DocClassifier_transformer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Required Packages"
   ]
  },
  {
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   "outputs": [
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     "name": "stderr",
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     "text": [
      "/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning:\n",
      "\n",
      "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: hazm in /opt/conda/lib/python3.10/site-packages (0.10.0)\n",
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     ]
    }
   ],
   "source": [
    "# Install required packages\n",
    "!pip install hazm\n",
    "!pip install clean-text[gpl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
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    "execution": {
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   "source": [
    "# Import required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.utils import shuffle\n",
    "import hazm\n",
    "from cleantext import clean\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from tqdm.notebook import tqdm\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import copy\n",
    "import collections\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n",
    "from transformers import BertConfig, BertTokenizer\n",
    "from transformers import TFBertModel, TFBertForSequenceClassification\n",
    "from transformers import glue_convert_examples_to_features\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cCTKvthyQlOG"
   },
   "source": [
    "## Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "colab": {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اسم کتاب   No one writes to the Colonel\\nترجمش...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>کتاب خوبی است</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>راستش خیلی خوشم نیومد ازش!</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>هر هشت تای این داستانها توی کتاب \"بهترین داستا...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>سلام چرا این نمونه نداره که بدونیم کدوم داستان...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             comment  rate\n",
       "0  اسم کتاب   No one writes to the Colonel\\nترجمش...   0.0\n",
       "1  طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...   5.0\n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...   5.0\n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...   2.0\n",
       "4                                      کتاب خوبی است   3.0\n",
       "5                        راستش خیلی خوشم نیومد ازش!    3.0\n",
       "6  کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...   5.0\n",
       "7  هر هشت تای این داستانها توی کتاب \"بهترین داستا...   3.0\n",
       "8  سلام چرا این نمونه نداره که بدونیم کدوم داستان...   1.0\n",
       "9  دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...   2.0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the pandas library for data manipulation and analysis\n",
    "import pandas as pd\n",
    "\n",
    "# Load the CSV file into a DataFrame\n",
    "data = pd.read_csv('/kaggle/input/taghchemain/taghche.csv')\n",
    "\n",
    "# Select only the 'comment' and 'rate' columns for further analysis\n",
    "data = data[['comment', 'rate']]\n",
    "\n",
    "# Display the first 10 rows of the DataFrame to get an overview of the data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bxf9Q8zvQsj6"
   },
   "source": [
    "### Fixing Conflicts\n",
    "\n",
    "As you might be realized, the dataset has some structural problems, as shown below.\n",
    "\n",
    "![alt text](https://res.cloudinary.com/m3hrdadfi/image/upload/v1596398432/kaggle/data-structure-flaws_xoqprk.png)\n",
    "\n",
    "For simplicity, I fix this problem by removing rows with the `rate` value of `None`. Furthermore, the dataset contains duplicated rows and missing values in the comment section."
   ]
  },
  {
   "cell_type": "code",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data information\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69829 entries, 0 to 69828\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   comment  69808 non-null  object \n",
      " 1   rate     69790 non-null  float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 1.1+ MB\n",
      "None \n",
      "\n",
      "missing values stats\n",
      "comment    21\n",
      "rate       39\n",
      "dtype: int64 \n",
      "\n",
      "some missing values\n",
      "                                                comment  rate\n",
      "5343                           مسأله حجاب و محمد رضاشاه   NaN\n",
      "7455  سوژه ای با این اهمیت و منحصربه فرد بودن، تجربه...   NaN\n",
      "7456                                                NaN   NaN\n",
      "7457                                                NaN   NaN\n",
      "7680  نویسنده رویکرد قابل تامل و پدیدارشناسانه ای به...   NaN \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the general information about the DataFrame\n",
    "print('data information')\n",
    "print(data.info(), '\\n')\n",
    "\n",
    "# Print the statistics of missing values in the DataFrame\n",
    "print('missing values stats')\n",
    "print(data.isnull().sum(), '\\n')\n",
    "\n",
    "# Print the first 5 rows where the 'rate' column has missing values\n",
    "print('some missing values')\n",
    "print(data[data['rate'].isnull()].iloc[:5], '\\n')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 219,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data information\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 64157 entries, 0 to 64156\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   comment  64157 non-null  object \n",
      " 1   rate     64157 non-null  float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 1002.6+ KB\n",
      "None \n",
      "\n",
      "missing values stats\n",
      "comment    0\n",
      "rate       0\n",
      "dtype: int64 \n",
      "\n",
      "some missing values\n",
      "Empty DataFrame\n",
      "Columns: [comment, rate]\n",
      "Index: [] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Handle conflicts in the dataset structure\n",
    "# For simplicity, remove invalid data combinations\n",
    "\n",
    "# Replace ratings that are 6 or higher with None\n",
    "# This assumes that valid ratings should be between 0 and 5\n",
    "data['rate'] = data['rate'].apply(lambda r: r if r < 6 else None)\n",
    "\n",
    "# Drop rows where the 'rate' column has missing values\n",
    "data = data.dropna(subset=['rate'])\n",
    "\n",
    "# Drop rows where the 'comment' column has missing values\n",
    "data = data.dropna(subset=['comment'])\n",
    "\n",
    "# Remove duplicate comments, keeping only the first occurrence\n",
    "data = data.drop_duplicates(subset=['comment'], keep='first')\n",
    "\n",
    "# Reset the index of the DataFrame\n",
    "data = data.reset_index(drop=True)\n",
    "\n",
    "# Print the general information about the DataFrame\n",
    "print('data information')\n",
    "print(data.info(), '\\n')\n",
    "\n",
    "# Print the statistics of missing values in the DataFrame\n",
    "print('missing values stats')\n",
    "print(data.isnull().sum(), '\\n')\n",
    "\n",
    "# Print the first 5 rows where the 'rate' column has missing values\n",
    "print('some missing values')\n",
    "print(data[data['rate'].isnull()].iloc[:5], '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CWmMbGFCQ1Rf"
   },
   "source": [
    "## Preprocessing\n",
    "\n",
    "The comments have different lengths based on words! Detecting the most normal range could help us find the maximum length of the sequences for the preprocessing step. On the other hand, we suppose that the minimum word combination for having a meaningful phrase for our learning process is 1."
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: 1 \tMax: 2691\n"
     ]
    }
   ],
   "source": [
    "# Calculate the length of comments based on the number of words\n",
    "# This uses hazm's word_tokenize function to split comments into words and then counts the number of words\n",
    "data['comment_len_by_words'] = data['comment'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
    "\n",
    "# Determine the minimum and maximum comment lengths\n",
    "# This provides insights into the range of comment lengths in the dataset\n",
    "min_max_len = data[\"comment_len_by_words\"].min(), data[\"comment_len_by_words\"].max()\n",
    "\n",
    "# Print the minimum and maximum comment lengths\n",
    "# This helps understand the variation in comment lengths\n",
    "print(f'Min: {min_max_len[0]} \\tMax: {min_max_len[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texts with word length of greater than 0 and less than 256 includes 98.82% of the whole!\n"
     ]
    }
   ],
   "source": [
    "def data_gl_than(data, less_than=100.0, greater_than=0.0, col='comment_len_by_words'):\n",
    "    \"\"\"\n",
    "    Calculate the percentage of comments with lengths greater than 'greater_than' and less than or equal to 'less_than'.\n",
    "\n",
    "    Parameters:\n",
    "    data (DataFrame): The DataFrame containing the data.\n",
    "    less_than (float): The upper bound for the comment length.\n",
    "    greater_than (float): The lower bound for the comment length.\n",
    "    col (str): The column name that contains the comment lengths.\n",
    "\n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "    # Extract the lengths of the comments from the specified column\n",
    "    data_length = data[col].values\n",
    "\n",
    "    # Count the number of comments that have a length greater than 'greater_than' and less than or equal to 'less_than'\n",
    "    data_glt = sum([1 for length in data_length if greater_than < length <= less_than])\n",
    "\n",
    "    # Calculate the percentage of such comments relative to the total number of comments\n",
    "    data_glt_rate = (data_glt / len(data_length)) * 100\n",
    "\n",
    "    # Print the result\n",
    "    print(f'Texts with word length of greater than {greater_than} and less than {less_than} includes {data_glt_rate:.2f}% of the whole!')\n",
    "\n",
    "# Call the function with specific bounds\n",
    "data_gl_than(data, 256, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T17:21:23.118056Z",
     "iopub.status.busy": "2024-07-10T17:21:23.117784Z",
     "iopub.status.idle": "2024-07-10T17:21:23.175388Z",
     "shell.execute_reply": "2024-07-10T17:21:23.174531Z",
     "shell.execute_reply.started": "2024-07-10T17:21:23.118032Z"
    },
    "id": "kliWRX7-3U1c",
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# Define minimum and maximum limits for comment length\n",
    "minlim, maxlim = 0, 256\n",
    "\n",
    "# Remove comments with a length of fewer than three words or more than 256 words\n",
    "data['comment_len_by_words'] = data['comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else None)\n",
    "\n",
    "# Drop rows where the 'comment_len_by_words' column has missing values\n",
    "data = data.dropna(subset=['comment_len_by_words'])\n",
    "\n",
    "# Reset the index of the DataFrame\n",
    "data = data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T17:21:23.176793Z",
     "iopub.status.busy": "2024-07-10T17:21:23.176489Z",
     "iopub.status.idle": "2024-07-10T17:21:23.239016Z",
     "shell.execute_reply": "2024-07-10T17:21:23.238195Z",
     "shell.execute_reply.started": "2024-07-10T17:21:23.176767Z"
    },
    "id": "q5UgJoPlQ4l6",
    "outputId": "46e8df36-9705-446b-bbc4-75ec2d4b1b32",
    "trusted": true
   },
   "outputs": [
    {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# This initializes an empty figure to which we will add traces\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add a histogram trace to the figure\n",
    "# The x-axis data is taken from the 'comment_len_by_words' column\n",
    "fig.add_trace(go.Histogram(\n",
    "    x=data['comment_len_by_words'],\n",
    "    marker=dict(\n",
    "        color='rgb(0, 123, 255)',  # Set bar color to a blue shade\n",
    "        line=dict(\n",
    "            color='rgb(8, 48, 107)',  # Set bar border color to a darker blue\n",
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    "        ),\n",
    "    ),\n",
    "    opacity=0.75,  # Set the opacity of the bars for a slight transparency effect\n",
    "))\n",
    "\n",
    "# Update the layout of the figure\n",
    "# This includes setting titles for the plot and axes, and customizing the appearance\n",
    "fig.update_layout(\n",
    "    title=dict(\n",
    "        text='Distribution of Word Counts Within Comments',  # Set the title of the plot\n",
    "        font=dict(size=24),  # Set the font size of the title\n",
    "        x=0.5,  # Center the title on the plot\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=dict(\n",
    "            text='Word Count',  # Set the x-axis title\n",
    "            font=dict(size=18),  # Set the font size of the x-axis title\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set the font size of the x-axis tick labels\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title=dict(\n",
    "            text='Frequency',  # Set the y-axis title\n",
    "            font=dict(size=18),  # Set the font size of the y-axis title\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set the font size of the y-axis tick labels\n",
    "    ),\n",
    "    bargap=0.2,  # Set the gap between individual bars\n",
    "    bargroupgap=0.2,  # Set the gap between groups of bars\n",
    "    template='plotly_white'  # Use the 'plotly_white' template for a cleaner look\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
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    "execution": {
     "iopub.execute_input": "2024-07-10T17:21:23.242716Z",
     "iopub.status.busy": "2024-07-10T17:21:23.242435Z",
     "iopub.status.idle": "2024-07-10T17:21:23.247984Z",
     "shell.execute_reply": "2024-07-10T17:21:23.247082Z",
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    "outputId": "fc8648f3-72f6-40d4-bd56-0f0fbeded96a",
    "trusted": true
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 6 unique rates: [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]\n"
     ]
    }
   ],
   "source": [
    "# Extract unique 'rate' values from the 'data' DataFrame, sort them, and convert them into a list\n",
    "unique_rates = list(sorted(data['rate'].unique()))\n",
    "\n",
    "# Print the number of unique rates and the list of unique rates\n",
    "print(f'We have {len(unique_rates)} unique rates: {unique_rates}')"
   ]
  },
  {
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    "outputId": "72bd971d-8a32-4ac3-9110-912136a98646",
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   "source": [
    "# Create a figure for the plot\n",
    "fig = go.Figure()\n",
    "\n",
    "# Group the data by 'rate' and count the occurrences of each rate\n",
    "groupby_rate = data.groupby('rate')['rate'].count()\n",
    "\n",
    "# Add a bar trace to the figure\n",
    "fig.add_trace(go.Bar(\n",
    "    x=list(sorted(groupby_rate.index)),  # Set the x-axis data to the sorted unique rates\n",
    "    y=groupby_rate.tolist(),  # Set the y-axis data to the frequency counts of each rate\n",
    "    text=groupby_rate.tolist(),  # Display the frequency counts as text on the bars\n",
    "    textposition='auto',  # Automatically position the text within the bars\n",
    "    marker=dict(\n",
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    "        line=dict(\n",
    "            color='rgb(107, 48, 8)',  # Set bar border color to a darker orange/brown\n",
    "            width=1.5,  # Set the width of the bar borders\n",
    "        ),\n",
    "    ),\n",
    "    opacity=0.75  # Set the opacity of the bars for a slight transparency effect\n",
    "))\n",
    "\n",
    "# Update the layout of the figure\n",
    "# This includes titles for the plot and axes, as well as setting bar gaps\n",
    "fig.update_layout(\n",
    "    title=dict(\n",
    "        text='Distribution of Rates Within Comments',  # Set the title of the plot\n",
    "        font=dict(size=24),  # Set title font size\n",
    "        x=0.5,  # Center the title\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=dict(\n",
    "            text='Rate',  # Set the x-axis title\n",
    "            font=dict(size=18),  # Set x-axis title font size\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set x-axis tick font size\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title=dict(\n",
    "            text='Frequency',  # Set the y-axis title\n",
    "            font=dict(size=18),  # Set y-axis title font size\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set y-axis tick font size\n",
    "    ),\n",
    "    bargap=0.2,  # Set the gap between individual bars\n",
    "    bargroupgap=0.2,  # Set the gap between groups of bars\n",
    "    template='plotly_white'  # Set the plot template for a cleaner look\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wMn63nr5RCz2"
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   "source": [
    "For our case, I transformed the rate in a range of 0.0 to 5.0 to a categorical form of negative, neutral, and positive with thresholds for positive and neutral, determined from our base model. If the rate is less than than 2.0, it is labeled as **negative**, else if it is less than 4.0, it is labeled as **neutral**, otherwise specified as **positive**."
   ]
  },
  {
   "cell_type": "code",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>rate</th>\n",
       "      <th>comment_len_by_words</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اسم کتاب   No one writes to the Colonel\\nترجمش...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>کتاب خوبی است</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             comment  rate  \\\n",
       "0  اسم کتاب   No one writes to the Colonel\\nترجمش...   0.0   \n",
       "1  طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...   5.0   \n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...   5.0   \n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...   2.0   \n",
       "4                                      کتاب خوبی است   3.0   \n",
       "\n",
       "   comment_len_by_words     label  \n",
       "0                  49.0  negative  \n",
       "1                  20.0  positive  \n",
       "2                  45.0  positive  \n",
       "3                  20.0   neutral  \n",
       "4                   3.0   neutral  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to label the sentiment based on rating thresholds\n",
    "def label_sentiment(rate, positive_threshold, neutral_threshold):\n",
    "    \"\"\"\n",
    "    Labels sentiment based on rating thresholds.\n",
    "\n",
    "    Args:\n",
    "    - rate (int or float): The numerical rating to evaluate.\n",
    "    - positive_threshold (int or float): The minimum rating value that qualifies as 'positive'.\n",
    "    - neutral_threshold (int or float): The minimum rating value that qualifies as 'neutral'; ratings below this are considered 'negative'.\n",
    "\n",
    "    Returns:\n",
    "    - str: The sentiment label ('positive', 'neutral', or 'negative') based on the rating.\n",
    "    \"\"\"\n",
    "    # Check if the rating is greater than or equal to the positive threshold\n",
    "    if rate >= positive_threshold:\n",
    "        return 'positive'\n",
    "    # If the rating is not 'positive', check if it is greater than or equal to the neutral threshold\n",
    "    elif rate >= neutral_threshold:\n",
    "        return 'neutral'\n",
    "    # If the rating is neither 'positive' nor 'neutral', label the sentiment as 'negative'\n",
    "    else:\n",
    "        return 'negative'\n",
    "    \n",
    "# Apply the label_sentiment function to the 'rate' column to create a new 'label' column\n",
    "data['label'] = data['rate'].apply(lambda t: label_sentiment(t, 4.0, 2.0))\n",
    "# Print unique labels\n",
    "labels = list(sorted(data['label'].unique()))\n",
    "# Display the first 5 rows of the DataFrame to get an overview of the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fCQfM5DmRGG-"
   },
   "source": [
    "# Cleaning\n",
    "Cleaning is the final step in this section. Our cleaned method includes these steps:\n",
    "\n",
    "- fixing unicodes\n",
    "- removing specials like a phone number, email, url, new lines, ...\n",
    "- cleaning HTMLs\n",
    "- normalizing\n",
    "- removing emojis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'> Note that I didn't use the preprocessing functions of this cell in this notebook, but used the one of the next cell. It is tested that the other one for our case and data, works better and has a 0.5% more accuracy </font>"
   ]
  },
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   "source": [
    "def cleanhtml(raw_html):\n",
    "    \"\"\"\n",
    "    Remove HTML tags from a string.\n",
    "    \n",
    "    Args:\n",
    "    raw_html (str): The raw HTML string to be cleaned.\n",
    "    \n",
    "    Returns:\n",
    "    str: The string with HTML tags removed.\n",
    "    \"\"\"\n",
    "    # Compile a regular expression pattern to match HTML tags\n",
    "    cleanr = re.compile('<.*?>')\n",
    "    \n",
    "    # Use the sub() method to replace HTML tags with an empty string\n",
    "    cleantext = re.sub(cleanr, '', raw_html)\n",
    "    \n",
    "    return cleantext\n",
    "\n",
    "def cleaning(text):\n",
    "    \"\"\"\n",
    "    Clean and normalize a given text string by performing several transformations.\n",
    "    \n",
    "    Args:\n",
    "    text (str): The text string to be cleaned.\n",
    "    \n",
    "    Returns:\n",
    "    str: The cleaned and normalized text string.\n",
    "    \"\"\"\n",
    "    # Strip leading and trailing whitespace from the text\n",
    "    text = text.strip()\n",
    "    \n",
    "    # Perform regular cleaning using the 'clean' function from the 'cleantext' library\n",
    "    text = clean(text,\n",
    "        fix_unicode=True,            # Fix broken unicode characters\n",
    "        to_ascii=False,              # Do not convert text to ASCII\n",
    "        lower=True,                  # Convert text to lowercase\n",
    "        no_line_breaks=True,         # Remove line breaks\n",
    "        no_urls=True,                # Remove URLs\n",
    "        no_emails=True,              # Remove email addresses\n",
    "        no_phone_numbers=True,       # Remove phone numbers\n",
    "        no_numbers=False,            # Do not remove numbers\n",
    "        no_digits=False,             # Do not remove digits\n",
    "        no_currency_symbols=True,    # Remove currency symbols\n",
    "        no_punct=False,              # Do not remove punctuation\n",
    "        replace_with_url=\"\",         # Replace URLs with an empty string\n",
    "        replace_with_email=\"\",       # Replace email addresses with an empty string\n",
    "        replace_with_phone_number=\"\",# Replace phone numbers with an empty string\n",
    "        replace_with_number=\"\",      # Replace numbers with an empty string\n",
    "        replace_with_digit=\"0\",      # Replace digits with '0'\n",
    "        replace_with_currency_symbol=\"\", # Replace currency symbols with an empty string\n",
    "    )\n",
    "\n",
    "    # Remove HTML tags from the text using the cleanhtml function\n",
    "    text = cleanhtml(text)\n",
    "\n",
    "    # Normalization (commented out, can be enabled if needed)\n",
    "    # normalizer = hazm.Normalizer()\n",
    "    # text = normalizer.normalize(text)\n",
    "\n",
    "    # Compile a regular expression pattern to match weird patterns (e.g., emojis, symbols)\n",
    "    wierd_pattern = re.compile(\"[\"\n",
    "        u\"\\U0001F600-\\U0001F64F\"  # Emoticons\n",
    "        u\"\\U0001F300-\\U0001F5FF\"  # Symbols & pictographs\n",
    "        u\"\\U0001F680-\\U0001F6FF\"  # Transport & map symbols\n",
    "        u\"\\U0001F1E0-\\U0001F1FF\"  # Flags (iOS)\n",
    "        u\"\\U00002702-\\U000027B0\"\n",
    "        u\"\\U000024C2-\\U0001F251\"\n",
    "        u\"\\U0001f926-\\U0001f937\"\n",
    "        u'\\U00010000-\\U0010ffff'\n",
    "        u\"\\u200d\"\n",
    "        u\"\\u2640-\\u2642\"\n",
    "        u\"\\u2600-\\u2B55\"\n",
    "        u\"\\u23cf\"\n",
    "        u\"\\u23e9\"\n",
    "        u\"\\u231a\"\n",
    "        u\"\\u3030\"\n",
    "        u\"\\ufe0f\"\n",
    "        u\"\\u2069\"\n",
    "        u\"\\u2066\"\n",
    "        u\"\\u2068\"\n",
    "        u\"\\u2067\"\n",
    "        \"]+\", flags=re.UNICODE)\n",
    "\n",
    "    # Remove weird patterns from the text\n",
    "    text = wierd_pattern.sub(r'', text)\n",
    "\n",
    "    # Remove extra spaces and hashtags from the text\n",
    "    text = re.sub(\"#\", \"\", text)\n",
    "    text = re.sub(\"\\s+\", \" \", text)\n",
    "\n",
    "    return text"
   ]
  },
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>rate</th>\n",
       "      <th>comment_len_by_words</th>\n",
       "      <th>label</th>\n",
       "      <th>cleaned_comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اسم کتاب   No one writes to the Colonel\\nترجمش...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>negative</td>\n",
       "      <td>اسم کتاب ترجمش میشه کسی به سرهنگ نامه نمینویسد...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>positive</td>\n",
       "      <td>طاقچه عزیز نام کتاب کسی به سرهنگ نامه نمینویسد...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>positive</td>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>کتاب خوبی است</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>کتاب خوبی است</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>راستش خیلی خوشم نیومد ازش!</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>راستش خیلی خوشم نیومد ازش</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>positive</td>\n",
       "      <td>کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>هر هشت تای این داستانها توی کتاب \"بهترین داستا...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>هر هشت تای این داستانها توی کتاب بهترین داستان...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>سلام چرا این نمونه نداره که بدونیم کدوم داستان...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>negative</td>\n",
       "      <td>سلام چرا این نمونه نداره که بدونیم کدوم داستان...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             comment  rate  \\\n",
       "0  اسم کتاب   No one writes to the Colonel\\nترجمش...   0.0   \n",
       "1  طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...   5.0   \n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...   5.0   \n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...   2.0   \n",
       "4                                      کتاب خوبی است   3.0   \n",
       "5                        راستش خیلی خوشم نیومد ازش!    3.0   \n",
       "6  کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...   5.0   \n",
       "7  هر هشت تای این داستانها توی کتاب \"بهترین داستا...   3.0   \n",
       "8  سلام چرا این نمونه نداره که بدونیم کدوم داستان...   1.0   \n",
       "9  دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...   2.0   \n",
       "\n",
       "   comment_len_by_words     label  \\\n",
       "0                  49.0  negative   \n",
       "1                  20.0  positive   \n",
       "2                  45.0  positive   \n",
       "3                  20.0   neutral   \n",
       "4                   3.0   neutral   \n",
       "5                   6.0   neutral   \n",
       "6                  11.0  positive   \n",
       "7                  36.0   neutral   \n",
       "8                  28.0  negative   \n",
       "9                  16.0   neutral   \n",
       "\n",
       "                                     cleaned_comment  \n",
       "0  اسم کتاب ترجمش میشه کسی به سرهنگ نامه نمینویسد...  \n",
       "1  طاقچه عزیز نام کتاب کسی به سرهنگ نامه نمینویسد...  \n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...  \n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...  \n",
       "4                                      کتاب خوبی است  \n",
       "5                         راستش خیلی خوشم نیومد ازش   \n",
       "6  کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...  \n",
       "7  هر هشت تای این داستانها توی کتاب بهترین داستان...  \n",
       "8  سلام چرا این نمونه نداره که بدونیم کدوم داستان...  \n",
       "9  دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess(text):\n",
    "    \"\"\"\n",
    "    Preprocess and normalize the given text string by performing several transformations.\n",
    "    \n",
    "    Args:\n",
    "    text (str): The text string to be preprocessed.\n",
    "    \n",
    "    Returns:\n",
    "    str: The preprocessed and normalized text string.\n",
    "    \"\"\"\n",
    "    # Define a regular expression pattern that matches one or more newline characters\n",
    "    pattern = re.compile(r\"\\n+\")\n",
    "    \n",
    "    # Replace multiple newlines with a single newline\n",
    "    text = pattern.sub(\"\\n\", text)\n",
    "    \n",
    "    # Remove special characters and replace newline characters with spaces\n",
    "    text = re.sub(r'\\\\n|\\n', ' ', text)\n",
    "    \n",
    "    # Remove non-Persian characters and digits\n",
    "    text = re.sub(r'[^آ-ی\\s\\u200c]', ' ', text)\n",
    "    \n",
    "    # Define a regular expression pattern that matches one or more spaces\n",
    "    pattern = re.compile(r\" +\")\n",
    "    \n",
    "    # Replace multiple spaces with a single space\n",
    "    text = pattern.sub(\" \", text)\n",
    "    \n",
    "    return text\n",
    "\n",
    "# Apply the preprocess function to the 'comment' column of the DataFrame\n",
    "data['cleaned_comment'] = data['comment'].apply(preprocess)\n",
    "\n",
    "# # Apply the cleaning function to the 'comment' column of the DataFrame\n",
    "# data['cleaned_comment'] = data['comment'].apply(cleaning)\n",
    "\n",
    "# Remove duplicate rows from the DataFrame\n",
    "data = data.drop_duplicates()\n",
    "\n",
    "# Drop rows with empty or NaN values in the 'comment' or 'rate' columns\n",
    "data.dropna(subset=['comment', 'rate'], inplace=True)\n",
    "\n",
    "# Display the first 10 rows of the cleaned DataFrame\n",
    "data.head(10)"
   ]
  },
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       "      <td>49.0</td>\n",
       "      <td>negative</td>\n",
       "      <td>اسم کتاب ترجمش میشه کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>positive</td>\n",
       "      <td>طاقچه عزیز نام کتاب کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>positive</td>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>کتاب خوبی است</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>کتاب خوبی است</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>راستش خیلی خوشم نیومد ازش!</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>راستش خیلی خوشم نیومد ازش</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>positive</td>\n",
       "      <td>کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>هر هشت تای این داستانها توی کتاب \"بهترین داستا...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>هر هشت تای این داستانها توی کتاب بهترین داستان...</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>سلام چرا این نمونه نداره که بدونیم کدوم داستان...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>negative</td>\n",
       "      <td>سلام چرا این نمونه نداره که بدونیم کدوم داستان...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             comment  rate  \\\n",
       "0  اسم کتاب   No one writes to the Colonel\\nترجمش...   0.0   \n",
       "1  طاقچه عزیز،نام کتاب\"کسی به سرهنگ نامه نمینویسد...   5.0   \n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...   5.0   \n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...   2.0   \n",
       "4                                      کتاب خوبی است   3.0   \n",
       "5                        راستش خیلی خوشم نیومد ازش!    3.0   \n",
       "6  کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...   5.0   \n",
       "7  هر هشت تای این داستانها توی کتاب \"بهترین داستا...   3.0   \n",
       "8  سلام چرا این نمونه نداره که بدونیم کدوم داستان...   1.0   \n",
       "9  دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...   2.0   \n",
       "\n",
       "   comment_len_by_words     label  \\\n",
       "0                  49.0  negative   \n",
       "1                  20.0  positive   \n",
       "2                  45.0  positive   \n",
       "3                  20.0   neutral   \n",
       "4                   3.0   neutral   \n",
       "5                   6.0   neutral   \n",
       "6                  11.0  positive   \n",
       "7                  36.0   neutral   \n",
       "8                  28.0  negative   \n",
       "9                  16.0   neutral   \n",
       "\n",
       "                                     cleaned_comment  \\\n",
       "0  اسم کتاب ترجمش میشه کسی به سرهنگ نامه نمینویسد...   \n",
       "1  طاقچه عزیز نام کتاب کسی به سرهنگ نامه نمینویسد...   \n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...   \n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...   \n",
       "4                                      کتاب خوبی است   \n",
       "5                         راستش خیلی خوشم نیومد ازش    \n",
       "6  کتابی شیوا و بینظیر یکی دیگر از شاهکار های اقا...   \n",
       "7  هر هشت تای این داستانها توی کتاب بهترین داستان...   \n",
       "8  سلام چرا این نمونه نداره که بدونیم کدوم داستان...   \n",
       "9  دارم ب این جمع بندی میرسم ک مارکز هیچ جوره نوی...   \n",
       "\n",
       "   cleaned_comment_len_by_words  \n",
       "0                            40  \n",
       "1                            16  \n",
       "2                            40  \n",
       "3                            19  \n",
       "4                             3  \n",
       "5                             5  \n",
       "6                            11  \n",
       "7                            31  \n",
       "8                            27  \n",
       "9                            15  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the length of comments based on their words\n",
    "data['cleaned_comment_len_by_words'] = data['cleaned_comment'].apply(lambda t: len(hazm.word_tokenize(t)))\n",
    "\n",
    "# Remove comments with fewer than three words\n",
    "data['cleaned_comment_len_by_words'] = data['cleaned_comment_len_by_words'].apply(lambda len_t: len_t if minlim < len_t <= maxlim else len_t)\n",
    "\n",
    "# Drop rows with NaN values in 'cleaned_comment_len_by_words'\n",
    "data = data.dropna(subset=['cleaned_comment_len_by_words'])\n",
    "\n",
    "# Reset the index of the DataFrame after dropping rows\n",
    "data = data.reset_index(drop=True)\n",
    "\n",
    "# Display the first 10 rows of the cleaned DataFrame\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T17:21:27.771263Z",
     "iopub.status.busy": "2024-07-10T17:21:27.770900Z",
     "iopub.status.idle": "2024-07-10T17:21:27.782790Z",
     "shell.execute_reply": "2024-07-10T17:21:27.781740Z",
     "shell.execute_reply.started": "2024-07-10T17:21:27.771229Z"
    },
    "id": "GlBnHyl0RIzx",
    "outputId": "32816f18-f745-4ff7-a127-15c93a94f9d7",
    "trusted": true
   },
   "outputs": [
    {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>اسم کتاب ترجمش میشه کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>طاقچه عزیز نام کتاب کسی به سرهنگ نامه نمینویسد...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>کتاب خوبی است</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                             comment     label\n",
       "0  اسم کتاب ترجمش میشه کسی به سرهنگ نامه نمینویسد...  negative\n",
       "1  طاقچه عزیز نام کتاب کسی به سرهنگ نامه نمینویسد...  positive\n",
       "2  بنظرم این اثر مارکز خیلی از صد سال تنهایی که ب...  positive\n",
       "3  به نظر کتاب خوبی میومد اما من از ترجمش خوشم نی...   neutral\n",
       "4                                      کتاب خوبی است   neutral"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select columns 'cleaned_comment' and 'label' from the DataFrame 'data'\n",
    "data = data[['cleaned_comment', 'label']]\n",
    "\n",
    "# Rename the columns to 'comment' and 'label'\n",
    "data.columns = ['comment', 'label']\n",
    "\n",
    "# Display the first few rows of the updated DataFrame\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T17:21:27.784363Z",
     "iopub.status.busy": "2024-07-10T17:21:27.784063Z",
     "iopub.status.idle": "2024-07-10T17:21:27.793740Z",
     "shell.execute_reply": "2024-07-10T17:21:27.792846Z",
     "shell.execute_reply.started": "2024-07-10T17:21:27.784338Z"
    },
    "id": "48SPjTNFRKhQ",
    "outputId": "4f79d426-5172-4ba6-d46e-67251638fb0e",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 3 labels: ['negative', 'neutral', 'positive']\n"
     ]
    }
   ],
   "source": [
    "# Print unique labels\n",
    "print(f'We have {len(labels)} labels: {labels}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ny0D77atzqWT"
   },
   "source": [
    "### Handling Unbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T17:21:27.795239Z",
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     "iopub.status.idle": "2024-07-10T17:21:27.855868Z",
     "shell.execute_reply": "2024-07-10T17:21:27.854986Z",
     "shell.execute_reply.started": "2024-07-10T17:21:27.795208Z"
    },
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    "outputId": "ec69c7be-248f-42bb-d388-5b31efd394d0",
    "trusted": true
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   "outputs": [
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   "source": [
    "# Create a figure for the plot\n",
    "fig = go.Figure()\n",
    "\n",
    "# Group the data by 'label' and count the occurrences of each label\n",
    "groupby_label = data.groupby('label')['label'].count()\n",
    "\n",
    "# Add a bar trace to the figure\n",
    "fig.add_trace(go.Bar(\n",
    "    x=list(sorted(groupby_label.index)),  # Set the x-axis data to the sorted unique labels\n",
    "    y=groupby_label.tolist(),  # Set the y-axis data to the frequency counts of each label\n",
    "    text=groupby_label.tolist(),  # Display the frequency counts as text on the bars\n",
    "    textposition='auto',  # Automatically position the text within the bars\n",
    "    marker=dict(\n",
    "        color='rgb(0, 123, 255)',  # Set bar color to a blue shade\n",
    "        line=dict(\n",
    "            color='rgb(8, 48, 107)',  # Set bar border color to a darker blue\n",
    "            width=1.5,  # Set the width of the bar borders\n",
    "        ),\n",
    "    ),\n",
    "    opacity=0.75  # Set the opacity of the bars for a slight transparency effect\n",
    "))\n",
    "\n",
    "# Update the layout of the figure\n",
    "fig.update_layout(\n",
    "    title=dict(\n",
    "        text='Distribution of Labels Within Comments',  # Set the title of the plot\n",
    "        font=dict(size=24),  # Set title font size\n",
    "        x=0.5,  # Center the title\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=dict(\n",
    "            text='Label',  # Set the x-axis title\n",
    "            font=dict(size=18),  # Set x-axis title font size\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set x-axis tick font size\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title=dict(\n",
    "            text='Frequency',  # Set the y-axis title\n",
    "            font=dict(size=18),  # Set y-axis title font size\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set y-axis tick font size\n",
    "    ),\n",
    "    bargap=0.2,  # Set the gap between individual bars\n",
    "    bargroupgap=0.2,  # Set the gap between groups of bars\n",
    "    template='plotly_white'  # Set the plot template for a cleaner look\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Q0yEPxd-tb-"
   },
   "source": [
    "Again, for making things simple. We cut the dataset randomly based on the fewer label, the negative class."
   ]
  },
  {
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    },
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28368 entries, 0 to 28367\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   comment  28368 non-null  object\n",
      " 1   label    28368 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 443.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Filter data into separate DataFrames based on 'label' values\n",
    "negative_data = data[data['label'] == 'negative']\n",
    "positive_data = data[data['label'] == 'positive']\n",
    "neutral_data = data[data['label'] == 'neutral']\n",
    "\n",
    "# Determine the smallest length among the three filtered DataFrames\n",
    "cutting_point = min(len(negative_data), len(positive_data), len(neutral_data))\n",
    "\n",
    "# If the cutting_point is less than or equal to the length of negative_data, sample down negative_data\n",
    "if cutting_point <= len(negative_data):\n",
    "    negative_data = negative_data.sample(n=cutting_point).reset_index(drop=True)\n",
    "\n",
    "# If the cutting_point is less than or equal to the length of positive_data, sample down positive_data\n",
    "if cutting_point <= len(positive_data):\n",
    "    positive_data = positive_data.sample(n=cutting_point).reset_index(drop=True)\n",
    "\n",
    "# If the cutting_point is less than or equal to the length of neutral_data, sample down neutral_data\n",
    "if cutting_point <= len(neutral_data):\n",
    "    neutral_data = neutral_data.sample(n=cutting_point).reset_index(drop=True)\n",
    "\n",
    "# Concatenate the sampled DataFrames back into a single DataFrame\n",
    "new_data = pd.concat([negative_data, positive_data, neutral_data])\n",
    "\n",
    "# Shuffle the rows of the new DataFrame\n",
    "new_data = new_data.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "# Display summary information about the new DataFrame\n",
    "new_data.info()"
   ]
  },
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    "# Create a figure for the plot\n",
    "fig = go.Figure()\n",
    "\n",
    "# Group the data by 'label' and count the occurrences of each label\n",
    "groupby_label = new_data.groupby('label')['label'].count()\n",
    "\n",
    "# Add a bar trace to the figure\n",
    "fig.add_trace(go.Bar(\n",
    "    x=list(sorted(groupby_label.index)),  # Set the x-axis data to the sorted unique labels\n",
    "    y=groupby_label.tolist(),  # Set the y-axis data to the frequency counts of each label\n",
    "    text=groupby_label.tolist(),  # Display the frequency counts as text on the bars\n",
    "    textposition='auto',  # Automatically position the text within the bars\n",
    "    marker=dict(\n",
    "        color='rgb(0, 123, 255)',  # Set bar color to a blue shade\n",
    "        line=dict(\n",
    "            color='rgb(8, 48, 107)',  # Set bar border color to a darker blue\n",
    "            width=1.5,  # Set the width of the bar borders\n",
    "        ),\n",
    "    ),\n",
    "    opacity=0.75  # Set the opacity of the bars for a slight transparency effect\n",
    "))\n",
    "\n",
    "# Update the layout of the figure\n",
    "fig.update_layout(\n",
    "    title=dict(\n",
    "        text='Distribution of Labels Within Comments After Balancing Labels via Resampling',  # Set the title of the plot\n",
    "        font=dict(size=24),  # Set title font size\n",
    "        x=0.5,  # Center the title\n",
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    "    ),\n",
    "    yaxis=dict(\n",
    "        title=dict(\n",
    "            text='Frequency',  # Set the y-axis title\n",
    "            font=dict(size=18),  # Set y-axis title font size\n",
    "        ),\n",
    "        tickfont=dict(size=14),  # Set y-axis tick font size\n",
    "    ),\n",
    "    bargap=0.2,  # Set the gap between individual bars\n",
    "    bargroupgap=0.2,  # Set the gap between groups of bars\n",
    "    template='plotly_white'  # Set the plot template for a cleaner look\n",
    ")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
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  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ترجمه اش چرت بود انگار از سر رفع تکلیف ترجمه کرده</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>آموزنده هست بقیه کتاب مثل نمونه گذاشته شده نیس...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>عالیییه من خیلی وقته منتظر این کتابم ممنون از ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>هرکی این کتاب رو نخوونه نصف عمرش برفناست من کت...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>مزخلف</td>\n",
       "      <td>negative</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>در کل از نظر من کتابهای موفقیت رو باید به روز ...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>قشنگ بود ولی فکر کنم اکثرا کارای ایشون با سانس...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>داستان شخصیت پردازی قوی ای نداشت مخاطب سنیش هم...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>سلام فایل کتاب اصلاح شد لطفا فایل قبلی را حذف ...</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>خیلی ناراحت کننده است</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                             comment     label\n",
       "0  ترجمه اش چرت بود انگار از سر رفع تکلیف ترجمه کرده  positive\n",
       "1  آموزنده هست بقیه کتاب مثل نمونه گذاشته شده نیس...   neutral\n",
       "2  عالیییه من خیلی وقته منتظر این کتابم ممنون از ...  negative\n",
       "3  هرکی این کتاب رو نخوونه نصف عمرش برفناست من کت...  positive\n",
       "4                                              مزخلف  negative\n",
       "5  در کل از نظر من کتابهای موفقیت رو باید به روز ...   neutral\n",
       "6  قشنگ بود ولی فکر کنم اکثرا کارای ایشون با سانس...  positive\n",
       "7  داستان شخصیت پردازی قوی ای نداشت مخاطب سنیش هم...   neutral\n",
       "8  سلام فایل کتاب اصلاح شد لطفا فایل قبلی را حذف ...   neutral\n",
       "9                             خیلی ناراحت کننده است   negative"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the updated DataFrame\n",
    "new_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RpLObddZROJm"
   },
   "source": [
    "## Train,Validation,Test split\n",
    "\n",
    "To achieve a globalized model, we need to split the cleaned dataset into train, valid, test sets due to size of the data. In this assignment, I have considered a rate of **0.1** for both *valid*, *test* sets. For splitting, I use `train_test_split` provided by Sklearn package with stratifying on the label for preserving the distribution balance."
   ]
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     "text": [
      "(22694, 3)\n",
      "(2837, 3)\n",
      "(2837, 3)\n"
     ]
    }
   ],
   "source": [
    "# Map labels to numerical ids and add a new column 'label_id' to new_data\n",
    "new_data['label_id'] = new_data['label'].apply(lambda t: labels.index(t))\n",
    "\n",
    "# Split new_data into train and test sets (80% train, 20% test), stratified by 'label'\n",
    "train, test = train_test_split(new_data, test_size=0.2, random_state=1, stratify=new_data['label'])\n",
    "\n",
    "# Further split test set into test and validation sets (50% test, 50% validation), stratified by 'label'\n",
    "test, valid = train_test_split(test, test_size=0.5, random_state=1, stratify=test['label'])\n",
    "\n",
    "# Reset index for train, validation, and test sets to ensure continuous integer indices\n",
    "train = train.reset_index(drop=True)\n",
    "valid = valid.reset_index(drop=True)\n",
    "test = test.reset_index(drop=True)\n",
    "\n",
    "# Extract comments and label_ids for train, validation, and test sets\n",
    "x_train, y_train = train['comment'].values.tolist(), train['label_id'].values.tolist()\n",
    "x_valid, y_valid = valid['comment'].values.tolist(), valid['label_id'].values.tolist()\n",
    "x_test, y_test = test['comment'].values.tolist(), test['label_id'].values.tolist()\n",
    "\n",
    "# Print shapes of train, validation, and test sets to verify sizes\n",
    "print(train.shape)\n",
    "print(valid.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Model Training** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "17sghNPPRaOz"
   },
   "source": [
    "Now, I will load my model and my tokenizer and train and evaluate the model on the tokenized train dataset. I will do this processes using *TensorFlow*.\n",
    "\n",
    "As you may know, the BERT model input is a combination of 3 embeddings.\n",
    "- Token embeddings: WordPiece token vocabulary (WordPiece is another word segmentation algorithm, similar to BPE)\n",
    "- Segment embeddings: for pair sentences [A-B] marked as $E_A$ or $E_B$ mean that it belongs to the first sentence or the second one.\n",
    "- Position embeddings: specify the position of words in a sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G9eY5tJrwLUZ"
   },
   "source": [
    "## TensorFlow Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X7Ro-8_pw_uw"
   },
   "source": [
    "### Configuration"
   ]
  },
  {
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   "execution_count": 237,
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    "execution": {
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   "source": [
    "# General configuration parameters\n",
    "MAX_LEN = 128              # Maximum sequence length\n",
    "TRAIN_BATCH_SIZE = 64      # Batch size for training\n",
    "VALID_BATCH_SIZE = 64      # Batch size for validation\n",
    "TEST_BATCH_SIZE = 64       # Batch size for testing\n",
    "\n",
    "EPOCHS = 3                 # Number of epochs for training\n",
    "EEVERY_EPOCH = 1000        # Number of steps to print progress during each epoch\n",
    "LEARNING_RATE = 2e-5       # Learning rate for the optimizer\n",
    "CLIP = 0.0                 # Gradient clipping threshold\n",
    "\n",
    "MODEL_NAME_OR_PATH = 'HooshvareLab/bert-fa-base-uncased'  # Pre-trained model name or path\n",
    "OUTPUT_PATH = '/content/bert-fa-base-uncased-sentiment-taaghceh/pytorch_model.bin'  # Path to save the trained model\n",
    "\n",
    "# Create the directory to save the trained model if it does not exist\n",
    "os.makedirs(os.path.dirname(OUTPUT_PATH), exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "colab": {
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     "height": 51
    },
    "execution": {
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    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label2id: {'negative': 0, 'neutral': 1, 'positive': 2}\n",
      "id2label: {0: 'negative', 1: 'neutral', 2: 'positive'}\n"
     ]
    }
   ],
   "source": [
    "# Create a dictionary mapping labels to numerical ids\n",
    "label2id = {label: i for i, label in enumerate(labels)}\n",
    "\n",
    "# Create a dictionary mapping numerical ids back to labels\n",
    "id2label = {v: k for k, v in label2id.items()}\n",
    "\n",
    "# Print label2id dictionary\n",
    "print(f'label2id: {label2id}')\n",
    "\n",
    "# Print id2label dictionary\n",
    "print(f'id2label: {id2label}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "execution": {
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    "trusted": true
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e77fa88c20144e5c80a6d85883f443b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/1.20M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"negative\",\n",
      "    \"1\": \"neutral\",\n",
      "    \"2\": \"positive\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"negative\": 0,\n",
      "    \"neutral\": 1,\n",
      "    \"positive\": 2\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.41.2\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 100000\n",
      "}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning:\n",
      "\n",
      "`resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize a BERT tokenizer using the pre-trained model specified in MODEL_NAME_OR_PATH\n",
    "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME_OR_PATH, force_download=True)\n",
    "\n",
    "# Create a BERT configuration object using the pre-trained model and additional custom settings\n",
    "config = BertConfig.from_pretrained(\n",
    "    MODEL_NAME_OR_PATH,  # Specify the pre-trained model name or path\n",
    "    **{                  # Additional custom settings passed as keyword arguments\n",
    "        'label2id': label2id,  # Mapping from labels to numerical ids\n",
    "        'id2label': id2label   # Mapping from numerical ids back to labels\n",
    "    })\n",
    "\n",
    "# Print the configuration details in JSON format\n",
    "print(config.to_json_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ppvfKLczSFAG"
   },
   "source": [
    "### Input Embeddings / Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "execution": {
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    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22694/22694 [00:00<00:00, 377919.83it/s]\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/data/processors/glue.py:66: FutureWarning:\n",
      "\n",
      "This function will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py\n",
      "\n",
      "100%|██████████| 22694/22694 [00:02<00:00, 10047.26it/s]\n",
      "100%|██████████| 2837/2837 [00:00<00:00, 170402.99it/s]\n",
      "100%|██████████| 2837/2837 [00:00<00:00, 9869.47it/s]\n",
      "100%|██████████| 2837/2837 [00:00<00:00, 314071.86it/s]\n",
      "100%|██████████| 2837/2837 [00:00<00:00, 9971.31it/s] \n",
      "100%|██████████| 2837/2837 [00:00<00:00, 316023.70it/s]\n",
      "100%|██████████| 2837/2837 [00:00<00:00, 778644.19it/s]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from transformers import glue_convert_examples_to_features\n",
    "\n",
    "class InputExample:\n",
    "    \"\"\" A single example for simple sequence classification. \"\"\"\n",
    "\n",
    "    def __init__(self, guid, text_a, text_b=None, label=None):\n",
    "        \"\"\" Constructs a InputExample. \"\"\"\n",
    "        self.guid = guid\n",
    "        self.text_a = text_a\n",
    "        self.text_b = text_b\n",
    "        self.label = label\n",
    "\n",
    "def make_examples(tokenizer, x, y=None, maxlen=128, output_mode=\"classification\", is_tf_dataset=True):\n",
    "    \"\"\"\n",
    "    Converts input texts and labels into examples and features suitable for BERT model training.\n",
    "\n",
    "    Args:\n",
    "    tokenizer (transformers.PreTrainedTokenizer): Tokenizer object for tokenizing input texts.\n",
    "    x (list): List of input texts or tuples of input texts (for sequence classification).\n",
    "    y (list, optional): List of labels corresponding to input texts. Default is None.\n",
    "    maxlen (int, optional): Maximum sequence length for input texts. Default is 128.\n",
    "    output_mode (str, optional): Output mode for the task (e.g., \"classification\"). Default is \"classification\".\n",
    "    is_tf_dataset (bool, optional): Whether to return a TensorFlow dataset or numpy arrays. Default is True.\n",
    "\n",
    "    Returns:\n",
    "    tf.data.Dataset or tuple of numpy arrays: Depending on is_tf_dataset, returns either a TensorFlow dataset\n",
    "                                             or a tuple of numpy arrays containing input_ids, attention_masks,\n",
    "                                             token_type_ids, and labels.\n",
    "    list: List of features converted from InputExamples.\n",
    "\n",
    "    \"\"\"\n",
    "    examples = []\n",
    "    y = y if isinstance(y, list) or isinstance(y, np.ndarray) else [None] * len(x)\n",
    "\n",
    "    # Create InputExamples from input texts and labels\n",
    "    for i, (_x, _y) in tqdm(enumerate(zip(x, y)), position=0, total=len(x)):\n",
    "        guid = \"%s\" % i\n",
    "        label = int(_y)\n",
    "\n",
    "        if isinstance(_x, str):\n",
    "            text_a = _x\n",
    "            text_b = None\n",
    "        else:\n",
    "            assert len(_x) == 2\n",
    "            text_a = _x[0]\n",
    "            text_b = _x[1]\n",
    "\n",
    "        examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))\n",
    "\n",
    "    # Convert InputExamples to features using glue_convert_examples_to_features function\n",
    "    features = glue_convert_examples_to_features(\n",
    "        examples,\n",
    "        tokenizer,\n",
    "        maxlen,\n",
    "        output_mode=output_mode,\n",
    "        label_list=list(np.unique(y)))\n",
    "\n",
    "    all_input_ids = []\n",
    "    all_attention_masks = []\n",
    "    all_token_type_ids = []\n",
    "    all_labels = []\n",
    "\n",
    "    # Process features into input arrays for TensorFlow dataset or numpy arrays\n",
    "    for f in tqdm(features, position=0, total=len(examples)):\n",
    "        if is_tf_dataset:\n",
    "            all_input_ids.append(tf.constant(f.input_ids))\n",
    "            all_attention_masks.append(tf.constant(f.attention_mask))\n",
    "            all_token_type_ids.append(tf.constant(f.token_type_ids))\n",
    "            all_labels.append(tf.constant(f.label))\n",
    "        else:\n",
    "            all_input_ids.append(f.input_ids)\n",
    "            all_attention_masks.append(f.attention_mask)\n",
    "            all_token_type_ids.append(f.token_type_ids)\n",
    "            all_labels.append(f.label)\n",
    "\n",
    "    if is_tf_dataset:\n",
    "        # Create TensorFlow dataset from input arrays\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(({\n",
    "            'input_ids': all_input_ids,\n",
    "            'attention_mask': all_attention_masks,\n",
    "            'token_type_ids': all_token_type_ids\n",
    "        }, all_labels))\n",
    "\n",
    "        return dataset, features\n",
    "\n",
    "    # Return tuple of numpy arrays if is_tf_dataset=False\n",
    "    xdata = [np.array(all_input_ids), np.array(all_attention_masks), np.array(all_token_type_ids)]\n",
    "    ydata = all_labels\n",
    "\n",
    "    return [xdata, ydata], features\n",
    "\n",
    "# Create training dataset and examples using make_examples function\n",
    "train_dataset_base, train_examples = make_examples(tokenizer, x_train, y_train, maxlen=128)\n",
    "\n",
    "# Create validation dataset and examples using make_examples function\n",
    "valid_dataset_base, valid_examples = make_examples(tokenizer, x_valid, y_valid, maxlen=128)\n",
    "\n",
    "# Create test dataset and examples using make_examples function\n",
    "test_dataset_base, test_examples = make_examples(tokenizer, x_test, y_test, maxlen=128)\n",
    "\n",
    "# Create test dataset and examples as numpy arrays (not TensorFlow dataset)\n",
    "[xtest, ytest], test_examples = make_examples(tokenizer, x_test, y_test, maxlen=128, is_tf_dataset=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     input_ids: [    2  3430 12477  4082 19715     4     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0     0     0     0     0\n",
      "     0     0     0     0     0     0     0     0]\n",
      "attention_mask: [1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "token_type_ids: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "        target: 2\n"
     ]
    }
   ],
   "source": [
    "# Iterate over the first example in the train_dataset_base\n",
    "for value in train_dataset_base.take(1):\n",
    "    # Print input_ids tensor\n",
    "    print(f'     input_ids: {value[0][\"input_ids\"]}')\n",
    "    # Print attention_mask tensor\n",
    "    print(f'attention_mask: {value[0][\"attention_mask\"]}')\n",
    "    # Print token_type_ids tensor\n",
    "    print(f'token_type_ids: {value[0][\"token_type_ids\"]}')\n",
    "    # Print target (label) tensor\n",
    "    print(f'        target: {value[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "execution": {
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    {
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     "text": [
      "354 44\n"
     ]
    }
   ],
   "source": [
    "def get_training_dataset(dataset, batch_size):\n",
    "    \"\"\"\n",
    "    Creates a training dataset pipeline.\n",
    "\n",
    "    Args:\n",
    "    dataset (tf.data.Dataset): TensorFlow dataset containing training examples.\n",
    "    batch_size (int): Batch size for training.\n",
    "\n",
    "    Returns:\n",
    "    tf.data.Dataset: Processed training dataset ready for model training.\n",
    "\n",
    "    \"\"\"\n",
    "    # Repeat the dataset indefinitely\n",
    "    dataset = dataset.repeat()\n",
    "    # Shuffle the dataset with a buffer size of 2048\n",
    "    dataset = dataset.shuffle(2048)\n",
    "    # Batch the dataset with the specified batch size\n",
    "    dataset = dataset.batch(batch_size)\n",
    "\n",
    "    return dataset\n",
    "\n",
    "def get_validation_dataset(dataset, batch_size):\n",
    "    \"\"\"\n",
    "    Creates a validation dataset pipeline.\n",
    "\n",
    "    Args:\n",
    "    dataset (tf.data.Dataset): TensorFlow dataset containing validation examples.\n",
    "    batch_size (int): Batch size for validation.\n",
    "\n",
    "    Returns:\n",
    "    tf.data.Dataset: Processed validation dataset ready for model evaluation.\n",
    "\n",
    "    \"\"\"\n",
    "    # Batch the dataset with the specified batch size\n",
    "    dataset = dataset.batch(batch_size)\n",
    "\n",
    "    return dataset\n",
    "\n",
    "# Create training dataset using get_training_dataset function\n",
    "train_dataset = get_training_dataset(train_dataset_base, TRAIN_BATCH_SIZE)\n",
    "\n",
    "# Create validation dataset using get_validation_dataset function\n",
    "valid_dataset = get_validation_dataset(valid_dataset_base, VALID_BATCH_SIZE)\n",
    "\n",
    "# Calculate steps per epoch for training and validation\n",
    "train_steps = len(train_examples) // TRAIN_BATCH_SIZE\n",
    "valid_steps = len(valid_examples) // VALID_BATCH_SIZE\n",
    "\n",
    "# Print the calculated steps for training and validation\n",
    "print(train_steps, valid_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PAi2TF78VNux"
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T17:22:15.692097Z",
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     "iopub.status.idle": "2024-07-10T17:22:21.992349Z",
     "shell.execute_reply": "2024-07-10T17:22:21.991431Z",
     "shell.execute_reply.started": "2024-07-10T17:22:15.692072Z"
    },
    "id": "WwsARx2QVLjK",
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c748eca9d20442b2bae513582a018963",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tf_model.h5:   0%|          | 0.00/963M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
      "\n",
      "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at HooshvareLab/bert-fa-base-uncased and are newly initialized: ['classifier']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "def build_model(model_name, config, learning_rate=3e-5):\n",
    "    \"\"\"\n",
    "    Builds a TensorFlow model for sequence classification using a pre-trained BERT model.\n",
    "\n",
    "    Args:\n",
    "    model_name (str): Pre-trained model name or path.\n",
    "    config (transformers.PretrainedConfig): Configuration object for the BERT model.\n",
    "    learning_rate (float, optional): Learning rate for optimizer. Default is 3e-5.\n",
    "\n",
    "    Returns:\n",
    "    tf.keras.Model: Compiled BERT-based model for sequence classification.\n",
    "\n",
    "    \"\"\"\n",
    "    # Load pre-trained BERT model for sequence classification\n",
    "    model = TFBertForSequenceClassification.from_pretrained(model_name, force_download=True, config=config)\n",
    "\n",
    "    # Define optimizer, loss function, and metrics for the model\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "    metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n",
    "\n",
    "    # Compile the model with optimizer, loss function, and metrics\n",
    "    model.compile(optimizer=optimizer, loss=loss, metrics=[metric])\n",
    "\n",
    "    return model\n",
    "\n",
    "# Build BERT-based model for sequence classification\n",
    "model = build_model(MODEL_NAME_OR_PATH, config, learning_rate=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jJrah6BGVfA2"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "execution": {
     "iopub.execute_input": "2024-07-10T17:22:21.994060Z",
     "iopub.status.busy": "2024-07-10T17:22:21.993455Z",
     "iopub.status.idle": "2024-07-10T17:40:05.397669Z",
     "shell.execute_reply": "2024-07-10T17:40:05.396657Z",
     "shell.execute_reply.started": "2024-07-10T17:22:21.994031Z"
    },
    "id": "i3qOIz1WVZLw",
    "outputId": "d4e6d1a0-c0e7-4cd7-ef7d-7592ff185abb",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "354/354 [==============================] - 406s 960ms/step - loss: 0.8791 - accuracy: 0.5870 - val_loss: 0.8411 - val_accuracy: 0.6165\n",
      "Epoch 2/3\n",
      "354/354 [==============================] - 328s 926ms/step - loss: 0.7511 - accuracy: 0.6641 - val_loss: 0.8851 - val_accuracy: 0.6080\n",
      "Epoch 3/3\n",
      "354/354 [==============================] - 327s 925ms/step - loss: 0.6227 - accuracy: 0.7357 - val_loss: 0.9510 - val_accuracy: 0.6154\n",
      "FINAL ACCURACY MEAN:  0.61328125\n",
      "CPU times: user 10min 52s, sys: 2min 12s, total: 13min 4s\n",
      "Wall time: 17min 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Train the BERT-based model\n",
    "r = model.fit(\n",
    "    train_dataset,                           # Training dataset\n",
    "    validation_data=valid_dataset,           # Validation dataset\n",
    "    steps_per_epoch=train_steps,             # Number of steps per epoch for training\n",
    "    batch_size=128,                          # Batch size for training\n",
    "    validation_steps=valid_steps,            # Number of steps per epoch for validation\n",
    "    epochs=EPOCHS,                           # Number of epochs\n",
    "    verbose=1                               # Verbosity mode (1 for progress bar)\n",
    ")\n",
    "\n",
    "# Extract validation accuracy from training history\n",
    "final_accuracy = r.history['val_accuracy']\n",
    "print('FINAL ACCURACY MEAN: ', np.mean(final_accuracy))\n",
    "\n",
    "# Save the trained model\n",
    "model.save_pretrained(os.path.dirname(OUTPUT_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hdJdgOecf3Ez"
   },
   "source": [
    "### Evaluation / Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T17:40:05.399283Z",
     "iopub.status.busy": "2024-07-10T17:40:05.399001Z",
     "iopub.status.idle": "2024-07-10T17:40:29.861692Z",
     "shell.execute_reply": "2024-07-10T17:40:29.860752Z",
     "shell.execute_reply.started": "2024-07-10T17:40:05.399251Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 24s 161ms/step\n",
      "Classification Report:\n",
      "               precision    recall  f1-score      support\n",
      "Negative       0.606283  0.612051  0.609153   946.000000\n",
      "Neutral        0.593023  0.485201  0.533721   946.000000\n",
      "Positive       0.637184  0.747090  0.687774   945.000000\n",
      "accuracy       0.614734  0.614734  0.614734     0.614734\n",
      "macro avg      0.612163  0.614781  0.610216  2837.000000\n",
      "weighted avg   0.612155  0.614734  0.610189  2837.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Overall Metrics:\n",
      "Accuracy: 0.6147\n",
      "Precision: 0.6122\n",
      "Recall: 0.6147\n",
      "F1 Score: 0.6102\n"
     ]
    }
   ],
   "source": [
    "def evaluate_model(y_true, y_pred, class_names):\n",
    "    \"\"\"\n",
    "    Evaluates the performance of a classification model using various metrics and visualizations.\n",
    "\n",
    "    Args:\n",
    "    - y_true (array-like): True labels of the data.\n",
    "    - y_pred (array-like): Predicted labels of the data.\n",
    "    - class_names (list): List of class names in the same order as the confusion matrix.\n",
    "\n",
    "    Returns:\n",
    "    - pd.DataFrame: DataFrame containing the classification report.\n",
    "    \"\"\"\n",
    "    # Generate and print the classification report\n",
    "    report = classification_report(y_true, y_pred, target_names=class_names, output_dict=True)\n",
    "    report_df = pd.DataFrame(report).transpose()\n",
    "    print(\"Classification Report:\\n\", report_df)\n",
    "\n",
    "    # Generate and display the confusion matrix as a heatmap\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "    # Calculate and print overall metrics: accuracy, precision, recall, and F1 score\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, average='weighted')\n",
    "    recall = recall_score(y_true, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "\n",
    "    metrics = {\n",
    "        \"Accuracy\": accuracy,\n",
    "        \"Precision\": precision,\n",
    "        \"Recall\": recall,\n",
    "        \"F1 Score\": f1\n",
    "    }\n",
    "\n",
    "    print(\"\\nOverall Metrics:\")\n",
    "    for metric, value in metrics.items():\n",
    "        print(f\"{metric}: {value:.4f}\")\n",
    "    \n",
    "    return report_df\n",
    "\n",
    "# Perform predictions on the test dataset\n",
    "predictions = model.predict(xtest)\n",
    "\n",
    "# Extract predicted labels from predictions\n",
    "ypred = predictions[0].argmax(axis=-1).tolist()\n",
    "\n",
    "# Evaluate the model\n",
    "report_df = evaluate_model(ytest, ypred, [\"Negative\", \"Neutral\", \"Positive\"])"
   ]
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