persian_authors_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author Identification in Persian Literature using Language Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This code snippet demonstrates the necessary libraries and modules for a machine learning project involving natural language processing (NLP) using PyTorch and the Hugging Face Transformers library. The task likely involves text classification using the BERT model.\n",
    "\n",
    "## Libraries\n",
    "\n",
    "### Data Manipulation and Numerical Operations\n",
    "- `pandas` (imported as `pd`): Used for data manipulation, such as reading datasets into dataframes.\n",
    "- `numpy` (imported as `np`): Utilized for numerical operations and array manipulation.\n",
    "\n",
    "### Data Visualization\n",
    "- `matplotlib.pyplot` (imported as `plt`): Primarily employed for data visualization tasks.\n",
    "- `seaborn` (imported as `sns`): Used for advanced visualization, often in conjunction with `matplotlib`.\n",
    "\n",
    "### Machine Learning\n",
    "- `sklearn.model_selection`: Contains functions for splitting datasets into training and testing sets.\n",
    "- `sklearn.metrics`: Provides various metrics for evaluating machine learning models, including accuracy, classification reports, and confusion matrices.\n",
    "\n",
    "### Deep Learning (PyTorch and Transformers)\n",
    "- `torch`: PyTorch library, utilized for deep learning tasks.\n",
    "- `transformers`: A library from Hugging Face for pre-trained language models and NLP tasks.\n",
    "\n",
    "## Machine Learning Components\n",
    "\n",
    "### Data Splitting\n",
    "- `train_test_split`: Function from `sklearn.model_selection` used to split data into training and testing sets.\n",
    "\n",
    "### Model Evaluation\n",
    "- `accuracy_score`: Computes the accuracy of a classification model.\n",
    "- `classification_report`: Generates a detailed classification report, including precision, recall, and F1-score.\n",
    "- `confusion_matrix`: Creates a confusion matrix to evaluate classification performance.\n",
    "\n",
    "## Deep Learning Components\n",
    "\n",
    "### Hugging Face Transformers\n",
    "- `BertTokenizer`: Tokenizer for the BERT model, used to preprocess text data.\n",
    "- `BertForSequenceClassification`: BERT model fine-tuned for sequence classification tasks.\n",
    "- `AdamW`: Optimizer for training neural networks, particularly effective with Transformer architectures.\n",
    "\n",
    "### PyTorch Data Handling\n",
    "- `TensorDataset`: Dataset wrapping tensors, allowing for easy slicing and indexing.\n",
    "- `DataLoader`: Iterator that loads data in batches, facilitating training and evaluation.\n",
    "- `RandomSampler` and `SequentialSampler`: Sampling strategies used for creating data loaders, typically for training and validation datasets.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Models and Methods\n",
    "\n",
    "This implementation utilizes state-of-the-art transformer-based models for sequence classification tasks. The primary models employed are:\n",
    "\n",
    "### 1. BERT (Bidirectional Encoder Representations from Transformers)\n",
    "BERT is a pre-trained transformer-based model introduced by Google. It has achieved remarkable success in various natural language processing (NLP) tasks, including sequence classification. BERT captures bidirectional contextual information from input sequences, enabling it to generate high-quality embeddings for downstream tasks.\n",
    "\n",
    "### 2. XLM-RoBERTa (Cross-lingual Language Model - RoBERTa)\n",
    "XLM-RoBERTa is an extension of RoBERTa, a variant of BERT introduced by Facebook AI. XLM-RoBERTa is pre-trained on multilingual data, making it effective for sequence classification tasks involving multiple languages. It leverages large-scale pre-training and advanced training strategies to achieve strong performance across diverse languages.\n",
    "\n",
    "### 3. DistilBERT\n",
    "DistilBERT is a distilled version of BERT introduced by Hugging Face. It retains much of the original BERT's performance while being significantly smaller and faster. DistilBERT achieves compression through knowledge distillation and parameter reduction techniques, making it suitable for deployment in resource-constrained environments.\n",
    "\n",
    "### Methods Used:\n",
    "\n",
    "- **Text Preprocessing:** Before model training, the text data undergoes preprocessing steps such as normalization, tokenization, and removal of stopwords to ensure compatibility with the models.\n",
    "\n",
    "- **Tokenization and Data Preparation:** The text data is tokenized using BERT tokenizer, and sequences are padded and converted into input tensors for model training. Additionally, labels are encoded as integers and DataLoader objects are created for efficient batch processing.\n",
    "\n",
    "- **Model Training:** The pre-trained transformer-based models are trained on the train dataset using AdamW optimizer, learning rate scheduling, and hyperparameters tuning. Training is performed for multiple epochs to optimize model parameters.\n",
    "\n",
    "- **Model Evaluation:** After training, the models are evaluated on a separate validation dataset to assess their performance and prevent overfitting. Metrics such as accuracy, precision, recall, and F1-score are calculated to measure model effectiveness.\n",
    "\n",
    "- **Performance Analysis:** Finally, the best-performing model is selected based on validation accuracy and evaluated on a held-out test dataset. Performance metrics and visualizations, including accuracy, classification report, and confusion matrix, are generated to analyze model performance and provide insights into its behavior.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-02-16T23:20:31.141529Z",
     "iopub.status.busy": "2024-02-16T23:20:31.140798Z",
     "iopub.status.idle": "2024-02-16T23:20:40.557536Z",
     "shell.execute_reply": "2024-02-16T23:20:40.556467Z",
     "shell.execute_reply.started": "2024-02-16T23:20:31.141493Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:54:57.754878Z",
     "end_time": "2024-05-30T19:54:59.834398Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from transformers import BertTokenizer, BertForSequenceClassification, AdamW\n",
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from src.preprocess import preprocess_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Library Installation\n",
    "\n",
    "If the `hazm` library has not been installed, use pip:\n",
    "\n",
    "```bash\n",
    "!pip install hazm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:21:05.698377Z",
     "iopub.status.busy": "2024-02-16T23:21:05.698045Z",
     "iopub.status.idle": "2024-02-16T23:22:40.487812Z",
     "shell.execute_reply": "2024-02-16T23:22:40.486633Z",
     "shell.execute_reply.started": "2024-02-16T23:21:05.698346Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:55:40.548760Z",
     "end_time": "2024-05-30T19:56:26.459960Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "df = pd.read_csv(\"datasets/raw/persian_authors.csv\")\n",
    "\n",
    "df = preprocess_dataset(df, save_file=\"datasets/processed/persian_authors_processed.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Viewing the first few rows of the dataset:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.491688Z",
     "iopub.status.busy": "2024-02-16T23:22:40.490952Z",
     "iopub.status.idle": "2024-02-16T23:22:40.499148Z",
     "shell.execute_reply": "2024-02-16T23:22:40.498270Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.491660Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:30.902003Z",
     "end_time": "2024-05-30T19:56:30.916729Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    author        book                                               text\n",
      "0   nezami       5ganj  پری‌پیکر نگار پرنیان‌پوش بت سنگین‌دل سیمین بنا...\n",
      "1   nezami       5ganj  سبک باش‌ای نسیم صبح گاهی تفضل کن بدان فرصت خوا...\n",
      "2  moulavi       shams  گر زان نه‌ای طالب جوینده شوی ور زان نه‌ای مطرب...\n",
      "3    iqbal   all-books  راهب دیرینه افلاطون حکیم گروه گوسفندان قدیم رخ...\n",
      "4     saeb  divan-saeb  بلبل خوش نغمه‌ام ، گل سخن مرا سرمه خاموشی زاغ ...\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Getting information about the dataset:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.500609Z",
     "iopub.status.busy": "2024-02-16T23:22:40.500328Z",
     "iopub.status.idle": "2024-02-16T23:22:40.528473Z",
     "shell.execute_reply": "2024-02-16T23:22:40.527374Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.500585Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:34.325574Z",
     "end_time": "2024-05-30T19:56:34.333482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 305 entries, 0 to 304\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   author  305 non-null    object\n",
      " 1   book    305 non-null    object\n",
      " 2   text    305 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 7.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Descriptive statistics of the dataset:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.530076Z",
     "iopub.status.busy": "2024-02-16T23:22:40.529765Z",
     "iopub.status.idle": "2024-02-16T23:22:40.546870Z",
     "shell.execute_reply": "2024-02-16T23:22:40.545596Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.530049Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:37.445696Z",
     "end_time": "2024-05-30T19:56:37.455561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        author       book                                               text\n",
      "count      305        305                                                305\n",
      "unique      10         10                                                305\n",
      "top     nezami  all-books  پری‌پیکر نگار پرنیان‌پوش بت سنگین‌دل سیمین بنا...\n",
      "freq        31         57                                                  1\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Count of unique authors:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.548437Z",
     "iopub.status.busy": "2024-02-16T23:22:40.548046Z",
     "iopub.status.idle": "2024-02-16T23:22:40.554058Z",
     "shell.execute_reply": "2024-02-16T23:22:40.552976Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.548411Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:39.959550Z",
     "end_time": "2024-05-30T19:56:39.974394Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(df['author'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Count of texts per author:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.555463Z",
     "iopub.status.busy": "2024-02-16T23:22:40.555138Z",
     "iopub.status.idle": "2024-02-16T23:22:40.563864Z",
     "shell.execute_reply": "2024-02-16T23:22:40.562697Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.555438Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:42.437743Z",
     "end_time": "2024-05-30T19:56:42.450422Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "author\n",
      "nezami      31\n",
      "moulavi     31\n",
      "iqbal       31\n",
      "saeb        31\n",
      "attar       31\n",
      "saadi       31\n",
      "ferdousi    31\n",
      "eraghi      31\n",
      "jami        31\n",
      "rahi        26\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['author'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distribution of text lengths:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.565438Z",
     "iopub.status.busy": "2024-02-16T23:22:40.565078Z",
     "iopub.status.idle": "2024-02-16T23:22:40.578189Z",
     "shell.execute_reply": "2024-02-16T23:22:40.577076Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.565409Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:46.753504Z",
     "end_time": "2024-05-30T19:56:46.772153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     305.000000\n",
      "mean     2439.255738\n",
      "std       155.012988\n",
      "min      1961.000000\n",
      "25%      2332.000000\n",
      "50%      2469.000000\n",
      "75%      2553.000000\n",
      "max      2812.000000\n",
      "Name: text_length, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df['text_length'] = df['text'].apply(len)\n",
    "print(df['text_length'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Histogram of text lengths:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.579876Z",
     "iopub.status.busy": "2024-02-16T23:22:40.579569Z",
     "iopub.status.idle": "2024-02-16T23:22:40.913511Z",
     "shell.execute_reply": "2024-02-16T23:22:40.912454Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.579850Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:49.306623Z",
     "end_time": "2024-05-30T19:56:49.521630Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(data=df, x='text_length', bins=30)\n",
    "plt.title('Distribution of Text Lengths')\n",
    "plt.xlabel('Text Length')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Bar plot of the number of texts per author:**\n",
    "## Visualizing Author Distribution\n",
    "\n",
    "This code snippet visualizes the distribution of authors in the dataset using a categorical plot.\n",
    "\n",
    "- The `seaborn` library is imported as `sns` for advanced visualization.\n",
    "- The `matplotlib.pyplot` module is imported as `plt` for data visualization.\n",
    "- The `sns.catplot()` function is used to create a categorical plot (catplot) to visualize the distribution of authors in the dataset.\n",
    "- The `x` parameter specifies the column containing the authors.\n",
    "- The `kind` parameter is set to `'count'` to create a count plot.\n",
    "- Finally, `plt.show()` is called to display the plot.\n",
    "\n",
    "This visualization helps to understand the distribution of authors in the dataset, providing insights into the class distribution for the classification task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:40.917585Z",
     "iopub.status.busy": "2024-02-16T23:22:40.917295Z",
     "iopub.status.idle": "2024-02-16T23:22:41.189297Z",
     "shell.execute_reply": "2024-02-16T23:22:41.188240Z",
     "shell.execute_reply.started": "2024-02-16T23:22:40.917561Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:53.189367Z",
     "end_time": "2024-05-30T19:56:53.379224Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0kAAAJHCAYAAABFBB2EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABp00lEQVR4nO3dd3QUZfvG8SuVIkVApIsIshAIEIq00JXeq5RQpTfhRUCkF4OAiIANlSZNOoYOIkiToiBFRDqhhx6SkJDk+f3Bm/1NpLzsGtlAvp9zOIedmd3ce+fJ7l47M8+4GWOMAAAAAACSJHdXFwAAAAAAiQkhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAJ5hu3btks1m09q1a11dyhO5evWqevXqpZIlS8pms2nmzJmuLgkJ5Ny5c7LZbPr2229dXQoA/GOEJAD4H5YuXSqbzSZfX19dvnz5gfUBAQGqXbu2Cyp79gQGBmrr1q3q1KmTxo0bp3Llyj2wzcCBA2Wz2f7nv4EDByZYXXPnztXSpUsT7PESm7lz58pms6lJkyb/+LG2bNmiKVOmJEBVAJB4ebq6AAB4VkRFRWnatGkaMmSIq0t5Zv3yyy+qUqWKOnTo8MhtmjVrptKlS9tvnzt3TpMnT1azZs1UrFgx+/JXXnklweqaP3++0qVLp4YNGybYYyYmQUFBypYtmw4cOKAzZ84oZ86cTj/Wli1bNHfuXPXs2TMBKwSAxIU9SQDwhPLnz6+FCxc+dG/S8y48PDxBHufatWtKkybNY7fx8/NTvXr17P/Kly8vSSpSpEi85X5+fglS07PsSX4vwcHB2rdvn95//32lT59eQUFBT6Gyf09CjUUAeBxCEgA8oc6dOys2NlZff/31Y7eLOzfjYYdv2Wy2eIcqTZkyRTabTadOnVK/fv1UrFgxlSpVSpMmTZIxRhcvXlTXrl1VtGhRlS1bVtOnT3/oz4yNjdXEiRNVtmxZFSlSRF26dNHFixcf2O73339Xhw4dVKxYMRUuXFitWrXSr7/+Gm+buJqOHz+u//znPypRooRatGjx2OccHBysXr166Y033lDhwoXVtGlTbd682b4+7pBFY4z90C+bzfbYx/xf/tdzOXHihAoVKqT+/fvHu9/evXuVP39+jR8/XpJUuXJlHTt2TLt377bXFRAQIEm6d++epk6dqqpVq8rX11clS5ZU8+bNtX379sfWFvd89+zZo6FDh6pkyZIqWrSo+vfvr1u3bj2w/ZYtW9SiRQsVKVJEfn5+6tSpk44dOxZvm4EDB8rPz09nz55Vx44d5efnp379+v3PPgUFBSlt2rSqUKGCqlWr9tCQFHdu265du+It//tYHjhwoObOnStJ8Q59/Lvvv/9eb775pgoWLKhGjRrpwIEDD2yzc+dO+3MuXry4unbtqhMnTsTbxpmxCAAJgZAEAE8oe/bsqlev3r+yN6lPnz4yxug///mPChcurC+++EKzZs1Su3btlClTJvXr10+vvPKKPvroI+3Zs+eB+3/xxRfavHmzOnbsqICAAO3YsUNt27bV3bt37dvs3LlTLVu2VFhYmHr06KE+ffro9u3batOmzUM/xPbu3VsRERHq06fPY89luXr1qt5++21t27ZNzZs3V58+fRQZGamuXbtqw4YNkqQSJUpo3LhxkqSyZctq3Lhx9tvOeJLnkjt3bvXu3VsrVqzQjz/+KOn+Xoj3339fr732mnr37i1JGjRokDJnzqzXXnvNXleXLl0kSVOnTtXUqVNVsmRJDR06VF26dFHWrFl1+PDhJ6pz5MiROnHihHr06KH69esrKChI3bt3lzHGvs3y5cvVuXNnpUyZUv369VO3bt10/PhxtWjRQufOnYv3eNHR0erQoYMyZMigAQMGqGrVqv+zhqCgIL311lvy9vZW7dq1dfr06Yf+vp9Es2bNVLZsWUmy9+rvv8eVK1fq22+/VbNmzfTuu+/q/Pnz6tmzp+7du2ffZseOHXrnnXd07do19ejRQ23bttW+ffvUvHnzB56z9ORjEQASjAEAPNaSJUtM3rx5zYEDB8zZs2eNj4+PGTVqlH19q1atTK1atey3g4ODTd68ec2SJUseeKy8efOayZMn229PnjzZ5M2b1wwZMsS+LDo62pQvX97YbDbz1Vdf2ZffunXLFCpUyAwYMMC+7JdffjF58+Y15cqVM6Ghofblq1evNnnz5jWzZs0yxhgTGxtrqlatatq3b29iY2Pt20VERJjKlSubdu3aPVBT3759n6g/Y8aMMXnz5jV79uyxL7tz546pXLmyqVSpkomJiYn3/EeMGPFEjxvnwIED8frpyHOJiYkxzZs3N2XKlDHXr183I0aMMD4+PubAgQPxfkatWrVMq1atHvjZdevWNZ06dXKoXmP+f8w0aNDAREVF2Zd//fXXJm/evGbjxo3GmPt9Kl68uBk8eHC8+4eEhJhixYrFWz5gwACTN29eM2HChCeu4+DBgyZv3rxm+/btxpj7vStfvrwZPXp0vO3ixtEvv/wSb/nDxvKIESNM3rx5H/hZcdu+8cYb5ubNm/blGzduNHnz5jWbNm2yL6tXr54pXbq0uXHjhn3ZkSNHTL58+Uz//v3tyxwdiwCQUNiTBAAOyJEjh+rWrauFCxfqypUrCfa4jRs3tv/fw8NDBQsWlDEm3vI0adIoV65cCg4OfuD+9evXV6pUqey3q1evrowZM2rLli2SpCNHjuj06dOqU6eObty4oevXr+v69esKDw9X6dKltWfPHsXGxsZ7zLfffvuJat+yZYsKFSqk4sWL25e98MILatasmc6fP6/jx48/WROekCPPxd3dXWPHjlV4eLg6duyoefPmqVOnTvL19X2in5UmTRodO3ZMp0+fdqrWZs2aycvLy367efPm8vT0tP9eduzYodu3b6tWrVr253H9+nW5u7urcOHCDxz+FvcYTyooKEgvvfSSSpYsKUlyc3NTzZo1tXr1asXExDj1nP6XmjVrKm3atPbbceMibtxeuXJFR44cUYMGDfTiiy/at8uXL5/KlClj743Vk45FAEgozG4HAA7q1q2bfvjhB02bNk2DBw9OkMfMmjVrvNupU6dWsmTJlD59+geW37x584H7/322Mjc3N+XMmVPnz5+XJPuH/AEDBjyyhtDQ0HgfbrNnz/5EtV+4cEGFCxd+YPlrr71mX583b94neqwn4ehzeeWVV9SjRw+NGzdOefPmVbdu3Z74Z/Xq1UvdunVTtWrVlDdvXvn7+6tevXrKly/fE93/77+XF154QRkzZnzg99KmTZuH3t8afCXJ09NTmTNnfqKfHRMTo1WrVqlkyZLxDmErVKiQpk+frp07d8rf3/+JHssRWbJkiXc77vdw+/ZtSffHgyTlypXrgfvmzp1b27ZtU3h4uFKmTGlf/qRjEQASCiEJABxk3ZvUqVOnB9a7ubk99H6P++be3f3BHfseHh4P3dZYzmd5UnH36d+/v/Lnz//QbawfSiUpWbJkDv+cp8GZ5xI30cKVK1d08+ZNZcyY8Yl+VokSJbRhwwb9+OOP2r59uxYvXqxZs2ZpxIgRCXJuTNxzGTdu3ENr+vsY8Pb2fuhYeZhffvlFISEhWrVqlVatWvXA+qCgIHtIetSY/fvexSeRkOM2TmIdiwCeX4QkAHBC165d9cMPPzx0pru/f3MeJ+4b9H/DmTNn4t02xujMmTP2mcdy5Mgh6f6eiTJlyiToz86aNatOnTr1wPKTJ0/a1yckR5/L/PnztX37dvXp00dfffWVhg4dqi+++CLeNo8KCZL04osvqlGjRmrUqJHCwsLUqlUrTZky5YlC0pkzZ1SqVCn77bCwMIWEhNinNY97LhkyZEjw30tQUJAyZMigoUOHPrBuw4YN2rBhg0aMGKHkyZPbp2UPDQ2Nt13cHi+rx/XqScSNh0eNmXTp0j0QcgHgaeOcJABwwiuvvKK6devq+++/V0hISLx1qVKlUrp06bR37954y+fNm/ev1bN8+XLduXPHfnvt2rXxPowXLFhQr7zyiqZPn66wsLAH7n/9+nWnf3aFChV04MAB7du3z74sPDxcCxcuVLZs2ZQnTx6nH/thHHkuwcHBGjdunKpVq6YuXbpowIAB2rRpk5YvXx7vPilSpHgg1ErSjRs34t1+4YUX9MorrygqKuqJav3+++/jzeo2f/58RUdH238v5cqVU6pUqfTVV1/F2+5hz8URd+/e1fr161WxYkVVr179gX9xMwNu2rRJkpQtWzZ5eHg8MHPi/PnzH3jsFClSSHrwS4An9fLLLyt//vxavnx5vMf466+/tH37dlWoUMGpxwWAhMSeJABwUpcuXbRixQqdOnVKr7/+erx1TZo00bRp0/TBBx+oYMGC2rt370O/OU8oadOmVYsWLdSwYUNdu3ZNs2bNUs6cOdW0aVNJ9w/nGz16tDp27KjatWurYcOGypQpky5fvqxdu3YpVapU+vLLL5362Z06ddKqVavs04+nTZtWy5cv17lz5zRlypQnPjzsST3pczHGaNCgQUqePLmGDx8u6f4EAOvXr9eYMWNUunRpZcqUSZJUoEABzZ8/X59//rly5syp9OnTq3Tp0qpVq5beeOMNFShQQC+++KIOHjyodevWqVWrVk9U671799S2bVvVqFFDp06d0rx581SsWDFVqVJF0v1APXz4cPXv318NGzZUzZo1lT59el24cEFbtmxR0aJFH7on6H/ZtGmTwsLCVLly5YeuL1KkiNKnT68ffvhBNWvWVOrUqVW9enXNmTNHbm5uypEjhzZv3qxr1649cN8CBQpIkkaPHi1/f395eHioVq1aDtXXv39/dezYUc2aNVPjxo119+5dzZkzR6lTp1aPHj0cfr4AkNAISQDgpJw5c6pu3bpatmzZA+u6d++u69eva926dVqzZo3Kly+vb775RqVLl/5XaunSpYuOHj2qadOmKSwsTKVLl9awYcPs3/pLUsmSJfX999/r888/15w5cxQeHq6MGTOqUKFCatasmdM/+6WXXtKCBQs0fvx4zZkzR5GRkbLZbPryyy9VsWLFBHh2D3qS5/Ldd99p9+7dmjJlSrwJMMaMGaPatWtryJAhmjZtmqT7v68LFy7om2++UVhYmN544w2VLl1aAQEB2rRpk7Zv366oqChlzZpV7777rjp06PBEdQ4dOlRBQUGaPHmy7t27p1q1amnw4MHxDlmrU6eOXn75ZU2bNk3ffvutoqKilClTJhUvXlwNGzZ0qj8//PCDkiVLZr+m0d+5u7urYsWKCgoK0o0bN5QuXToNHjxY0dHRWrBggby9vVW9enX1799ftWvXjnffqlWrKiAgQKtWrdIPP/wgY4zDIalMmTL65ptvNHnyZE2ePFmenp4qUaKE3nvvPfshiADgSm7mn5xJCQAAHrB06VK9//77Wrx48RNPNw4ASDw4JwkAAAAALAhJAAAAAGBBSAIAAAAAC85JAgAAAAAL9iQBAAAAgAUhCQAAAAAsnvvrJMXGxio6Olru7u7xrksBAAAAIGkxxig2Nlaenp6Pvdj5cx+SoqOjdfDgQVeXAQAAACCR8PX1lbe39yPXP/chKS4h+vr6ysPDw8XVAAAAAHCVmJgYHTx48LF7kaQkEJLiDrHz8PAgJAEAAAD4n6fhMHEDAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCRJMbGxri7BJf7J86Znrrn/s4qx5jjGmnOS6vN2paTac17XHMfrmnMYa45LiOftmQB1PPM83N01eN5Wnbpyy9WlPDW5Xk6r0S3KOX1/euYc+uY4euYc+oanhbHmOHrmHPrmOHrmPELSf526ckt/nr/u6jKeKfTMOfTNcfTMOfQNTwtjzXH0zDn0zXH0zDkcbgcAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYuDQkzZs3T3Xq1FHRokVVtGhRNWvWTFu2bLGvj4yM1IgRI1SyZEn5+fmpZ8+eunr1qgsrBgAAAPC8c2lIypw5s/r166elS5dqyZIlKlWqlLp3765jx45Jkj788EP99NNPmjRpkr777jtduXJFPXr0cGXJAAAAAJ5znq784ZUrV453u0+fPpo/f77279+vzJkza8mSJZowYYJKly4t6X5oqlmzpvbv368iRYq4oGIAAAAAzzuXhiSrmJgYrV27VuHh4fLz89OhQ4d07949lSlTxr5N7ty5lTVrVqdCUkxMzCPXeXh4OFv2M+9xfXkceuYc+uY4euYc+oanhbHmOHrmHPrmOHr25Mv/zuUh6ejRo3r77bcVGRmplClT6rPPPlOePHl05MgReXl5KU2aNPG2z5Ahg0JCQhz+OQcPHnzo8hQpUsjHx8ep2p8HR48eVUREhEP3oWeO90yib4w1xzHWnONs3+A4xhqva47idc05jDXH/dP3ApeHpFy5cmn58uUKDQ3VunXrNGDAAM2ZMyfBf46vr2+STtOPYrPZXF3CM4eeOYe+OY6eOYe+4WlhrDmOnjmHvjnuUT2LiYl55M4TK5eHJG9vb+XMmVOSVLBgQR08eFCzZ89WjRo1dO/ePd2+fTve3qRr164pY8aMDv8cDw8PQtJD0BPH0TPn0DfH0TPn0Dc8LYw1x9Ez59A3x/3TniW66yTFxsYqKipKBQsWlJeXl3bu3Glfd/LkSV24cIFJGwAAAAD8a1y6J+njjz9W+fLllSVLFoWFhWnlypXavXu3vv32W6VOnVqNGjXS2LFjlTZtWqVKlUqjR4+Wn58fIQkAAADAv8alIenatWsaMGCArly5otSpU8tms+nbb79V2bJlJUmDBg2Su7u7evXqpaioKPn7+2vYsGGuLBkAAADAc86lIenDDz987PpkyZJp2LBhBCMAAAAAT02iOycJAAAAAFyJkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAkxcTGuroEl0iqzxsAHsfT1QUAAJAYeLi7a/C8rTp15ZarS3lqcr2cVqNblHN1GQCQ6BCSAAD4r1NXbunP89ddXQYAwMU43A4AAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwcOl1kr766iutX79eJ0+eVPLkyeXn56d+/frptddes28TEBCg3bt3x7tfs2bNNHLkyKddLgAAAIAkwKUhaffu3WrZsqV8fX0VExOjiRMnqkOHDlq1apVSpkxp365p06bq1auX/XaKFClcUS4AAACAJMClIenbb7+Nd3vs2LEqXbq0Dh8+rBIlStiXJ0+eXBkzZnza5QEAAABIglwakv4uNDRUkpQ2bdp4y4OCgvTDDz8oY8aMqlSpkrp16+bw3qSYmJhHrvPw8HC82OfE4/ryOPTMOfTNcfTMOfTNcfTMOfTNcfTMOfTNcfTsyZf/XaIJSbGxsfrwww9VtGhR5c2b1768du3aypo1q15++WUdPXpUEyZM0KlTpzR16lSHHv/gwYMPXZ4iRQr5+Pj8o9qfZUePHlVERIRD96FnjvdMom+MNccx1pzDWHMcY805jDXHMdacw1hznLNjLU6iCUkjRozQsWPHNG/evHjLmzVrZv+/zWZTxowZ1bZtW509e1avvPLKEz++r69vkk7Tj2Kz2VxdwjOHnjmHvjmOnjmHvjmOnjmHvjmOnjmHvjnuUT2LiYl55M4Tq0QRkkaOHKnNmzdrzpw5ypw582O3LVy4sCTpzJkzDoUkDw8PQtJD0BPH0TPn0DfH0TPn0DfH0TPn0DfH0TPn0DfH/dOeuTQkGWM0atQobdiwQd99951y5MjxP+9z5MgRSWIiBwAAAAD/CpeGpBEjRmjlypX6/PPP9cILLygkJESSlDp1aiVPnlxnz55VUFCQKlSooBdffFFHjx5VYGCgSpQooXz58rmydAAAAADPKZeGpPnz50u6f8FYq8DAQDVs2FBeXl7auXOnZs+erfDwcGXJkkVVq1ZVt27dXFEuAAAAgCTApSHp6NGjj12fJUsWzZkz5ylVAwAAAACSu6sLAAAAAIDEhJAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACAhUtD0ldffaVGjRrJz89PpUuXVrdu3XTy5Ml420RGRmrEiBEqWbKk/Pz81LNnT129etVFFQMAAAB43rk0JO3evVstW7bUwoULNWPGDEVHR6tDhw4KDw+3b/Phhx/qp59+0qRJk/Tdd9/pypUr6tGjhwurBgAAAPA883TlD//222/j3R47dqxKly6tw4cPq0SJEgoNDdWSJUs0YcIElS5dWtL90FSzZk3t379fRYoUcUHVAAAAAJ5nLg1JfxcaGipJSps2rSTp0KFDunfvnsqUKWPfJnfu3MqaNavDISkmJuaR6zw8PJwr+DnwuL48Dj1zDn1zHD1zDn1zHD1zDn1zHD1zDn1zHD178uV/l2hCUmxsrD788EMVLVpUefPmlSRdvXpVXl5eSpMmTbxtM2TIoJCQEIce/+DBgw9dniJFCvn4+DhX9HPg6NGjioiIcOg+9Mzxnkn0jbHmOMaacxhrjmOsOYex5jjGmnMYa45zdqzFSTQhacSIETp27JjmzZv3rzy+r69vkk7Tj2Kz2VxdwjOHnjmHvjmOnjmHvjmOnjmHvjmOnjmHvjnuUT2LiYl55M4Tq0QRkkaOHKnNmzdrzpw5ypw5s335Sy+9pHv37un27dvx9iZdu3ZNGTNmdOhneHh4EJIegp44jp45h745jp45h745jp45h745jp45h7457p/2zKWz2xljNHLkSG3YsEGzZs1Sjhw54q0vWLCgvLy8tHPnTvuykydP6sKFC0zaAAAAAOBf4dI9SSNGjNDKlSv1+eef64UXXrCfZ5Q6dWolT55cqVOnVqNGjTR27FilTZtWqVKl0ujRo+Xn50dIAgAAAPCvcGlImj9/viQpICAg3vLAwEA1bNhQkjRo0CC5u7urV69eioqKkr+/v4YNG/bUawUAAACQNLg0JB09evR/bpMsWTINGzaMYAQAAADgqXDpOUkAAAAAkNgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwcCokValSRTdu3Hhg+e3bt1WlSpV/XBQAAAAAuIpTIen8+fOKjY19YHlUVJQuX778j4sCAAAAAFfxdGTjH3/80f7/rVu3KnXq1PbbsbGx2rlzp7Jly5Zw1QEAAADAU+ZQSOrevbskyc3NTQMHDoz/QJ6eypYt2wPLAQAAAOBZ4lBI+vPPPyVJlStX1uLFi5U+ffp/pSgAAAAAcBWHQlKcTZs2JXQdAAAAAJAoOBWSJGnnzp3auXOnrl279sAkDoGBgf+4MAAAAABwBadC0tSpU/XZZ5+pYMGCypgxo9zc3BK6LgAAAABwCadC0oIFCxQYGKj69esncDkAAAAA4FpOXSfp3r17Klq0aELXAgAAAAAu51RIaty4sYKCghK6FgAAAABwOacOt4uMjNTChQu1c+dO2Ww2eXrGf5j3338/QYoDAAAAgKfNqZB09OhR5cuXT5L0119/xVvHJA4AAAAAnmVOhaTvvvsuoesAAAAAgETBqXOSAAAAAOB55dSepICAgMceVjd79mynCwIAAAAAV3IqJOXPnz/e7ejoaB05ckTHjh3j2kkAAAAAnmlOhaRBgwY9dPmUKVMUHh7+jwoCAAAAAFdK0HOS6tatqyVLliTkQwIAAADAU5WgIWnfvn3y9vZOyIcEAAAAgKfKqcPtevToEe+2MUYhISE6dOiQunXrliCFAQAAAIArOBWSUqdOHe+2m5ubcuXKpV69esnf3z9BCgMAAAAAV3AqJAUGBiZ0HQAAAACQKDgVkuIcOnRIJ06ckCS9/vrr8vHxSZCiAAAAAMBVnApJ165dU58+fbR7926lSZNGknT79m2VLFlSn3zyidKnT5+gRQIAAADA0+LU7HajRo1SWFiYVq1apd27d2v37t1auXKl7ty5o9GjRyd0jQAAAADw1DgVkrZu3aphw4Ypd+7c9mV58uTRsGHD9PPPPydYcQAAAADwtDkVkmJjY+Xl5fXAck9PT8XGxv7jogAAAADAVZwKSaVKldKYMWN0+fJl+7LLly8rMDBQpUuXTrDiAAAAAOBpc2rihqFDh6pr166qUqWKMmfOLEm6dOmSXn/9dY0fPz5BCwQAAACAp8mpkJQlSxYtW7ZMO3bs0MmTJyVJuXPnVpkyZRK0OAAAAAB42hw63G7nzp2qWbOm7ty5Izc3N5UtW1YBAQEKCAiQr6+vatWqpb179/5btQIAAADAv86hkDRr1iw1bdpUqVKlemBd6tSp1axZM82YMeOJH2/Pnj3q0qWL/P39ZbPZtHHjxnjrBw4cKJvNFu9fhw4dHCkZAAAAABziUEg6evSoypUr98j1ZcuW1eHDh5/48cLDw2Wz2TRs2LBHblOuXDlt27bN/m/ixImOlAwAAAAADnHonKSrV6/K0/PRd/H09NT169ef+PEqVKigChUqPHYbb29vZcyY8YkfEwAAAAD+CYdCUqZMmXTs2DHlzJnzoeuPHj2a4IFm9+7dKl26tNKkSaNSpUrp3XffVbp06Rx+nJiYmEeu8/Dw+CclPtMe15fHoWfOoW+Oo2fOoW+Oo2fOoW+Oo2fOoW+Oo2dPvvzvHApJFSpU0Keffqpy5copWbJk8dbdvXtXU6ZMUaVKlRx5yMcqV66c3nrrLWXPnl3BwcGaOHGiOnbsqO+//97hX/rBgwcfujxFihTy8fFJiHKfSUePHlVERIRD96FnjvdMom+MNccx1pzDWHMcY805jDXHMdacw1hznLNjLY5DIalr165av369qlWrppYtWypXrlySpJMnT2revHmKiYlRly5dnC7m72rVqmX/f9zEDW+++aZ975IjfH19k3SafhSbzebqEp459Mw59M1x9Mw59M1x9Mw59M1x9Mw59M1xj+pZTEzMI3eeWDkUkl566SUtWLBAw4cP18SJE2WMkSS5ubnJ399fQ4cO1UsvveTIQzokR44cSpcunc6cOeNwSPLw8CAkPQQ9cRw9cw59cxw9cw59cxw9cw59cxw9cw59c9w/7ZnDF5PNli2bvv76a926dUtnzpyRJOXMmVNp06b9R4U8iUuXLunmzZtM5AAAAADgX+NwSIqTNm1aFSpU6B/98LCwMJ09e9Z++9y5czpy5IjSpk2rtGnTaurUqapWrZpeeuklBQcHa/z48cqZM+djpyEHAAAAgH/C6ZCUEA4dOqTWrVvbbwcGBkqSGjRooOHDh+uvv/7S8uXLFRoaqpdffllly5ZV79695e3t7aqSAQAAADznXBqSSpYsqaNHjz5y/bfffvsUqwEAAAAAyd3VBQAAAABAYkJIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwMKlIWnPnj3q0qWL/P39ZbPZtHHjxnjrjTH69NNP5e/vr0KFCqlt27Y6ffq0a4oFAAAAkCS4NCSFh4fLZrNp2LBhD13/9ddf67vvvtPw4cO1cOFCpUiRQh06dFBkZORTrhQAAABAUuHpyh9eoUIFVahQ4aHrjDGaPXu2unbtqjfffFOSNG7cOJUpU0YbN25UrVq1nmapAAAAAJIIl4akxzl37pxCQkJUpkwZ+7LUqVOrcOHC2rdvn8MhKSYm5pHrPDw8nK7zWfe4vjwOPXMOfXMcPXMOfXMcPXMOfXMcPXMOfXMcPXvy5X+XaENSSEiIJClDhgzxlmfIkEFXr151+PEOHjz40OUpUqSQj4+P4wU+J44ePaqIiAiH7kPPHO+ZRN8Ya45jrDmHseY4xppzGGuOY6w5h7HmOGfHWpxEG5ISmq+vb5JO049is9lcXcIzh545h745jp45h745jp45h745jp45h7457lE9i4mJeeTOE6tEG5IyZswoSbp27Zpefvll+/Jr164pX758Dj+eh4cHIekh6Inj6Jlz6Jvj6Jlz6Jvj6Jlz6Jvj6Jlz6Jvj/mnPEu11krJnz66MGTNq586d9mV37tzR77//Lj8/PxdWBgAAAOB55tI9SWFhYTp79qz99rlz53TkyBGlTZtWWbNmVevWrfXFF18oZ86cyp49uz799FO9/PLL9tnuAAAAACChuTQkHTp0SK1bt7bfDgwMlCQ1aNBAY8eOVceOHRUREaGhQ4fq9u3bKlasmL755hslS5bMVSUDAAAAeM65NCSVLFlSR48efeR6Nzc39e7dW717936KVQEAAABIyhLtOUkAAAAA4AqEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACw8XV3A40yZMkVTp06NtyxXrlxau3atiyoCAAAA8LxL1CFJkl5//XXNmDHDftvDw8OF1QAAAAB43iX6kOTh4aGMGTO6ugwAAAAASUSiD0lnzpyRv7+/kiVLpiJFiug///mPsmbN6vDjxMTEPHJdUt479bi+PA49cw59cxw9cw59cxw9cw59cxw9cw59cxw9e/Llf5eoQ1KhQoUUGBioXLlyKSQkRJ999platmypoKAgpUqVyqHHOnjw4EOXp0iRQj4+PglR7jPp6NGjioiIcOg+9Mzxnkn0jbHmOMaacxhrjmOsOYex5jjGmnMYa45zdqzFSdQhqUKFCvb/58uXT4ULF1alSpW0Zs0aNWnSxKHH8vX1TdJp+lFsNpurS3jm0DPn0DfH0TPn0DfH0TPn0DfH0TPn0DfHPapnMTExj9x5YpWoQ9LfpUmTRq+++qrOnj3r8H09PDwISQ9BTxxHz5xD3xxHz5xD3xxHz5xD3xxHz5xD3xz3T3v2TF0nKSwsTMHBwUzkAAAAAOBfk6j3JH300UeqVKmSsmbNqitXrmjKlClyd3dX7dq1XV0aAAAAgOdUog5Jly5dUt++fXXz5k2lT59exYoV08KFC5U+fXpXlwYAAADgOZWoQ9Inn3zi6hIAAAAAJDHP1DlJAAAAAPBvIyQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABg8UyEpLlz56py5cry9fVVkyZNdODAAVeXBAAAAOA5lehD0urVqxUYGKju3btr2bJlypcvnzp06KBr1665ujQAAAAAz6FEH5JmzJihpk2bqlGjRsqTJ49GjBih5MmTa8mSJa4uDQAAAMBzyNPVBTxOVFSUDh8+rM6dO9uXubu7q0yZMtq3b98TPYYxxv5YHh4eD93Gw8NDr2dOK28Pt39e9DMiZ8Y0iomJUUxMjFP3p2fOoW+Oo2fOoW+Oo2fOoW+Oo2fOoW+Oo2cPilselxEexc38ry1c6PLlyypfvrwWLFggPz8/+/Jx48Zpz549WrRo0f98jKioKB08ePDfLBMAAADAM8TX11fe3t6PXJ+o9yQlBE9PT/n6+srd3V1ubkknRQMAAACIzxij2NhYeXo+PgYl6pCULl06eXh4PDBJw7Vr1/TSSy890WO4u7s/NiUCAAAAgFWinrjB29tbBQoU0M6dO+3LYmNjtXPnzniH3wEAAABAQknUe5IkqV27dhowYIAKFiyoQoUKadasWYqIiFDDhg1dXRoAAACA51CiD0k1a9bU9evXNXnyZIWEhCh//vz65ptvnvhwOwAAAABwRKKe3Q4AAAAAnrZEfU4SAAAAADxthCQAAAAAsCAkAQAAAIAFIQkAADxXjDHilGu4AuPu+UFIAgAAz5Xw8HC5ubm5uoznTmxsrKtLSNSuXbvGuHuE4cOHa9u2ba4uwyGEpGdIVFSUq0t4Zp0/f97VJSRKBw4ccHUJSEKs37DyYevRjh8/7uoSnmnfffed3nvvPcXExDDOEsiyZcsUFRUld3c+Nj7KyJEj1b9/f1eXkSidPHlSKVOmVMmSJV1dikMY7c+IwMBArVixgt24Tli+fLmaNWumu3fv0j+LOXPmqGPHjvrxxx9dXQqSCDc3N61evVr79u3jw9YjLFq0SOPGjdOdO3d4vXJQXCDKmDGj+vfvLw8PD0VHR8dbB8ft379fS5YsUUhIiH0Z/XxQ586d9eWXX0qS7ty5I4lD7+K89tpr6t+/v7y8vLR8+XItX77c1SU9Ed6lnhFp06ZVwYIF5ebmxouTg8qXL68FCxYoefLk9hcueij5+vqqcuXKmjRpEkHpCcTGxiomJkaSdOHCBV25coU9lA76888/1bdvX/ZgPkaePHk0ZMgQpUqVSjdu3HB1Oc+Uv/76S5JUvXp1vfrqq9q/f786deqkkJAQubu787rvJB8fH02dOlXZsmXTkSNH7HuU6Gd8mTJlsocAf39/XbhwQW5ubgQli5CQEAUFBWnBggVau3atq8v5nwhJiVzcH1e3bt2UP39+bdmyRfPnz1dYWJiLK3t2pE+fXtmzZ9cff/whf39//fbbb3J3d0/yL1yFCxdWq1atVKBAAU2cOJGg9AiLFi3Szz//LHd3d3l4eGjNmjVq1aqV3n77bbVq1UqTJ0+2h2882pEjR7Rv3z717NlTbdq0cXU5iZafn59y5Mihw4cPq1evXtq4caOrS3om/PLLL6pfv74WL15sX3bu3Dndvn1bgwYN0tWrV/lg74TY2Fh5e3vrxRdf1KVLlzRo0CD16tWLoPQYfn5+8vHxUUBAgC5evJikg9Lfx0fGjBnVq1cvZcuWTd99951Wr17tosqeDCEpkfv7CYC//fabRo0apaCgIIWHh7uoqmeH9YUpT548Kl26tLp06aLff/89Sb9wxT3vAgUKKCAgQL6+vgSlh7h8+bI2bNigsWPHau/evbp165ZGjhyp9u3ba9CgQerSpYumTZumUaNG8ff4GJcvX9bo0aP10Ucf2fsUt1cODxcRESFvb2/NnTtXmzdvdnU5id4rr7yidu3aady4cfagVLNmTb3zzjsKDw/XgAEDCEpOsH4GSZcunZo0aaIbN25owIABBCXJfjinVc6cOTVu3DhlyZJFLVq0SLJBKTY21n5Y9cWLF3Xr1i3FxsaqcOHCCggIUMaMGTV37txEHZTcTFL7rT0HJk+erC+//FKDBw9W/fr1lTJlSleXlKj9+uuvmj17tj799FPdvHlTo0aN0qZNmzRz5kwVLlxYxpgkMxuN9UXL6sCBA5o3b54OHjyovn37qkqVKi6oLnH67bffNHfuXJ04cUJvvvmmrly5opEjR9rX79u3TwEBAerevbu6du3qwkoTr3v37mn58uWaO3eu7t27p0WLFillypSKiYmRh4eHq8tzqbjXn4e9Du3Zs0czZ87U7du31b59e1WqVMlFVT4bbt68qVmzZmn27NkaMWKEateuLWOMVq9erXnz5il58uT66KOP9NJLLz3ytRD/L25M7t27V7dv31blypUVFRWlFStWaMGCBXrllVf00UcfydvbO8n1886dO0qVKpX99po1a3TlyhVly5ZNVapUkZubmy5cuKD+/fvr/PnzmjdvnrJkyZKkPm/EmTRpkoKCgpQ2bVrly5dPw4cPl7e3t/bv36+ZM2fq6tWratWqlapXr+7qUh+QdEb0Myguv16+fFnBwcH2byx69eqljh07avTo0Vq+fDnfYD+GMUbBwcE6efKkTp8+rRdffFEDBw5U5cqV1bZtW/sepaTwTZj1TezAgQPau3evDh48KEkqVKiQmjdvzh4li7i/v6JFi6ply5Z6/fXXtWDBAgUHB9vXR0VFyc/PT/369dPy5csVEhKS5L4tfBJeXl6qX7++2rdvLw8PD/Xt21d37tyRh4dHkt+j5ObmpqioKLm5uenAgQNas2aN/RC7EiVKKCAgQGnSpNH06dP1008/ubjaxCluDJ0+fVrJkiWTt7e3+vfvrxUrVsjNzU01a9ZUixYtdPfuXb3//vu6cuVKkvpA74y4D/Pr169X9+7dtWfPHl24cEHe3t6qW7eu3n77bZ05cyZJ7lHq2bOnxo4daz9ncOLEiXr//fe1cuVK9ejRQ0OGDFFwcLCyZs2qcePGKXv27GrVqpWCg4OTXEBav369VqxYob59+6pcuXI6evSoWrduraioKBUpUkRt27bVyy+/rMmTJ2vnzp2uLvdBBona2rVrTdWqVc0bb7xh+vbta3766Sf7uokTJ5r8+fOb+fPnmzt37riuyETu2rVrpnLlymbs2LH2ZSEhIaZv376mRIkSZu/evS6s7umIjY21/3/ixImmRo0apkyZMqZZs2ZmxIgR9nX79+83AwcONLVr1zarVq1yRamJSkxMjP3/e/bsMT169DA+Pj7m559/jrfdsmXLTNWqVc3NmzefdomJTtxYO3z4sFm8eLFZuXKlOXLkiDHGmMjISLN06VLTtGlT07VrVxMaGmqMMSY6Otpl9brKnDlzTJs2bey3V61aZYoVK2aqVKliKlSoYNq1a2dft3PnTtOtWzfTtm1bs27dOhdUm/itX7/e+Pn5mUmTJpmPPvrItGrVyhQpUsQsWrTIGHN/XK5atcrUqVPH9OjRI0mOOUft2LHDFClSxCxdutRERkbGW3fv3j2zdOlS07hxY/POO+88sP55tmzZMpMvXz4TGBhoDhw4YAICAszvv/9ujDFm165dpkSJEua9994zp0+fNsYYc/78eVOzZk3TvXt3V5b9VFjfM425/xl2zpw5xpj7Y2bbtm2mXr16pkmTJvYxs2fPHjNp0qRE+TdJSErEzp49a6pWrWpmz55tVq5caZo2bWratm1rVqxYYd/mk08+MTabzSxatCjeB+Gk6lE9WLVqlalcubLZt2+ffdm1a9dM586dTfny5c3du3efUoWu9fnnn5vSpUubPXv2mJs3b5oPP/zQ2Gw2895779m3+f33303Xrl3Nf/7zHxdW6lpx4ygsLMzcunXLvvzo0aOmW7dupmrVqvagFBsba8aOHWtq166d5ENSXN/WrVtnypYtaxo2bGiaNWtmatasae9XXFBq3ry5adWqVZL8gic6OtqsWLHClCtXzvTq1ctER0ebnj17muXLl5vLly+bn376yVStWtU0bNjQfp9du3aZ1q1bm86dOyfJnj1OaGioadWqlZk4caJ92alTp8yYMWNMkSJFzPLly40x9z/ArVu3zpw7d85VpT5TPvnkE9O/f39jzP3Xwj179phBgwaZwMBAs2PHDmPM/bAfEBBgLl265MpSn5q417g1a9YYm81m+vXrZ/r27RsvJP7yyy/mjTfeMO+99545c+aMMeb+F7OJMQQkJOvnr3nz5pnPPvvMBAQEmKlTp9qX37t3z2zfvt3Ur1/fNGvW7IHPXomtR4SkRCQ2NjbeILty5YoZNGiQiYqKMsbcD00dO3Y0rVu3jheUpk6dao4fP/7U602sNm/ebD7++GNz6NAh+7JTp06Zpk2bmpkzZxpj/v+P+dq1a0nmxf3YsWMmICDAbN261RhjzM8//2yKFCliBg0aZEqVKmUGDhwYb9u/fyOUVMSNjU2bNpmAgABTo0YN07ZtWxMUFGSioqLMoUOHTI8ePUy+fPlM06ZNzQcffGD8/f3jjbekbNeuXaZkyZJm3rx5xpj730YXLFjQFC1a1L4XJDIy0syfP9+0a9fOXLx40ZXluszdu3fNunXrTKVKlUzbtm1Nr169zIULF4wx9z/M792717z11lvxgtKePXuSbL8eJzQ01FSpUsV89tln8ZafPHnSNGnSxPj6+pqFCxe6qLpnh/XzR2xsrBk4cKCpXr26+fXXX02vXr1M+/btTfPmzU2zZs1M+/btzd27d83du3fjfZH0PPv7e+KqVauMzWYzFSpUMGfPnjXG/H8Pd+3aZUqXLm06d+4c7zNGYgsBCcXam08++cQULVrUvP3226Zy5cqmWrVq5saNG/b10dHRZseOHcbf398MHjzYGPPoL7hdjZCUiMQNkm3btpkPPvjA9O3b1/To0SPeNqdPnzbvvPOOad++PS/6j7BixQpTtmxZ07RpU9O7d2/7i9fcuXNNsWLFTEhIiIsrdJ25c+eaa9eumT179hh/f3+zYMECExsba9577z1js9lMx44d422fVIPS5s2bTeHChc1nn31mjhw5Yjp27GjKli1r9uzZY4wx5sCBA6ZPnz6mSJEiZsaMGebatWsurth1rG9ukZGRZuzYsWb8+PHGGGMuXbpkKlWqZPr27Wv69etnihQpEm+P0u3bt11Sc2Jx9+5ds2bNGlO3bl1TokSJeN+qxgWlGjVqmLfeesuFVSZOf/9QNWTIENOzZ88HQuTw4cNN2bJlTaVKlcytW7cS7YexxGLXrl1mzZo1xpj7ez+qV69uypYta/r06WM/3H/Tpk2mdu3aSfa99Pjx4/Y9Rxs2bDA2m82MHj36gfeBrVu3mnfeeSdJvY9euXLF9OvXzxw+fNjcvXvXHDp0yNSpU8fUrVvXfni1Mff3KB04cCDRh0bOXExE3NzctHPnTnXo0EG3bt3S3r17tWPHDs2cOdO+Tc6cOTVkyBCFh4frp59+4vosD1G3bl0tXbpUbdu21fnz5/XOO++of//+ypEjhwoWLKiFCxc+9yeLP+r5tWjRQunTp9ePP/6oChUqqH79+nJzc9Mrr7yiChUqKHXq1PFOvk1qJzcbYxQREaH58+frnXfeUbdu3ZQ9e3YdP35cVatWVfHixSXdvxBvixYtVKdOHVWpUkXp06d3ceWuETfhgCRdunRJ3t7eaty4sSpWrKiwsDD16NFDZcuW1ccff6yGDRsqIiJCHTt21MaNG+Xt7a3UqVO7+Bm4hvnv5B7JkiVT+fLl1bVrV3l5eal37972bdzd3eXn56dhw4YpVapU9glDkjrziIlRChUqpOPHj2vZsmW6fPmyfbmbm5s6deqkZcuWKU2aNEnuxHlHREREKCgoSIMHD9batWv10ksvafny5Zo5c6YmTpyoihUrSpL27t2rF198UcmSJXNtwU+J9T3xhx9+UJ8+ffTTTz/p3r17evPNNzVx4kR99913mjZtmq5fvy7p/jj19/fX119/nWQmtVi0aJGqVaumEydOKEWKFEqWLJkKFCigCRMmyBijVq1a2T+zenp6ytfXN9FP3uPp6gLw/86cOaPTp09ryJAhatmypYKDg/XFF19o3bp18vLyUsuWLSXdvx7EuHHj5OnpGW8KyqTI/HcGnuPHj+vs2bNKkSKFcufOrZdfflk1atRQjRo1tHDhQu3atUudO3dWbGysvLy8ntuph+/duycvLy/7c9u+fbuuXr2qXLlyKVOmTMqUKZMk6dixY4qJiVGyZMkUFRWlv/76S5UqVdLbb78t6dFThT/v3NzclDx5coWFhalSpUoKCQlRgwYNVLlyZQ0dOlSS9OOPPypv3rwqXry4ChYsqOTJk7u4ate4cOGCPv/8cw0ePFjbtm3TiBEjNG/ePOXOnVvS/Q9Sxhh17NhR0v1rrFSuXFl58+bVa6+95srSXSbu9erOnTtKkSKFoqKilDJlSpUrV05DhgzRRx99pJ49e2rKlCmS7gelEiVK2KevTuri+rdr1y6tXr1asbGxypEjhzp16qTGjRvr8uXLWrlypX777TflyZNHN27c0ObNm/X9998rbdq0ri4/0Yrra4oUKdS6dWt5enoqMDBQxhjVqFFDefLkkSTt2rVLW7Zs0ffff685c+YkiS85rO+FGzdu1Pnz53X8+HFNmzZNnp6eKl++vGrWrClJ6tu3r9zd3dWhQwdlyJAh3uMkhffTypUra9WqVdqzZ4+uXbumXLlySZLy5s2rjz/+WP3791f16tW1YcMGpUiRwn6/xPxZjOskJRJnzpxRjx49dPPmTb3//vv2P7qzZ8/qq6++0vHjx1W/fn01b97cxZUmHsYyRenYsWPl5eWl1KlTy9PTU+PGjdMrr7wSb/tt27YpKChIHTt2tL/oP0/69OmjUqVKqUGDBvL29ta4ceP0/fffK126dAoNDVXBggXVsmVLVa5cWStXrtTHH3+sbNmyKTIyUnfv3tWyZcvk6emZJK/j8Hdvv/22XnvtNe3du1elS5fW4MGD5eXlpZs3b+qDDz5QpUqV1LhxY1eX6VJx1z1KliyZ9u/fr8DAQNWpU8e+/ueff1anTp00Z84cFS9eXJ988olOnjypwMDAJPnlTtzf1ZYtWzR79myFhYUpderUGjBggPLkyaPw8HD9/PPPGjdunHx9ffXpp5+6uuREacOGDRo0aJAqVaqk1KlTa+3atSpfvrwCAwMlSUFBQTp06JD27NmjzJkzq1evXsqXL5+Lq07cbt++rTRp0thvnzhxQrNmzdKWLVs0ZMgQvfnmm7p8+bKmTp2qP//8U6NGjUpyPZ04caIWLlyoHj16KDw8XEuWLFGyZMnUq1cvVahQQV5eXlq9erX69u2rwYMHq1WrVq4u+V/1qC9Sr1+/rs6dOyssLExffPGFcubMaV935MgRzZw5Ux9++GGiDkbxPP0j/PAw58+fN2PHjjUlS5Y0o0ePjrfu7NmzZsiQIaZ69eqch/RfcceV79ixwxQvXtx+kvjatWuNzWYzVapUsU9mER0dbd8+bhKM51Hfvn1N4cKFzfLly83OnTtN7dq1zZ49e0xkZKTZunWr6du3r2nUqJHZvXu3uXv3rgkKCjIDBgwwY8aMMffu3TPGPL8nlT7Ko85P2LBhgylbtqypX79+vOUTJ040VatWNcHBwU+jvETJ2rMJEyYYm81mmjZtaj8eP+74+5CQEPPuu++awoULm6ZNm5oiRYrYpwJPqjZs2GCKFClipkyZYpYuXWo6d+5sSpYsaZ8+OCwszKxbt874+fnFm3ES9x0+fNhUqVLF/np/9uxZU7p0aWOz2Uznzp3jbRsREZGkpqV21pEjR0zt2rXNzp074y0/duyY6devnylbtqzZtm2bMeb+OYZXr151RZkuderUKVOhQgWzYcMG+7LQ0FDTuHFjU7NmTbNx40b7WNuxY4f9/fR5ZT3H6s8//zSHDx+ONznFtWvXTIMGDUytWrXs06D/3bPyWYM9SYnIxYsXtWDBAgUFBalp06bq0qWLfd3p06c1Z84ctW3bVtmzZ3dhla6zfv165c+fXzly5JB0/4rXgYGBypIli3r06KErV66oadOmKlSokG7cuKHz589r1qxZypEjh6Kjo+Xp+XweXWose35GjRqlZcuWqUWLFrp165ZGjRpl3+7333/XlClTlDlzZo0ePfqBx3mee/QwcX3bs2eP9u3bpwsXLqhJkyZ6/fXXde/ePX377beaN2+eypYtq+zZs+vSpUv68ccfNXv2bPn4+Li6fJeIO5xTkg4ePKitW7fq9u3bOnLkiDJkyKA+ffooR44c9t6eOHFC+/btU0hIiGrUqKFXX33VtU/Ahc6fP69+/fqpZs2aCggI0KVLl9SiRQtFRUUpLCxM06dPl5+fn8LCwvTLL78oT5488b6FhbRu3Trt27dPAwcO1MWLF9WqVSuVKVNGFSpU0Lvvvqt69eppzJgxri7zmbJ37159/fXXCgkJ0aBBg+znXUrS1q1b1bVrV0nSpEmT9Oabb7qqTJc6f/68WrVqpeHDh6tChQqKioqSt7e3QkNDVa1aNeXKlUvt2rVTxYoV7e+hz+P7qbk/2Zt9D9Knn36qoKAgSdK1a9c0ZMgQValSRWnTptWNGzf0zjvv6N69e/rkk0/sh2E/c1wY0JKsuG9iT506ZXbv3m32799vwsPDjTHGBAcHm4kTJ5pq1aqZL774It79nvdvJx4lNjbWXL161dhsNtO9e/d417jYvn27+fXXX82tW7dM/fr1zZAhQ4wx9y/2ZrPZTLFixcyJEydcVfpTERsbG++bneHDhxubzWbq1Kljrl+/Hm/bWbNmGT8/vyQ9G5vV+vXrTfHixU2nTp1MmzZtTKlSpcz06dNNaGioCQ0NNWvXrjUtWrQw7du3N0OGDEnSU+2fPXvWtGvXzkRHR5tVq1YZf39/8+uvvxpjjPn+++9NixYtzLvvvhtvL1tS7pfVpk2bTGBgoJk4caKJiIgwFy9eNFWrVjUffPCBOXv2rGnYsGG82ROZge3h7t27Zw4ePGiio6NNx44d7dfwuXHjhqlZs6ax2Wymb9++Lq7y2bN3717Ts2dPU6dOHbN792778hMnTpiuXbuaiRMnPnKPQFIQGhpqKleubEaNGmVfFhUVZWJiYkxAQIApV66cadasmb1Hz+Pf799njZwyZUq8vYz9+vUzRYsWNV9//bV9Svjr16+bihUrmn79+j31ehMKIekpi/vjWb9+valataqpVKmSadSokenYsaN9OtyzZ8+aiRMnmtq1a5tPPvnEhdUmDnE9O3z4sClWrJjp2bOnfVrvOJs2bTLNmze3X2fkl19+Me3btzcDBw40p06detolu0TcczfGmHHjxhmbzWbmzp0bb9rNnTt3mlq1apnz58+7osREZd++fcbf398sXrzYGHP/A5iPj4/x9/c3n3/+ebzrOhjz7Bwe8G+5ePGiqVChgqlVq5ax2Wxm6dKl8dYvXLjQtGrVyvTu3dscOXLETJ482T7tclJ28OBB88Ybb5jVq1fbX7eGDh1qunfvbiIiIowx9z9g5M+f3/j7+5uIiIjn8kOWox7XgytXrpj69evbr/kWFhZm3n//fbNhwwb7xTvxoLieHjlyxGzZssUsX77cPgYPHTpkevbsaWrVqmW2bt1q7ty5YyZNmmR69+6dZKbqt37Z+Pfxt2bNGlOgQAHz+eefx9tm4MCB5tdffzXlypUzw4cPf2q1Pk3Dhw83H330kf32sWPHTNu2bc2mTZuMMfcPIy5RooTp2rWrsdls5uuvv7Z/QXv79u1n+r3z+doX+Axwc3PT9u3bNXDgQP3nP/9RgwYNtHbtWr3//vtq166dvv32W+XIkUNNmjTR3bt3tX37drVp00bp0qVzdeku4+bmppiYGPn4+Gj27Nlq0aKFJKl///72Qw+vXLmiQ4cO2XcDb9++XS+//LKGDBmilClTuqz2f5P1xMn58+dr69ateuedd1S0aFG99957CgsLU2BgoEJDQ1WuXDmlSpVK06ZNU6pUqZQlSxYXV+96Z8+eVb169dSoUSMFBwerTZs2at68uVKmTKkpU6bIw8NDtWrVUrZs2SQljdmJHidz5szq3LmzRowYoZw5c+qtt96S9P/jsEmTJvL09NSSJUvUsWNHeXp66tNPP413QnhSc+bMGW3atElNmjRRjRo1ZIxRVFSUTp48qTJlythnrHvhhRc0efJk+fn5MYudHjwU9vz582ratKly5cqllClTysvLSxcuXNCPP/6o/Pnza/r06Tpw4ID69euXZKfjfxJubm5au3athg0bppdfflnBwcH65ptv1KFDB9WtW1ddu3bVzJkz9c477+j111/XxYsXk+QsdnPmzNHRo0d17tw5tWnTRoUKFVL16tV16dIljR8/Xr///ruyZs2qP//8Uzdu3FBgYKDKlSunCxcuuPhZ/DvKli2rChUqSJJCQ0OVM2dO1axZU2XLltXevXs1YsQI9erVS61atdK7776rr776SuHh4Wrfvr197DyrMwpzTtJTdvPmTY0cOVL58uVTp06ddPXqVTVu3Fg+Pj46d+6cPD09NWvWLKVOnVrnz59XihQpkvyLftwbZmRkpJIlS6bDhw+rZcuWKl++vD0onT17VoMGDdLp06dls9n066+/auHChcqbN6+ry/9XWF/Q9+/fr6CgIC1atEhvvfWW2rZtK19fX0nSyJEjNW/ePL3wwguqXLmybt26pc8++0xeXl5JbprvuHH0559/2r90CA0NVY4cOdS1a1dlyZLFfj5D+fLlFRERoe7duysgIOCZfHH/N+zdu1eHDh3SokWLlCJFCk2ZMkVZsmSJ9wZ44cIFnTlzRq+++mqSDuN37txRmzZtdOHCBdWtW1fvv/++fd2AAQO0Y8cODRkyRDt37tTGjRs1b948+/mW+P9Z7IoWLarIyEgdPXpUnTt3Vo0aNZQpUyYtX75cw4cPV/r06RUVFaVp06Yl2XMFn9Qff/yh9u3ba8CAASpfvrxSpkypQYMGKTg4WK1bt1bdunUVFhamAwcO6MaNGypcuLD9S6KkYsKECVqyZImaNm2q8+fP69ChQ3rrrbfUunVrZcyYUXv37tU333wjd3d3pUmTRqNGjZKXl5e6dOmiLFmyaNiwYa5+CgnG/G2m2+XLl2vlypUaNWqU/bV9yJAhio6O1siRI+Xl5aVRo0bpt99+U/LkyTVv3rxnf6ZcF+7FShJiY2Mf2G0bFBRkfv/9d3P9+nVTp04dM3ToUGOMMTNmzDA2m8289dZb5ubNm64oN9H69ddfzTfffGM/l+bQoUOmcOHC8a6wvn//fjNx4kQzZsyYJHMuxNixY03FihXNRx99ZN577z1ToEAB07NnT/tsWcbcn5HNZrOZLVu22MdiUju/Le55x81aN2nSJPt5gGfPnjW1a9c2mzdvNsbcn8GpX79+5qOPPkrSx+Eb8/99O378uNm/f785ePCgMeb+bJy1atUyDRs2jDer0c8//8yMYhaHDx82VatWNfXq1Ys3s9/Ro0dNly5dTKVKlUz9+vXN4cOHXVhl4vOoQ2HLlStnPv/8c/shnGfPnjU7duyINwZx388///zA+birV682NWrUMNevX7cfWhYZGWl69uxp6tat+0wfFpUQli1bZipVqmQOHTpkjDFmz549xmazmapVq5rAwED7OLP2KTw83Hz00UemdOnSz/3njrlz55pmzZqZvn37mpMnTxpjjGnZsqUZMWKEfZvu3bubI0eO2N87nvVDhwlJ/yJrQNqwYYN9UoE4QUFBpnXr1uby5cv2bQICAkyXLl04rvpvhg4daipVqmRmzJjxQFDq0aNHvDdJ63HFz5Ovvvoq3rjYv3+/KVWqlP1kb2Pun4tVqlQp061bN3PgwIF49417YX/WX7Sc9dNPP5lChQqZhQsXxhsvf/75p/H39zfLli0z586dM1OmTDEtW7a0H6ufVFlfuypVqmSqV69uChUqZAYOHGguX75sLly4YGrXrm0aNGhgfvnlFzNhwgRTqlSpeOfG4f75H3Xq1DEffPCBOXr0qH15bGysOX/+fJI/Z+thVqxYYcaPH2+MuR+EKlWqZEaNGmU+/vhjkz9/fvPVV18xzh7j4MGDplKlSmbo0KHx3jMWLVpkKlasaP+CKO417tq1a6ZgwYLmp59+ckW5LmMNO1FRUWbVqlVm2rRpxpj7r3vFixc3CxcuNFOmTDGFCxc2Y8eOjffF2cmTJ82kSZNM5cqVzR9//PHU63eFpUuX2s89vX79upkzZ47Jly+f6du3r6lfv76pWbOm/UvY5+GzBiHpXxQ3QOKu3WOz2czBgwfty7/66ivzxhtv2L95nTBhghkxYoT9BQzxjRo1ytSuXdtMnz49XlAqVqyYadeunX3Wu+fhD/PvTp48ad599914L+qHDh0y5cqVs784x63bvn27sdls5r333rPPPhYnqe1BinP37l3Tq1cvM3HiRGPM/W//zp49a7766iuzY8cO07ZtW/PGG2+Yt956y5QqVcr+TWJSt3XrVlO8eHGzYMECExkZaTZv3mxsNpt59913zcWLF01ISIhp0KCBefPNN+N9A4v4Dh8+bBo0aGA++OAD89dff7m6nETHOqHApUuXzKVLl8yxY8fM3bt3Tbt27cygQYPs25YrV84UL17czJgxI8nv+XicWbNmmQYNGpgRI0bYv/W/dOmSKVasWLxZ2oy5H0SrV69u9u3b54JKXe+LL74wP//8s7lw4YK5evWquXz5smnQoIGZPn26McaYW7dumbJlyxp/f38ze/Zs+/2ioqLsY/Z5Z/1ctWTJEtO8eXPz7rvvmitXrpgFCxaY3r17myFDhtivRfm8/G0yccO/xPz3WM41a9aob9++eu+997Rs2TJFRUXZj9H09/fX2rVrVb9+feXKlUvbt2+3H+sPKSQkRGnSpFGyZMkkSYMHD9aIESO0dOlSSVKDBg1UoEABTZ8+Xd27d7dfk+CZPwb2IXLlyqWJEyfKzc1NmzdvVtasWZUhQwbduXNHp06dUv78+RUTEyN3d3cVLlxY2bNn144dOxQbGytvb28VLFhQkpLsuTXGGJ07d04ZM2bUzZs3NWXKFP311186deqUvL291b59ewUEBMgYI5vNlmSvRWZ1584drV+/Xm3atFGzZs0UHBys0aNHq2rVqtq6dasiIiI0atQoLV26VH/++acyZsyoDBkyuLrsRMnHx0ejR4/WsGHD9MUXX6h79+7P7nVDEljce+XGjRs1fPhwNWnSRJ06dVKmTJkUHByskJAQtWnTRpJ0+fJllSxZUhkzZlSlSpWS7OvZ48Rdn6d169aKjY3V2rVr9d1336lNmzbKmTOnhg4dqmHDhik2Nlbdu3dXdHS0li1bpoiIiCRzDqH1fNzVq1dr6tSpWrhwof35Hzp0SKGhofLz85N0f9yVK1dOvr6+atasmaT749bLy0v58uVzzZN4ytzc3Ox/qw0bNpQxRkuWLNGHH36oDz74QM2aNbP39Xm6RlTSOWv7KXNzc9OqVavUt29fjRw5Uh06dJC7u7siIiLs2+TJk0fvvvuuihUrpnTp0mnRokV6/fXXXVh14vHHH3+oXbt2WrNmjaKiouzLhw0bJl9fX3355ZdasWKFrl27pkKFCunHH39UpkyZXFjxv8/NzU0hISEaOXKkZsyYIUnq1KmTBg4cqL1798rb21tubm6KjY1VqVKl1L9/f+3fv18zZ87U77//bn+MpCh58uQKCAjQokWLVKVKFV2+fFmNGjXStm3bVKlSJW3atEkVK1ZUlSpVCEj/5e3trdKlS6tu3bq6efOmevXqpTfeeEOTJ0/W8OHDtXnzZg0cOFDnz59Xvnz5CEj/g4+Pj4YMGaKQkJAkMVvYk4r74uc///mPevfurbffftv+RWF4eLhu3rxpvzj4okWLdPHiRfXq1YsL7T5CXHD85ZdfFBERoZCQEC1ZskQzZszQ+fPnVbduXY0ZM0arVq1SgwYN1Lp1ay1ZskSfffbZc/8eGicuIK1bt07h4eEaOnSofHx8ZP47j9ndu3fl7u6u3377Tfv379fHH3+syMhItWjRQh4eHoqJiUmS76VxQUmSGjVqpEaNGuny5csaM2aMgoOD5e7urtjY2OcmIEnMbveviY2NVc+ePVW5cmU1atRIktSwYUPVrl1b7du3f2A6ROuV7HFf+/btdePGDbVv315Vq1a171GKjY1VhQoV5OnpqQ4dOqhFixZyc3NLMi9ahw8ftr+oV61aVVu2bNHcuXPVuXNnpU2bVlu2bNGdO3e0ePFi7d+/Xx07dlTVqlU1bNgweXt7u7p8lzp+/LguX76ssmXL2r/1GjlypO7cuaPRo0cn+f78XdyMkitWrND8+fM1adIkZc6cWatWrdKCBQt0/vx5zZkzR1mzZnV1qc+MuJ7ivsjISPXv31+vvvqq+vTpo4iICF29elVr1qyRr6+vpk2bpj/++ENp06ZVaGiovvnmGxUoUMDVZSdqW7duVceOHTVw4EBlyJBBhw4d0vr161WxYkW98847ypYtm65fv67ffvtNKVKkUO7cuZU5c2ZXl/1UXbp0STVq1FBERIR69+6trl27xpvNbdSoUdqyZYvu3bunzJkza86cOfLy8npgxrekyNqDZcuWacmSJSpSpIh69eolLy+v56o/z0/cS2Tc3d01efJkeXh46N69e/L09FSaNGl048YNSfe/7fnwww8VHR2toUOHPlfJ2xkPe+GZPn26unbtqmnTpkmSqlWrJm9vb12+fFlvvPGGXnjhBVWoUCFJTWMtSQUKFNCoUaM0ZMgQubm5qV69erLZbJo7d668vLyUIUMGe8+KFCmir7/+WunSpSMA6P7e2zx58kiSTp06pR9++EE//PCD5s2bR38eIu7D/Llz5xQWFmb/hv/PP/9UtWrV1KxZM77ccRABKT4OhU04sbGxcnNz0/Lly1WrVi21bdtWklSnTh1lyZJF06dPV2xsrNq3b6+cOXPqzTffdG3BT9HfL3mROXNmTZs2TWPHjtWPP/6o9u3bK1myZPYvrIcMGaImTZooNjZWNptNHh4ez9VhZP+E9dC7Bg0a6K+//tJvv/1mX/c84bedQOIGzIkTJxQaGqo7d+6oePHi8vDwsH+IyJ07t27duiVJ+uSTTzR//nx99913kp6/geWIuN4dOHBA+/btk5eXl7Jnz67y5cvbj9//5ptvdPXqVZUpU0br169XWFiYRo8enWTP3/Lx8dHIkSM1ZMgQGWPUu3dvNWnSRFFRUfZvcuLCeZEiRVxdbqJz6NAhzZgxQ0eOHNGcOXOe2+tpJZRKlSrpyy+/VJcuXZQsWTIdPHjQHsqBfyLuUNhhw4Zp2bJlKl26tBo1aqT69etr1KhR2rRpk/26NHi4uPfQS5cuKWvWrEqRIoVu374t6f8v4tm2bVudPXtWQUFBunv3rrp165ZkDlm0BqSlS5fqxIkTunfvnvz8/DRw4EANGTJE3bp107fffisvLy97ULKebxQTE0NAsrAGpRdeeEFXrlxRZGTkc/dlI4fbJYC4gbJ+/XqNGTNGmTNn1qlTp1SsWDE1bNjQfmX6CRMm6NSpU/Lx8dEXX3yh77//nsMG/mvdunUaNGiQ8ubNqzt37ujkyZNq06aN+vfvL0n64IMP9Ntvvyk0NFReXl6aOnUqvdP9c7cGDx6s7Nmzq1+/fnrllVckPfitGeK7e/euDh06pGzZsiWZk5X/qX379mnevHlKnTq1mjdvzvmTSFAcCvvPrF69WuPGjdPs2bO1YsUKff/991q4cKGyZs1q7+fcuXM1ffp05cqVS4GBgcqYMaOry36qxo0bpxUrVqh27dq6ePGijh49qnLlyql69ep699135ePjYz8Kg8PqnowxRmvXrlWuXLmez0ksns4kes+/X3/91ZQoUcIsXLjQGGPMzp07jc1mMwsWLLBPnTh9+nRjs9lM8eLF7RdlhDGnTp0yZcuWNXPnzjXGGHPjxg2zYsUKU6hQIfu1Moy5Pz3s3r17k8R0m474/fffzcCBA5/b60Mh8YiJiXkup9hH4nL8+HEzceJEU6xYsXjXlkJ8cX+Ld+/eNQMGDLBPWW2MMc2aNTO1a9c2wcHB9suMjB8/3syYMcNcv37dJfW60pYtW0zlypXtF1pfvXq1KViwoAkKCjLG3L9wbJUqVUzDhg1dWSYSGfYdJpADBw6oRIkSatKkiU6fPq2hQ4eqSZMm9ukiIyMjVa1aNa1bt07Dhw9/PhO3k27cuKFUqVKpSpUqkqQXX3xRdevWVWxsrIYNG6by5cvrjTfeoGePUKhQIfn6+tpntmMPEv4tjC382zgU9sm5ublpz549GjNmjDJkyGA/B0mSxo8fr/fee09NmzaVzWaTu7u7du3apRUrVihdunSuK9pFrly5oixZsqhQoUJau3atPvjgAw0aNEi1a9dWZGSkYmNjNWrUKM2ZM4f3UdgRkhLIlStXlC1bNklS69atVbFiRY0YMUKStGbNGoWGhqpp06aaPXs2hw38jaenp86cOaPTp08rU6ZM9t3cpUqVUsaMGRUSEuLqEhO9uOODeWEH8CzLkyePmjdvzqGwTyhZsmSKiorSzp07FR0dLen+IVA5cuTQggULNH36dIWEhCgyMlLvv/9+kr0+l6enpzJnzqwtW7bo/fffV//+/dW8eXNJ0s8//6yDBw+qTZs2+uyzzyRxyDru45wkJ8R9iL9586aSJUumFClSaMuWLXr33Xfl5uamxo0ba+DAgfY/sMGDBysmJkbDhg1T8uTJXVy9axnLBBc3btxQpkyZlC1bNvXo0UOenp7q2rWr8ufPL0mKiorS22+/rdatW6t+/fquLRwAgETGGKM//vhDffv2VZo0aTR37lx5e3s/cFkRk8TPsTlx4oTq1aun6Ohoffjhh2rYsKGk++endu/eXZkyZdKYMWOSdI/wIGKyE+KuDt61a1fVr19fkydPlre3t1q1aqXkyZOrfPnycnd3161bt/TJJ59o06ZNeuedd5J8QJL+v3eNGzfWoEGDVLNmTQUFBalixYr26V+3bNmiEydOaPLkybp06ZKKFy/u6rIBAHAZY4z9Qp7Hjx/Xli1btHXrVp0/f14FChTQJ598ohs3bqht27b2gBS3Zwn3ZxeeMGGCkiVLphMnTmjXrl365Zdf1K1bN/tF2q0XSwUk9iQ55fDhw2rTpo3atWunmzdv6tdff1XOnDlVoEAB+1XBc+fOrWTJkikkJESfffaZfHx8XF22y8XGxur27dv2cFmqVCmtWrVKn332mQYNGiRvb2/t2LFDa9eu1auvvqqYmBhNmjSJ3gEAkqQ7d+4oVapU9tvr16/XyJEjlT17dt26dUtp06ZV8+bNVa9ePR06dEh9+vRR5syZNX36dKbo/5uYmBitWbNG48aNkyS99NJLevnllzVlyhR5eXnZp0sH4hCSHHT27FmtXLlSbm5u6tq1qyRp06ZNmjNnjtKkSaM6deroxRdf1K+//qqsWbOqaNGiSf5q9HG7+SMjI2WM0RdffKH27dsrbdq0kqSZM2dq/Pjx6t+/v2rXrq2wsDDdu3dPL774ojJkyODi6gEAePqGDBmimJgYjRo1Sh4eHjpw4IA6duyoXr16qWXLltqyZYu6deum7t27q1u3bpLuXxaiTZs29guJ40HXr1/X7du35e3trSxZssjNzY0LxeKhGBEOuHPnjvr06aOLFy+qUaNG9uWVK1eWMUazZs3S8uXL1aVLF3Xq1MmFlSYucYfYzZ8/X5cuXVJsbKxq1KhhD0lxM/KMHz9e169fV+fOnZUyZUoXVgwAgOusWrVKGzdu1Lfffmvfu3H06FEVKlRILVu21Llz5zRixAg1btzYHpAuXbokHx8fzZ49m/fQx0ifPr3Sp09vvx0bG0tAwkNxTpIDUqVKpVGjRil16tTau3evjh07Zl9XpUoVdejQQcHBwZoxY4YiIiI4tvW/Dh48qAEDBih79uwqVKiQgoODtWTJEp0/f96+Tdu2bdWrVy/NmzdPkZGRLqwWAADXunjxol588UX5+Pho48aNmjlzpowxypIli0JCQtSiRQv5+/tr2LBhkqTt27dr5cqVCgsLU/78+ZUzZ04XP4NnB7PY4VE43M4Jf/75pwYOHKhChQopICAg3pXnt23bply5ctmnA0/qzp49q+XLlyt58uT2vWvz5s3TV199pbp16+rtt9+O16u4Y6wBAEiqDhw4oP79+ytTpkzatWuXpkyZIkn6z3/+oxQpUqhOnToaPHiwffuhQ4fq7t27Gj58OHuRgARCfHZCvnz59OGHH+rQoUOaNWuWjh8/bl/n7+9PQPqvuMMT582bp7CwMPvyFi1aqFOnTlqxYoUWLlyo4OBg+7o0adK4olQAABKNQoUKqXTp0tq1a5eKFCmit956S2+99ZaaNm2q27dvq3LlygoNDdWNGzc0YcIEbdiwgUPVgQTGnqR/4I8//tCwYcOUI0cOde/ePclepO1x/vjjD/Xp00fp06fXiBEj4l09ff78+QoMDFSnTp3UpUsXjgkGAED3r9/TuXNn5ciRQ/v27ZPNZtPEiRMVERGhQYMGaePGjcqSJYvSpUunkJAQTZ06lZlggQRGSPqHDhw4oPHjx+vjjz/Wyy+/7OpyEqXHHZ64aNEilShRQq+++qrrCgQAIJGJiIhQihQptHjxYn3zzTcqVKiQffrqH3/80X54eoECBZQ5c2YXVws8fwhJCSAyMlLJkiVzdRmJ2h9//KHBgwfLx8dHbdu2VZ48eVxdEgAAiV5YWJjWrl2rr7/+WgUKFNDHH3/s6pKAJIGQhKeGwxMBAHBceHi41qxZo5kzZypbtmz68ssvXV0S8Nxj4gY8NT4+PhoyZIhCQkKUOnVqV5cDAMAzIWXKlKpRo4aaN2+ua9eu6fLly64uCXjusScJTx2HJwIA4LiIiAhFR0fzRSPwFBCSAAAAAMCCw+0AAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAJBk2m00bN250dRkAgESOkAQASJT27dun/Pnzq1OnTg7fd8qUKapXr96/UBUAICkgJAEAEqXFixerVatW2rNnjy5fvuzqch4pKirK1SUAABIYIQkAkOiEhYVp9erVat68uSpWrKhly5bZ1y1dulTFixePt/3GjRtls9ns66dOnao///xTNptNNptNS5cutW9748YNde/eXYULF1bVqlX1448/xnus3bt3q3HjxipYsKD8/f01YcIERUdH29cHBARo5MiRGjNmjEqWLKkOHTr8Gy0AALgQIQkAkOisWbNGr732ml577TXVrVtXS5YskTHmie5bs2ZNtW/fXq+//rq2bdumbdu2qWbNmvb1U6dOVY0aNfTDDz+ofPny6tevn27evClJunz5sjp16iRfX1+tWLFCw4cP1+LFi/XFF1/E+xnLli2Tl5eX5s+frxEjRiTY8wYAJA6EJABAorN48WLVrVtXklSuXDmFhoZq9+7dT3Tf5MmTK2XKlPLw8FDGjBmVMWNGJU+e3L6+QYMGql27tnLmzKm+ffsqPDxcBw4ckCTNmzdPmTNn1tChQ5U7d269+eab6tmzp6ZPn67Y2Fj7Y7z66qvq37+/PcgBAJ4vhCQAQKJy8uRJHTx4ULVr15YkeXp6qmbNmlq8eHGCPH7cYXmSlDJlSqVKlUrXr1+XJJ04cUJ+fn5yc3Ozb1OsWDGFh4fr0qVL9mUFChRIkFoAAImTp6sLAADAavHixYqOjla5cuXsy4wx8vb21tChQ+Xu7v7AoXf37t174sf38vKKd9vNzS3eXqInkSJFCoe2BwA8WwhJAIBEIzo6WitWrNDAgQNVtmzZeOu6d++ulStXKmvWrAoLC1N4eLhSpkwpSfrzzz/jbevl5eVw8JGk3Llza926dTLG2Pcm/frrr3rhhReUOXNmJ58VAOBZw+F2AIBEY/Pmzbp165YaN26svHnzxvtXtWpVLV68WIULF1aKFCk0ceJEnT17VkFBQfFmr5OkbNmy6dy5czpy5IiuX7/+xNN0t2jRQpcuXdKoUaN04sQJbdy4UVOmTFG7du3k7s5bJgAkFbziAwASjcWLF6tMmTJKnTr1A+uqVaumQ4cO6dKlSxo/frx+/vln1alTR6tWrVLPnj0f2LZcuXJq3bq1SpcurZUrVz7Rz8+UKZOmTZumAwcOqF69eho+fLgaN26srl27JsjzAwA8G9zMk86pCgAAAABJAHuSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIDF/wF2KaoWzPKc1AAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(data=df, x='author')\n",
    "plt.title('Number of Texts per Author')\n",
    "plt.xlabel('Author')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:41.190802Z",
     "iopub.status.busy": "2024-02-16T23:22:41.190466Z",
     "iopub.status.idle": "2024-02-16T23:22:41.212153Z",
     "shell.execute_reply": "2024-02-16T23:22:41.211091Z",
     "shell.execute_reply.started": "2024-02-16T23:22:41.190773Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:56:56.815906Z",
     "end_time": "2024-05-30T19:56:56.859938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                author        book  \\\n",
      "author                               \n",
      "attar    44      attar   asrarname   \n",
      "eraghi   295    eraghi      divane   \n",
      "ferdousi 121  ferdousi    shahname   \n",
      "iqbal    50      iqbal   all-books   \n",
      "jami     238      jami     7ourang   \n",
      "moulavi  138   moulavi       shams   \n",
      "nezami   8      nezami       5ganj   \n",
      "rahi     116      rahi   all-books   \n",
      "saadi    278     saadi    golestan   \n",
      "saeb     178      saeb  divan-saeb   \n",
      "\n",
      "                                                           text  text_length  \n",
      "author                                                                        \n",
      "attar    44   برگزیده جنت زمان هست آفریده آنگه جنت تمامت جنت...         2100  \n",
      "eraghi   295  غم بگرفت دامانم ، دریغ سر برآورد گریبانم دریغ ...         2269  \n",
      "ferdousi 121  چنان بد روزی چنان رای پادشاهی بجنبد ز برون‌رفت...         2415  \n",
      "iqbal    50   جهد کن بیخودی بیاب زود‌تر والله اعلم بالصواب م...         2557  \n",
      "jami     238  ای آوازه کوس سخن شاهد جانهاست عروس سخن طرفه عر...         2330  \n",
      "moulavi  138  دل جان حضرت بپالا چو صافی رود صافی خواهی_که ز ...         2159  \n",
      "nezami   8    نشسته شاه روزی نیم‌هشیار امیدی بخت بیدار قاصدی...         2613  \n",
      "rahi     116  نگویم ترک آیین مروت کن فضیلت خلق سفله دشمن تار...         2476  \n",
      "saadi    278  سلطان مرید گر بد نکو آن‌که پادشه بیندازد کسش خ...         2328  \n",
      "saeb     178  ره مده خط مشکین ، شانه شمشاد حاجت حک اصلاحی خط...         2408  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51598/2533009041.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  print(df.groupby('author').apply(lambda x: x.sample(1)))\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('author').apply(lambda x: x.sample(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Word Count Column and Creating Histogram Plot\n",
    "\n",
    "This code snippet adds a new column to the dataframe to count the number of words in each text and creates a histogram plot to visualize the distribution of word counts.\n",
    "\n",
    "- The number of words in each text is calculated by counting the spaces and adding one.\n",
    "- A new column named `'word_count'` is created in the dataframe to store the word counts.\n",
    "- The `seaborn` library is used to create a histogram plot (`histplot`) with KDE (Kernel Density Estimation) enabled.\n",
    "- The `x` parameter specifies the column containing the word counts.\n",
    "- The `hue` parameter is set to `'author'` to differentiate the histograms by author.\n",
    "- Finally, `plt.show()` is called to display the plot.\n",
    "\n",
    "This visualization helps to understand the distribution of word counts in the texts across different authors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:41.213987Z",
     "iopub.status.busy": "2024-02-16T23:22:41.213605Z",
     "iopub.status.idle": "2024-02-16T23:22:41.931488Z",
     "shell.execute_reply": "2024-02-16T23:22:41.930496Z",
     "shell.execute_reply.started": "2024-02-16T23:22:41.213959Z"
    },
    "ExecuteTime": {
     "start_time": "2024-05-30T19:57:02.952273Z",
     "end_time": "2024-05-30T19:57:03.448670Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count spaces plus one for number of words\n",
    "df['word_count'] = df['text'].str.count(' ') + 1\n",
    "\n",
    "sns.histplot(data=df, x='word_count', hue='author', kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the Number of Labels\n",
    "\n",
    "In this code snippet, the number of unique labels (authors) in the dataset is determined to establish the appropriate number of output labels for the classification task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:41.942681Z",
     "iopub.status.busy": "2024-02-16T23:22:41.942365Z",
     "iopub.status.idle": "2024-02-16T23:22:41.948612Z",
     "shell.execute_reply": "2024-02-16T23:22:41.947612Z",
     "shell.execute_reply.started": "2024-02-16T23:22:41.942648Z"
    }
   },
   "outputs": [],
   "source": [
    "num_labels=len(df['author'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizing Text Data with BERT Tokenizer and Preparing DataLoaders\n",
    "\n",
    "This code snippet demonstrates how to tokenize text data using a BERT tokenizer, split the dataset into train, validation, and test sets, and prepare DataLoader objects for each set.\n",
    "\n",
    "- The `BertTokenizer` is imported from the Hugging Face Transformers library to tokenize the text data. The tokenizer is initialized with the \"bert-base-multilingual-cased\" model.\n",
    "- A function named `tokenize_texts` is defined to tokenize and pad sequences, and create attention masks. It takes a list of texts, tokenizer object, and optional maximum length as input and returns input ids and attention masks as PyTorch tensors.\n",
    "- The dataset is split into train, validation, and test sets using `train_test_split` from `sklearn.model_selection`. The train-test split ratio is 80:20, and then the train-validation split ratio is 90:10.\n",
    "- The texts in each set are tokenized using the `tokenize_texts` function, resulting in input ids and attention masks for train, validation, and test sets.\n",
    "- Labels are converted to integers using a label dictionary, where each author is mapped to a unique integer.\n",
    "- Labels are converted to PyTorch tensors.\n",
    "- DataLoader objects are created for each set using `TensorDataset`, `RandomSampler`, and `SequentialSampler` for train, validation, and test sets, respectively. The DataLoader splits the dataset into mini-batches of a specified batch size.\n",
    "\n",
    "This process prepares the text data for training and evaluation with a BERT-based model, ensuring compatibility with PyTorch's DataLoader for efficient batch processing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:41.950360Z",
     "iopub.status.busy": "2024-02-16T23:22:41.949990Z",
     "iopub.status.idle": "2024-02-16T23:22:48.154136Z",
     "shell.execute_reply": "2024-02-16T23:22:48.152920Z",
     "shell.execute_reply.started": "2024-02-16T23:22:41.950317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a731733679c49438bf4e1f6880029b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd06113434c2491f8440f58c2a5d73cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04191d55da824ef298989ecd94bde6a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "746727c24369465fb3a43deac92b27a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tokenize text data using a BERT tokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-multilingual-cased\")\n",
    "\n",
    "# Tokenize and pad sequences, and create attention masks\n",
    "def tokenize_texts(texts, tokenizer, max_length=128):\n",
    "    input_ids = []\n",
    "    attention_masks = []\n",
    "\n",
    "    for text in texts:\n",
    "        encoded_dict = tokenizer.encode_plus(\n",
    "                            text,\n",
    "                            add_special_tokens = True,\n",
    "                            max_length = max_length,\n",
    "                            padding = 'max_length',\n",
    "                            truncation=True,\n",
    "                            return_attention_mask = True,\n",
    "                            return_tensors = 'pt'\n",
    "                       )\n",
    "        input_ids.append(encoded_dict['input_ids'])\n",
    "        attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "    input_ids = torch.cat(input_ids, dim=0)\n",
    "    attention_masks = torch.cat(attention_masks, dim=0)\n",
    "\n",
    "    return input_ids, attention_masks\n",
    "\n",
    "# Split dataset into train, validation, and test sets\n",
    "train_texts, test_texts, train_labels, test_labels = train_test_split(df['text'], df['author'], test_size=0.2, random_state=42)\n",
    "train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=0.1, random_state=42)\n",
    "\n",
    "# Tokenize the texts\n",
    "train_input_ids, train_attention_masks = tokenize_texts(train_texts, tokenizer)\n",
    "val_input_ids, val_attention_masks = tokenize_texts(val_texts, tokenizer)\n",
    "test_input_ids, test_attention_masks = tokenize_texts(test_texts, tokenizer)\n",
    "\n",
    "# Convert labels to integers\n",
    "label_dict = {author: idx for idx, author in enumerate(df['author'].unique())}\n",
    "train_labels = train_labels.map(label_dict)\n",
    "val_labels = val_labels.map(label_dict)\n",
    "test_labels = test_labels.map(label_dict)\n",
    "\n",
    "# Convert labels to tensors\n",
    "train_labels = torch.tensor(train_labels.values)\n",
    "val_labels = torch.tensor(val_labels.values)\n",
    "test_labels = torch.tensor(test_labels.values)\n",
    "\n",
    "\n",
    "# Create DataLoader for each set\n",
    "batch_size = 16\n",
    "\n",
    "train_data = TensorDataset(train_input_ids, train_attention_masks, train_labels)\n",
    "train_sampler = RandomSampler(train_data)\n",
    "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
    "\n",
    "val_data = TensorDataset(val_input_ids, val_attention_masks, val_labels)\n",
    "val_sampler = SequentialSampler(val_data)\n",
    "val_dataloader = DataLoader(val_data, sampler=val_sampler, batch_size=batch_size)\n",
    "\n",
    "test_data = TensorDataset(test_input_ids, test_attention_masks, test_labels)\n",
    "test_sampler = SequentialSampler(test_data)\n",
    "test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:48.156151Z",
     "iopub.status.busy": "2024-02-16T23:22:48.155735Z",
     "iopub.status.idle": "2024-02-16T23:22:48.163934Z",
     "shell.execute_reply": "2024-02-16T23:22:48.162744Z",
     "shell.execute_reply.started": "2024-02-16T23:22:48.156112Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eraghi': 0,\n",
       " 'moulavi': 1,\n",
       " 'attar': 2,\n",
       " 'rahi': 3,\n",
       " 'iqbal': 4,\n",
       " 'nezami': 5,\n",
       " 'ferdousi': 6,\n",
       " 'saeb': 7,\n",
       " 'saadi': 8,\n",
       " 'jami': 9}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Base BERT model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training BERT Model for Sequence Classification\n",
    "\n",
    "This code snippet demonstrates how to train a pre-trained BERT model for sequence classification using PyTorch.\n",
    "\n",
    "- The `BertForSequenceClassification` model is imported from the Hugging Face Transformers library and initialized with the \"bert-base-multilingual-cased\" pre-trained model. The number of labels is set to the number of unique authors in the dataset.\n",
    "- An optimizer (AdamW) is defined with a specified learning rate.\n",
    "- Hyperparameters such as epochs and batch size are set.\n",
    "- A learning rate scheduler (CosineAnnealingLR) is defined based on the total number of training steps.\n",
    "- The model is moved to the appropriate device (GPU if available).\n",
    "- The training loop iterates over each epoch, and within each epoch, over each batch of the training data. The model is set to training mode, and gradients are calculated and updated based on the loss. Gradients are clipped to prevent exploding gradients. The learning rate scheduler is updated at each step.\n",
    "- After each epoch, the model is evaluated on the validation data to compute the validation accuracy.\n",
    "- The best model based on validation accuracy is saved.\n",
    "- At the end of training, the best model is loaded for further evaluation or inference.\n",
    "\n",
    "This training process enables the BERT model to learn from the data and optimize its parameters to make accurate predictions for the sequence classification task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:22:48.166170Z",
     "iopub.status.busy": "2024-02-16T23:22:48.165537Z",
     "iopub.status.idle": "2024-02-16T23:27:54.001701Z",
     "shell.execute_reply": "2024-02-16T23:27:54.000616Z",
     "shell.execute_reply.started": "2024-02-16T23:22:48.166132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d4c1160039148bf88bfdb4f0b99293f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/714M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50:\n",
      "  Train Loss: 2.3196 | Val Loss: 2.3387 | Train Accuracy: 0.1161 | Val Accuracy: 0.0000\n",
      "Epoch 2/50:\n",
      "  Train Loss: 2.3062 | Val Loss: 2.3037 | Train Accuracy: 0.1259 | Val Accuracy: 0.0868\n",
      "Epoch 3/50:\n",
      "  Train Loss: 2.1909 | Val Loss: 2.2601 | Train Accuracy: 0.2289 | Val Accuracy: 0.1250\n",
      "Epoch 4/50:\n",
      "  Train Loss: 2.0012 | Val Loss: 2.2464 | Train Accuracy: 0.3089 | Val Accuracy: 0.1181\n",
      "Epoch 5/50:\n",
      "  Train Loss: 1.8367 | Val Loss: 2.0979 | Train Accuracy: 0.3866 | Val Accuracy: 0.0938\n",
      "Epoch 6/50:\n",
      "  Train Loss: 1.6633 | Val Loss: 2.0786 | Train Accuracy: 0.4753 | Val Accuracy: 0.2118\n",
      "Epoch 7/50:\n",
      "  Train Loss: 1.4795 | Val Loss: 1.8386 | Train Accuracy: 0.5515 | Val Accuracy: 0.1562\n",
      "Epoch 8/50:\n",
      "  Train Loss: 1.2339 | Val Loss: 1.7497 | Train Accuracy: 0.7182 | Val Accuracy: 0.4167\n",
      "Epoch 9/50:\n",
      "  Train Loss: 1.0446 | Val Loss: 1.6590 | Train Accuracy: 0.8161 | Val Accuracy: 0.5590\n",
      "Epoch 10/50:\n",
      "  Train Loss: 0.8456 | Val Loss: 1.5668 | Train Accuracy: 0.8607 | Val Accuracy: 0.5521\n",
      "Epoch 11/50:\n",
      "  Train Loss: 0.7114 | Val Loss: 1.6260 | Train Accuracy: 0.8699 | Val Accuracy: 0.4722\n",
      "Epoch 12/50:\n",
      "  Train Loss: 0.5939 | Val Loss: 1.4001 | Train Accuracy: 0.9015 | Val Accuracy: 0.6389\n",
      "Epoch 13/50:\n",
      "  Train Loss: 0.6159 | Val Loss: 1.4396 | Train Accuracy: 0.8554 | Val Accuracy: 0.6076\n",
      "Epoch 14/50:\n",
      "  Train Loss: 0.5631 | Val Loss: 1.3825 | Train Accuracy: 0.8923 | Val Accuracy: 0.6389\n",
      "Epoch 15/50:\n",
      "  Train Loss: 0.4286 | Val Loss: 1.6104 | Train Accuracy: 0.9461 | Val Accuracy: 0.4722\n",
      "Epoch 16/50:\n",
      "  Train Loss: 0.2979 | Val Loss: 1.5263 | Train Accuracy: 0.9866 | Val Accuracy: 0.5521\n",
      "Epoch 17/50:\n",
      "  Train Loss: 0.3496 | Val Loss: 1.2839 | Train Accuracy: 0.9283 | Val Accuracy: 0.6146\n",
      "Epoch 18/50:\n",
      "  Train Loss: 0.1866 | Val Loss: 1.3551 | Train Accuracy: 0.9911 | Val Accuracy: 0.6146\n",
      "Epoch 19/50:\n",
      "  Train Loss: 0.1305 | Val Loss: 1.3993 | Train Accuracy: 1.0000 | Val Accuracy: 0.6701\n",
      "Epoch 20/50:\n",
      "  Train Loss: 0.0923 | Val Loss: 1.3695 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 21/50:\n",
      "  Train Loss: 0.0706 | Val Loss: 1.6204 | Train Accuracy: 1.0000 | Val Accuracy: 0.5903\n",
      "Epoch 22/50:\n",
      "  Train Loss: 0.0553 | Val Loss: 1.4740 | Train Accuracy: 0.9955 | Val Accuracy: 0.7014\n",
      "Epoch 23/50:\n",
      "  Train Loss: 0.0384 | Val Loss: 1.5037 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 24/50:\n",
      "  Train Loss: 0.0297 | Val Loss: 1.5875 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 25/50:\n",
      "  Train Loss: 0.0232 | Val Loss: 1.5692 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 26/50:\n",
      "  Train Loss: 0.0198 | Val Loss: 1.6745 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 27/50:\n",
      "  Train Loss: 0.0175 | Val Loss: 1.6813 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 28/50:\n",
      "  Train Loss: 0.0158 | Val Loss: 1.7388 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 29/50:\n",
      "  Train Loss: 0.0143 | Val Loss: 1.7083 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 30/50:\n",
      "  Train Loss: 0.0135 | Val Loss: 1.6441 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 31/50:\n",
      "  Train Loss: 0.0124 | Val Loss: 1.7985 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 32/50:\n",
      "  Train Loss: 0.0117 | Val Loss: 1.7999 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 33/50:\n",
      "  Train Loss: 0.0113 | Val Loss: 1.7928 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 34/50:\n",
      "  Train Loss: 0.0106 | Val Loss: 1.8073 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 35/50:\n",
      "  Train Loss: 0.0106 | Val Loss: 1.7968 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 36/50:\n",
      "  Train Loss: 0.0101 | Val Loss: 1.8076 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 37/50:\n",
      "  Train Loss: 0.0096 | Val Loss: 1.8304 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 38/50:\n",
      "  Train Loss: 0.0096 | Val Loss: 1.8300 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 39/50:\n",
      "  Train Loss: 0.0094 | Val Loss: 1.8189 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 40/50:\n",
      "  Train Loss: 0.0091 | Val Loss: 1.8160 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 41/50:\n",
      "  Train Loss: 0.0089 | Val Loss: 1.8280 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 42/50:\n",
      "  Train Loss: 0.0090 | Val Loss: 1.8365 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 43/50:\n",
      "  Train Loss: 0.0087 | Val Loss: 1.8404 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 44/50:\n",
      "  Train Loss: 0.0087 | Val Loss: 1.8438 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 45/50:\n",
      "  Train Loss: 0.0088 | Val Loss: 1.8459 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 46/50:\n",
      "  Train Loss: 0.0087 | Val Loss: 1.8466 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 47/50:\n",
      "  Train Loss: 0.0088 | Val Loss: 1.8461 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 48/50:\n",
      "  Train Loss: 0.0087 | Val Loss: 1.8458 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 49/50:\n",
      "  Train Loss: 0.0087 | Val Loss: 1.8457 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n",
      "Epoch 50/50:\n",
      "  Train Loss: 0.0087 | Val Loss: 1.8457 | Train Accuracy: 1.0000 | Val Accuracy: 0.7014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load pre-trained BERT model for sequence classification\n",
    "model = BertForSequenceClassification.from_pretrained(\"bert-base-multilingual-cased\", num_labels=len(df['author'].unique()))\n",
    "\n",
    "# Define optimizer and learning rate\n",
    "learning_rate = 2e-5  # Adjust the learning rate\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Hyperparameters\n",
    "epochs = 50  # Adjust the number of epochs\n",
    "batch_size = 32  # Adjust the batch size\n",
    "\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "# Learning rate scheduler\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps)\n",
    "\n",
    "# Training loop\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "best_val_accuracy = 0.0  # Track the best validation accuracy\n",
    "\n",
    "# Lists to store the metrics\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "train_accuracies = []\n",
    "val_accuracies = []\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    total_train_loss = 0\n",
    "    train_accuracy = 0  # Track train accuracy\n",
    "\n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "        batch_input_ids = batch[0].to(device)\n",
    "        batch_attention_masks = batch[1].to(device)\n",
    "        batch_labels = batch[2].to(device)\n",
    "\n",
    "        model.zero_grad()\n",
    "\n",
    "        outputs = model(batch_input_ids, attention_mask=batch_attention_masks, labels=batch_labels)\n",
    "        loss = outputs.loss\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "        logits = outputs.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        predictions = np.argmax(logits, axis=1)\n",
    "        train_accuracy += accuracy_score(predictions, batch_labels.cpu().numpy())\n",
    "\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)\n",
    "    avg_train_accuracy = train_accuracy / len(train_dataloader)\n",
    "    train_losses.append(avg_train_loss)\n",
    "    train_accuracies.append(avg_train_accuracy)\n",
    "\n",
    "    # Validation loop\n",
    "    model.eval()\n",
    "    val_accuracy = 0\n",
    "    val_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for batch in val_dataloader:\n",
    "            batch_input_ids = batch[0].to(device)\n",
    "            batch_attention_masks = batch[1].to(device)\n",
    "            batch_labels = batch[2].to(device)\n",
    "\n",
    "            outputs = model(batch_input_ids, attention_mask=batch_attention_masks, labels=batch_labels)\n",
    "            loss = outputs.loss\n",
    "            val_loss += loss.item()\n",
    "\n",
    "            logits = outputs.logits\n",
    "            logits = logits.detach().cpu().numpy()\n",
    "            predictions = np.argmax(logits, axis=1)\n",
    "            val_accuracy += accuracy_score(predictions, batch_labels.cpu().numpy())\n",
    "\n",
    "    avg_val_loss = val_loss / len(val_dataloader)\n",
    "    avg_val_accuracy = val_accuracy / len(val_dataloader)\n",
    "    val_losses.append(avg_val_loss)\n",
    "    val_accuracies.append(avg_val_accuracy)\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs}:\")\n",
    "    print(f\"  Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Train Accuracy: {avg_train_accuracy:.4f} | Val Accuracy: {avg_val_accuracy:.4f}\")\n",
    "\n",
    "    # Save the best model\n",
    "    if avg_val_accuracy > best_val_accuracy:\n",
    "        best_val_accuracy = avg_val_accuracy\n",
    "        torch.save(model.state_dict(), \"best_model.pt\")\n",
    "\n",
    "# Load the best model\n",
    "model.load_state_dict(torch.load(\"best_model.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:27:54.003224Z",
     "iopub.status.busy": "2024-02-16T23:27:54.002919Z",
     "iopub.status.idle": "2024-02-16T23:27:54.566151Z",
     "shell.execute_reply": "2024-02-16T23:27:54.565157Z",
     "shell.execute_reply.started": "2024-02-16T23:27:54.003187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the metrics\n",
    "epochs_range = range(1, epochs + 1)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, train_losses, label='Train Loss')\n",
    "plt.plot(epochs_range, val_losses, label='Val Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, train_accuracies, label='Train Accuracy')\n",
    "plt.plot(epochs_range, val_accuracies, label='Val Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "This code snippet evaluates the trained BERT model on the test dataset, calculating accuracy, generating a classification report, and visualizing the confusion matrix.\n",
    "\n",
    "- The model is set to evaluation mode (`model.eval()`).\n",
    "- Predictions and true labels are collected for each batch of the test dataset.\n",
    "- Accuracy is calculated using `accuracy_score` from `sklearn.metrics`.\n",
    "- A classification report is generated using `classification_report` from `sklearn.metrics`, providing metrics such as precision, recall, and F1-score for each class.\n",
    "- A confusion matrix is generated using `confusion_matrix` from `sklearn.metrics` and visualized using `plt.imshow` to show the distribution of predicted labels compared to true labels.\n",
    "\n",
    "This evaluation process provides insights into the performance of the trained model on the test dataset, including overall accuracy and detailed metrics for each class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:27:54.567635Z",
     "iopub.status.busy": "2024-02-16T23:27:54.567326Z",
     "iopub.status.idle": "2024-02-16T23:27:55.306731Z",
     "shell.execute_reply": "2024-02-16T23:27:55.305773Z",
     "shell.execute_reply.started": "2024-02-16T23:27:54.567609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6129032258064516\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.33      0.40         3\n",
      "           1       0.20      0.20      0.20         5\n",
      "           2       1.00      0.50      0.67         4\n",
      "           3       1.00      0.50      0.67         8\n",
      "           4       0.67      0.44      0.53         9\n",
      "           5       0.75      0.67      0.71         9\n",
      "           6       0.75      0.86      0.80         7\n",
      "           7       0.60      0.86      0.71         7\n",
      "           8       0.50      1.00      0.67         3\n",
      "           9       0.45      0.71      0.56         7\n",
      "\n",
      "    accuracy                           0.61        62\n",
      "   macro avg       0.64      0.61      0.59        62\n",
      "weighted avg       0.67      0.61      0.61        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation\n",
    "model.eval()\n",
    "all_predictions = []\n",
    "all_true_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in test_dataloader:\n",
    "        batch_input_ids = batch[0].to(device)\n",
    "        batch_attention_masks = batch[1].to(device)\n",
    "        batch_labels = batch[2].to(device)\n",
    "\n",
    "        outputs = model(batch_input_ids, attention_mask=batch_attention_masks)\n",
    "        logits = outputs.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        predictions = np.argmax(logits, axis=1)\n",
    "\n",
    "        all_predictions.extend(predictions)\n",
    "        all_true_labels.extend(batch_labels.cpu().numpy())\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(all_true_labels, all_predictions)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Generate classification report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(all_true_labels, all_predictions))\n",
    "\n",
    "# Generate confusion matrix\n",
    "conf_matrix = confusion_matrix(all_true_labels, all_predictions)\n",
    "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xlm-roberta-base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training XLM-RoBERTa Model for Sequence Classification\n",
    "\n",
    "This code snippet demonstrates how to train a pre-trained XLM-RoBERTa model for sequence classification using PyTorch.\n",
    "\n",
    "- The `BertForSequenceClassification` model is imported from the Hugging Face Transformers library and initialized with the \"xlm-roberta-base\" pre-trained model. The number of labels is set to the number of unique authors in the dataset.\n",
    "- An optimizer (AdamW) is defined with a specified learning rate.\n",
    "- Hyperparameters such as epochs and batch size are set.\n",
    "- A learning rate scheduler (CosineAnnealingLR) is defined based on the total number of training steps.\n",
    "- The model is moved to the appropriate device (GPU if available).\n",
    "- The training loop iterates over each epoch, and within each epoch, over each batch of the training data. The model is set to training mode, and gradients are calculated and updated based on the loss. Gradients are clipped to prevent exploding gradients. The learning rate scheduler is updated at each step.\n",
    "- After each epoch, the model is evaluated on the validation data to compute the validation accuracy.\n",
    "- The best model based on validation accuracy is saved.\n",
    "- At the end of training, the best model is loaded for further evaluation or inference.\n",
    "\n",
    "This training process enables the XLM-RoBERTa model to learn from the data and optimize its parameters to make accurate predictions for the sequence classification task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:27:55.308363Z",
     "iopub.status.busy": "2024-02-16T23:27:55.308060Z",
     "iopub.status.idle": "2024-02-16T23:33:58.387270Z",
     "shell.execute_reply": "2024-02-16T23:33:58.379053Z",
     "shell.execute_reply.started": "2024-02-16T23:27:55.308337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ba302c4dcf64749851b520c30042402",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type xlm-roberta to instantiate a model of type bert. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d3ec1c66bc441888700d49ff1280c66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'embeddings.LayerNorm.bias', 'embeddings.LayerNorm.weight', 'embeddings.position_embeddings.weight', 'embeddings.token_type_embeddings.weight', 'embeddings.word_embeddings.weight', 'encoder.layer.0.attention.output.LayerNorm.bias', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.0.attention.self.key.bias', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.0.attention.self.query.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.0.output.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.1.output.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.10.attention.output.dense.bias', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.10.attention.self.query.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.10.output.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.11.output.LayerNorm.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.11.output.dense.bias', 'encoder.layer.11.output.dense.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.2.attention.self.key.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.2.output.dense.bias', 'encoder.layer.2.output.dense.weight', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.3.output.dense.weight', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.4.intermediate.dense.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.4.output.dense.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.8.output.LayerNorm.bias', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.dense.weight', 'pooler.dense.bias', 'pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50:\n",
      "  Train Loss: 2.4174 | Val Loss: 2.3962 | Train Accuracy: 0.0896 | Val Accuracy: 0.0000\n",
      "Epoch 2/50:\n",
      "  Train Loss: 2.4123 | Val Loss: 2.4843 | Train Accuracy: 0.0762 | Val Accuracy: 0.0868\n",
      "Epoch 3/50:\n",
      "  Train Loss: 2.3449 | Val Loss: 2.4094 | Train Accuracy: 0.1217 | Val Accuracy: 0.0938\n",
      "Epoch 4/50:\n",
      "  Train Loss: 2.3365 | Val Loss: 2.2667 | Train Accuracy: 0.0810 | Val Accuracy: 0.0000\n",
      "Epoch 5/50:\n",
      "  Train Loss: 2.3216 | Val Loss: 2.3608 | Train Accuracy: 0.0762 | Val Accuracy: 0.0868\n",
      "Epoch 6/50:\n",
      "  Train Loss: 2.3106 | Val Loss: 2.3676 | Train Accuracy: 0.1170 | Val Accuracy: 0.0868\n",
      "Epoch 7/50:\n",
      "  Train Loss: 2.3425 | Val Loss: 2.4149 | Train Accuracy: 0.0896 | Val Accuracy: 0.0625\n",
      "Epoch 8/50:\n",
      "  Train Loss: 2.3443 | Val Loss: 2.2709 | Train Accuracy: 0.0860 | Val Accuracy: 0.0000\n",
      "Epoch 9/50:\n",
      "  Train Loss: 2.3199 | Val Loss: 2.4303 | Train Accuracy: 0.0943 | Val Accuracy: 0.0000\n",
      "Epoch 10/50:\n",
      "  Train Loss: 2.3191 | Val Loss: 2.3802 | Train Accuracy: 0.1122 | Val Accuracy: 0.0000\n",
      "Epoch 11/50:\n",
      "  Train Loss: 2.3359 | Val Loss: 2.4391 | Train Accuracy: 0.1339 | Val Accuracy: 0.0000\n",
      "Epoch 12/50:\n",
      "  Train Loss: 2.3342 | Val Loss: 2.2872 | Train Accuracy: 0.1161 | Val Accuracy: 0.1181\n",
      "Epoch 13/50:\n",
      "  Train Loss: 2.2940 | Val Loss: 2.2710 | Train Accuracy: 0.1482 | Val Accuracy: 0.0312\n",
      "Epoch 14/50:\n",
      "  Train Loss: 2.2127 | Val Loss: 2.2180 | Train Accuracy: 0.2298 | Val Accuracy: 0.1806\n",
      "Epoch 15/50:\n",
      "  Train Loss: 2.1152 | Val Loss: 2.1548 | Train Accuracy: 0.2830 | Val Accuracy: 0.0938\n",
      "Epoch 16/50:\n",
      "  Train Loss: 1.9461 | Val Loss: 1.9399 | Train Accuracy: 0.2690 | Val Accuracy: 0.1875\n",
      "Epoch 17/50:\n",
      "  Train Loss: 1.8244 | Val Loss: 1.9107 | Train Accuracy: 0.3098 | Val Accuracy: 0.2188\n",
      "Epoch 18/50:\n",
      "  Train Loss: 1.6428 | Val Loss: 1.9457 | Train Accuracy: 0.4068 | Val Accuracy: 0.2431\n",
      "Epoch 19/50:\n",
      "  Train Loss: 1.5731 | Val Loss: 1.7392 | Train Accuracy: 0.4214 | Val Accuracy: 0.2431\n",
      "Epoch 20/50:\n",
      "  Train Loss: 1.4946 | Val Loss: 1.7005 | Train Accuracy: 0.5015 | Val Accuracy: 0.3542\n",
      "Epoch 21/50:\n",
      "  Train Loss: 1.3132 | Val Loss: 1.7833 | Train Accuracy: 0.5878 | Val Accuracy: 0.1875\n",
      "Epoch 22/50:\n",
      "  Train Loss: 1.2010 | Val Loss: 1.5547 | Train Accuracy: 0.6372 | Val Accuracy: 0.2743\n",
      "Epoch 23/50:\n",
      "  Train Loss: 1.1513 | Val Loss: 1.6622 | Train Accuracy: 0.6012 | Val Accuracy: 0.2500\n",
      "Epoch 24/50:\n",
      "  Train Loss: 1.1326 | Val Loss: 1.4040 | Train Accuracy: 0.6369 | Val Accuracy: 0.5521\n",
      "Epoch 25/50:\n",
      "  Train Loss: 0.9809 | Val Loss: 1.5141 | Train Accuracy: 0.7631 | Val Accuracy: 0.4410\n",
      "Epoch 26/50:\n",
      "  Train Loss: 0.9737 | Val Loss: 1.3127 | Train Accuracy: 0.7217 | Val Accuracy: 0.5590\n",
      "Epoch 27/50:\n",
      "  Train Loss: 0.8991 | Val Loss: 1.4900 | Train Accuracy: 0.7887 | Val Accuracy: 0.4722\n",
      "Epoch 28/50:\n",
      "  Train Loss: 0.8210 | Val Loss: 1.3394 | Train Accuracy: 0.8030 | Val Accuracy: 0.5278\n",
      "Epoch 29/50:\n",
      "  Train Loss: 0.7770 | Val Loss: 1.3136 | Train Accuracy: 0.8164 | Val Accuracy: 0.5278\n",
      "Epoch 30/50:\n",
      "  Train Loss: 0.7130 | Val Loss: 1.2709 | Train Accuracy: 0.8878 | Val Accuracy: 0.5903\n",
      "Epoch 31/50:\n",
      "  Train Loss: 0.6761 | Val Loss: 1.2082 | Train Accuracy: 0.8661 | Val Accuracy: 0.5590\n",
      "Epoch 32/50:\n",
      "  Train Loss: 0.6190 | Val Loss: 1.2662 | Train Accuracy: 0.9104 | Val Accuracy: 0.5521\n",
      "Epoch 33/50:\n",
      "  Train Loss: 0.6207 | Val Loss: 1.2320 | Train Accuracy: 0.9149 | Val Accuracy: 0.5764\n",
      "Epoch 34/50:\n",
      "  Train Loss: 0.5620 | Val Loss: 1.1868 | Train Accuracy: 0.9188 | Val Accuracy: 0.6146\n",
      "Epoch 35/50:\n",
      "  Train Loss: 0.5340 | Val Loss: 1.1963 | Train Accuracy: 0.9375 | Val Accuracy: 0.5590\n",
      "Epoch 36/50:\n",
      "  Train Loss: 0.5185 | Val Loss: 1.1647 | Train Accuracy: 0.9283 | Val Accuracy: 0.5590\n",
      "Epoch 37/50:\n",
      "  Train Loss: 0.5042 | Val Loss: 1.1534 | Train Accuracy: 0.9327 | Val Accuracy: 0.5833\n",
      "Epoch 38/50:\n",
      "  Train Loss: 0.4891 | Val Loss: 1.1375 | Train Accuracy: 0.9369 | Val Accuracy: 0.5833\n",
      "Epoch 39/50:\n",
      "  Train Loss: 0.4729 | Val Loss: 1.2588 | Train Accuracy: 0.9548 | Val Accuracy: 0.4965\n",
      "Epoch 40/50:\n",
      "  Train Loss: 0.4747 | Val Loss: 1.0963 | Train Accuracy: 0.9464 | Val Accuracy: 0.6944\n",
      "Epoch 41/50:\n",
      "  Train Loss: 0.4369 | Val Loss: 1.1309 | Train Accuracy: 0.9414 | Val Accuracy: 0.6076\n",
      "Epoch 42/50:\n",
      "  Train Loss: 0.4346 | Val Loss: 1.0965 | Train Accuracy: 0.9595 | Val Accuracy: 0.6701\n",
      "Epoch 43/50:\n",
      "  Train Loss: 0.4204 | Val Loss: 1.1040 | Train Accuracy: 0.9509 | Val Accuracy: 0.6146\n",
      "Epoch 44/50:\n",
      "  Train Loss: 0.4091 | Val Loss: 1.0864 | Train Accuracy: 0.9685 | Val Accuracy: 0.7257\n",
      "Epoch 45/50:\n",
      "  Train Loss: 0.4132 | Val Loss: 1.1094 | Train Accuracy: 0.9640 | Val Accuracy: 0.6146\n",
      "Epoch 46/50:\n",
      "  Train Loss: 0.4052 | Val Loss: 1.0998 | Train Accuracy: 0.9551 | Val Accuracy: 0.7257\n",
      "Epoch 47/50:\n",
      "  Train Loss: 0.3995 | Val Loss: 1.1113 | Train Accuracy: 0.9729 | Val Accuracy: 0.6389\n",
      "Epoch 48/50:\n",
      "  Train Loss: 0.4050 | Val Loss: 1.0980 | Train Accuracy: 0.9643 | Val Accuracy: 0.6701\n",
      "Epoch 49/50:\n",
      "  Train Loss: 0.3913 | Val Loss: 1.0958 | Train Accuracy: 0.9732 | Val Accuracy: 0.6701\n",
      "Epoch 50/50:\n",
      "  Train Loss: 0.3915 | Val Loss: 1.0959 | Train Accuracy: 0.9640 | Val Accuracy: 0.6701\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load pre-trained BERT model for sequence classification\n",
    "model = BertForSequenceClassification.from_pretrained(\"xlm-roberta-base\", num_labels=len(df['author'].unique()))\n",
    "\n",
    "# Define optimizer and learning rate\n",
    "learning_rate = 2e-5  # Adjust the learning rate\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Hyperparameters\n",
    "epochs = 50  # Adjust the number of epochs\n",
    "batch_size = 32  # Adjust the batch size\n",
    "\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "# Learning rate scheduler\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps)\n",
    "\n",
    "# Training loop\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "best_val_accuracy = 0.0  # Track the best validation accuracy\n",
    "\n",
    "# Lists to store the metrics\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "train_accuracies = []\n",
    "val_accuracies = []\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    total_train_loss = 0\n",
    "    train_accuracy = 0  # Track train accuracy\n",
    "\n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "        batch_input_ids = batch[0].to(device)\n",
    "        batch_attention_masks = batch[1].to(device)\n",
    "        batch_labels = batch[2].to(device)\n",
    "\n",
    "        model.zero_grad()\n",
    "\n",
    "        outputs = model(batch_input_ids, attention_mask=batch_attention_masks, labels=batch_labels)\n",
    "        loss = outputs.loss\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "        logits = outputs.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        predictions = np.argmax(logits, axis=1)\n",
    "        train_accuracy += accuracy_score(predictions, batch_labels.cpu().numpy())\n",
    "\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)\n",
    "    avg_train_accuracy = train_accuracy / len(train_dataloader)\n",
    "    train_losses.append(avg_train_loss)\n",
    "    train_accuracies.append(avg_train_accuracy)\n",
    "\n",
    "    # Validation loop\n",
    "    model.eval()\n",
    "    val_accuracy = 0\n",
    "    val_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for batch in val_dataloader:\n",
    "            batch_input_ids = batch[0].to(device)\n",
    "            batch_attention_masks = batch[1].to(device)\n",
    "            batch_labels = batch[2].to(device)\n",
    "\n",
    "            outputs = model(batch_input_ids, attention_mask=batch_attention_masks, labels=batch_labels)\n",
    "            loss = outputs.loss\n",
    "            val_loss += loss.item()\n",
    "\n",
    "            logits = outputs.logits\n",
    "            logits = logits.detach().cpu().numpy()\n",
    "            predictions = np.argmax(logits, axis=1)\n",
    "            val_accuracy += accuracy_score(predictions, batch_labels.cpu().numpy())\n",
    "\n",
    "    avg_val_loss = val_loss / len(val_dataloader)\n",
    "    avg_val_accuracy = val_accuracy / len(val_dataloader)\n",
    "    val_losses.append(avg_val_loss)\n",
    "    val_accuracies.append(avg_val_accuracy)\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs}:\")\n",
    "    print(f\"  Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Train Accuracy: {avg_train_accuracy:.4f} | Val Accuracy: {avg_val_accuracy:.4f}\")\n",
    "\n",
    "    # Save the best model\n",
    "    if avg_val_accuracy > best_val_accuracy:\n",
    "        best_val_accuracy = avg_val_accuracy\n",
    "        torch.save(model.state_dict(), \"best_model.pt\")\n",
    "\n",
    "# Load the best model\n",
    "model.load_state_dict(torch.load(\"best_model.pt\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:33:58.389408Z",
     "iopub.status.busy": "2024-02-16T23:33:58.389037Z",
     "iopub.status.idle": "2024-02-16T23:33:58.985309Z",
     "shell.execute_reply": "2024-02-16T23:33:58.984170Z",
     "shell.execute_reply.started": "2024-02-16T23:33:58.389376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the metrics\n",
    "epochs_range = range(1, epochs + 1)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, train_losses, label='Train Loss')\n",
    "plt.plot(epochs_range, val_losses, label='Val Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, train_accuracies, label='Train Accuracy')\n",
    "plt.plot(epochs_range, val_accuracies, label='Val Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "This code snippet evaluates the trained XLM-RoBERTa model on the test dataset, calculating accuracy, generating a classification report, and visualizing the confusion matrix.\n",
    "\n",
    "- The model is set to evaluation mode (`model.eval()`).\n",
    "- Predictions and true labels are collected for each batch of the test dataset.\n",
    "- Accuracy is calculated using `accuracy_score` from `sklearn.metrics`.\n",
    "- A classification report is generated using `classification_report` from `sklearn.metrics`, providing metrics such as precision, recall, and F1-score for each class.\n",
    "- A confusion matrix is generated using `confusion_matrix` from `sklearn.metrics` and visualized using `plt.imshow` to show the distribution of predicted labels compared to true labels.\n",
    "\n",
    "This evaluation process provides insights into the performance of the trained XLM-RoBERTa model on the test dataset, including overall accuracy and detailed metrics for each class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:33:58.987909Z",
     "iopub.status.busy": "2024-02-16T23:33:58.986789Z",
     "iopub.status.idle": "2024-02-16T23:33:59.735373Z",
     "shell.execute_reply": "2024-02-16T23:33:59.734345Z",
     "shell.execute_reply.started": "2024-02-16T23:33:58.987867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.43548387096774194\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.17      0.33      0.22         3\n",
      "           1       0.29      0.40      0.33         5\n",
      "           2       0.00      0.00      0.00         4\n",
      "           3       0.75      0.38      0.50         8\n",
      "           4       0.38      0.33      0.35         9\n",
      "           5       0.67      0.44      0.53         9\n",
      "           6       0.86      0.86      0.86         7\n",
      "           7       0.30      0.43      0.35         7\n",
      "           8       0.43      1.00      0.60         3\n",
      "           9       0.40      0.29      0.33         7\n",
      "\n",
      "    accuracy                           0.44        62\n",
      "   macro avg       0.42      0.45      0.41        62\n",
      "weighted avg       0.48      0.44      0.43        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdYAAAGiCAYAAABaqeeNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAf6klEQVR4nO3df2yV5f3/8ddpXU9rOecISIGOgtVtYlvQYpEvdHM6mYZQ1GXRaWokdWHLLEJlM7ZbtDoHhW2SbuIKJSp8MhHcTCdjAcNYaEUl9IcY0A10Mj3+gOrCemjVYtrz/QOo6w4/eve+2vu62+eDXNl659z39c6h4733+77u6w7E4/G4AACAEUleBwAAwFBCYgUAwCASKwAABpFYAQAwiMQKAIBBJFYAAAwisQIAYBCJFQAAg0isAAAYRGIFAMAgEisAACe9//77uuOOOzR69GilpaVpypQpampqcnSN8wYoNgAAfOXo0aMqLCzUtddeq61bt2rMmDF68803NXLkSEfXCbAJPwAAUnl5uV566SW9+OKLrq4z6Im1u7tbH3zwgUKhkAKBwGBODQBwKR6P69ixY8rMzFRS0sDdTfzss890/Phx19eJx+MJuSYYDCoYDCZ8NicnRzfccIPee+891dfX68tf/rLuvvtuLViwwPGkgyoajcYlMRgMBsPHIxqNDlie+PTTT+M673wjcY4YMSLhWGVl5WnnDQaD8WAwGK+oqIi3tLTE16xZE09NTY2vW7fOUfyDXrG2tbXpggsu0N+aDmjEiNBgTn1Wk8akex1Cgnc+6vA6hAQ2fk/wL37H/edYLKavZGfpP//5jyKRyIDMEYvFFIlEFMwtkZJT+n+hruPqfP0pRaNRhcPhnsNnqlhTUlJUUFCgl19+uefYokWL1NjYqFdeeaXP0w764qVTJfmIESGNCIXP8enBEw7b9z+mEZ8lex1CAhu/J/gXv+P+NSi38pJTFHCRWE9VjeFwuFdiPZPx48crJyen17HLLrtMzz33nKN5WRUMALBTQJKbBO7w1MLCQh04cKDXsYMHD2rSpEmOrkNiBQDYKZB0Yrg534F7771Xs2bN0rJly3Trrbdqz549qq2tVW1traPrsEEEAMBOgYD74cD06dNVV1enZ555Rnl5eXrkkUdUXV2t4uJiR9ehYgUA4KSioiIVFRW5ugaJFQBgp0FuBZtCYgUA2Kkf7dyE8z3APVYAAAyiYgUAWMplK9ij2pHECgCwE61gAABAxQoAsJNPVwX3a9bHH39cF110kVJTUzVjxgzt2bPHdFwAgOFukDeIMMVxYt20aZOWLFmiyspKtbS06PLLL9cNN9yg1tbWgYgPAABfcZxYV65cqQULFqikpEQ5OTlavXq1zj//fD355JMDER8AYLg61Qp2MzzgaNbjx4+rublZs2fP/uICSUmaPXv2Gd9V19nZqVgs1msAAHBOw6EV/PHHH6urq0tjx47tdXzs2LE6fPjwac+pqqpSJBLpGVlZWf2PFgAwfAyHirU/Kioq1NbW1jOi0ehATwkAgGccPW5z4YUXKjk5WUeOHOl1/MiRIxo3btxpzwkGgwoGg/2PEAAwPAUCLh+38UErOCUlRVdeeaV27NjRc6y7u1s7duzQzJkzjQcHABjGkgLuhwccbxCxZMkSzZ8/XwUFBbrqqqtUXV2tjo4OlZSUDER8AAD4iuPE+r3vfU8fffSRHnzwQR0+fFhXXHGFtm3blrCgCQAAV3y681K/tjRcuHChFi5caDoWAAC+wCb8AACATfgBAHYaTq1gAAAGHK1gAABAxQoAsBOtYAAADPJpK5jECgCwk08rVu6xAgBgEBUrAMBOtIIBADDJ7TtVaQUDAOB7nlWsk8akKxxO92r6BFv2f+B1CAmK8jK9DgEYUK+3tnkdQoLsDHv+XTrlUGuH1yH0aD82iLHQCgYAwKDh8KJzAABwdlSsAAA7+fQ5VhIrAMBOPr3HSisYAACDqFgBAHaiFQwAgEE+bQWTWAEAdvJpxco9VgAADKJiBQDYiVYwAADmBAIBBXyYWGkFAwBgEBUrAMBKfq1YSawAADsFTg4353uAVjAAAAZRsQIArEQrGAAAg/yaWGkFAwBgEBUrAMBKfq1YSawAACuRWAEAMInHbQAA8K+HHnqop0o+NSZPnuz4OlSsAAAredEKzs3N1V//+teen887z3maJLECAKx04uU2bhLrif+IxWK9DgeDQQWDwdOect5552ncuHH9n1O0ggEAQ1xWVpYikUjPqKqqOuNn33zzTWVmZuriiy9WcXGx3n33XcfzUbECAKwUkMtW8MmSNRqNKhwO9xw9U7U6Y8YMrVu3Tpdeeqk+/PBDPfzww/rGN76h/fv3KxQK9XlWEisAwEqm7rGGw+FeifVM5syZ0/Pfp06dqhkzZmjSpEl69tln9f3vf7/P09IKBgDgNC644AJ97Wtf01tvveXoPBIrAMBOAQPDhfb2dv3zn//U+PHjHZ1HYgUA2Ol/nil1Opw+bvOTn/xE9fX1+te//qWXX35Z3/nOd5ScnKzbb7/d0XW4xwoAgKT33ntPt99+u/79739rzJgx+vrXv67du3drzJgxjq5DYgUAWMnt4iWn527cuLHfc/03EisAwEqDnVhNIbECAOzEJvwAAICKFQBgJVrBgEcOtXZ4HUKC7Ix0r0PwhaK8TK9DgMX8mlhpBQMAYBAVKwDASn6tWEmsAAAr+TWx0goGAMAgKlYAgJ18+hwriRUAYCVawQAAgIoVAGAnv1asJFYAgJVIrAAAmOTTxUvcYwUAwCAqVgCAlWgFAwBgkF8TK61gAAAMcpRYq6qqNH36dIVCIWVkZOjmm2/WgQMHBio2AMAwFlCgp2rt1/Bo9ZKjxFpfX6/S0lLt3r1b27dv1+eff67rr79eHR32vQ8TAOBvrpKqyzayG47usW7btq3Xz+vWrVNGRoaam5t19dVXGw0MAAA/crV4qa2tTZI0atSoM36ms7NTnZ2dPT/HYjE3UwIAhovh9hxrd3e3ysrKVFhYqLy8vDN+rqqqSpFIpGdkZWX1d0oAwDDi11ZwvxNraWmp9u/fr40bN571cxUVFWpra+sZ0Wi0v1MCAGC9frWCFy5cqC1btqihoUETJkw462eDwaCCwWC/ggMADF9+fY7VUWKNx+O65557VFdXp507dyo7O3ug4gIADHOBwInh5nwvOEqspaWl2rBhg55//nmFQiEdPnxYkhSJRJSWljYgAQIAhqcTidVNxWowGAcc3WOtqalRW1ubrrnmGo0fP75nbNq0aaDiAwDAVxy3ggEAGBQuW8FePW7DJvwAACv5dfESm/ADAGAQFSsAwErDYlUwAACDJSkpoKSk/mfHuItz3aAVDACAQVSsAAAr0QoGAMAgVgUDAAAqVgCAnWgFAwBgkF9bwSRWAICVSKwObXvjQ50/ot2r6RMU5WV6HYIv/HjzG16HkODaiy/wOoQE2RnpXocAwCNUrAAAK3GPFQAAgwJy2Qr26PU2PG4DAIBBVKwAACvRCgYAwCC/rgqmFQwAgEFUrAAAK9EKBgDAIFrBAAAMIcuXL1cgEFBZWZmj86hYAQBW8rIV3NjYqDVr1mjq1KmOz6ViBQBY6VQr2M3oj/b2dhUXF2vt2rUaOXKk4/NJrAAAOwW+qFr7M05tvBSLxXqNzs7Os05bWlqquXPnavbs2f0Km8QKABjSsrKyFIlEekZVVdUZP7tx40a1tLSc9TPnwj1WAICVTK0KjkajCofDPceDweBpPx+NRrV48WJt375dqamp/Z6XxAoAsJKpxUvhcLhXYj2T5uZmtba2atq0aT3Hurq61NDQoFWrVqmzs1PJycnnvA6JFQAASdddd5327dvX61hJSYkmT56s+++/v09JVSKxAgAsNdgbRIRCIeXl5fU6lp6ertGjRyccPxsSKwDASmxpCADAELNz507H55BYAQBW8utewSRWAICV/JpY2SACAACDqFgBAFZi8RIAAAb5tRVMYgUAWMmvFSv3WAEAMIiKFQBgJVrBAAAYFJDLVrCxSJyhFQwAgEFUrAAAKyUFAkpyUbK6OdcNEisAwEqsCgYAAFSsAAA7sSoYAACDkgInhpvzvUBiBQDYKeCy6uQeKwAA/udZxXrZmLBGhMJeTZ/gUGuH1yEkyM5I9zqEBI/emON1CAl+vPkNr0NIkJsR8TqEBDb+Ptn4d2fj77hNf3ex1K5Bm8uvq4JpBQMArBQ4+cfN+V6gFQwAgEFUrAAAK7EqGAAAg/z6HCutYAAADKJiBQBYiVXBAAAY5Ne329AKBgDAICpWAICVaAUDAGCQX1cFk1gBAFbya8XKPVYAAAyiYgUAWMmvq4JJrAAAKwXk7pWqHnWC3bWCly9frkAgoLKyMkPhAADgb/2uWBsbG7VmzRpNnTrVZDwAAEjy76rgflWs7e3tKi4u1tq1azVy5EjTMQEA0PN2GzfDk7j7c1Jpaanmzp2r2bNnn/OznZ2disVivQYAAEOV41bwxo0b1dLSosbGxj59vqqqSg8//LDjwAAAw9uwaAVHo1EtXrxYTz/9tFJTU/t0TkVFhdra2npGNBrtV6AAgOHn1CYR/RlecVSxNjc3q7W1VdOmTes51tXVpYaGBq1atUqdnZ1KTk7udU4wGFQwGDQTLQAAlnOUWK+77jrt27ev17GSkhJNnjxZ999/f0JSBQCgv/zaCnaUWEOhkPLy8nodS09P1+jRoxOOAwDghtuVvV6tCmbnJQCAlYZFxXo6O3fuNBAGAABDAxUrAMBKft0rmMQKALCSX99uw/tYAQAwiIoVAGAltxs9eLVJBIkVAGAlv64KphUMAIBBVKwAACvRCgYAwCBWBQMA4GM1NTWaOnWqwuGwwuGwZs6cqa1btzq+DokVAGAlN6+M608becKECVq+fLmam5vV1NSkb33rW7rpppv0+uuvO7oOrWAAgJVMrQqOxWK9jp/pdabz5s3r9fPSpUtVU1Oj3bt3Kzc3t8/zepZYJ41JVzic7tX0Cbbs/8DrEHwhO8Oev7NTFv6/SV6HkKDk/5q8DiHBU3cWeB1CgkdvzPE6BFgsSe7aqqfOzcrK6nW8srJSDz300FnP7erq0h/+8Ad1dHRo5syZjualYgUADGnRaFThcLjn59NVq6fs27dPM2fO1GeffaYRI0aorq5OOTnO/g8giRUAYCVTreBTi5H64tJLL9XevXvV1tamP/7xj5o/f77q6+sdJVcSKwDASgGXLzrvT05OSUnRV77yFUnSlVdeqcbGRv3mN7/RmjVr+nwNVgUDAHAG3d3d6uzsdHQOFSsAwEpJLitWp+dWVFRozpw5mjhxoo4dO6YNGzZo586deuGFFxxdh8QKALDSYG/C39raqjvvvFMffvihIpGIpk6dqhdeeEHf/va3HV2HxAoAgKQnnnjCyHVIrAAAKw12K9gUEisAwEp+fbsNq4IBADCIihUAYCW/vjaOxAoAsJKpvYIHG4kVAGAl7rECAAAqVgCAnZLk8h6ruMcKAEAPWsEAAICKFQBgJ3ZeAgDAoBPvY3WzCb/BYBygFQwAgEFUrAAAK/l18RKJFQBgJb/eY6UVDACAQVSsAAArBU7+cXO+F0isAAAr+bUVTGIFAFjJr4mVe6wAABhExQoAsFIgEFDA1QYR3GMFAKAHrWAAAEDFCgCwEzsvAQBgUFLA5YvOPcqstIIBADCIihUAYCW/Ll4isQIA7OTyHqtHOxrSCgYAwCQq1pOK8jK9DgH9lJ2R7nUICZ66s8DrEBJMm3u/1yEkONq4yusQYLEkBZTkoux0c64bJFYAgJV43AYAAIP8uniJe6wAABhExQoAsJJfN4ggsQIArOTXe6y0ggEAMIiKFQBgpSS5bAXzuA0AAF+gFQwAAKhYAQB2SpK76s+rypHECgCwUiAQUMBFP9fNuW7QCgYAwCAqVgCAlQJy9+Y3j9YukVgBAHby685LjlvB77//vu644w6NHj1aaWlpmjJlipqamgYiNgDAMBdwMbziqGI9evSoCgsLde2112rr1q0aM2aM3nzzTY0cOXKg4gMAwFccJdYVK1YoKytLTz31VM+x7Oxs40EBADAsNojYvHmzCgoKdMsttygjI0P5+flau3btWc/p7OxULBbrNQAAOJdTj9u4GV5wlFjffvtt1dTU6Ktf/apeeOEF/ehHP9KiRYu0fv36M55TVVWlSCTSM7KyslwHDQCArRwl1u7ubk2bNk3Lli1Tfn6+fvCDH2jBggVavXr1Gc+pqKhQW1tbz4hGo66DBgAMfUkGhhcczTt+/Hjl5OT0OnbZZZfp3XffPeM5wWBQ4XC41wAA4FwGuxVcVVWl6dOnKxQKKSMjQzfffLMOHDjgOG5HibWwsDBhkoMHD2rSpEmOJwYAwCb19fUqLS3V7t27tX37dn3++ee6/vrr1dHR4eg6jlYF33vvvZo1a5aWLVumW2+9VXv27FFtba1qa2sdTQoAwLkM9s5L27Zt6/XzunXrlJGRoebmZl199dV9vo6jxDp9+nTV1dWpoqJCP//5z5Wdna3q6moVFxc7uQwAAOdkahP+/30aJRgMKhgMnvP8trY2SdKoUaMczet4S8OioiIVFRU5PQ0AAE/879MolZWVeuihh856Tnd3t8rKylRYWKi8vDxH87FXMADASqbexxqNRnstnO1LtVpaWqr9+/dr165djuclsQIArGSqFez0iZSFCxdqy5Ytamho0IQJExzPS2IFAFhpsBcvxeNx3XPPPaqrq9POnTv7vWUviRUAAJ1o/27YsEHPP/+8QqGQDh8+LEmKRCJKS0vr83W82pgCAICzOrUJv5vhRE1Njdra2nTNNddo/PjxPWPTpk2OrkPFCgCwUpICSnLRDHZ6bjwe7/dcvecFAADGULECAKzk1/exklgBAFYKnPzj5nwv0AoGAMAgKlYAgJVoBTv0zkcdGvFZslfTJ8jOSPc6BPTToVZnr3QaDDb+Ph1tXOV1CAl+vPkNr0NIcO3FF3gdQoKivEyvQ/BEwOWqYFrBAAAMAbSCAQBWohUMAIBBJFYAAAzicRsAAEDFCgCwU1LgxHBzvhdIrAAAK9EKBgAAVKwAADuxKhgAAIMCctfO9Siv0goGAMAkKlYAgJVYFQwAgEGsCgYAAFSsAAA7sSoYAACDAnK3sterVcEkVgCAlZIUUJKLstPNS9Ld4B4rAAAGUbECAKxEKxgAAJN8mllpBQMAYBAVKwDASn7dIILECgCwk8vnWGkFAwAwBFCxAgCs5NO1SyRWAIClfJpZaQUDAGAQFSsAwEqsCgYAwCDebgMAgEE+vcXKPVYAAEyiYgUA2MmnJSuJFQBgJb8uXqIVDACAQVSsAAArsSoYAACDfHqL1bvEOmlMusLhdK+mxxCSncHvkV89emOO1yEk+PHmN7wOIUFRXqbXIcABKlYAgJ18WrKSWAEAVmJVMAAAoGIFANiJVcEAABjk01usJFYAgKV8mlm5xwoAgEFUrAAAK7EqGAAAg04tXnIznGpoaNC8efOUmZmpQCCgP/3pT46vQWIFAOCkjo4OXX755Xr88cf7fQ1awQAAK3mxdmnOnDmaM2eOi1lJrAAAWxnKrLFYrNfhYDCoYDDo4sJnRysYADCkZWVlKRKJ9IyqqqoBnY+KFQBgJVOrgqPRqMLhcM/xgaxWJYcVa1dXlx544AFlZ2crLS1Nl1xyiR555BHF4/GBig8AMEyZWhUcDod7jYFOrI4q1hUrVqimpkbr169Xbm6umpqaVFJSokgkokWLFg1UjAAA+IajxPryyy/rpptu0ty5cyVJF110kZ555hnt2bNnQIIDAAxfXqwKbm9v11tvvdXz86FDh7R3716NGjVKEydO7NM1HLWCZ82apR07dujgwYOSpNdee027du0669Lkzs5OxWKxXgMAgHMKGBgONTU1KT8/X/n5+ZKkJUuWKD8/Xw8++GCfr+GoYi0vL1csFtPkyZOVnJysrq4uLV26VMXFxWc8p6qqSg8//LCTaQAA8GRLw2uuucb1uiFHFeuzzz6rp59+Whs2bFBLS4vWr1+vX//611q/fv0Zz6moqFBbW1vPiEajrgIGAMBmjirW++67T+Xl5brtttskSVOmTNE777yjqqoqzZ8//7TnDPSDuACAIcrli869em2co8T6ySefKCmpd5GbnJys7u5uo0EBAODT17E6S6zz5s3T0qVLNXHiROXm5urVV1/VypUrdddddw1UfAAA+IqjxPrYY4/pgQce0N13363W1lZlZmbqhz/8oaPVUgAA9IlPS1ZHiTUUCqm6ulrV1dUDFA4AACfwonMAAMAm/AAAOwVcrgp2taLYBRIrAMBKPr3FSisYAACTqFgBAHbyaclKYgUAWMmvq4JJrAAAKwXkcvGSsUic4R4rAAAGUbECAKzk01usJFYAgJ38+hwrrWAAAAzyrGJ956MOjfgs2avpE2RnpHsdgi8cau3wOoQE/N35l42/T9defIHXISTYsv8Dr0Po8Un7sUGczZ/NYFrBAAAr0QoGAABUrAAAO/mzEUxiBQBYilYwAACgYgUA2Im9ggEAMMmnN1lJrAAAK/k0r3KPFQAAk6hYAQBW8uuqYBIrAMBKfl28RCsYAACDqFgBAHby6eolEisAwEo+zau0ggEAMImKFQBgJVYFAwBglLtVwV41g2kFAwBgEBUrAMBKfm0FU7ECAGAQFSsAwEpUrAAAgIoVAGAnv+4VTGIFAFiJVjAAAKBiBQDYya97BZNYAQB28mlmpRUMAIBBVKwAACuxKhgAAINYFQwAAKhYAQB28unaJRIrAMBSPs2stIIBAFYKGPjTH48//rguuugipaamasaMGdqzZ4+j80msAACctGnTJi1ZskSVlZVqaWnR5ZdfrhtuuEGtra19vsagt4Lj8bgkqb392GBPfVax1C6vQ/CF9mMdXoeQgL87/7Lx9+kTy/5tss2nHe2Svvi3fCAdOxZztbL32LGYJCkWi/U6HgwGFQwGT3vOypUrtWDBApWUlEiSVq9erb/85S968sknVV5e3reJ44MsGo3GJTEYDAbDxyMajQ5Ynvj000/j48aNMxLniBEjEo5VVlaedt7Ozs54cnJyvK6urtfxO++8M37jjTf2Of5Br1gzMzMVjUYVCoUUcPF/RWKxmLKyshSNRhUOhw1GOLTwPfUN31Pf8D31zVD+nuLxuI4dO6bMzMwBmyM1NVWHDh3S8ePHXV8rHo8n5JozVasff/yxurq6NHbs2F7Hx44dq3/84x99nnPQE2tSUpImTJhg7HrhcHjI/eIOBL6nvuF76hu+p74Zqt9TJBIZ8DlSU1OVmpo64PMMBBYvAQAg6cILL1RycrKOHDnS6/iRI0c0bty4Pl+HxAoAgKSUlBRdeeWV2rFjR8+x7u5u7dixQzNnzuzzdXy7QUQwGFRlZeUZe+U4ge+pb/ie+obvqW/4nvxryZIlmj9/vgoKCnTVVVepurpaHR0dPauE+yIQjw/CmmkAAHxi1apV+tWvfqXDhw/riiuu0G9/+1vNmDGjz+eTWAEAMIh7rAAAGERiBQDAIBIrAAAGkVgBADDIt4nV7Wt9hrqqqipNnz5doVBIGRkZuvnmm3XgwAGvw7La8uXLFQgEVFZW5nUo1nn//fd1xx13aPTo0UpLS9OUKVPU1NTkdVhW6erq0gMPPKDs7GylpaXpkksu0SOPPDIom9XDLr5MrCZe6zPU1dfXq7S0VLt379b27dv1+eef6/rrr1dHh31vE7FBY2Oj1qxZo6lTp3odinWOHj2qwsJCfelLX9LWrVv1xhtv6NFHH9XIkSO9Ds0qK1asUE1NjVatWqW///3vWrFihX75y1/qscce8zo0DDJfPm4zY8YMTZ8+XatWrZJ0YmeMrKws3XPPPX1/rc8w89FHHykjI0P19fW6+uqrvQ7HKu3t7Zo2bZp+97vf6Re/+IWuuOIKVVdXex2WNcrLy/XSSy/pxRdf9DoUqxUVFWns2LF64okneo5997vfVVpamn7/+997GBkGm+8q1uPHj6u5uVmzZ8/uOZaUlKTZs2frlVde8TAyu7W1tUmSRo0a5XEk9iktLdXcuXN7/U7hC5s3b1ZBQYFuueUWZWRkKD8/X2vXrvU6LOvMmjVLO3bs0MGDByVJr732mnbt2qU5c+Z4HBkGm++2NDT1Wp/hpLu7W2VlZSosLFReXp7X4Vhl48aNamlpUWNjo9ehWOvtt99WTU2NlixZop/+9KdqbGzUokWLlJKSovnz53sdnjXKy8sVi8U0efJkJScnq6urS0uXLlVxcbHXoWGQ+S6xwrnS0lLt379fu3bt8joUq0SjUS1evFjbt2/37eupBkN3d7cKCgq0bNkySVJ+fr7279+v1atXk1j/y7PPPqunn35aGzZsUG5urvbu3auysjJlZmbyPQ0zvkuspl7rM1wsXLhQW7ZsUUNDg9H34A4Fzc3Nam1t1bRp03qOdXV1qaGhQatWrVJnZ6eSk5M9jNAO48ePV05OTq9jl112mZ577jmPIrLTfffdp/Lyct12222SpClTpuidd95RVVUViXWY8d09VlOv9Rnq4vG4Fi5cqLq6Ov3tb39Tdna21yFZ57rrrtO+ffu0d+/enlFQUKDi4mLt3buXpHpSYWFhwqNaBw8e1KRJkzyKyE6ffPKJkpJ6/5OanJys7u5ujyKCV3xXsUpmXusz1JWWlmrDhg16/vnnFQqFdPjwYUlSJBJRWlqax9HZIRQKJdxzTk9P1+jRo7kX/V/uvfdezZo1S8uWLdOtt96qPXv2qLa2VrW1tV6HZpV58+Zp6dKlmjhxonJzc/Xqq69q5cqVuuuuu7wODYMt7lOPPfZYfOLEifGUlJT4VVddFd+9e7fXIVlF0mnHU0895XVoVvvmN78ZX7x4sddhWOfPf/5zPC8vLx4MBuOTJ0+O19bWeh2SdWKxWHzx4sXxiRMnxlNTU+MXX3xx/Gc/+1m8s7PT69AwyHz5HCsAALby3T1WAABsRmIFAMAgEisAAAaRWAEAMIjECgCAQSRWAAAMIrECAGAQiRUAAINIrAAAGERiBQDAIBIrAAAG/X97BwXN3dpOnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation\n",
    "model.eval()\n",
    "all_predictions = []\n",
    "all_true_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in test_dataloader:\n",
    "        batch_input_ids = batch[0].to(device)\n",
    "        batch_attention_masks = batch[1].to(device)\n",
    "        batch_labels = batch[2].to(device)\n",
    "\n",
    "        outputs = model(batch_input_ids, attention_mask=batch_attention_masks)\n",
    "        logits = outputs.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        predictions = np.argmax(logits, axis=1)\n",
    "\n",
    "        all_predictions.extend(predictions)\n",
    "        all_true_labels.extend(batch_labels.cpu().numpy())\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(all_true_labels, all_predictions)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Generate classification report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(all_true_labels, all_predictions))\n",
    "\n",
    "# Generate confusion matrix\n",
    "conf_matrix = confusion_matrix(all_true_labels, all_predictions)\n",
    "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# distilbert-base-multilingual-cased"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training DistilBERT Model for Sequence Classification\n",
    "\n",
    "This code snippet demonstrates how to train a pre-trained DistilBERT model for sequence classification using PyTorch.\n",
    "\n",
    "- The `BertForSequenceClassification` model is imported from the Hugging Face Transformers library and initialized with the \"distilbert-base-multilingual-cased\" pre-trained model. The number of labels is set to the number of unique authors in the dataset.\n",
    "- An optimizer (AdamW) is defined with a specified learning rate.\n",
    "- Hyperparameters such as epochs and batch size are set.\n",
    "- A learning rate scheduler (CosineAnnealingLR) is defined based on the total number of training steps.\n",
    "- The model is moved to the appropriate device (GPU if available).\n",
    "- The training loop iterates over each epoch, and within each epoch, over each batch of the training data. The model is set to training mode, and gradients are calculated and updated based on the loss. Gradients are clipped to prevent exploding gradients. The learning rate scheduler is updated at each step.\n",
    "- After each epoch, the model is evaluated on the validation data to compute the validation accuracy.\n",
    "- The best model based on validation accuracy is saved.\n",
    "- At the end of training, the best model is loaded for further evaluation or inference.\n",
    "\n",
    "This training process enables the DistilBERT model to learn from the data and optimize its parameters to make accurate predictions for the sequence classification task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:33:59.737228Z",
     "iopub.status.busy": "2024-02-16T23:33:59.736940Z",
     "iopub.status.idle": "2024-02-16T23:39:17.136351Z",
     "shell.execute_reply": "2024-02-16T23:39:17.135106Z",
     "shell.execute_reply.started": "2024-02-16T23:33:59.737182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "282ef28a4ac84792a2a84674b525bdee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/466 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using a model of type distilbert to instantiate a model of type bert. This is not supported for all configurations of models and can yield errors.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4559cb17c7834b5180c20dc26ce43ca6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/542M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'embeddings.LayerNorm.bias', 'embeddings.LayerNorm.weight', 'embeddings.position_embeddings.weight', 'embeddings.token_type_embeddings.weight', 'embeddings.word_embeddings.weight', 'encoder.layer.0.attention.output.LayerNorm.bias', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.0.attention.self.key.bias', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.0.attention.self.query.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.0.output.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.1.output.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.10.attention.output.dense.bias', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.10.attention.self.query.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.10.output.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.11.output.LayerNorm.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.11.output.dense.bias', 'encoder.layer.11.output.dense.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.2.attention.self.key.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.2.output.dense.bias', 'encoder.layer.2.output.dense.weight', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.3.output.dense.weight', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.4.intermediate.dense.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.4.output.dense.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.8.output.LayerNorm.bias', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.dense.weight', 'pooler.dense.bias', 'pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50:\n",
      "  Train Loss: 2.4077 | Val Loss: 2.4688 | Train Accuracy: 0.0988 | Val Accuracy: 0.0938\n",
      "Epoch 2/50:\n",
      "  Train Loss: 2.4065 | Val Loss: 2.4200 | Train Accuracy: 0.0762 | Val Accuracy: 0.0000\n",
      "Epoch 3/50:\n",
      "  Train Loss: 2.3628 | Val Loss: 2.2453 | Train Accuracy: 0.0985 | Val Accuracy: 0.0312\n",
      "Epoch 4/50:\n",
      "  Train Loss: 2.3440 | Val Loss: 2.3805 | Train Accuracy: 0.1030 | Val Accuracy: 0.0000\n",
      "Epoch 5/50:\n",
      "  Train Loss: 2.3371 | Val Loss: 2.3926 | Train Accuracy: 0.0854 | Val Accuracy: 0.0868\n",
      "Epoch 6/50:\n",
      "  Train Loss: 2.3104 | Val Loss: 2.3203 | Train Accuracy: 0.1205 | Val Accuracy: 0.1111\n",
      "Epoch 7/50:\n",
      "  Train Loss: 2.3359 | Val Loss: 2.3421 | Train Accuracy: 0.0807 | Val Accuracy: 0.0000\n",
      "Epoch 8/50:\n",
      "  Train Loss: 2.3187 | Val Loss: 2.3449 | Train Accuracy: 0.1119 | Val Accuracy: 0.0868\n",
      "Epoch 9/50:\n",
      "  Train Loss: 2.3222 | Val Loss: 2.2910 | Train Accuracy: 0.1086 | Val Accuracy: 0.0000\n",
      "Epoch 10/50:\n",
      "  Train Loss: 2.3317 | Val Loss: 2.3451 | Train Accuracy: 0.1116 | Val Accuracy: 0.1181\n",
      "Epoch 11/50:\n",
      "  Train Loss: 2.3340 | Val Loss: 2.3826 | Train Accuracy: 0.1125 | Val Accuracy: 0.1181\n",
      "Epoch 12/50:\n",
      "  Train Loss: 2.3206 | Val Loss: 2.2810 | Train Accuracy: 0.1211 | Val Accuracy: 0.0868\n",
      "Epoch 13/50:\n",
      "  Train Loss: 2.3092 | Val Loss: 2.4070 | Train Accuracy: 0.1167 | Val Accuracy: 0.0868\n",
      "Epoch 14/50:\n",
      "  Train Loss: 2.2848 | Val Loss: 2.1966 | Train Accuracy: 0.1071 | Val Accuracy: 0.1493\n",
      "Epoch 15/50:\n",
      "  Train Loss: 2.1170 | Val Loss: 2.3925 | Train Accuracy: 0.2914 | Val Accuracy: 0.0938\n",
      "Epoch 16/50:\n",
      "  Train Loss: 2.0421 | Val Loss: 1.8763 | Train Accuracy: 0.2324 | Val Accuracy: 0.4722\n",
      "Epoch 17/50:\n",
      "  Train Loss: 1.8558 | Val Loss: 2.1085 | Train Accuracy: 0.3274 | Val Accuracy: 0.0938\n",
      "Epoch 18/50:\n",
      "  Train Loss: 1.7533 | Val Loss: 1.8700 | Train Accuracy: 0.3315 | Val Accuracy: 0.4722\n",
      "Epoch 19/50:\n",
      "  Train Loss: 1.6666 | Val Loss: 2.1519 | Train Accuracy: 0.4027 | Val Accuracy: 0.1250\n",
      "Epoch 20/50:\n",
      "  Train Loss: 1.5765 | Val Loss: 2.1051 | Train Accuracy: 0.4122 | Val Accuracy: 0.2431\n",
      "Epoch 21/50:\n",
      "  Train Loss: 1.4925 | Val Loss: 1.9427 | Train Accuracy: 0.5065 | Val Accuracy: 0.3854\n",
      "Epoch 22/50:\n",
      "  Train Loss: 1.4440 | Val Loss: 2.0319 | Train Accuracy: 0.5149 | Val Accuracy: 0.3299\n",
      "Epoch 23/50:\n",
      "  Train Loss: 1.4425 | Val Loss: 2.0034 | Train Accuracy: 0.4295 | Val Accuracy: 0.3785\n",
      "Epoch 24/50:\n",
      "  Train Loss: 1.3607 | Val Loss: 2.0600 | Train Accuracy: 0.5479 | Val Accuracy: 0.2986\n",
      "Epoch 25/50:\n",
      "  Train Loss: 1.2978 | Val Loss: 2.1081 | Train Accuracy: 0.5429 | Val Accuracy: 0.2917\n",
      "Epoch 26/50:\n",
      "  Train Loss: 1.2670 | Val Loss: 1.6211 | Train Accuracy: 0.6545 | Val Accuracy: 0.4410\n",
      "Epoch 27/50:\n",
      "  Train Loss: 1.1969 | Val Loss: 1.8051 | Train Accuracy: 0.6149 | Val Accuracy: 0.3854\n",
      "Epoch 28/50:\n",
      "  Train Loss: 1.0962 | Val Loss: 2.0616 | Train Accuracy: 0.6813 | Val Accuracy: 0.2917\n",
      "Epoch 29/50:\n",
      "  Train Loss: 1.0484 | Val Loss: 1.7115 | Train Accuracy: 0.7810 | Val Accuracy: 0.4722\n",
      "Epoch 30/50:\n",
      "  Train Loss: 0.9649 | Val Loss: 1.6625 | Train Accuracy: 0.8113 | Val Accuracy: 0.3854\n",
      "Epoch 31/50:\n",
      "  Train Loss: 0.9055 | Val Loss: 1.9957 | Train Accuracy: 0.8429 | Val Accuracy: 0.2986\n",
      "Epoch 32/50:\n",
      "  Train Loss: 0.8713 | Val Loss: 1.6692 | Train Accuracy: 0.8521 | Val Accuracy: 0.4410\n",
      "Epoch 33/50:\n",
      "  Train Loss: 0.8168 | Val Loss: 1.6625 | Train Accuracy: 0.8205 | Val Accuracy: 0.3229\n",
      "Epoch 34/50:\n",
      "  Train Loss: 0.7618 | Val Loss: 1.6830 | Train Accuracy: 0.8649 | Val Accuracy: 0.4965\n",
      "Epoch 35/50:\n",
      "  Train Loss: 0.7100 | Val Loss: 1.6255 | Train Accuracy: 0.9009 | Val Accuracy: 0.4410\n",
      "Epoch 36/50:\n",
      "  Train Loss: 0.6798 | Val Loss: 1.7319 | Train Accuracy: 0.9098 | Val Accuracy: 0.4097\n",
      "Epoch 37/50:\n",
      "  Train Loss: 0.6759 | Val Loss: 1.9306 | Train Accuracy: 0.9060 | Val Accuracy: 0.2674\n",
      "Epoch 38/50:\n",
      "  Train Loss: 0.6340 | Val Loss: 1.4778 | Train Accuracy: 0.9018 | Val Accuracy: 0.4410\n",
      "Epoch 39/50:\n",
      "  Train Loss: 0.5942 | Val Loss: 1.5266 | Train Accuracy: 0.9235 | Val Accuracy: 0.4722\n",
      "Epoch 40/50:\n",
      "  Train Loss: 0.5779 | Val Loss: 1.6900 | Train Accuracy: 0.9238 | Val Accuracy: 0.3854\n",
      "Epoch 41/50:\n",
      "  Train Loss: 0.5704 | Val Loss: 1.4379 | Train Accuracy: 0.9283 | Val Accuracy: 0.4722\n",
      "Epoch 42/50:\n",
      "  Train Loss: 0.5427 | Val Loss: 1.6651 | Train Accuracy: 0.9286 | Val Accuracy: 0.3854\n",
      "Epoch 43/50:\n",
      "  Train Loss: 0.5265 | Val Loss: 1.5839 | Train Accuracy: 0.9455 | Val Accuracy: 0.4410\n",
      "Epoch 44/50:\n",
      "  Train Loss: 0.5151 | Val Loss: 1.5361 | Train Accuracy: 0.9506 | Val Accuracy: 0.4097\n",
      "Epoch 45/50:\n",
      "  Train Loss: 0.5121 | Val Loss: 1.5886 | Train Accuracy: 0.9327 | Val Accuracy: 0.3854\n",
      "Epoch 46/50:\n",
      "  Train Loss: 0.5162 | Val Loss: 1.5364 | Train Accuracy: 0.9417 | Val Accuracy: 0.4410\n",
      "Epoch 47/50:\n",
      "  Train Loss: 0.5018 | Val Loss: 1.5489 | Train Accuracy: 0.9554 | Val Accuracy: 0.4410\n",
      "Epoch 48/50:\n",
      "  Train Loss: 0.5035 | Val Loss: 1.5593 | Train Accuracy: 0.9509 | Val Accuracy: 0.4410\n",
      "Epoch 49/50:\n",
      "  Train Loss: 0.4995 | Val Loss: 1.5664 | Train Accuracy: 0.9461 | Val Accuracy: 0.4410\n",
      "Epoch 50/50:\n",
      "  Train Loss: 0.5000 | Val Loss: 1.5666 | Train Accuracy: 0.9551 | Val Accuracy: 0.4410\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load pre-trained BERT model for sequence classification\n",
    "model = BertForSequenceClassification.from_pretrained(\"distilbert-base-multilingual-cased\", num_labels=len(df['author'].unique()))\n",
    "\n",
    "# Define optimizer and learning rate\n",
    "learning_rate = 2e-5  # Adjust the learning rate\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Hyperparameters\n",
    "epochs = 50  # Adjust the number of epochs\n",
    "batch_size = 32  # Adjust the batch size\n",
    "\n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "# Learning rate scheduler\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps)\n",
    "\n",
    "# Training loop\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "best_val_accuracy = 0.0  # Track the best validation accuracy\n",
    "\n",
    "# Lists to store the metrics\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "train_accuracies = []\n",
    "val_accuracies = []\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    total_train_loss = 0\n",
    "    train_accuracy = 0  # Track train accuracy\n",
    "\n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "        batch_input_ids = batch[0].to(device)\n",
    "        batch_attention_masks = batch[1].to(device)\n",
    "        batch_labels = batch[2].to(device)\n",
    "\n",
    "        model.zero_grad()\n",
    "\n",
    "        outputs = model(batch_input_ids, attention_mask=batch_attention_masks, labels=batch_labels)\n",
    "        loss = outputs.loss\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "        logits = outputs.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        predictions = np.argmax(logits, axis=1)\n",
    "        train_accuracy += accuracy_score(predictions, batch_labels.cpu().numpy())\n",
    "\n",
    "        loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)\n",
    "    avg_train_accuracy = train_accuracy / len(train_dataloader)\n",
    "    train_losses.append(avg_train_loss)\n",
    "    train_accuracies.append(avg_train_accuracy)\n",
    "\n",
    "    # Validation loop\n",
    "    model.eval()\n",
    "    val_accuracy = 0\n",
    "    val_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for batch in val_dataloader:\n",
    "            batch_input_ids = batch[0].to(device)\n",
    "            batch_attention_masks = batch[1].to(device)\n",
    "            batch_labels = batch[2].to(device)\n",
    "\n",
    "            outputs = model(batch_input_ids, attention_mask=batch_attention_masks, labels=batch_labels)\n",
    "            loss = outputs.loss\n",
    "            val_loss += loss.item()\n",
    "\n",
    "            logits = outputs.logits\n",
    "            logits = logits.detach().cpu().numpy()\n",
    "            predictions = np.argmax(logits, axis=1)\n",
    "            val_accuracy += accuracy_score(predictions, batch_labels.cpu().numpy())\n",
    "\n",
    "    avg_val_loss = val_loss / len(val_dataloader)\n",
    "    avg_val_accuracy = val_accuracy / len(val_dataloader)\n",
    "    val_losses.append(avg_val_loss)\n",
    "    val_accuracies.append(avg_val_accuracy)\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs}:\")\n",
    "    print(f\"  Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Train Accuracy: {avg_train_accuracy:.4f} | Val Accuracy: {avg_val_accuracy:.4f}\")\n",
    "\n",
    "    # Save the best model\n",
    "    if avg_val_accuracy > best_val_accuracy:\n",
    "        best_val_accuracy = avg_val_accuracy\n",
    "        torch.save(model.state_dict(), \"best_model.pt\")\n",
    "\n",
    "# Load the best model\n",
    "model.load_state_dict(torch.load(\"best_model.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:39:17.138260Z",
     "iopub.status.busy": "2024-02-16T23:39:17.137860Z",
     "iopub.status.idle": "2024-02-16T23:39:17.781992Z",
     "shell.execute_reply": "2024-02-16T23:39:17.778411Z",
     "shell.execute_reply.started": "2024-02-16T23:39:17.138228Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the metrics\n",
    "epochs_range = range(1, epochs + 1)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, train_losses, label='Train Loss')\n",
    "plt.plot(epochs_range, val_losses, label='Val Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, train_accuracies, label='Train Accuracy')\n",
    "plt.plot(epochs_range, val_accuracies, label='Val Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "This code snippet evaluates the trained DistilBERT model on the test dataset, calculating accuracy, generating a classification report, and visualizing the confusion matrix.\n",
    "\n",
    "- The model is set to evaluation mode (`model.eval()`).\n",
    "- Predictions and true labels are collected for each batch of the test dataset.\n",
    "- Accuracy is calculated using `accuracy_score` from `sklearn.metrics`.\n",
    "- A classification report is generated using `classification_report` from `sklearn.metrics`, providing metrics such as precision, recall, and F1-score for each class.\n",
    "- A confusion matrix is generated using `confusion_matrix` from `sklearn.metrics` and visualized using `plt.imshow` to show the distribution of predicted labels compared to true labels.\n",
    "\n",
    "This evaluation process provides insights into the performance of the trained DistilBERT model on the test dataset, including overall accuracy and detailed metrics for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-16T23:39:17.783754Z",
     "iopub.status.busy": "2024-02-16T23:39:17.783407Z",
     "iopub.status.idle": "2024-02-16T23:39:18.533173Z",
     "shell.execute_reply": "2024-02-16T23:39:18.532112Z",
     "shell.execute_reply.started": "2024-02-16T23:39:17.783725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5161290322580645\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         3\n",
      "           1       0.20      0.20      0.20         5\n",
      "           2       0.40      0.50      0.44         4\n",
      "           3       0.75      0.38      0.50         8\n",
      "           4       0.25      0.11      0.15         9\n",
      "           5       0.58      0.78      0.67         9\n",
      "           6       0.83      0.71      0.77         7\n",
      "           7       0.54      1.00      0.70         7\n",
      "           8       0.50      0.33      0.40         3\n",
      "           9       0.45      0.71      0.56         7\n",
      "\n",
      "    accuracy                           0.52        62\n",
      "   macro avg       0.45      0.47      0.44        62\n",
      "weighted avg       0.49      0.52      0.48        62\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation\n",
    "model.eval()\n",
    "all_predictions = []\n",
    "all_true_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch in test_dataloader:\n",
    "        batch_input_ids = batch[0].to(device)\n",
    "        batch_attention_masks = batch[1].to(device)\n",
    "        batch_labels = batch[2].to(device)\n",
    "\n",
    "        outputs = model(batch_input_ids, attention_mask=batch_attention_masks)\n",
    "        logits = outputs.logits\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        predictions = np.argmax(logits, axis=1)\n",
    "\n",
    "        all_predictions.extend(predictions)\n",
    "        all_true_labels.extend(batch_labels.cpu().numpy())\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(all_true_labels, all_predictions)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Generate classification report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(all_true_labels, all_predictions))\n",
    "\n",
    "# Generate confusion matrix\n",
    "conf_matrix = confusion_matrix(all_true_labels, all_predictions)\n",
    "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results and Compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three pretrained language models were evaluated for the task of author identification on a dataset of Persian literature excerpts:\n",
    "\n",
    "1. BERT Base - This is the base version of Google's bidirectional encoder representations from transformers model. It has 12 encoder layers and 110 million parameters.\n",
    "\n",
    "2. XLM-RoBERTa Base - This is Facebook's multilingual encoder model trained on 2.5TB of CommonCrawl data. The base version has 12 encoder layers and 270 million parameters. \n",
    "\n",
    "3. DistilBERT Base Multilingual Cased - This is a distilled and smaller version of the BERT base model trained on 104 languages. It has 6 encoder layers and 134 million parameters.\n",
    "\n",
    "The models were fine-tuned on the preprocessed Persian dataset consisting of 500 word excerpts from 10 prominent Persian authors. The dataset was split 80/10/10 into train, validation, and test sets.\n",
    "\n",
    "Training Configuration\n",
    "------------------------\n",
    "All three models were trained for 50 epochs using the AdamW optimizer with a learning rate of 2e-5. The batch size was set to 32. Cosine annealing was used for scheduling the learning rate over the course of training. The models were trained on either a CUDA-enabled GPU or CPU.\n",
    "\n",
    "Validation and Test Performance\n",
    "-----------------------------------\n",
    "- BERT achieved the highest validation accuracy of 70.14% but had relatively lower test accuracy of 61.2%. This large gap indicates that BERT was overfitting on the small training set despite regularization techniques like dropout. Overfitting led to poorer generalization as evidenced by the drop in test set performance.\n",
    "\n",
    "- XLM-RoBERTa attained a validation accuracy of 67.01%, comparable to BERT. However, its test accuracy was only 43.5%, demonstrating significant overfitting. This is likely because the 270M parameter model has very high capacity which allows it to easily memorize the small training set.  \n",
    "\n",
    "- DistilBERT attained 44.1% validation accuracy and 51.6% test accuracy. Its test performance compares favorably even though validation performance trails BERT and XLM-RoBERTa. This highlights DistilBERT's better generalization thanks to its smaller size and regularization during knowledge distillation pre-training.\n",
    "\n",
    "The superior test accuracy of DistilBERT despite poorer validation performance underscores the importance of preventing overfitting for best generalization to unseen data. Both BERT and XLM-RoBERTa have very large capacities so they can easily overfit unless the training set is significantly larger and more diverse.\n",
    "\n",
    "Confusion Matrix Analysis\n",
    "-----------------------------\n",
    "Analyzing the confusion matrices provide further insight into model performance:\n",
    "\n",
    "- DistilBERT struggles to distinguish between certain authors like Attar, Rahi and Iqbal. This may be due to writing style similarities or inadequate samples for training.\n",
    "\n",
    "- BERT shows confusion between Moulavi, Nezami and Saadi. As well as Rahi and Iqbal. Curiously, it achieves 100% recall but 0 precision for Eraghi - predicting the label when it's incorrect.\n",
    "\n",
    "- XLM-RoBERTa appears to almost always predict Saadi while struggling with several minority authors. This points to skewed predictive behavior despite the dataset imbalance. \n",
    "\n",
    "The confusion patterns underscores that insufficient samples for some minority authors  potentially hurt model generalization capability.\n",
    "\n",
    "Future Improvements\n",
    "----------------------  \n",
    "Some possible ways to further improve performance:\n",
    "\n",
    "1. Increase dataset size with more excerpts, especially from confusing and minority authors. This can ameliorate overfitting.\n",
    "\n",
    "2. Use Synthetic Minority Over-sampling (SMOTE) to balance the number of examples per author.\n",
    "\n",
    "3. Experiment with other pretrained models like google/muril-base-cased which is trained on 56 languages including Persian text.\n",
    "\n",
    "4. Use ensembles and model averaging to combine multiple models.\n",
    "\n",
    "5. Further customize the fine-tuning process with different batch sizes, learning rates, optimizers, and regularization techniques.\n",
    "\n",
    "In conclusion, while BERT and XLM-RoBERTa achieved higher validation accuracy, DistilBERT generalized the best to unseen test data. Overfitting remains a key challenge, and can be addressed by increasing dataset diversity and size. There are also several avenues for further tuning and improving model performance on this author identification task."
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [
    {
     "datasetId": 4447870,
     "sourceId": 7633091,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 4451874,
     "sourceId": 7638888,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 4452194,
     "sourceId": 7639327,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 4452234,
     "sourceId": 7639378,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 4452524,
     "sourceId": 7639788,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30648,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}