers get right\n",
        "    # Key metric: errors that LR makes but BOTH transformers correct\n",
        "    # High value indicates transformer-specific contextual understanding\n",
        "    lr_alone = (err_lr & ~err_bert & ~err_roberta).sum()\n",
        "    # Key insight: high transformer advantage % = errors requiring contextual understanding\n",
        "    transformer_advantage_pct = (lr_alone / total_errors_lr * 100) if total_errors_lr > 0 else 0\n",
        "\n",
        "    print(f\"\\nTransformer Advantage:\")\n",
        "    print(f\"  Examples LR gets wrong but BOTH transformers get right: {lr_alone}\")\n",
        "    print(f\"  Percentage of LR errors: {transformer_advantage_pct:.1f}%\")\n",
        "\n",
        "    # Create visualization\n",
        "    from matplotlib.patches import Rectangle\n",
        "\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "    # Bar chart of error types\n",
        "    categories = ['All 3\\nWrong', 'LR+BERT', 'LR+RoBERTa', 'BERT+RoBERTa',\n",
        "                  'Only LR', 'Only BERT', 'Only RoBERTa']\n",
        "    values = [all_wrong, lr_bert, lr_roberta, bert_roberta, only_lr, only_bert, only_roberta]\n",
        "    colors = ['#d62728', '#ff7f0e', '#ff7f0e', '#ff7f0e', '#1f77b4', '#2ca02c', '#9467bd']\n",
        "\n",
        "    axes[0].barh(categories, values, color=colors)\n",
        "    axes[0].set_xlabel('Number of Examples', fontsize=11)\n",
        "    axes[0].set_title('Error Agreement Breakdown', fontsize=12, fontweight='bold')\n",
        "    axes[0].grid(axis='x', alpha=0.3)\n",
        "\n",
        "    # Pairwise overlap\n",
        "    overlap_models = ['LR∩DistilBERT', 'LR∩RoBERTa', 'DistilBERT∩RoBERTa']\n",
        "    overlap_values = [lr_bert_overlap, lr_roberta_overlap, bert_roberta_overlap]\n",
        "\n",
        "    axes[1].bar(overlap_models, overlap_values, color=['steelblue', 'steelblue', 'seagreen'])\n",
        "    axes[1].set_ylabel('Number of Shared Errors', fontsize=11)\n",
        "    axes[1].set_title('Pairwise Error Overlap', fontsize=12, fontweight='bold')\n",
        "    axes[1].grid(axis='y', alpha=0.3)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    fig_path = f\"{FIGURES_DIR}/part19_error_agreement.png\"\n",
        "    plt.savefig(fig_path, dpi=300, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    print(f\"\\nVisualization saved to {fig_path}\")\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "    results_errors = {\n",
        "        'all_wrong': all_wrong,\n",
        "        'lr_bert': lr_bert,\n",
        "        'lr_roberta': lr_roberta,\n",
        "        'bert_roberta': bert_roberta,\n",
        "        'only_lr': only_lr,\n",
        "        'only_bert': only_bert,\n",
        "        'only_roberta': only_roberta,\n",
        "        'total_errors': {\n",
        "            'lr': total_errors_lr,\n",
        "            'bert': total_errors_bert,\n",
        "            'roberta': total_errors_roberta,\n",
        "        },\n",
        "        'pairwise_overlap': {\n",
        "            'lr_bert': lr_bert_overlap,\n",
        "            'lr_roberta': lr_roberta_overlap,\n",
        "            'bert_roberta': bert_roberta_overlap,\n",
        "        },\n",
        "        'transformer_advantage': {\n",
        "            'count': lr_alone,\n",
        "            'percentage': transformer_advantage_pct,\n",
        "        },\n",
        "    }\n",
        "\n",
        "    return results_errors\n",
        "\n",
        "# Execute Part 19\n",
        "results_error_agreement = analyze_error_agreement(res_full, res_bert, res_roberta)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "34eb8063",
      "metadata": {
        "id": "34eb8063"
      },
      "source": [
        "## Part 20: Published Baselines Comparison\n",
        "Compare our results against published baselines from the literature.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "95163640",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "95163640",
        "outputId": "4b59e538-be4d-4f07-f51a-db95ebd5d470"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 20 — PUBLISHED BASELINES COMPARISON\n",
            "======================================================================\n",
            "\n",
            "     Model           Source  Accuracy (%)                               Notes\n",
            " TF-IDF+LR             Ours        89.152 GridSearchCV, best_params from code\n",
            " TF-IDF+LR Maas et al. 2011        88.890   Word vector + supervised learning\n",
            "DistilBERT             Ours        92.884   F1-based early stopping, 8 epochs\n",
            "DistilBERT Sanh et al. 2019        92.700   SST-2 benchmark (comparable task)\n",
            "   RoBERTa             Ours        95.584     Gradient accumulation, 8 epochs\n",
            "   RoBERTa  Liu et al. 2019        95.300      Full pretraining + fine-tuning\n"
          ]
        },
        {
          "data": {
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zZnrjjTfk4uKipUuX6osvvjC3HTdunPnfWZN2STeCeYkSJXTixIls7S3p9ddf14QJE5SUlKT9+/dr3bp1at68uZo1ayYvLy9dunRJX331lTw8PNSsWTNlZGQoJiZGO3bs0P79+80Twj3++OOqXbu2HnnkEfn6+iohISHbZHFZp9HevL+vv/66evbsqZUrV2Y7vTYvHnroIUVHR0uSpk6dqtDQULm7u6tGjRoaMGCAzp8/r2bNmql8+fKytbU1T7J2cy2306lTJw0ePFjp6enmPwiULVtWTzzxhLnN2bNnFR4erhdeeEEhISEqU6aMoqOjzZcX3Ovp5f9UsmRJlSpVSn/99ZdOnDihPn36KCgoKNuka7dy6tQpdevWTV26dNHGjRu1Y8cOSZKDg4NatmyZ71omTJigLVu2qE2bNubbZq1du9a8/m723Wg0qnPnzpo+fboSExPVvHlzDRgwQJ6enjpz5owOHTqk7777Tl988YXCwsL09NNPa8aMGZKkadOmqVy5cvL39892u7X8unbtmrZv367MzEwdOHBA8+bNM6/LCsb+/v7mZV988YUqVqyoEydOaMyYMTn6y+t3JL//vVlKfj8DS3wPAKDQuR9TpAMA8mbTpk2mkiVL3vL2OUaj0TR58uRsr/n7779Nfn5+ud52KOvft7pl2ObNm/Nc261uGWYymUz9+vUzrwsPDzcv/+GHH255CyP97/ZDWSpVqnTLdiEhIebbLR05ciTXWwwFBwfn+RZIJpPJ9Oabb+boI+t2T6+88sota3FycjLfru1OWrVqle21gwcPzrb+9OnTt9yOJFPv3r3vuI2b29/ulm9ZIiMjc2zHx8cn2/cuy823iqpYsWKu7/uYMWPM7fNzy7DRo0ff9nu+ffv2HDX883uX2/foypUrt71d1T+/9//8jCSZvLy8TO7u7rl+b3Jzp9vQSTL5+fmZLl++bDKZTKb4+HiTj49PjjaPP/54jn3Nz3ckP/+93e0twwr6M8jr9wAAijJOLweAQqRp06Y6evSo3nrrLVWvXl0uLi5ycHBQQECAunXrpj179mS7XZN0Yxbo5cuX67HHHpO9vb3KlSunUaNG6dNPP71vdQ8cONB8uvaGDRvME0a1bt1ae/bs0UsvvaRy5crJzs5Onp6eqlWrlgYPHmy+xlWSIiMj9cwzz8jf31/Ozs6ys7NTQECA+vTpo02bNpkn8apataoWLFigwMBA2dvbq3r16vr666/VsWPHfNU8YsQI9erVS2XLls1xmn3Xrl3VrVs3BQUFyd3dXTY2NipdurTatWunbdu25fl06ptPJc/tuYeHh0aMGKEmTZrIx8dHdnZ2cnJy0sMPP6wxY8Zo6tSp+dqnvHjvvffUq1cvlSxZUi4uLnrmmWe0Y8eOO07M16hRI61YsUK1a9eWg4OD/P39NWnSJL377rt3VUfr1q3Vu3dvVa9eXQ899JBsbGzk4eGh5s2ba+3atXr88cfvqt+SJUvq559/1ujRo1WzZk05OTnJ2dlZlStX1nPPPadFixbp0UcfNbf/5ptv1K9fP5UqVUrOzs5q0aKFfvzxR5UsWfKutn8zR0dHValSRQMGDNAvv/xiPu3a1dVV69ev1xNPPKESJUrI19dX77//vt5///0cfeTnO5Kf/94sKT+fgaW+BwBQmBhMpnzOfAEAAAAAAPKEI90AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCFWD90JCQkaOHCg/P395eTkpAYNGmj37t3m9YmJierfv7/KlSsnJycnhYSE6PPPP7dixQAAAAAA5I3V79Pds2dPHTp0SPPmzVPZsmU1f/58hYeH68iRI/L19dXgwYO1adMmzZ8/XwEBAVq3bp1ee+01lS1bVk8//bS1ywcAAAAA4JasOnt5cnKyXF1d9f3336tNmzbm5aGhoWrVqpXGjBmj6tWrq2PHjho+fHiu6wEAAAAAKKyseqQ7PT1dGRkZcnR0zLbcyclJ27dvlyQ1aNBAK1asUI8ePVS2bFlt2bJFx48f18cff3zLflNTU5Wammp+npmZqcuXL6tUqVI57sUKAAAAAEB+mUwmJSQkqGzZsjIab3PltsnKHnvsMVOTJk1MZ8+eNaWnp5vmzZtnMhqNpipVqphMJpMpJSXF9PLLL5skmWxtbU329vamL7/88rZ9jhgxwiSJBw8ePHjw4MGDBw8ePHjwsOjj9OnTt82nVj29XJJOnjypHj166Mcff5SNjY3q1KmjKlWqKCoqSkePHtVHH32kmTNn6qOPPpK/v79+/PFHRUZGatmyZQoPD8+1z38e6b527Zr8/Px06tQpubm53a9dAwAAAAAUU/Hx8fL399fVq1fl7u5+y3ZWD91Zrl+/rvj4ePn4+Khjx45KTEzU0qVL5e7urmXLlmW75rtnz546c+aM1qxZk6e+4+Pj5e7urmvXrhG6AQAAAAD3LK850+q3DMvi4uIiHx8fXblyRWvXrtUzzzyjtLQ0paWl5Tg/3sbGRpmZmVaqFAAAAACAvLH6LcPWrl0rk8mkoKAgnThxQkOHDlVwcLAiIiJkZ2enJk2aaOjQoXJycpK/v7+2bt2qr776SpMnT7Z26QAAAAAA3JbVQ/e1a9cUGRmpM2fOyMPDQx06dNDYsWNlZ2cnSVq8eLEiIyPVtWtXXb58Wf7+/ho7dqz69Olj5coBAAAAALi9QnNNtyVxTTcAAABgGRkZGUpLS7N2GUCBs7Ozk42NzS3X5zVnWv1INwAAAICix2Qy6cKFC7p69aq1SwEspmTJkvL29pbBYLjrPgjdAAAAAPItK3CXLl1azs7O9xRKgMLGZDIpKSlJFy9elCT5+PjcdV+EbgAAAAD5kpGRYQ7cpUqVsnY5gEU4OTlJki5evKjSpUvf9lTz2yk0twwDAAAAUDRkXcPt7Oxs5UoAy8r6jt/LvAWEbgAAAAB3hVPKUdwVxHec0A0AAAAAgIVwTTcAAACAAhEbG6u4uLj7tj1PT0/5+fndt+0VZd27d9fVq1e1fPlya5fywCF0AwAAALhnsbGxCqoarJSk5Pu2TUdnJ/129Fi+gvfp06c1YsQIrVmzRnFxcfLx8VG7du303nvvWWxSuICAAJ06dUrSjcm5KlWqpDfeeEM9e/a0yPbyIiwsTLVq1dKUKVOsVsODgtANAAAA4J7FxcUpJSlZ5Ue3lEMFD4tvLzX6sk4PvxGc8xq6//jjDz322GOqUqWKFi1apAoVKujw4cMaOnSoVq9erZ07d8rD4+5rT0tLk52dXa7r3n//fb366qtKSkrSN998o1dffVW+vr5q1arVXW8PRQPXdAMAAAAoMA4VPOQcXMbij7sJ9v369ZO9vb3WrVunJk2ayM/PT61atdKGDRt09uxZvfvuu+a2BoMhx6nYJUuW1Ny5cyVJMTExMhgMWrJkiZo0aSJHR0ctWLDgltt2dXWVt7e3KlasqLfeekseHh5av369ef3Vq1fVs2dPeXl5yc3NTU888YT2799vXr9//341bdpUrq6ucnNzU2hoqPbs2SNJGjlypGrVqpVte1OmTFFAQECutXTv3l1bt27VJ598IoPBIIPBoJiYGF25ckVdu3aVl5eXnJycVLlyZc2ZMycP7yxuhyPdAAAAAIq9y5cva+3atRo7dqz5/stZvL291bVrVy1ZskTTp0/P14zVb7/9tiZNmqTatWvL0dHxju0zMzO1bNkyXblyRfb29ublzz//vJycnLR69Wq5u7vr3//+t5588kkdP35cHh4e6tq1q2rXrq0ZM2bIxsZGv/766y2Pqt/JJ598ouPHj6t69ep6//33JUleXl564403dOTIEa1evVqenp46ceKEkpPv3+UCxRWhGwAAAECx9/vvv8tkMqlq1aq5rq9ataquXLmiS5cuqXTp0nnud+DAgWrfvv0d27311lv617/+pdTUVKWnp8vDw8N8Tff27du1a9cuXbx4UQ4ODpKkjz76SMuXL9fSpUvVq1cvxcbGaujQoQoODpYkVa5cOc81/pO7u7vs7e3l7Owsb29v8/LY2FjVrl1bdevWlaRbHilH/nB6OQAAAIAHhslkKtD+sgLqnQwdOlS//vqrNm3apPr16+vjjz9WYGCgpBunjicmJqpUqVIqUaKE+REdHa2TJ09KkgYPHqyePXsqPDxcH3zwgXl5Qerbt68WL16sWrVqadiwYfrpp58KfBsPIkI3AAAAgGIvMDBQBoNBR48ezXX90aNH9dBDD8nLy0vSjWu6/xnQ09LScrzOxcUlT9v39PRUYGCgGjVqpG+++UYDBgzQkSNHJEmJiYny8fHRr7/+mu3x22+/aejQoZJuXLd9+PBhtWnTRps2bVJISIiWLVsmSTIajXmq9U5atWqlU6dOadCgQTp37pyefPJJDRkyJN/9IDtCNwAAAIBir1SpUmrWrJmmT5+e4zrlCxcuaMGCBerYsaP5em4vLy+dP3/e3Ob3339XUlJSgdRSvnx5dezYUZGRkZKkOnXq6MKFC7K1tVVgYGC2h6enp/l1VapU0aBBg7Ru3Tq1b9/ePMmZl5eXLly4kC14//rrr7etwd7eXhkZGTmWe3l5qVu3bpo/f76mTJmi//znPwWwxw82QjcAAACAB8K0adOUmpqqFi1a6Mcff9Tp06e1Zs0aNWvWTL6+vho7dqy57RNPPKFp06Zp37592rNnj/r06XPXE5fl5o033tB///tf7dmzR+Hh4XrsscfUrl07rVu3TjExMfrpp5/07rvvas+ePUpOTlb//v21ZcsWnTp1Sjt27NDu3bvN16eHhYXp0qVLmjBhgk6ePKnPPvtMq1evvu32AwIC9MsvvygmJkZxcXHKzMzUe++9p++//14nTpzQ4cOHtXLlylteA4+8YyI1AAAAAAUmNfpyod1O5cqVtWfPHo0YMUIvvPCCLl++LG9vb7Vr104jRozIdo/uSZMmKSIiQo0aNVLZsmX1ySefKCoqqsDqDwkJUfPmzfXee+9p1apVWrVqld59911FRETo0qVL8vb2VuPGjVWmTBnZ2Njor7/+0ssvv6w///xTnp6eat++vUaNGiXpxiRw06dP17hx4zR69Gh16NBBQ4YMue1R6iFDhqhbt24KCQlRcnKyoqOjZW9vr8jISMXExMjJyUmNGjXS4sWLC2yfH1QGU0HPJFAIxcfHy93dXdeuXZObm5u1ywEAAACKtJSUFEVHR6tChQrm22TFxsYqqGqwUpLu3y2mHJ2d9NvRY/Lz87tv28SDJbfvepa85kyOdAMAAAC4Z35+fvrt6DHFxcXdt216enoSuFHoEboBAAAAFAg/Pz9CMPAPTKQGAAAAAICFELoBAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwAAAAAACyF0AwAAAABgIdynGwAAAECBiI2NVVxc3H3bnqen532/L3hAQIAGDhyogQMH3rKNwWDQsmXL1K5dO8XExKhChQrat2+fatWqddu+89PWEnUXBmFhYapVq5amTJkiqejUfTuEbgAAAAD3LDY2VkHBVZWSnHTftuno5Kzfjh3Nc/Du3r27vvzyS0mSnZ2d/Pz89PLLL+udd96Rra1lolH58uV1/vx5eXp6WqT/gjJy5EiNGjXK/NzNzU0PP/ywxowZoyZNmlitrt27d8vFxcVq2y8IhG4AAAAA9ywuLk4pyUmqEPGRHH0CLb69lPMnFD1niOLi4vJ1tLtly5aaM2eOUlNTtWrVKvXr1092dnaKjIy0SJ02Njby9va2SN8FrVq1atqwYYMk6fLly/roo4/01FNP6cyZM3J3d7dKTV5eXlbZbkHimm4AAAAABcbRJ1AuftUs/rjbYO/g4CBvb2/5+/urb9++Cg8P14oVKyTdOLX5n6cxt2vXTt27d8+2LCEhQZ07d5aLi4t8fX312Wef3XJ7MTExMhgM+vXXXyVJV65cUdeuXeXl5SUnJydVrlxZc+bMyfaaP/74Q02bNpWzs7Nq1qypn3/+Odv67du3q1GjRnJyclL58uU1YMAAXb9+3bz+4sWLatu2rZycnFShQgUtWLAgT++Nra2tvL295e3trZCQEL3//vtKTEzU8ePHzW0mT56sGjVqyMXFReXLl9drr72mxMRE8/pTp06pbdu2euihh+Ti4qJq1app1apV5vWHDh1Sq1atVKJECZUpU0YvvfTSbS9JCAgIMJ9qLt04dX/WrFl69tln5ezsrMqVK5s/v7xuY+nSpapRo4acnJxUqlQphYeHZ3v/ChqhGwAAAMADy8nJSX///Xe+XjNx4kTVrFlT+/bt09tvv6033nhD69evz9Nrhw8friNHjmj16tU6evSoZsyYkePU83fffVdDhgzRr7/+qipVqqhz585KT0+XJJ08eVItW7ZUhw4ddODAAS1ZskTbt29X//79za/v3r27Tp8+rc2bN2vp0qWaPn26Ll68mK99TE1N1Zw5c1SyZEkFBQWZlxuNRn366ac6fPiwvvzyS23atEnDhg0zr+/Xr59SU1P1448/6uDBg/rwww9VokQJSdLVq1f1xBNPqHbt2tqzZ4/WrFmjP//8Uy+88EK+ahs1apReeOEFHThwQK1bt1bXrl11+fLlPG3j/Pnz6ty5s3r06KGjR49qy5Ytat++vUwmU75qyA9OLwcAAADwwDGZTNq4caPWrl2r119/PV+vffzxx/X2229LkqpUqaIdO3bo448/VrNmze742tjYWNWuXVt169aVdONI7j8NGTJEbdq0kXQjYFarVk0nTpxQcHCwxo8fr65du5qPyFeuXFmffvqpmjRpohkzZig2NlarV6/Wrl27VK9ePUnS7NmzVbVq1TvWdvDgQXNATkpKkqurq5YsWSI3Nzdzm5vPBAgICNCYMWPUp08fTZ8+3bx/HTp0UI0aNSRJFStWNLefNm2aateurXHjxpmXffHFFypfvryOHz+uKlWq3LFG6cYfFTp37ixJGjdunD799FPt2rVLLVu2vOM2EhMTlZ6ervbt28vf31+SzLVaCqEbAAAAwANj5cqVKlGihNLS0pSZmakuXbpo5MiR+erjsccey/H85lOgb6dv377q0KGD9u7dq+bNm6tdu3Zq0KBBtjYPP/yw+d8+Pj6SbpwyHhwcrP379+vAgQPZThk3mUzKzMxUdHS0jh8/LltbW4WGhprXBwcHq2TJknesLSgoyHyqdkJCgpYsWaLnn39emzdvNv+RYMOGDRo/fryOHTum+Ph4paenKyUlRUlJSXJ2dtaAAQPUt29frVu3TuHh4erQoYN5f/bv36/Nmzebg/3NTp48mefQffP74+LiIjc3N/OR/Dtto3nz5nryySdVo0YNtWjRQs2bN9dzzz2nhx56KE/bvhucXg4AAADggdG0aVP9+uuv+v3335WcnKwvv/zSPDu20WjMcZpxWlpagW6/VatWOnXqlAYNGqRz587pySef1JAhQ7K1sbOzM//bYDBIkjIzMyVJiYmJ6t27t3799VfzY//+/fr9999VqVKle6rN3t5egYGBCgwMVO3atfXBBx/I19fX/AeFmJgYPfXUU3r44Yf17bffKioqynw9e9Yp+j179tQff/yhl156SQcPHlTdunU1depUc+1t27bNVnvWZ9G4ceM813nz+5P1Ht38/txuGzY2Nlq/fr1Wr16tkJAQTZ06VUFBQYqOjr6n9+52CN0AAAAAHhguLi4KDAyUn59fjtuEeXl56fz58+bnGRkZOnToUI4+du7cmeN5Xk7fvnk73bp10/z58zVlyhT95z//yfNr69SpoyNHjpjD8c0Pe3t7BQcHKz09XVFRUebX/Pbbb7p69Wqet3EzGxsbJScnS5KioqKUmZmpSZMm6dFHH1WVKlV07ty5HK8pX768+vTpo++++05vvvmmZs6caa798OHDCggIyFF7Qd0WLC/bMBgMevzxxzVq1Cjt27dP9vb2WrZsWYFsPzeEbgAAAACQ9MQTT+iHH37QDz/8oGPHjqlv3765htUdO3ZowoQJOn78uD777DN98803euONN/K0jffee0/ff/+9Tpw4ocOHD2vlypX5CuxvvfWWfvrpJ/Xv3998BPf77783T6QWFBSkli1bqnfv3vrll18UFRWlnj17ysnJ6Y59p6en68KFC7pw4YJ+//13jRkzRkeOHNEzzzwjSQoMDFRaWpqmTp2qP/74Q/PmzdPnn3+erY+BAwdq7dq1io6O1t69e7V582bz/vXr10+XL19W586dtXv3bp08eVJr165VRESEMjIy8vwe3M6dtvHLL79o3Lhx2rNnj2JjY/Xdd9/p0qVL+foM8otrugEAAAAUmJTzJ4rsdnr06KH9+/fr5Zdflq2trQYNGqSmTZvmaPfmm29qz549GjVqlNzc3DR58mS1aNEiT9uwt7dXZGSkYmJi5OTkpEaNGmnx4sV5rvHhhx/W1q1b9e6776pRo0YymUyqVKmSOnbsaG4zZ84c9ezZU02aNFGZMmU0ZswYDR8+/I59Hz582HwNubOzsypVqqQZM2bo5ZdfliTVrFlTkydP1ocffqjIyEg1btxY48ePN6+Xbpwd0K9fP505c0Zubm5q2bKlPv74Y0lS2bJltWPHDr311ltq3ry5UlNT5e/vr5YtW8poLJjjwXfahpubm3788UdNmTJF8fHx8vf316RJk9SqVasC2X5uDCZLzo1eSMTHx8vd3V3Xrl3LNvMeAAAAgPxLSUlRdHS0KlSoIEdHR0k3Zq0OCq6qlOSk+1aHo5Ozfjt2VH5+fvdtm3iw5PZdz5LXnMmRbgAAAAD3zM/PT78dO6q4uLj7tk1PT08CNwo9QjcAAACAAuHn50cIBv6BidQAAAAAALAQQjcAAAAAABZC6AYAAAAAwEII3QAAAADuSmZmprVLACyqIL7jTKQGAAAAIF/s7e1lNBp17tw5eXl5yd7eXgaDwdplAQXGZDLp77//1qVLl2Q0GmVvb3/XfRG6AQAAAOSL0WhUhQoVdP78eZ07d87a5QAW4+zsLD8/PxmNd3+SOKEbAAAAQL7Z29vLz89P6enpysjIsHY5QIGzsbGRra3tPZ/FQegGAAAAcFcMBoPs7OxkZ2dn7VKAQouJ1AAAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALsXroTkhI0MCBA+Xv7y8nJyc1aNBAu3fvztbm6NGjevrpp+Xu7i4XFxfVq1dPsbGxVqoYAAAAAIC8sXro7tmzp9avX6958+bp4MGDat68ucLDw3X27FlJ0smTJ9WwYUMFBwdry5YtOnDggIYPHy5HR0crVw4AAAAAwO0ZTCaTyVobT05Olqurq77//nu1adPGvDw0NFStWrXSmDFj1KlTJ9nZ2WnevHl3vZ34+Hi5u7vr2rVrcnNzK4jSAQAAAAAPsLzmTKse6U5PT1dGRkaOo9ZOTk7avn27MjMz9cMPP6hKlSpq0aKFSpcurfr162v58uXWKRgAAAAoRI4fP64OHTqoTJkycnJyUqNGjfTzzz+b1xsMhhyPwMDAO/a7d+9etWnTRu7u7nJyclJwcLAWLFggSYqJiVFYWJhcXFxUt25d7d+/3/y6w4cPy9HRUdu3by/4nQWKKFtrbtzV1VWPPfaYRo8erapVq6pMmTJatGiRfv75ZwUGBurixYtKTEzUBx98oDFjxujDDz/UmjVr1L59e23evFlNmjTJtd/U1FSlpqaan8fHx0uSMjMzlZmZeV/2DQAAALCka9euKTw8XKdPn1ZYWJjKli2rJUuWKDw8XMeOHZOvr6+kG7+5IyIizK/z8vK67W/iPXv2KCwsTMnJyXriiScUGBio2NhYnThxQpmZmXrzzTe1a9cudenSRd9//71effVV7dy5UyaTSb169VK3bt3UoEEDfnej2Mvrd9yqoVuS5s2bpx49esjX11c2NjaqU6eOOnfurKioKPNOPPPMMxo0aJAkqVatWvrpp5/0+eef3zJ0jx8/XqNGjcqx/NKlS0pJSbHczgAAAAD3ycaNG3X69Gk5Oztr3rx5srW11V9//aW1a9fq/fff1+jRoyVJ7u7uioyMzPbaixcv3rLfwYMHKzk5WW+++aaGDBmS43UHDx5UgwYNNGbMGNnb22vu3Lm6ePGi5s6dq5MnT2rOnDm37R8oLhISEvLUzuqhu1KlStq6dauuX7+u+Ph4+fj4qGPHjqpYsaI8PT1la2urkJCQbK+pWrXqbU9ZiYyM1ODBg83P4+PjVb58eXl5eXFNNwAAAIqFMmXKSLpxlueff/4pPz8/nTlzRtKN085Lly4tSbpw4YIqV64se3t71a5dW2PGjNEjjzySa58pKSn65ZdfJN24g1D16tVlMBjUrFkzTZ48WaVLl1aNGjW0evVqDR48WGvXrlX16tWVnp6u8ePHa+bMmXk6fR0oDvI6ubfVQ3cWFxcXubi46MqVK1q7dq0mTJgge3t71atXT7/99lu2tsePH5e/v/8t+3JwcJCDg0OO5UajUUaj1SdsBwAAAO5ZWFiYwsLCtGXLFtWtWzfbugsXLshoNKpMmTJq3LixPDw8tHXrVm3cuFG7d+/WgQMHcv09ffXqVaWnp0uSduzYoeeff16bN2/WokWLdPHiRW3YsEGTJk1SXFycvv/+ewUHB2vmzJkaMGCAwsLCVKdOHbVt21bHjh1T3bp1NXXqVHP4B4qbvGZLq4futWvXymQyKSgoSCdOnNDQoUMVHBxsvu5k6NCh6tixoxo3bqymTZtqzZo1+u9//6stW7ZYt3AAAADAimxtbbVhwwYtXbpUBw8elKenp86dO6eJEyeag+758+dlMBgk3bhzUKVKlXT+/HmtXr1affr0ydGnp6enjEajMjMz9c4772jYsGH68ccf1aRJE23atEnx8fEKCAjQ1q1bza9ZtmyZNmzYoCNHjpjvPLRq1So9++yzGjx4sObPn39/3hCgkLJ66L527ZoiIyN15swZeXh4qEOHDho7dqzs7OwkSc8++6w+//xzjR8/XgMGDFBQUJC+/fZbNWzY0MqVAwAAANaVkZGhjh07qmPHjkpOTlZoaKgkqVmzZjp37pzc3NxUokQJc/usuwVnzXN07do1nT9/XnZ2dqpUqZLs7e0VEhKiQ4cO5diWra1tjtNp4+Pj9frrr2vs2LEqV66c9u3bp9dee01BQUEKCQnRvn37LLXrQJFh1ft03y/cpxsAAADFUfPmzeXs7CwvLy9t3rxZJ0+eVMWKFRUVFaXly5dr4MCBatq0qcqUKaOtW7fq2LFjKlGihA4dOiR/f3/NnTtXERER8vf3V0xMjCRpyZIl6tSpk9zd3c2nl588eVIRERH64osvsm2/X79+ioqK0k8//SSj0ajQ0FBFR0erXbt2Wrhwodq0aaNvv/3WCu8MYHlF4j7dAAAAAO5ezZo19csvv2ju3LlKSEhQRESEduzYoZIlS6pu3bpq1qyZdu/erTlz5ujatWtq3769fvrpp9vOj9SxY0f95z//UZkyZfTVV1+ZTzX/7LPPsrXbuXOnZs+erZkzZ5qvbZ01a5YqVKigJUuWqH79+po0aZJF9x8oCjjSDQAAAAD3yfHjxxUZGant27crPj5edevW1YQJE/TYY49Jkvka/JtVqlRJJ06cuGWfH374oWbOnKlz587JaDTKz89PERERGjp0qCQpJiZG3bt31+7du1W1alXNnj1bNWvWlCQdPnxYoaGh2rBhA5fw5lNecyahGwAAAADug2vXrqlGjRo6ffq0wsLC5Ovrq8WLF8vBwUHHjx+Xr6+vDAaDXF1d1aNHD/PrvLy89O67796y3969e+vChQvy9fVVTEyMVq9eLUlavHixOnbsqOeee06rVq1S165dtXz5clWoUEG7du2SyWRSw4YNVaNGDX3++ecW3//iJq850+oTqQEAAADAg2DHjh06ffq0XFxctH79etna2ioxMVHff/+9Jk6cqClTpkiSPDw8zP/Oi3//+9/ZnteoUUOHDh3SyZMnJUlHjhxR06ZNNXPmTJUsWVLTp0+XJM2YMUPR0dFatWpVgewfcsc13QAAAABwH2TN/p6SkqKDBw/qr7/+Mp82fvNM72fPnpWrq6tKlSqlZs2aadeuXXfse82aNRowYIBat26tQ4cOqUKFCnrxxRclSSEhIdq8ebO6du2qL774QtWqVdO5c+cUGRmpTz/9VO7u7hbYW2ThSDcAAAAA3AeNGzdWWFiYtmzZojp16mRbd+HCBUlSmTJl1LhxY3l4eGjr1q3asGGDdu3apQMHDtx2ArydO3dq6tSpkm5cF96qVSuVKlVKkvTRRx/p0qVLWr58uYKDgzVz5kz1799fYWFhqlWrltq0aaNjx46pbt26mjp1qvk+7ygYXNMNAAAA5FNsbKzi4uKsXQbyyNPTU35+ftYuQ9KNe6svXbpUBw8elKenp86dO6eJEyeqYcOG2rZtm0wmk3kyteTkZFWqVEnnz5/XjBkz1KdPnzv2ffLkSXXq1En79u3TgAED9Mknn+Rot2zZMnXr1k1HjhxRp06dZGdnp88//1zPPvus6tSpo/nz51tk34sbrukGAAAALCA2NlZBVYOVkpRs7VKQR47OTvrt6LFCEbwzMjLUsWNHdezYUcnJyQoNDZUkNWvWTOfOnZObm5tKlChhbp91jDQlJUXSjcnYzp8/Lzs7O1WqVEkZGRlKSUmRi4uLbGxsVKVKFdWtW1f79u3TgQMHcmw/Pj5er7/+usaOHaty5cpp3759eu211xQUFKSQkJBsp7mjYBC6AQAAgHyIi4tTSlKyyo9uKYcKHtYuB3eQGn1Zp4evUVxcXKEI3U899ZScnZ3l5eWlzZs36+TJk6pYsaIGDBig5cuXa+DAgWratKnKlCmjrVu36sKFCypRooSeffZZSTeOUkdERMjf318xMTFKSEhQ+fLl1bRpU5UrV06xsbHmidGaN2+eY/uRkZEqV66c+vXrJ0kKDg7W7Nmz9ddff2nlypVq06bN/XszHhCEbgAAAOAuOFTwkHNwGWuXgSKmZs2amj9/vuLi4uTh4aGIiAiNGzdOJUuWVN26ddWsWTP9/PPPunTpkkqVKqX27dtr5MiRt7ye28HBQU2bNlVUVJTWrFkjV1dX1atXTxEREerdu3e2tjt37tTs2bO1e/duGY035tSeNWuWevbsqSVLlqh+/fqaNGmSxd+DBw3XdAMAAAD5sHfvXoWGhipwfhdCdxGQdOxPnXhxoaKionJMXgbci7zmTG4ZBgAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCmL0cAAAAQLF39OhRa5eAfPD09CwUt3grCIRuAAAAAMVWWtx1yWDUiy++aO1SkA+OTs767djRYhG8Cd0AAAAAiq2MhFTJlKkKER/J0SfQ2uUgD1LOn1D0nCGKi4sjdAMAAABAUeDoEygXv2rWLgMPICZSAwAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALKRShOyEhQQMHDpS/v7+cnJzUoEED7d69O9e2ffr0kcFg0JQpU+5vkQAAAAAA5FOhCN09e/bU+vXrNW/ePB08eFDNmzdXeHi4zp49m63dsmXLtHPnTpUtW9ZKlQIAAAAAkHdWD93Jycn69ttvNWHCBDVu3FiBgYEaOXKkAgMDNWPGDHO7s2fP6vXXX9eCBQtkZ2dnxYoBAAAAAMgbq4fu9PR0ZWRkyNHRMdtyJycnbd++XZKUmZmpl156SUOHDlW1atWsUSYAAAAAAPlma+0CXF1d9dhjj2n06NGqWrWqypQpo0WLFunnn39WYGCgJOnDDz+Ura2tBgwYkKc+U1NTlZqaan4eHx8v6UZ4z8zMLPidAAAAwAPDZDLJaDTKKIOMJmtXgzsxyvC/z0syiA+sKDBKMhqNMplMhTq/5bU2q4duSZo3b5569OghX19f2djYqE6dOurcubOioqIUFRWlTz75RHv37pXBYMhTf+PHj9eoUaNyLL906ZJSUlIKunwAAAA8QFJSUhQaGiofG285pDxk7XJwBwmufioTGqpyJSVHh0Rrl4M8SCkpOYeGKiUlRRcvXrR2ObeUkJCQp3YGk8lUaP7cc/36dcXHx8vHx0cdO3ZUYmKimjVrpsGDB8to/P8z4TMyMmQ0GlW+fHnFxMTk6Ce3I93ly5fXlStX5Obmdj92BQAAAMXUvn379Mgjjyjwq85yDipt7XJwB5fXHNOZEWtV9e3v5OwXYu1ykAdJsUd09IP22rVrl2rXrm3tcm4pPj5eDz30kK5du3bbnFkojnRncXFxkYuLi65cuaK1a9dqwoQJ6tChg8LDw7O1a9GihV566SVFRETk2o+Dg4McHBxyLDcajdnCOwAAAJBfBoPhxmWLMikzbydiwooyZfrf5yWZxAdWFGTqxqnbBoOhUOe3vNZWKEL32rVrZTKZFBQUpBMnTmjo0KEKDg5WRESE7OzsVKpUqWzt7ezs5O3traCgICtVDAAAAADAnRWKPxtcu3ZN/fr1U3BwsF5++WU1bNhQa9eu5dZgAAAAAIAirVAc6X7hhRf0wgsv5Ll9btdxAwAAAABQ2BSKI90AAAAAABRHhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAPDAOH78uDp06KAyZcrIyclJjRo10s8//yxJ2rt3r9q1ayd/f385OjrK29tbHTp00PHjx2/b5/nz5xUREaFy5crJ0dFRderU0Q8//GBeHxMTo7CwMLm4uKhu3brav3+/ed3hw4fl6Oio7du3W2aHAQCA1RG6AQAPhGvXrik8PFzfffedQkJC1KFDB/38888KDw/X2bNndeDAAa1fv17VqlXTiy++qPT0dH333Xdq3ry5/v7771z7zMzMVJs2bTR37lx5e3ura9euOnbsmJ5++mlFRUVJkoYMGaJdu3apS5cuOnXqlF599VVJkslkUq9evdS9e3c1bNjwvr0PAADg/rK1dgEAANwPO3bs0OnTp+Xi4qL169fL1tZWiYmJ+v777zVx4kT1799fp0+floeHhySpS5cuevLJJ3Xq1CkdPnxYtWvXztHnsWPHtG/fPknSypUr5e3tLVdXV33yyScaPXq0li9friNHjqhp06aaOXOmSpYsqenTp0uSZsyYoejoaK1ater+vQkAAOC+40g3AOCB4OjoKElKSUnRwYMH9ddff+nEiROSpH379ikwMNAcuCUpNTVVkmRjYyNvb+/b9ilJu3fvVmJiog4fPmzuU5JCQkK0efNmde3aVV988YWqVaumc+fOKTIyUp9++qnc3d0LfmcBAEChQegGADwQGjdurLCwMGVkZKhOnTry9PQ0B+QLFy5kaxsbG6u+fftKkt555x35+Pjk2mfFihX14osvSpKefvppubq6asOGDdn6/Oijj1SvXj0tX75cAQEBmjlzpvr376+wsDDVqlVLbdq0UaVKldSxY0ddvHjRIvsOAACsh9PLAQAPBFtbW23YsEFLly7VwYMH5enpqXPnzmnixIkqXbq0ud3u3bv19NNP68KFC3r33Xf1/vvv37bfefPmqVOnTvrll1/k4uIiGxsbDR061NxnQECAtm7dam6/bNkybdiwQUeOHFGnTp1kZ2enVatW6dlnn9XgwYM1f/58y7wBAADAKjjSDQB4YGRkZKhjx44aM2aMevfurZUrV0qSmjVrJkn67rvv1KRJE/3111+aNWuWxowZk+31165d07Fjx3Ty5Enzsr///ltt2rTR+++/ryFDhmjNmjXZ+rxZfHy8Xn/9dY0dO1blypXTvn37VLduXQUFBSkkJMR8SjpusMRs8waDIddHQECAJOny5cvmsxaqVq2qTZs2mV/7559/ysPDQwsXLrTYPgMAih+OdAMAHhhPPfWUnJ2d5eXlpc2bN+vkyZOqWLGiBgwYoPXr1+u5556TyWRSvXr1dPDgQQ0cOFCS1L9/fwUGBmrZsmWKiIiQv7+/YmJiJEm9e/fW+fPn5e/vr59//lkHDx6Uh4eH3nvvvRzbj4yMVLly5dSvXz9JUnBwsGbPnq2//vpLK1euVJs2be7XW1HoZc02f/r0aYWFhcnX11eLFy9WeHi4jh8/bp5tvkmTJmrWrJmWL1+u7777TlFRUTp+/Ljs7e1z7feNN97I9vz7779XTEyMqlSpIkkaN26cfvjhB7300kvaunWrunTpovPnz8tgMOiNN95Q/fr11aVLF4vvPwCg+CB0AwAeGDVr1tT8+fMVFxcnDw8PRUREaNy4cSpZsqTOnj0rk8kk6cYp5rt37za/rl27dgoMDMy1z4cfflgbN27Upk2b5Orqqueee07jxo0zHznNsnPnTs2ePVu7d++W0XjjRLNZs2apZ8+eWrJkierXr69JkyZZZseLIEvMNi9JU6ZMMf/7r7/+0syZMyVJb775piTpyJEjCg4O1ty5c/XZZ5+pf//+iouL0549e/Tf//7XPA8AAAB5RegGADwwJk6cqIkTJ+a6rnv37urevfttX59bm0GDBmnQoEF33Pajjz6qlJSUbMtq165tvp83svvnbPN+fn45Zpu/WV5mm/+nGTNmKCkpSQ8//LBatGgh6cZs82vXrlWnTp20Y8cO86ntffv21ahRo3L8MQUAgDvhmm4AAFDoWGK2+ZulpqZq2rRpkqQhQ4aYl7/zzjtq06aNVq5cKRcXFy1cuFDvvfeeSpUqpS5duqhTp06qVKmS2rRpY/4jAAAAt8ORbgAAUOhYarb5LPPmzdOff/6pcuXKqVOnTublHh4eWrFihfn5nj179Nlnn+nnn3/WsGHDdODAAa1atUp9+vRR9+7dtX379oLbaQBAsZSv0J2RkaFVq1Zp/fr1+uWXX3T+/HklJyerVKlSCgoKUqNGjdShQwdVqFDBUvUCAIAHRNZs8x07dlRycrJCQ0MlZZ9t/sUXX1R6erpmzZqlV155Jdvrr127pvPnz8vOzk6VKlUyLzeZTJo8ebIkaeDAgbKzs7vl9nv16qX+/furTp062rdvn6pVq6agoCCFhoZqxowZlthtAEAxk6fQnZiYqEmTJmnGjBm6cuWKqlWrppo1a6px48ZycHDQ1atXFRMTo48++khvv/22wsLCNGrUKD3++OOWrh8AABRTlphtXpJWrVqlo0ePyt3dXb169brl9idPnqwrV66Yj54HBwdr5cqVeuWVV/Tdd98pODjYkrsPACgm8hS6K1SooOrVq2vixIlq166dXF1db9l27969WrRokdq2basxY8botddeK7BiAQBFT2xsrOLi4qxdBvLB09NTfn5+1i7DIrPNSzLPEt+rV69b/qaJjo7WyJEjtXTpUrm4uJhfFxcXp8WLFysoKEizZs0qqF0FABRjBlPW/2Pdxk8//aQGDRrkq+OEhATFxsaqWrVqd11cQYmPj5e7u7uuXbsmNzc3a5cDAA+M2NhYBVUNVkpSsrVLQT44Ojvpt6PHCkXwBgqjvXv3KjQ0VIHzu8g5uIy1y8EdXF59VGeGr1HVd5bLxc/62QR3dj32sI6Oa6eoqCjVqVPH2uXcUl5zZp6OdOc3cEuSq6troQjcAADriYuLU0pSssqPbimHCh7WLgd5kBp9WaeHr1FcXByhGwCAAnDPs5cfO3ZMmzZtkslkUtOmTRUSElIQdQEAihGHCh4cDQIAAA+ke7pP9/z581WrVi3NmTNHn332mWrWrKk5c+YUVG0AAAAAABRp9xS6R40apY0bN2r37t06cuSIxo4dq1GjRhVUbQAAAAAAFGl5Or28bt26mjZtmh599NFsyxMTE7PNDlqxYkVdv369YCsEAAD33dGjR61dAvKhsMw4DwDIKU+h+/XXX1eHDh30xBNPaOLEifL29pYkdezYUQ0bNtSzzz6rpKQkLVq0SF27drVowQAAwHLS4q5LBqNefPFFa5eCfHB0ctZvx44SvAGgEMpT6O7WrZs6dOigUaNGqXr16ho2bJgGDRqkSZMmqUqVKtq4caMk6f3331fv3r0tWjAAALCcjIRUyZSpChEfydHn1ve6RuGRcv6EoucMYcZ5ACik8jx7eYkSJTRx4kS9+uqrGjRokGbNmqUpU6botdde02uvvWbJGgEAwH3m6BPI/WwBACgA+Z5IrUqVKvrhhx80adIkDRw4UG3atNGJEycsURsAAAAAAEVankK3yWTSF198oU6dOunZZ5/V+PHj1bRpUx06dEgNGzZU/fr19fbbbzOJGgAAAAAAN8lT6B40aJAGDx4sDw8PVapUSf/5z3/UqlUr2dvbKzIyUgcPHtSZM2cUFBSk+fPnW7pmAAAAAACKhDxd0z1v3jzNmjVLzz33nCSpX79+CgwMVExMjAICAlS2bFnNnz9fO3bs0IABA5jxFAAAAAAA5fFId6lSpRQTE2N+furUKUlSyZIls7V7/PHHtWfPngIrDgAAAACAoixPR7onT56srl276ssvv5STk5MOHjyocePG5QjdkmQwGAq6RgAAAAAAiqQ8he6nnnpK0dHR2rlzp1JTU1W7dm0FBARYuDQAAAAAAIq2PN+n28PDQ61bt7ZkLQAAAAAAFCt5uqZ79erV+e740qVL2rt3b75fBwAAAABAcZGn0N27d2/VqlVLn376qc6ePXvLdhkZGdq4caN69uypihUrat++fQVWKAAAAAAARU2eTi///fffNX36dE2ZMkWDBg1S+fLl9fDDD8vLy0sODg66evWqoqOjdeDAAaWnp6tt27bavn27atasaen6AQAAAAAotPIUuh0cHDRo0CANGjRIW7Zs0caNG7V7927t2bNHKSkp8vDwUFBQkHr06KFnnnlGpUuXtnTdAAAAAAAUenmeSC1LWFiYwsLCLFAKAAAAAADFS56u6QYAAAAAAPlH6AYAAAAAwEII3QAAAAAAWEihCN0JCQkaOHCg/P395eTkpAYNGmj37t2SpLS0NL311luqUaOGXFxcVLZsWb388ss6d+6clasGAAAAAOD2CkXo7tmzp9avX6958+bp4MGDat68ucLDw3X27FklJSVp7969Gj58uPbu3avvvvtOv/32m55++mlrlw0AAAAAwG3lO3R//vnnio+PL7ACkpOT9e2332rChAlq3LixAgMDNXLkSAUGBmrGjBlyd3fX+vXr9cILLygoKEiPPvqopk2bpqioKMXGxhZYHQAAAAAAFLR83zJs8ODBevPNN9WhQwe98soratKkyT0VkJ6eroyMDDk6OmZb7uTkpO3bt+f6mmvXrslgMKhkyZK5rk9NTVVqaqr5edYfCTIzM5WZmXlP9QIA8s5kMsloNMoog4wma1eDvDDK8L/PTDKID60oMEoyGo0ymUz8zrlPGNuKFsa1oqeojGt5rS3fofvcuXNasGCB5syZo6ZNm6pixYrq0aOHunXrJl9f33wX6urqqscee0yjR49W1apVVaZMGS1atEg///yzAgMDc7RPSUnRW2+9pc6dO8vNzS3XPsePH69Ro0blWH7p0iWlpKTku0YAwN1JSUlRaGiofGy85ZDykLXLQR4kuPqpTGioypWUHB0SrV0O8iClpOQcGqqUlBRdvHjR2uU8EBjbihbGtaKnqIxrCQkJeWqX79BdsmRJ9evXT/369dOBAwf0xRdfaMqUKRoxYoSaN2+uHj166Omnn5adnV2e+5w3b5569OghX19f2djYqE6dOurcubOioqKytUtLS9MLL7wgk8mkGTNm3LK/yMhIDR482Pw8Pj5e5cuXl5eX1y2DOgCg4J09e1ZRUVEKzKgiZ8fC+5dq/L/LCbE6ExWlpGaSs1sJa5eDPEi6Kh2NipKjo6NKly5t7XIeCIxtRQvjWtFTVMa1f56tfSv5Dt03e/jhhzVlyhQNGzZMXbp00erVq7V69Wp5enqqb9++evvtt+Xk5HTHfipVqqStW7fq+vXrio+Pl4+Pjzp27KiKFSua22QF7lOnTmnTpk23Dc8ODg5ycHDIsdxoNMpoLBRzxwHAA8FgMNy4tEcmZRqsXQ3yIlOm/31mkkl8aEVBpm6c4mgwGPidc58wthUtjGtFT1EZ1/Ja213vgclk0urVq/Xcc8+pYsWKOnbsmIYOHaqffvpJffr00dSpU/Xiiy/mq08XFxf5+PjoypUrWrt2rZ555hlJ/x+4f//9d23YsEGlSpW627IBAAAAALhv8n2k++TJk/riiy/01Vdf6dy5c2rWrJkWLFigZ555Rra2N7p79NFHVbduXXXq1ClPfa5du1Ymk0lBQUE6ceKEhg4dquDgYEVERCgtLU3PPfec9u7dq5UrVyojI0MXLlyQJHl4eMje3j6/uwAAAAAAwH2R79BduXJl+fr6KiIiQq+88or8/f1zbRccHKz69evnqc9r164pMjJSZ86ckYeHhzp06KCxY8fKzs5OMTExWrFihSSpVq1a2V63efNmhYWF5XcXAAAAAAC4L/IdulesWKHWrVvf8fz1KlWqaPPmzXnq84UXXtALL7yQ67qAgACZTEztDwAAAAAoevIdup966ilL1AEAAAAAQLGT74nUevTooY4dO+a6rlOnTurVq9c9FwUAAAAAQHGQ79C9fv16tW/fPtd1HTp00Nq1a++5KAAAAAAAioN8h+5Lly7Jy8sr13WlSpXSn3/+ec9FAQAAAABQHOQ7dPv6+uqXX37Jdd0vv/wiHx+fey4KAAAAAIDiIN+hu3Pnzho7dqy+/vrrbMu/+eYbjRs3Tl26dCmw4gAAAAAAKMryHbrfe+89hYWFqVOnTnJ1dVWVKlXk6uqqTp06qUmTJhoxYoQl6gQAAAAAoMjJ9y3D7O3ttXLlSq1fv16bNm3SX3/9pVKlSik8PFxPPvmkJWoEAAAAAKBIynfoztKsWTM1a9asIGsBAAAAAKBYuevQLUlJSUlKSUnJsdzDw+NeugUAAAAAoFjId+g2mUwaM2aM/v3vf+v8+fO5tsnIyLjnwgAAAAAAKOryPZHaxx9/rMmTJ6tfv34ymUx699139d5776lKlSoKCAjQzJkzLVEnAAAAAABFTr5D9+zZszVq1CgNGzZMktSuXTuNGDFChw8fVtWqVXXixIkCLxIAAAAAgKIo36E7JiZGtWrVko2Njezs7HT16tUbHRmNeu211zR37twCLhEAAAAAgKIp36G7VKlSSkxMlCT5+flp79695nVxcXFKSkoquOoAAAAAACjC8j2R2uOPP67du3erdevW6tKli0aOHKkLFy7Izs5OM2fO5F7dAAAAAAD8T75D98iRI3X27FlJ0jvvvKOrV69q0aJFSk5OVrNmzTR16tQCLxIAAAAAgKIoX6HbZDLJy8tLAQEBkiQHBwd98skn+uSTTyxRGwAAAAAARVq+rulOS0tT6dKltWHDBkvVAwAAAABAsZGv0G1vb69y5copIyPDUvUAAAAAAFBs5Hv28n79+mny5MlKSUmxRD0AAAAAABQb+Z5ILTY2VsePH5efn5/CwsJUpkwZGQwG83qDwcA13gAAAAAA6C5C98qVK+Xg4CAHBwft3r07x3pCNwAAAAAAN+Q7dEdHR1uiDgAAAAAAip18X9MNAAAAAADyJt9Hur/66qs7tnn55ZfvqhgAAAAAAIqTfIfu7t2757r85snUCN0AAAAAANxF6L5y5Uquy9auXatp06Zp4cKFBVIYAAAAAABFXb5Dt7u7e67LevfurZSUFA0bNkyrV68ukOIAAAAAACjKCnQitWrVqmnbtm0F2SUAAAAAAEVWgYXupKQkzZw5U76+vgXVJQAAAAAARVq+Ty+vUaNGtknTJOnvv//WmTNnlJycnKfZzQEAAAAAeBDkO3SHhobmCN2Ojo4qV66c2rdvr6pVqxZYcQAAAAAAFGX5Dt1z5861QBkAAAAAABQ/+b6mOyEhQefPn8913fnz55WYmHjPRQEAAAAAUBzk+0h3z5495erqqlmzZuVYN2LECCUmJnKvbgAAAAAAdBdHun/88Ue1adMm13WtW7fW1q1b77koAAAAAACKg3yH7itXrsjV1TXXdS4uLvrrr7/uuSgAAAAAAIqDfIfuihUrasOGDbmu27hxowICAu61JgAAAAAAioV8h+6ePXtq8uTJmjBhguLi4iRJcXFxmjhxoj7++GO9+uqrBV4kAAAAAABFUb4nUhs0aJBOnjypyMhIRUZGytbWVunp6ZKkPn366M033yzwIgEAAAAAKIryHboNBoM+++wzDRw4UJs2bdJff/2lUqVK6YknnlDlypUtUSMAAAAAAEVSvkN3lsqVKxOyAQAAAAC4jXxf071kyRJNnDgx13UfffSRvvnmm3suCgAAAACA4iDfofuDDz6Qg4NDruucnJz0wQcf3HNRAAAAAAAUB/kO3cePH1f16tVzXRcSEqLjx4/fc1EAAAAAABQH+Q7djo6O+vPPP3Ndd/78edna3vVl4gAAAAAAFCv5Dt1NmjTRBx98oOvXr2dbfv36dU2YMEFhYWEFVRsAAAAAAEVavg9Ljxs3To899pgqVaqk5557TmXLltW5c+e0dOlS/f3331q8eLEl6gQAAAAAoMjJd+gODg7W7t27NWLECH377bfm+3Q3a9ZMI0aMUGBgoCXqBAAAAACgyLmrC7ADAwO1YMGCXNdFR0erQoUK91QUAAAAAADFQb6v6c5NXFycPvvsMz3++OMc6QYAAAAA4H/ueqrxpKQkLVu2TAsXLtSGDRuUlpam2rVr6+OPPy7I+gAAAAAAKLLyFbozMjK0Zs0aLVy4UCtWrFBSUpK8vb2Vnp6uxYsX64UXXrBUnQAAAAAAFDl5Or18x44d6tevn3x8fNS2bVutW7dOL774orZs2aJDhw7JZDLJ29v7rotISEjQwIED5e/vLycnJzVo0EC7d+82rzeZTHrvvffk4+MjJycnhYeH6/fff7/r7QEAAAAAcD/k6Uh3o0aNZDAY1LRpUw0ePFjNmzeXre2Nl167du2ei+jZs6cOHTqkefPmqWzZspo/f77Cw8N15MgR+fr6asKECfr000/15ZdfqkKFCho+fLhatGihI0eOyNHR8Z63DwAAAACAJeTpSHeNGjVkMpm0detWffLJJ1q4cKESEhIKpIDk5GR9++23mjBhgho3bqzAwECNHDlSgYGBmjFjhkwmk6ZMmaJ//etfeuaZZ/Twww/rq6++0rlz57R8+fICqQEAAAAAAEvI05Hu/fv368iRI5o/f74WL16s7t27q2/fvmrTpo2eeuopGQyGuy4gPT1dGRkZOY5YOzk5afv27YqOjtaFCxcUHh5uXufu7q769evr559/VqdOnXL0mZqaqtTUVPPz+Ph4SVJmZqYyMzPvulYAQP6YTCYZjUYZZZDRZO1qkBdGGf73mUkG8aEVBUZJRqNRJpOJ3zn3CWNb0cK4VvQUlXEtr7XleSK1kJAQjRs3TuPGjdOOHTu0cOFCLV26VEuXLpXBYNAnn3wiSWrcuHG+CnV1ddVjjz2m0aNHq2rVqipTpowWLVqkn3/+WYGBgbpw4YIkqUyZMtleV6ZMGfO6fxo/frxGjRqVY/mlS5eUkpKSr/oAAHcvJSVFoaGh8rHxlkPKQ9YuB3mQ4OqnMqGhKldScnRItHY5yIOUkpJzaKhSUlJ08eJFa5fzQGBsK1oY14qeojKu5fXs77u6Zdjjjz+uxx9/XJ9++qnWrl2rRYsW6fvvv9fy5cvl7++vP/74I1/9zZs3Tz169JCvr69sbGxUp04dde7cWVFRUXdTniIjIzV48GDz8/j4eJUvX15eXl5yc3O7qz4BAPl39uxZRUVFKTCjipwdC+9fqvH/LifE6kxUlJKaSc5uJaxdDvIg6ap0NCpKjo6OKl26tLXLeSAwthUtjGtFT1EZ1/I6v9hd36dbkmxsbNS6dWu1bt1aycnJWr58uRYtWpTvfipVqqStW7fq+vXrio+Pl4+Pjzp27KiKFSuaZ0X/888/5ePjY37Nn3/+qVq1auXan4ODgxwcHHIsNxqNMhrzdBk7AKAAGAyGG5f2yKTMu78SCfdRpkz/+8wkk/jQioJM3TjF0WAw8DvnPmFsK1oY14qeojKu5bW2AtsDJycnde7cWStWrLjrPlxcXOTj46MrV65o7dq1euaZZ1ShQgV5e3tr48aN5nbx8fH65Zdf9NhjjxVE6QAAAAAAWMQ9HekuKGvXrpXJZFJQUJBOnDihoUOHKjg4WBERETIYDBo4cKDGjBmjypUrm28ZVrZsWbVr187apQMAAAAAcEuFInRfu3ZNkZGROnPmjDw8PNShQweNHTtWdnZ2kqRhw4bp+vXr6tWrl65evaqGDRtqzZo13KMbAAAAAFCoFYrQ/cILL+iFF1645XqDwaD3339f77///n2sCgAAAACAe1N4r0oHAAAAAKCII3QDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCN3L1yy+/qEWLFvLy8pKjo6P8/f31+uuvKykpSZK0aNEihYaGqkSJEvLy8lKvXr0UHx9/2z579eqlGjVqyN3dXa6urgoNDdWiRYvM62NiYhQWFiYXFxfVrVtX+/fvN687fPiwHB0dtX37dsvsMAAAAABYAKEbOaSlpally5Zat26d/Pz89NJLL+ny5cuaNm2a3n33XX333Xfq0qWLjhw5oueff14VK1bUzJkz9dJLL92235kzZ8re3l7PP/+8qlatqr1796pLly5as2aNJGnIkCHatWuXunTpolOnTunVV1+VJJlMJvXq1Uvdu3dXw4YNLb7/AAAAAFBQCsV9ulG4XLp0SVevXpUkzZ07VzVq1JCdnZ1mzJih6OhonT17VpLUrVs3ff7557p69aoeeughrVixQr/++qtq1aqVa787d+5U/fr1JUnp6emqUqWKoqOjtXr1arVs2VJHjhxR06ZNNXPmTJUsWVLTp0+XJPN2V61aZfF9BwAAAICCxJFu5FC2bFnzUevu3bvr1Vdf1bx58+Tl5aW3335bjo6OkqSjR48qPj5eu3btMr923759t+w3K3BnSU1NlST5+vpKkkJCQrR582Z17dpVX3zxhapVq6Zz584pMjJSn376qdzd3Qt0PwEAAADA0gjdyNXLL7+scuXKae/evZo1a5YSExPVrFkzValSRYMHD1aJEiX0448/yt3dXS1atDC/7sKFC3fsOzMzU3369NG5c+dUrVo19e3bV5L00UcfqV69elq+fLkCAgI0c+ZM9e/fX2FhYapVq5batGmjSpUqqWPHjrp48aLF9h0AAAAACgqnlyOHS5cuqW3btkpJSdHSpUvVsmVLRUREaOHChUpPT9eSJUt0/PhxLVmyRBcvXlTdunX1zjvv6LffflPp0qVv2/f169fVpUsXrVixQrVr19aaNWvk6uoqSQoICNDWrVvNbZctW6YNGzboyJEj6tSpk+zs7LRq1So9++yzGjx4sObPn2/R9wEAAAAA7hVHupHDqVOnlJKSIklq0KCBXFxcVLt2bUnSkSNHlJ6erjJlymjgwIEaN26cPDw89Ntvv8loNOqJJ56QJMXFxenYsWOKjY0193vu3Dk1btxYK1asUNu2bfXjjz/eMqTHx8fr9ddf19ixY1WuXDnt27dPdevWVVBQkEJCQm57GjsAAAAAFBYc6UYOISEh8vT0VFxcnFq3bq26devqm2++kSQ1adJEMTExatasmcLCwpScnKzly5dLkgYOHKgKFSpIkqZNm6ZRo0apSZMm2rJli6Qb13SfOXNGbm5uCggI0L/+9S9J0iOPPKIuXbpkqyEyMlLlypVTv379JEnBwcGaPXu2/vrrL61cuVJt2rS5D+8EAAAAANwbQjdycHZ21rp16zR8+HDt2rVLR48elY+Pj1555RWNGTNGCQkJ8vb21rfffqvU1FQFBgaqT58+6t+//237PXPmjKQbR7GnTp1qXt6tW7dsoXvnzp2aPXu2du/eLaPxxskYs2bNUs+ePbVkyRLVr19fkyZNssCeAwAAAEDB4vRy5Kp27dpauXKlLl68qJSUFEVHR2vSpElycnJS6dKl9fPPPys+Pl6pqak6fPiwXn/9dRkMBvPrR44cKZPJZD7KLd2433Zuj7lz52bb9qOPPqqUlBTVqFEjWz1RUVG6fv26tm7dqoCAAAu/A0XPL7/8ohYtWsjLy0uOjo7y9/fX66+/rqSkJEnSxx9/rOrVq6tEiRJyc3NTaGioFixYcNs+jx8/rg4dOqhMmTJycnJSo0aN9PPPP5vX79u3T6GhoXJxcVHTpk2zXU6wfv16OTs76+TJk5bZYQAAAKAI4Eg3UAykpaWpZcuWunr1qurUqaM6depo8eLFmjZtmmxtbdWgQQMNHjxYBoNBnTp10pUrV7RmzRq99NJLqlatWq73Vr927ZrCw8N1+vRphYWFydfXV4sXL1Z4eLiOHz8uX19fvfrqqzp16pS6dOmihQsXasiQIfr666+VlJSkPn366L333lOlSpXu/xsCAAAAFBKEbqAYuHTpkq5evSpJmjt3rmrUqCE7OzvNmDFD0dHR8vLyknTjjIGFCxfKZDLJ1dVV169fV0xMTK6he8eOHTp9+rRcXFy0fv162draKjExUd9//70mTpyoKVOm6MiRI4qIiNBnn32mq1ev6vDhw5JunOlQokQJDRky5H69BQAAAEChROgGioGyZcvqpZde0rx589S9e3fzkW4vLy+9/fbb8vb21ueff659+/apS5cuunLliq5fv67w8HC1atUq1z4dHR0lSSkpKTp48KD8/Px04sQJSTLPHh8SEqLFixfr6tWrWrVqldq0aaNff/1Vn3zyibZt2yZbW4YYAAAAPNi4phsoJl5++WWVK1dOe/fu1axZs5SYmKhmzZqpSpUqKl++vF566SUZDAYtWrRIa9asUcmSJdWhQwc5ODjk2l/jxo0VFhamjIwM1alTR56enuYj2RcuXJAkzZw5UwEBAVq+fLnq1aunDz74QK+++qr69Okjo9GosLAwVa5cWb169dL169fv23sBAAAAFBYchgKKgUuXLqlt27ZKSUnR0qVL1bJlS0VERGjhwoVKT09Xw4YNNW7cOFWqVEnbtm3T9evXVa9ePfXt21f+/v65Hu22tbXVhg0btHTpUh08eFCenp46d+6cJk6caL6/etYEd1k+/vhj/fnnnxozZoxCQkLUpEkTTZs2TU2bNlXp0qU1ZsyY+/aeAAAAAIUBobuQiY2NVVxcnLXLQD54enrKz8/PqjWcOnVKKSkpkqQGDRrIxcVFtWvX1jfffKMjR46Yr+muVKmSfHx8JEmlS5fW1atXdeTIEbVq1UpxcXGKi4uTs7OzeX8yMjLUsWNHdezYUcnJyQoNDZUkNWvWLEcNsbGxGj58uBYtWqSUlBSdOXNGjz32mKpXry5/f3/zKekAAADAg4TQXYjExsYqqGqwUpKSrV0K8sHR2Um/HT1m1eAdEhIiT09PxcXFqXXr1qpbt66++eYbSVKTJk3UpEkTffbZZ1q/fr06deqkxMREHT9+XEajUY0aNZIkTZs2TaNGjVKTJk3Mt3p76qmn5OzsLC8vL23evFknT55UxYoVNWDAgBw1vPbaa2rVqpXatm2rzMxMeXp66sMPP9TOnTu1d+9eDRo06L69HwAAAEBhQeguROLi4pSSlKzyo1vKoYKHtctBHqRGX9bp4WsUFxdn1dDt7OysdevWafjw4dq1a5eOHj0qHx8fvfLKKxozZoycnJw0ffp0zZgxQytXrpSNjY3q1aunYcOG6ZFHHrllvzVr1tT8+fMVFxcnDw8PRUREaNy4cSpZsmS2dkuWLNH27dt19OhRSZLRaNSiRYvUv39/LVu2TG3atNG7775rybcAAAAAKJQI3YWQQwUPOQeXsXYZKGJq166tlStX3nJ937591bdv31uuHzlypEaOHJlt2cSJEzVx4sQ7bjvrFPSbhYeH69ixY3d8LQAAAFCcMXs5AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAthIjWgAGTN2o3CrzDcVx0AAAAPDkI3cA/S4q5LBqNefPFFa5eCPHJ0ctZvx44SvAEAAHBfELqBe5CRkCqZMlUh4iM5+gRauxzcQcr5E4qeM8Tq91UHAADAg4PQDRQAR59AufhVs3YZAAAAAAoZJlIDAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhVg9dGdkZGj48OGqUKGCnJycVKlSJY0ePVomk8ncJjExUf3791e5cuXk5OSkkJAQff7551asGgAAAACAO7O1dgEffvihZsyYoS+//FLVqlXTnj17FBERIXd3dw0YMECSNHjwYG3atEnz589XQECA1q1bp9dee01ly5bV008/beU9AAAAAAAgd1Y/0v3TTz/pmWeeUZs2bRQQEKDnnntOzZs3165du7K16datm8LCwhQQEKBevXqpZs2a2doAAAAAAFDYWP1Id4MGDfSf//xHx48fV5UqVbR//35t375dkydPztZmxYoV6tGjh8qWLastW7bo+PHj+vjjj3PtMzU1Vampqebn8fHxkqTMzExlZmZadofugclkktFolFEGGU13bg/rM8rwv89MMogPrbAzSjIajTKZTIV6LChOGNeKHsa1ooex7f5jbCtaGNeKnqIyruW1NquH7rffflvx8fEKDg6WjY2NMjIyNHbsWHXt2tXcZurUqerVq5fKlSsnW1tbGY1GzZw5U40bN861z/Hjx2vUqFE5ll+6dEkpKSkW25d7lZKSotDQUPnYeMsh5SFrl4M8SHD1U5nQUJUrKTk6JFq7HNxBSknJOTRUKSkpunjxorXLeSAwrhU9jGtFD2Pb/cfYVrQwrhU9RWVcS0hIyFM7q4fur7/+WgsWLNDChQtVrVo1/frrrxo4cKDKli2rbt26SboRunfu3KkVK1bI399fP/74o/r166eyZcsqPDw8R5+RkZEaPHiw+Xl8fLzKly8vLy8vubm53bd9y6+zZ88qKipKgRlV5OxYeP+ig/93OSFWZ6KilNRMcnYrYe1ycAdJV6WjUVFydHRU6dKlrV3OA4FxrehhXCt6GNvuP8a2ooVxregpKuOao6NjntpZPXQPHTpUb7/9tjp16iRJqlGjhk6dOqXx48erW7duSk5O1jvvvKNly5apTZs2kqSHH35Yv/76qz766KNcQ7eDg4McHBxyLDcajTIarX4Z+y0ZDIYbp8DLpEyDtatBXmTK9L/PTDKJD62wy9SN04AMBkOhHguKE8a1oodxrehhbLv/GNuKFsa1oqeojGt5rc3qe5CUlJSjWBsbG/P58WlpaUpLS7ttGwAAAAAACiOrH+lu27atxo4dKz8/P1WrVk379u3T5MmT1aNHD0mSm5ubmjRpoqFDh8rJyUn+/v7aunWrvvrqq2yTrQEAAAAAUNhYPXRPnTpVw4cP12uvvaaLFy+qbNmy6t27t9577z1zm8WLFysyMlJdu3bV5cuX5e/vr7Fjx6pPnz5WrBwAAAAAgNuzeuh2dXXVlClTNGXKlFu28fb21pw5c+5fUQAAAAAAFACrX9MNAAAAAEBxRegGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALMTqoTsjI0PDhw9XhQoV5OTkpEqVKmn06NEymUzZ2h09elRPP/203N3d5eLionr16ik2NtZKVQMAAAAAcGe21i7gww8/1IwZM/Tll1+qWrVq2rNnjyIiIuTu7q4BAwZIkk6ePKmGDRvqlVde0ahRo+Tm5qbDhw/L0dHRytUDAAAAAHBrVg/dP/30k5555hm1adNGkhQQEKBFixZp165d5jbvvvuuWrdurQkTJpiXVapU6b7XCgAAAABAflj99PIGDRpo48aNOn78uCRp//792r59u1q1aiVJyszM1A8//KAqVaqoRYsWKl26tOrXr6/ly5dbsWoAAAAAAO7M6ke63377bcXHxys4OFg2NjbKyMjQ2LFj1bVrV0nSxYsXlZiYqA8++EBjxozRhx9+qDVr1qh9+/bavHmzmjRpkqPP1NRUpaammp/Hx8dLuhHgMzMz78+O3QWTySSj0SijDDKa7twe1meU4X+fmWQQH1phZ5RkNBplMpkK9VhQnDCuFT2Ma0UPY9v9x9hWtDCuFT1FZVzLa21WD91ff/21FixYoIULF6patWr69ddfNXDgQJUtW1bdunUz78gzzzyjQYMGSZJq1aqln376SZ9//nmuoXv8+PEaNWpUjuWXLl1SSkqKZXfoHqSkpCg0NFQ+Nt5ySHnI2uUgDxJc/VQmNFTlSkqODonWLgd3kFJScg4NVUpKii5evGjtch4IjGtFD+Na0cPYdv8xthUtjGtFT1EZ1xISEvLUzuqhe+jQoXr77bfVqVMnSVKNGjV06tQpjR8/Xt26dZOnp6dsbW0VEhKS7XVVq1bV9u3bc+0zMjJSgwcPNj+Pj49X+fLl5eXlJTc3N8vtzD06e/asoqKiFJhRRc6OhfcvOvh/lxNidSYqSknNJGe3EtYuB3eQdFU6GhUlR0dHlS5d2trlPBAY14oexrWih7Ht/mNsK1oY14qeojKu5XVib6uH7qSkJBmN2S8tt7GxMR/htre3V7169fTbb79la3P8+HH5+/vn2qeDg4McHBxyLDcajTm2VZgYDIYbp8DLpEyDtatBXmTK9L/PTDKJD62wy9SN04AMBkOhHguKE8a1oodxrehhbLv/GNuKFsa1oqeojGt5rc3qobtt27YaO3as/Pz8VK1aNe3bt0+TJ09Wjx49zG2GDh2qjh07qnHjxmratKnWrFmj//73v9qyZYv1CgcAAAAA4A6sHrqnTp2q4cOH67XXXtPFixdVtmxZ9e7dW++99565zbPPPqvPP/9c48eP14ABAxQUFKRvv/1WDRs2tGLlAAAAAADcntVDt6urq6ZMmaIpU6bctl2PHj2yHf0GAAAAAKCwK7wnyAMAAAAAUMQRugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIUQugEAAAAAsBBCNwAAAAAAFkLoBgAAAADAQgjdAAAAAABYCKEbAAAAAAALIXQDAAAAAGAhhG4AAAAAACyE0A0AAAAAgIXYWruA+8FkMkmS4uPjrVzJ7SUmJkqSMpPSlJGYauVqkBeZKemSpIzUJGUkJ1i5GtxJRmqSpBv/rRX28aC4YFwrehjXih7GtvuPsa1oYVwreorKuJZVW1bevBWD6U4tioEzZ86ofPny1i4DAAAAAFDMnD59WuXKlbvl+gcidGdmZurcuXNydXWVwWCwdjkoRuLj41W+fHmdPn1abm5u1i4HAO4Z4xqA4oZxDZZiMpmUkJCgsmXLymi89ZXbD8Tp5Uaj8bZ/eQDulZubG4M4gGKFcQ1AccO4Bktwd3e/YxsmUgMAAAAAwEII3QAAAAAAWAihG7gHDg4OGjFihBwcHKxdCgAUCMY1AMUN4xqs7YGYSA0AAAAAAGvgSDcAAAAAABZC6AYAAAAAwEII3QAAAAAAWAihGwCAIsZgMGj58uV3/fqRI0eqVq1a5ufdu3dXu3bt7rkuAACQE6EbRYrBYLjtY+TIkYqJicl13YsvvnjLfrds2SKDwaCrV69me24wGGQ0GuXu7q7atWtr2LBhOn/+fLbXjhw5Mtftbdiw4a72MSAgQFOmTMl13T/3zcPDQ02aNNG2bdvualsACpfu3bub//u2s7NTmTJl1KxZM33xxRfKzMw0tzt//rxatWqVpz5zC+hDhgzRxo0b81SHwWBQqVKl1LJlSx04cCBH37k9Fi9eLCn7WGowGOTl5aXWrVvr4MGDt339zWM6gOLpn+NdhQoVNGzYMKWkpOTp9f/8TWRvb6/AwECNGTNGN88TfavfacHBweY2YWFh5uWOjo6qUqWKxo8fL5PJdMvX3/wA7sTW2gUA+XFz4F2yZInee+89/fbbb+ZlJUqUUFxcnCRpw4YNqlatmnmdk5NTvrf322+/yc3NTfHx8dq7d68mTJig2bNna8uWLapRo4a5XbVq1XKEbA8Pj1z77N69uwICAu7px2TWvsXFxWns2LF66qmndPz4cZUpU+au+wRQOLRs2VJz5sxRRkaG/vzzT61Zs0ZvvPGGli5dqhUrVsjW1lbe3t73tI0SJUqoRIkSeapDki5cuKB//etfeuqppxQbG5ut3Zw5c9SyZctsy0qWLJntedZYeu7cOQ0dOlRt2rTRiRMn8jSmAyi+ssaZtLQ0RUVFqVu3bjIYDPrwww/z3EfWb6LU1FRt375dPXv2lI+Pj1555RVzm9x+p9naZo9Br776qt5//32lpqZq06ZN6tWrl0qWLKkhQ4aoT58+5nb16tVTr1699Oqrr97lXuNBxJFuFCne3t7mh7u7uwwGQ7ZlN/9AK1WqVI72+VW6dGl5e3urSpUq6tSpk3bs2CEvLy/17ds3W7usH8E3P+zt7e95f28la9+qV6+ud955R/Hx8frll18stj0A94+Dg4O8vb3l6+urOnXq6J133tH333+v1atXa+7cuZKyH73++++/1b9/f/n4+MjR0VH+/v4aP368pBtnzkjSs88+K4PBYH7+z9PLb1eHt7e3atWqpbffflunT5/WpUuXsrUrWbJkjvHP0dExW5ussbROnToaOHCgTp8+rWPHjuVrTAdQ/GSNM+XLl1e7du0UHh6u9evXS5JSU1M1YMAAlS5dWo6OjmrYsKF2796do4+s30T+/v7q2rWrHn/8ce3duzdbm9x+p3l6emZr4+zsbO4nIiJCDz/8sNavX68SJUpke52NjY1cXV3NzxcuXKgaNWrIxcVF5cuX12uvvabExETLvWkokgjdQD44OTmpT58+2rFjhy5evGjtcpScnKyvvvpKkiwa8gFY1xNPPKGaNWvqu+++y7Hu008/1YoVK/T111/rt99+04IFC8zhOusH6pw5c3T+/Plcf7DmRWJioubPn6/AwECVKlXqrvfj2rVr5lPPGbMA3OzQoUP66aefzGPDsGHD9O233+rLL7/U3r17FRgYqBYtWujy5cu37GPPnj2KiopS/fr177oOk8mkbdu26dixY3kap4xGoz799FMdPnxYX375pTZt2qRhw4bd9fZRPHF6OYqtBg0ayGj8/78rbdu2TbVr177nfrOuAYqJiVHp0qUlSQcPHsx2RCYkJES7du26523dSta+JSUlyWQyKTQ0VE8++aTFtgfA+oKDg3NcUy1JsbGxqly5sho2bCiDwSB/f3/zOi8vL0n/fzQ6P1auXGke165fvy4fHx+tXLky27gqSZ07d5aNjU22ZUeOHJGfn5/5ebly5cz9SNLTTz+d7XpKAA+mrHEmPT1dqampMhqNmjZtmq5fv64ZM2Zo7ty55vkrZs6cqfXr12v27NkaOnSouY+s30R///230tLS1KtXL7388svZtvPP32mS9OKLL+rzzz83P58+fbpmzZpl7sfR0VEDBgy44z4MHDjQ/O+AgACNGTNGffr00fTp0+/mLUExRehGsbVkyRJVrVrV/Lx8+fKSblzXc+rUKUlSo0aNtHr16nz1mzU5x80TZwQFBWnFihXm5w4ODuZ/L1iwQL179zY/T01NlcFg0EcffWRetnr1ajVq1CjPNSxZskTBwcE6dOiQhg0bprlz58rOzi5f+wGgaDGZTLlO2NO9e3c1a9ZMQUFBatmypZ566ik1b978nrfXtGlTzZgxQ5J05coVTZ8+Xa1atdKuXbuyBfuPP/5Y4eHh2V5btmzZbM+3bdsmZ2dn7dy5U+PGjcv2QxfAgytrnLl+/bo+/vhj2draqkOHDjpw4IDS0tL0+OOPm9va2dnpkUce0dGjR7P1kfV7Ly0tTYcOHdLrr7+uhx56SB988IG5zT9/p0mSm5tbtuddu3bVu+++qytXrmjEiBFq0KCBGjRocMd92LBhg8aPH69jx44pPj5e6enpSklJUVJSkpydne/mbUExROhGsVW+fHkFBgbmWL5q1SqlpaVJurvJ1bIG+6zTNyWZZ8zMzdNPP53tNKe33npLvr6+2f566uvrm68aypcvr8qVK6ty5cpKT0/Xs88+q0OHDmUL+wCKl6NHj6pChQo5ltepU0fR0dFavXq1NmzYoBdeeEHh4eFaunTpPW3PxcUl27g2a9Ysubu7a+bMmRozZox5ube39y3HvywVKlRQyZIlFRQUpIsXL6pjx4768ccf76k+AEXfzePMF198oZo1a2r27NmqV69envu4+fde1apVdfLkSQ0fPlwjR440zy9xu99pWdzd3c1tvv76awUGBurRRx/N8UfFm8XExOipp55S3759NXbsWHl4eGj79u165ZVX9PfffxO6YcY13Xjg+Pv7KzAwUIGBgfkOu8nJyfrPf/6jxo0bm0/bvBNXV1fz9gIDA+Xq6ioPD49sy+4m/Gd57rnnZGtry2lMQDG2adMmHTx4UB06dMh1vZubmzp27KiZM2dqyZIl+vbbb83XPdrZ2SkjI+Oea8i6hWJycvI99dOvXz8dOnRIy5Ytu+eaABQfRqNR77zzjv71r3+pUqVKsre3144dO8zr09LStHv3boWEhNy2HxsbG6Wnp+vvv/++61pKlCihN954Q0OGDMl2+7F/ioqKUmZmpiZNmqRHH31UVapU0blz5+56uyi+ONIN3MbFixeVkpKihIQERUVFacKECYqLi8t1MqOCdPbsWf3666/Zlt18OufNDAaDBgwYoJEjR6p37978VRUo4lJTU3XhwoVstwwbP368nnrqqRzXKUrS5MmT5ePjo9q1a8toNOqbb76Rt7e3+bZdAQEB2rhxox5//HE5ODjooYceylcd0o3Ty6dNm6bExES1bds2W7urV6+a22VxdXWVi4tLrv06Ozvr1Vdf1YgRI9SuXTvucQvA7Pnnn9fQoUM1Y8YM9e3bV0OHDpWHh4f8/Pw0YcIEJSUlZbsVmCT99ddfunDhgtLT03Xw4EF98sknatq0abbTx9PT03OMUwaD4ba3Wu3du7dGjx6tb7/9Vs8991yubQIDA5WWlqapU6eqbdu22rFjB5fPIFcc6QZuIygoSGXLllVoaKg++OADhYeH69ChQ3f8K+u9+uijj1S7du1sjx9++OGW7bt166a0tDRNmzbNonUBsLw1a9bIx8dHAQEBatmypTZv3qxPP/1U33//fY4Jy6QbAXfChAmqW7eu6tWrp5iYGK1atco84dmkSZO0fv16lS9fPl+TSWbV4ePjo/r162v37t365ptvFBYWlq1dRESEuV3WY+rUqbftu3///jp69Ki++eabPNcDoPiztbVV//79NWHCBI0dO1YdOnTQSy+9pDp16ujEiRNau3Ztjj8choeHm8fMXr16qXXr1lqyZEm2NocPH84xTt3qYEYWDw8Pvfzyyxo5cqQyMzNzbVOzZk1NnjxZH374oapXr64FCxaYb9kI3Mxgut05EwAAAAAA4K5xpBsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhRC6AQAAAACwEEI3AAAAAAAWQugGAAAAAMBCCN0AAAAAAFgIoRsAAAAAAAshdAMAAAAAYCGEbgAAAAAALITQDQAAAACAhfwfyHnR/j7rRB4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 20: Published Baselines Comparison\n",
        "# ==========================================================\n",
        "\n",
        "def compare_published_baselines(res_full, res_bert, res_roberta):\n",
        "    \"\"\"\n",
        "    Compare our results against published baselines from the literature.\n",
        "    Sources: Pang et al. 2002, Maas et al. 2011, Sanh et al. 2019, Liu et al. 2019.\n",
        "    \"\"\"\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(\"PART 20 — PUBLISHED BASELINES COMPARISON\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    baselines = pd.DataFrame({\n",
        "        'Model': ['TF-IDF+LR', 'TF-IDF+LR', 'DistilBERT', 'DistilBERT', 'RoBERTa', 'RoBERTa'],\n",
        "        'Source': ['Ours', 'Maas et al. 2011', 'Ours', 'Sanh et al. 2019', 'Ours', 'Liu et al. 2019'],\n",
        "        'Accuracy (%)': [\n",
        "            res_full['accuracy'] * 100,\n",
        "            88.89,  # Maas et al. 2011 reported ~88.9% with word vectors\n",
        "            res_bert['accuracy'] * 100,\n",
        "            92.7,   # DistilBERT paper reports ~92.7% on SST-2 (comparable)\n",
        "            res_roberta['accuracy'] * 100,\n",
        "            95.3,   # RoBERTa paper best on IMDB\n",
        "        ],\n",
        "        'Notes': [\n",
        "            f\"GridSearchCV, best_params from code\",\n",
        "            \"Word vector + supervised learning\",\n",
        "            f\"F1-based early stopping, {res_bert['epochs_run']} epochs\",\n",
        "            \"SST-2 benchmark (comparable task)\",\n",
        "            f\"Gradient accumulation, {res_roberta['epochs_run']} epochs\",\n",
        "            \"Full pretraining + fine-tuning\",\n",
        "        ],\n",
        "    })\n",
        "\n",
        "    print(\"\\n\" + baselines.to_string(index=False))\n",
        "\n",
        "    # Visualization\n",
        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
        "    models = ['TF-IDF+LR', 'DistilBERT', 'RoBERTa']\n",
        "    ours = [res_full['accuracy']*100, res_bert['accuracy']*100, res_roberta['accuracy']*100]\n",
        "    published = [88.89, 92.7, 95.3]\n",
        "\n",
        "    x = np.arange(len(models))\n",
        "    width = 0.35\n",
        "    ax.bar(x - width/2, ours, width, label='Our Results', color='#2ecc71', edgecolor='black')\n",
        "    ax.bar(x + width/2, published, width, label='Published Baselines', color='#3498db', edgecolor='black')\n",
        "\n",
        "    for i, (o, p) in enumerate(zip(ours, published)):\n",
        "        ax.text(i - width/2, o + 0.2, f'{o:.1f}%', ha='center', fontsize=9, fontweight='bold')\n",
        "        ax.text(i + width/2, p + 0.2, f'{p:.1f}%', ha='center', fontsize=9, fontweight='bold')\n",
        "\n",
        "    ax.set_ylabel('Accuracy (%)', fontsize=11)\n",
        "    ax.set_title('Our Results vs Published Baselines', fontsize=12, fontweight='bold')\n",
        "    ax.set_xticks(x)\n",
        "    ax.set_xticklabels(models)\n",
        "    ax.legend()\n",
        "    ax.set_ylim([85, 98])\n",
        "    ax.grid(axis='y', alpha=0.3)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(f'{FIGURES_DIR}/part20b_published_baselines.png', dpi=150, bbox_inches='tight')\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "    return baselines.to_dict('records')\n",
        "\n",
        "# Execute\n",
        "results_baselines = compare_published_baselines(res_full, res_bert, res_roberta)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69f8f99d",
      "metadata": {
        "id": "69f8f99d"
      },
      "source": [
        "## Part 21: Save All Results\n",
        "\n",
        "Save all analysis results to files for reporting and future reference."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "cc96f3d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc96f3d2",
        "outputId": "5556e82a-7af5-4794-a0bc-bd7bee98c49a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 21 — SAVING ALL RESULTS  [run_20260329_093626]\n",
            "======================================================================\n",
            "  Saved ./output/run_20260329_093626/run_metadata.json\n",
            "  Saved ./output/run_20260329_093626/results_lr.json\n",
            "  Saved ./output/run_20260329_093626/results_bert.json\n",
            "  Saved ./output/run_20260329_093626/results_roberta.json\n",
            "  Saved ./output/run_20260329_093626/results_comparison.json\n",
            "  Saved ./output/run_20260329_093626/error_analysis.json\n",
            "  Saved ./output/run_20260329_093626/linguistic_tests.json\n",
            "  Saved ./output/run_20260329_093626/part15_linguistic_results.json\n",
            "  Saved ./output/run_20260329_093626/part16_ablation_results.json\n",
            "  Saved ./output/run_20260329_093626/part17_calibration_results.json\n",
            "  Saved ./output/run_20260329_093626/part18_length_results.json\n",
            "  Saved ./output/run_20260329_093626/part19_error_agreement_results.json\n",
            "  Saved ./output/run_20260329_093626/part16b_hp_grid_results.json\n",
            "  Saved ./output/run_20260329_093626/part16c_roberta_hp_grid_results.json\n",
            "  Saved ./output/run_20260329_093626/part12b_gradient_attr_results.json\n",
            "  Saved ./output/run_20260329_093626/part20b_baselines_results.json\n",
            "  Figures: 25 files in ./output/run_20260329_093626/figures\n",
            "\n",
            "  ALSO SAVED TO DRIVE: /content/drive/MyDrive/IMDB_Results/run_20260329_093626\n",
            "  Your results are safe even if the runtime disconnects.\n",
            "\n",
            "  Total: 16 files + 25 figures saved\n",
            "  Run tag: run_20260329_093626\n",
            "\n",
            "======================================================================\n",
            "NOTEBOOK COMPLETE!\n",
            "======================================================================\n",
            "  GPU: NVIDIA L4\n",
            "  Peak VRAM: 8.29 GB\n",
            "  Run tag: run_20260329_093626\n",
            "  Local results: ./output/run_20260329_093626/\n",
            "  Drive results: /content/drive/MyDrive/IMDB_Results/run_20260329_093626/\n",
            "\n",
            "  All runs on Drive (3 total):\n",
            "    run_20260328_125944\n",
            "    run_20260328_172727\n",
            "    run_20260329_093626\n",
            "\n",
            "  You can now disconnect the runtime to stop using compute units.\n",
            "  Go to: Runtime -> Disconnect and delete runtime\n"
          ]
        }
      ],
      "source": [
        "# ==========================================================\n",
        "# Part 21: Save All Results to JSON\n",
        "# ==========================================================\n",
        "def save_all_results(res_full, res_subset, res_bert, res_roberta, linguistic,\n",
        "                     error_analysis_results, results_linguistic, results_ablation,\n",
        "                     results_calibration, results_length, results_error_agreement,\n",
        "                     results_hp_grid, results_roberta_hp_grid, results_gradient_attr, results_baselines):\n",
        "    \"\"\"\n",
        "    Consolidated save: writes ALL analysis results to JSON files.\n",
        "    Each run is saved to output/<run_tag>/ and optionally to Google Drive.\n",
        "    \"\"\"\n",
        "    def convert(obj):\n",
        "        if isinstance(obj, (np.integer,)): return int(obj)\n",
        "        if isinstance(obj, (np.floating,)): return float(obj)\n",
        "        if isinstance(obj, np.ndarray): return obj.tolist()\n",
        "        if isinstance(obj, dict): return {k: convert(v) for k, v in obj.items()}\n",
        "        if isinstance(obj, (list, tuple)): return [convert(v) for v in obj]\n",
        "        return obj\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(f\"PART 21 — SAVING ALL RESULTS  [{RUN_TAG}]\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    # --- Build run metadata ---\n",
        "    run_meta = {\n",
        "        \"run_tag\": RUN_TAG,\n",
        "        \"timestamp\": RUN_TIMESTAMP,\n",
        "        \"device\": str(DEVICE),\n",
        "        \"gpu_name\": torch.cuda.get_device_properties(0).name if DEVICE.type == \"cuda\" else \"N/A\",\n",
        "        \"vram_gb\": round(torch.cuda.get_device_properties(0).total_memory / (1024**3), 1) if DEVICE.type == \"cuda\" else 0,\n",
        "        \"peak_vram_gb\": round(torch.cuda.max_memory_allocated() / (1024**3), 2) if DEVICE.type == \"cuda\" else 0,\n",
        "        \"seed\": SEED,\n",
        "        \"bert_batch\": BERT_BATCH_SIZE,\n",
        "        \"roberta_batch\": ROBERTA_BATCH_SIZE,\n",
        "        \"fp16\": USE_FP16,\n",
        "        \"n_bootstrap\": N_BOOTSTRAP,\n",
        "    }\n",
        "\n",
        "    # --- Core results ---\n",
        "    res_full_save = {k: v for k, v in res_full.items() if k not in [\"predictions\", \"probabilities\", \"true_labels\"]}\n",
        "    res_bert_save = {k: v for k, v in res_bert.items() if k not in [\"predictions\", \"probabilities\", \"true_labels\"]}\n",
        "    res_roberta_save = {k: v for k, v in res_roberta.items() if k not in [\"predictions\", \"probabilities\", \"true_labels\"]}\n",
        "    res_subset_save = {k: v for k, v in res_subset.items() if k not in [\"predictions\", \"probabilities\", \"true_labels\"]}\n",
        "\n",
        "    files = {\n",
        "        \"run_metadata.json\": convert(run_meta),\n",
        "        \"results_lr.json\": convert({\"full_dataset\": res_full_save, \"subset\": res_subset_save}),\n",
        "        \"results_bert.json\": convert(res_bert_save),\n",
        "        \"results_roberta.json\": convert(res_roberta_save),\n",
        "        \"results_comparison.json\": convert({\n",
        "            \"LR_FULL\": {k: res_full[k] for k in [\"accuracy\",\"precision\",\"recall\",\"f1\",\"auc\"]},\n",
        "            \"LR_SUBSET\": {k: res_subset[k] for k in [\"accuracy\",\"precision\",\"recall\",\"f1\",\"auc\"]},\n",
        "            \"BERT\": {k: res_bert[k] for k in [\"accuracy\",\"precision\",\"recall\",\"f1\",\"auc\"]},\n",
        "            \"ROBERTA\": {k: res_roberta[k] for k in [\"accuracy\",\"precision\",\"recall\",\"f1\",\"auc\"]},\n",
        "        }),\n",
        "        \"error_analysis.json\": convert(error_analysis_results),\n",
        "        \"linguistic_tests.json\": convert(linguistic),\n",
        "        \"part15_linguistic_results.json\": convert(results_linguistic),\n",
        "        \"part16_ablation_results.json\": convert(results_ablation),\n",
        "        \"part17_calibration_results.json\": convert(results_calibration),\n",
        "        \"part18_length_results.json\": convert(results_length),\n",
        "        \"part19_error_agreement_results.json\": convert(results_error_agreement),\n",
        "        \"part16b_hp_grid_results.json\": convert(results_hp_grid),\n",
        "        \"part16c_roberta_hp_grid_results.json\": convert(results_roberta_hp_grid),\n",
        "        \"part12b_gradient_attr_results.json\": convert(results_gradient_attr),\n",
        "        \"part20b_baselines_results.json\": convert(results_baselines),\n",
        "    }\n",
        "\n",
        "    # Save to local output folder\n",
        "    for fname, data in files.items():\n",
        "        path = f\"{OUTPUT_DIR}/{fname}\"\n",
        "        with open(path, \"w\") as f:\n",
        "            json.dump(data, f, indent=2, default=str)\n",
        "        print(f\"  Saved {path}\")\n",
        "\n",
        "    # Copy figures list\n",
        "    local_figs = [f for f in os.listdir(FIGURES_DIR) if os.path.isfile(os.path.join(FIGURES_DIR, f))] if os.path.exists(FIGURES_DIR) else []\n",
        "    print(f\"  Figures: {len(local_figs)} files in {FIGURES_DIR}\")\n",
        "\n",
        "    # --- Save to Google Drive (persistent) ---\n",
        "    if SAVE_TO_DRIVE and DRIVE_OUTPUT_DIR:\n",
        "        import shutil\n",
        "        for fname in files:\n",
        "            shutil.copy2(f\"{OUTPUT_DIR}/{fname}\", f\"{DRIVE_OUTPUT_DIR}/{fname}\")\n",
        "        # Copy figures\n",
        "        if os.path.exists(FIGURES_DIR):\n",
        "            drive_figs = f\"{DRIVE_OUTPUT_DIR}/figures\"\n",
        "            if os.path.exists(drive_figs):\n",
        "                shutil.rmtree(drive_figs)\n",
        "            shutil.copytree(FIGURES_DIR, drive_figs)\n",
        "        print(f\"\\n  ALSO SAVED TO DRIVE: {DRIVE_OUTPUT_DIR}\")\n",
        "        print(f\"  Your results are safe even if the runtime disconnects.\")\n",
        "\n",
        "    print(f\"\\n  Total: {len(files)} files + {len(local_figs)} figures saved\")\n",
        "    print(f\"  Run tag: {RUN_TAG}\")\n",
        "\n",
        "# Execute Part 21\n",
        "save_all_results(res_full, res_subset, res_bert, res_roberta, linguistic,\n",
        "                 error_analysis_results, results_linguistic, results_ablation,\n",
        "                 results_calibration, results_length, results_error_agreement,\n",
        "                 results_hp_grid, results_roberta_hp_grid, results_gradient_attr, results_baselines)\n",
        "\n",
        "# Print runtime summary\n",
        "print(\"\\n\" + \"=\"*70)\n",
        "print(\"NOTEBOOK COMPLETE!\")\n",
        "print(\"=\"*70)\n",
        "if DEVICE.type == \"cuda\":\n",
        "    print(f\"  GPU: {torch.cuda.get_device_properties(0).name}\")\n",
        "    print(f\"  Peak VRAM: {torch.cuda.max_memory_allocated() / (1024**3):.2f} GB\")\n",
        "print(f\"  Run tag: {RUN_TAG}\")\n",
        "print(f\"  Local results: {OUTPUT_DIR}/\")\n",
        "if SAVE_TO_DRIVE:\n",
        "    print(f\"  Drive results: {DRIVE_OUTPUT_DIR}/\")\n",
        "    # List all previous runs on Drive\n",
        "    all_runs = sorted([d for d in os.listdir(DRIVE_BASE) if d.startswith(\"run_\")])\n",
        "    if len(all_runs) > 1:\n",
        "        print(f\"\\n  All runs on Drive ({len(all_runs)} total):\")\n",
        "        for r in all_runs:\n",
        "            print(f\"    {r}\")\n",
        "print(f\"\\n  You can now disconnect the runtime to stop using compute units.\")\n",
        "print(f\"  Go to: Runtime -> Disconnect and delete runtime\")\n"
      ]
    }
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