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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# **Imporitng libraries**"
      ],
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        "id": "hsNAR1xXdERs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "from nltk.util import ngrams\n",
        "from sklearn.utils import shuffle\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import string\n",
        "import re\n",
        "import nltk\n",
        "nltk.download(\"punkt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJhqSj-QdDzz",
        "outputId": "2f546feb-2cf8-4f77-f87f-3ad364b062f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Corpus**\n",
        "\n"
      ],
      "metadata": {
        "id": "V8kWsUi3cEU3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RjgR3FQXVpmR"
      },
      "outputs": [],
      "source": [
        "def read_file_sections(filename):\n",
        "    sections = []\n",
        "    current_section = \"\"\n",
        "\n",
        "    with open(filename, 'r', encoding='utf-8') as file:\n",
        "        for line in file:\n",
        "            if line.strip() == \"__________________________\":\n",
        "                if current_section:\n",
        "                    sections.append(current_section.strip())\n",
        "                    current_section = \"\"\n",
        "            else:\n",
        "                current_section += line\n",
        "\n",
        "    if current_section:\n",
        "        sections.append(current_section.strip())\n",
        "\n",
        "    return sections"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_pos= read_file_sections(r\"/content/product_pos.txt\")\n",
        "product_neg = read_file_sections(r\"/content/product_neg.txt\")"
      ],
      "metadata": {
        "id": "YaMLn517cP9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "book_pos = read_file_sections(r\"/content/book_pos.txt\")\n",
        "book_neg = read_file_sections(r\"/content/book_neg.txt\")"
      ],
      "metadata": {
        "id": "xOYwcVWLcawn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_product_neg = pd.DataFrame({'text': product_neg, 'label': 0})\n",
        "df_product_pos = pd.DataFrame({'text': product_pos, 'label': 1})\n",
        "df_book_neg = pd.DataFrame({'text': book_neg, 'label': 0})\n",
        "df_book_pos = pd.DataFrame({'text': book_pos, 'label': 1})\n",
        "\n",
        "# Concatenate and shuffle\n",
        "df = pd.concat([df_product_neg, df_product_pos,df_book_neg,df_book_pos], ignore_index=True)\n",
        "#df = pd.concat([df_product_neg, df_product_pos], ignore_index=True)\n",
        "\n",
        "df = shuffle(df).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "HSQsGqGucl80"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
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        "colab": {
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          "height": 363
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        "id": "L-6iKae5eks7",
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              "                                                text  label\n",
              "0  ఈ పుస్తకం APJ అబ్దుల్ కలాం సార్  మాజీ రాష్ర్టప...      1\n",
              "1  ఈ సర్వేని ఎలా ప్రారంభించాలో నాకు తెలియట్లేదు  ...      0\n",
              "2  నేను ఈ వివో y55s స్మార్ట్ఫోన్ను సమీక్షిస్తున్న...      0\n",
              "3  హలో ఫ్రెండ్స్ ఈ ఫోన్ కొనుగోలు చేయకూడదని నేను మ...      0\n",
              "4  రికనెక్ట్ అనేది కొత్త బ్రాండ్లలో ఒకటి  ఇది ఎలక...      0\n",
              "5  హాయ్ ఫ్రెండ్స్ నేను నికోన్ D80 కెమెరా గురించి ...      0\n",
              "6  అందరికీ హలో నేను ఒక నెల పాచు ఈ ఫోన్ ఉపయోగించాన...      0\n",
              "7  Dr  A P J  అబ్దుల్ కలాం సర్ భారతదేశంలో గొప్ప శ...      1\n",
              "8  మంచి అద్భుతమైన భావం మరియు కాంపాక్ట్ సైజు; చాలా...      1\n",
              "9  ఫక్ ఇట్ ఫక్ ది బుక్ ఫక్ ది రైటర్ ఛీ ఇది ఒక బుక...      0"
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              "      <td>ఈ పుస్తకం APJ అబ్దుల్ కలాం సార్  మాజీ రాష్ర్టప...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>నేను ఈ వివో y55s స్మార్ట్ఫోన్ను సమీక్షిస్తున్న...</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>అందరికీ హలో నేను ఒక నెల పాచు ఈ ఫోన్ ఉపయోగించాన...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Dr  A P J  అబ్దుల్ కలాం సర్ భారతదేశంలో గొప్ప శ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>మంచి అద్భుతమైన భావం మరియు కాంపాక్ట్ సైజు; చాలా...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ఫక్ ఇట్ ఫక్ ది బుక్ ఫక్ ది రైటర్ ఛీ ఇది ఒక బుక...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03020e03-aeab-4504-8119-a4c46f346267')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-03020e03-aeab-4504-8119-a4c46f346267 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-03020e03-aeab-4504-8119-a4c46f346267');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-697e5303-5440-496f-8acc-1cb5335b3147\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-697e5303-5440-496f-8acc-1cb5335b3147')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-697e5303-5440-496f-8acc-1cb5335b3147 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3goSn3uenRc",
        "outputId": "6b4d8cf5-6e85-4c45-8452-52d08295e197"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(399, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Removing Punctuations**"
      ],
      "metadata": {
        "id": "cENIgNcj4TEi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import string\n",
        "\n",
        "# Define a function to remove punctuation\n",
        "def remove_punctuation(text):\n",
        "    translator = str.maketrans('', '', string.punctuation)\n",
        "    return text.translate(translator)\n",
        "\n",
        "# Apply the function to the \"text\" column\n",
        "df['text'] = df['text'].apply(remove_punctuation)\n"
      ],
      "metadata": {
        "id": "y-qc92qu4Blp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQo6s3rI4EWj",
        "outputId": "33462a53-1017-434b-b6f7-d9c796982f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text     అందరికీ హలో నేను ఒక నెల పాచు ఈ ఫోన్ ఉపయోగించాన...\n",
              "label                                                    0\n",
              "Name: 6, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "KI4mmEUt7-OX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3781222b-9659-421b-c5cd-f282d9b9ed00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label\n",
              "0  ఈ పుస్తకం APJ అబ్దుల్ కలాం సార్  మాజీ రాష్ర్టప...      1\n",
              "1  ఈ సర్వేని ఎలా ప్రారంభించాలో నాకు తెలియట్లేదు  ...      0\n",
              "2  నేను ఈ వివో y55s స్మార్ట్ఫోన్ను సమీక్షిస్తున్న...      0\n",
              "3  హలో ఫ్రెండ్స్ ఈ ఫోన్ కొనుగోలు చేయకూడదని నేను మ...      0\n",
              "4  రికనెక్ట్ అనేది కొత్త బ్రాండ్లలో ఒకటి  ఇది ఎలక...      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-112c1f36-bd13-4c31-bb4e-3b1ba3e05af8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>0</th>\n",
              "      <td>ఈ పుస్తకం APJ అబ్దుల్ కలాం సార్  మాజీ రాష్ర్టప...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ఈ సర్వేని ఎలా ప్రారంభించాలో నాకు తెలియట్లేదు  ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>నేను ఈ వివో y55s స్మార్ట్ఫోన్ను సమీక్షిస్తున్న...</td>\n",
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              "      <th>3</th>\n",
              "      <td>హలో ఫ్రెండ్స్ ఈ ఫోన్ కొనుగోలు చేయకూడదని నేను మ...</td>\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-112c1f36-bd13-4c31-bb4e-3b1ba3e05af8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-112c1f36-bd13-4c31-bb4e-3b1ba3e05af8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ea2966dc-16ee-482f-99af-251f0036834a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea2966dc-16ee-482f-99af-251f0036834a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ea2966dc-16ee-482f-99af-251f0036834a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 399,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 397,\n        \"samples\": [\n          \"\\u0c07\\u0c26\\u0c3f \\u0c2e\\u0c02\\u0c1a\\u0c3f \\u0c2f\\u0c42\\u0c1c\\u0c30\\u0c4d \\u0c2b\\u0c4d\\u0c30\\u0c46\\u0c02\\u0c21\\u0c4d\\u0c32\\u0c40 \\u0c2e\\u0c4a\\u0c2c\\u0c46\\u0c56\\u0c32\\u0c4d \\u0c2e\\u0c30\\u0c3f\\u0c2f\\u0c41 \\u0c38\\u0c4c\\u0c02\\u0c21\\u0c4d \\u0c0e\\u0c2b\\u0c46\\u0c15\\u0c4d\\u0c1f\\u0c4d\\u0c38\\u0c4d \\u0c1a\\u0c3e\\u0c32\\u0c3e \\u0c2c\\u0c3e\\u0c17\\u0c41\\u0c28\\u0c4d\\u0c28\\u0c3e\\u0c2f\\u0c3f \\u0c2e\\u0c30\\u0c3f\\u0c2f\\u0c41 \\u0c05\\u0c02\\u0c26\\u0c30\\u0c42 \\u0c09\\u0c2a\\u0c2f\\u0c4b\\u0c17\\u0c3f\\u0c02\\u0c1a\\u0c47 \\u0c35\\u0c3f\\u0c27\\u0c02\\u0c17\\u0c3e \\u0c21\\u0c3f\\u0c1c\\u0c46\\u0c56\\u0c28\\u0c4d \\u0c38\\u0c3e\\u0c2e\\u0c4d\\u0c38\\u0c02\\u0c17\\u0c4d 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\\u0c2a\\u0c4d\\u0c30\\u0c36\\u0c4d\\u0c28\\u0c32\\u0c41 \\u0c2a\\u0c30\\u0c3f\\u0c37\\u0c4d\\u0c15\\u0c30\\u0c3f\\u0c02\\u0c1a\\u0c2c\\u0c21\\u0c3f \\u0c09\\u0c02\\u0c1f\\u0c3e\\u0c2f\\u0c3f \\n\\u0c2e\\u0c30\\u0c3f\\u0c2f\\u0c41 \\u0c28\\u0c47\\u0c28\\u0c41 \\u0c35\\u0c4d\\u0c2f\\u0c15\\u0c4d\\u0c24\\u0c3f\\u0c17\\u0c24\\u0c02\\u0c17\\u0c3e \\u0c08 \\u0c2a\\u0c41\\u0c38\\u0c4d\\u0c24\\u0c15\\u0c02 \\u0c28\\u0c41\\u0c02\\u0c21\\u0c3f \\u0c17\\u0c4a\\u0c2a\\u0c4d\\u0c2a \\u0c17\\u0c48\\u0c21\\u0c46\\u0c28\\u0c4d\\u0c38\\u0c4d \\u0c2a\\u0c4a\\u0c02\\u0c26\\u0c3e\\u0c28\\u0c41  \\u0c2e\\u0c30\\u0c3f\\u0c2f\\u0c41 \\u0c26\\u0c40\\u0c28\\u0c3f\\u0c32\\u0c4b \\u0c15\\u0c3e\\u0c28\\u0c4d\\u0c38\\u0c46\\u0c2a\\u0c4d\\u0c1f\\u0c4d\\u0c38\\u0c4d \\u0c2c\\u0c3e\\u0c17\\u0c3e \\u0c35\\u0c3f\\u0c35\\u0c30\\u0c3f\\u0c02\\u0c1a\\u0c2c\\u0c21\\u0c3f \\u0c09\\u0c02\\u0c1f\\u0c3e\\u0c2f\\u0c3f  \\u0c15\\u0c3e\\u0c2c\\u0c1f\\u0c4d\\u0c1f\\u0c3f \\u0c35\\u0c3f\\u0c26\\u0c4d\\u0c2f\\u0c3e\\u0c30\\u0c4d\\u0c27\\u0c3f \\u0c0e\\u0c15\\u0c4d\\u0c15\\u0c41\\u0c35 \\u0c2e\\u0c3e\\u0c30\\u0c4d\\u0c15\\u0c41\\u0c32\\u0c41 \\u0c38\\u0c4d\\u0c15\\u0c4b\\u0c30\\u0c4d \\u0c1a\\u0c47\\u0c2f\\u0c21\\u0c3e\\u0c28\\u0c3f\\u0c15\\u0c3f \\u0c1a\\u0c3e\\u0c32\\u0c3e \\u0c38\\u0c39\\u0c3e\\u0c2f \\u0c2a\\u0c21\\u0c41\\u0c24\\u0c41\\u0c02\\u0c26\\u0c3f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Tokenization**"
      ],
      "metadata": {
        "id": "AVy43OSz5OvI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install indic-nlp-library\n",
        "!pip install indicnlp\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sjzmBIgBC5h",
        "outputId": "39b06c20-0bc2-42f1-dc19-f0c82385ecd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting indic-nlp-library\n",
            "  Downloading indic_nlp_library-0.92-py3-none-any.whl (40 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 kB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting sphinx-argparse (from indic-nlp-library)\n",
            "  Downloading sphinx_argparse-0.4.0-py3-none-any.whl (12 kB)\n",
            "Collecting sphinx-rtd-theme (from indic-nlp-library)\n",
            "  Downloading sphinx_rtd_theme-2.0.0-py2.py3-none-any.whl (2.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m27.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting morfessor (from indic-nlp-library)\n",
            "  Downloading Morfessor-2.0.6-py3-none-any.whl (35 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from indic-nlp-library) (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from indic-nlp-library) (1.25.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->indic-nlp-library) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->indic-nlp-library) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->indic-nlp-library) (2024.1)\n",
            "Requirement already satisfied: sphinx>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from sphinx-argparse->indic-nlp-library) (5.0.2)\n",
            "Requirement already satisfied: docutils<0.21 in /usr/local/lib/python3.10/dist-packages (from sphinx-rtd-theme->indic-nlp-library) (0.18.1)\n",
            "Collecting sphinxcontrib-jquery<5,>=4 (from sphinx-rtd-theme->indic-nlp-library)\n",
            "  Downloading sphinxcontrib_jquery-4.1-py2.py3-none-any.whl (121 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.1/121.1 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->indic-nlp-library) (1.16.0)\n",
            "Requirement already satisfied: sphinxcontrib-applehelp in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.0.8)\n",
            "Requirement already satisfied: sphinxcontrib-devhelp in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.0.6)\n",
            "Requirement already satisfied: sphinxcontrib-jsmath in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.0.1)\n",
            "Requirement already satisfied: sphinxcontrib-htmlhelp>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.0.5)\n",
            "Requirement already satisfied: sphinxcontrib-serializinghtml>=1.1.5 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.1.10)\n",
            "Requirement already satisfied: sphinxcontrib-qthelp in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.0.7)\n",
            "Requirement already satisfied: Jinja2>=2.3 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (3.1.3)\n",
            "Requirement already satisfied: Pygments>=2.0 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.16.1)\n",
            "Requirement already satisfied: snowballstemmer>=1.1 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.2.0)\n",
            "Requirement already satisfied: babel>=1.3 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.14.0)\n",
            "Requirement already satisfied: alabaster<0.8,>=0.7 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (0.7.16)\n",
            "Requirement already satisfied: imagesize in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.4.1)\n",
            "Requirement already satisfied: requests>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.31.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (24.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.3->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.5.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.5.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.5.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.5.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2024.2.2)\n",
            "Installing collected packages: morfessor, sphinxcontrib-jquery, sphinx-argparse, sphinx-rtd-theme, indic-nlp-library\n",
            "Successfully installed indic-nlp-library-0.92 morfessor-2.0.6 sphinx-argparse-0.4.0 sphinx-rtd-theme-2.0.0 sphinxcontrib-jquery-4.1\n",
            "Collecting indicnlp\n",
            "  Downloading indicnlp-0.0.1-py3-none-any.whl (13 kB)\n",
            "Installing collected packages: indicnlp\n",
            "Successfully installed indicnlp-0.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from indicnlp.tokenize import indic_tokenize\n",
        "\n",
        "# Function to tokenize text\n",
        "def tokenize_text(text):\n",
        "    return indic_tokenize.trivial_tokenize(text)\n",
        "\n",
        "# Create new column 'tokenized_text' with tokenized text\n",
        "df['tokenized_text'] = df['text'].apply(tokenize_text)\n",
        "\n",
        "# Print the updated dataframe with tokenized text column\n",
        "print(df)\n"
      ],
      "metadata": {
        "id": "JHMYBLy08FLN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19ed514f-5bfa-4bb5-9eb4-8a6197b199c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  text  label  \\\n",
            "0    ఈ పుస్తకం APJ అబ్దుల్ కలాం సార్  మాజీ రాష్ర్టప...      1   \n",
            "1    ఈ సర్వేని ఎలా ప్రారంభించాలో నాకు తెలియట్లేదు  ...      0   \n",
            "2    నేను ఈ వివో y55s స్మార్ట్ఫోన్ను సమీక్షిస్తున్న...      0   \n",
            "3    హలో ఫ్రెండ్స్ ఈ ఫోన్ కొనుగోలు చేయకూడదని నేను మ...      0   \n",
            "4    రికనెక్ట్ అనేది కొత్త బ్రాండ్లలో ఒకటి  ఇది ఎలక...      0   \n",
            "..                                                 ...    ...   \n",
            "394  ఇటీవల నా సోదరుడు ఈ సోనీ బ్రావియా LED కొనుగోలు ...      1   \n",
            "395  నేను వేగంగా ఈ పుస్తకాన్ని చదువుతాను  నాకు చాలా...      0   \n",
            "396  అవును నాకు ఇది చాలా ఇష్టం  దీని వేలి ముద్రణ సె...      1   \n",
            "397  గత నెలల్లో శశి దేశ్పాండే రచించిన పుస్తకం లాంగ్...      0   \n",
            "398  హలో ఫ్రెండ్స్  తీవ్రంగా నిరాశపరిచింది  నేను పూ...      0   \n",
            "\n",
            "                                        tokenized_text  \n",
            "0    [ఈ, పుస్తకం, APJ, అబ్దుల్, కలాం, సార్, మాజీ, ర...  \n",
            "1    [ఈ, సర్వేని, ఎలా, ప్రారంభించాలో, నాకు, తెలియట్...  \n",
            "2    [నేను, ఈ, వివో, y55s, స్మార్ట్ఫోన్ను, సమీక్షిస...  \n",
            "3    [హలో, ఫ్రెండ్స్, ఈ, ఫోన్, కొనుగోలు, చేయకూడదని,...  \n",
            "4    [రికనెక్ట్, అనేది, కొత్త, బ్రాండ్లలో, ఒకటి, ఇద...  \n",
            "..                                                 ...  \n",
            "394  [ఇటీవల, నా, సోదరుడు, ఈ, సోనీ, బ్రావియా, LED, క...  \n",
            "395  [నేను, వేగంగా, ఈ, పుస్తకాన్ని, చదువుతాను, నాకు...  \n",
            "396  [అవును, నాకు, ఇది, చాలా, ఇష్టం, దీని, వేలి, ము...  \n",
            "397  [గత, నెలల్లో, శశి, దేశ్పాండే, రచించిన, పుస్తకం...  \n",
            "398  [హలో, ఫ్రెండ్స్, తీవ్రంగా, నిరాశపరిచింది, నేను...  \n",
            "\n",
            "[399 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate max sequence length for tokenized text\n",
        "max_seq_length = max(len(tokens) for tokens in df['tokenized_text'])\n",
        "print(\"Maximum Sequence Length for Tokenized Text:\", max_seq_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fiih5XF-45Pf",
        "outputId": "dd7ffbb5-d7e6-45c4-ae1b-7901a7795716"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Sequence Length for Tokenized Text: 259\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Padding**"
      ],
      "metadata": {
        "id": "RKAMUR3L9nyb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "# Pad sequences to max_seq_length\n",
        "max_seq_length = 259\n",
        "padded_sequences = pad_sequences(df['tokenized_text'], maxlen=max_seq_length, dtype=object, padding='post', truncating='post', value='')\n",
        "\n",
        "# Update dataframe with padded sequences\n",
        "df['padded_text'] = padded_sequences.tolist()\n",
        "\n",
        "# Print the updated dataframe with padded text column\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmDF95z15aD8",
        "outputId": "868a72ba-8c2f-415c-ec33-dfd32b194dc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  text  label  \\\n",
            "0    ఈ పుస్తకం APJ అబ్దుల్ కలాం సార్  మాజీ రాష్ర్టప...      1   \n",
            "1    ఈ సర్వేని ఎలా ప్రారంభించాలో నాకు తెలియట్లేదు  ...      0   \n",
            "2    నేను ఈ వివో y55s స్మార్ట్ఫోన్ను సమీక్షిస్తున్న...      0   \n",
            "3    హలో ఫ్రెండ్స్ ఈ ఫోన్ కొనుగోలు చేయకూడదని నేను మ...      0   \n",
            "4    రికనెక్ట్ అనేది కొత్త బ్రాండ్లలో ఒకటి  ఇది ఎలక...      0   \n",
            "..                                                 ...    ...   \n",
            "394  ఇటీవల నా సోదరుడు ఈ సోనీ బ్రావియా LED కొనుగోలు ...      1   \n",
            "395  నేను వేగంగా ఈ పుస్తకాన్ని చదువుతాను  నాకు చాలా...      0   \n",
            "396  అవును నాకు ఇది చాలా ఇష్టం  దీని వేలి ముద్రణ సె...      1   \n",
            "397  గత నెలల్లో శశి దేశ్పాండే రచించిన పుస్తకం లాంగ్...      0   \n",
            "398  హలో ఫ్రెండ్స్  తీవ్రంగా నిరాశపరిచింది  నేను పూ...      0   \n",
            "\n",
            "                                        tokenized_text  \\\n",
            "0    [ఈ, పుస్తకం, APJ, అబ్దుల్, కలాం, సార్, మాజీ, ర...   \n",
            "1    [ఈ, సర్వేని, ఎలా, ప్రారంభించాలో, నాకు, తెలియట్...   \n",
            "2    [నేను, ఈ, వివో, y55s, స్మార్ట్ఫోన్ను, సమీక్షిస...   \n",
            "3    [హలో, ఫ్రెండ్స్, ఈ, ఫోన్, కొనుగోలు, చేయకూడదని,...   \n",
            "4    [రికనెక్ట్, అనేది, కొత్త, బ్రాండ్లలో, ఒకటి, ఇద...   \n",
            "..                                                 ...   \n",
            "394  [ఇటీవల, నా, సోదరుడు, ఈ, సోనీ, బ్రావియా, LED, క...   \n",
            "395  [నేను, వేగంగా, ఈ, పుస్తకాన్ని, చదువుతాను, నాకు...   \n",
            "396  [అవును, నాకు, ఇది, చాలా, ఇష్టం, దీని, వేలి, ము...   \n",
            "397  [గత, నెలల్లో, శశి, దేశ్పాండే, రచించిన, పుస్తకం...   \n",
            "398  [హలో, ఫ్రెండ్స్, తీవ్రంగా, నిరాశపరిచింది, నేను...   \n",
            "\n",
            "                                           padded_text  \n",
            "0    [ఈ, పుస్తకం, APJ, అబ్దుల్, కలాం, సార్, మాజీ, ర...  \n",
            "1    [ఈ, సర్వేని, ఎలా, ప్రారంభించాలో, నాకు, తెలియట్...  \n",
            "2    [నేను, ఈ, వివో, y55s, స్మార్ట్ఫోన్ను, సమీక్షిస...  \n",
            "3    [హలో, ఫ్రెండ్స్, ఈ, ఫోన్, కొనుగోలు, చేయకూడదని,...  \n",
            "4    [రికనెక్ట్, అనేది, కొత్త, బ్రాండ్లలో, ఒకటి, ఇద...  \n",
            "..                                                 ...  \n",
            "394  [ఇటీవల, నా, సోదరుడు, ఈ, సోనీ, బ్రావియా, LED, క...  \n",
            "395  [నేను, వేగంగా, ఈ, పుస్తకాన్ని, చదువుతాను, నాకు...  \n",
            "396  [అవును, నాకు, ఇది, చాలా, ఇష్టం, దీని, వేలి, ము...  \n",
            "397  [గత, నెలల్లో, శశి, దేశ్పాండే, రచించిన, పుస్తకం...  \n",
            "398  [హలో, ఫ్రెండ్స్, తీవ్రంగా, నిరాశపరిచింది, నేను...  \n",
            "\n",
            "[399 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Getting Embeddings**"
      ],
      "metadata": {
        "id": "K5pw7DsGCthJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertModel\n",
        "import pandas as pd\n",
        "\n",
        "# Load your dataframe 'df'\n",
        "# Assuming 'tokenized_text' and 'padded_text' columns are already populated\n",
        "\n",
        "# Load the tokenizer and model\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased')\n",
        "model = BertModel.from_pretrained('bert-base-multilingual-cased')\n",
        "\n",
        "# Define a function to get embeddings\n",
        "def get_embeddings(text):\n",
        "    tokens = tokenizer.encode(text, add_special_tokens=True, max_length=259, truncation=True)\n",
        "    input_ids = torch.tensor(tokens).unsqueeze(0)  # Add batch dimension\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_ids)\n",
        "        embeddings = outputs[0][:, 0, :]  # Extract the [CLS] token embedding\n",
        "    return embeddings.tolist()\n",
        "\n",
        "# Apply the function to your dataframe and update the 'text_embeddings' column\n",
        "df['text_embeddings'] = df['padded_text'].apply(get_embeddings)\n",
        "\n",
        "# Save or use the updated dataframe with embeddings\n",
        "df.to_csv('df_with_embeddings.csv', index=False)\n"
      ],
      "metadata": {
        "id": "5fjTfP8CC3XT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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        "outputId": "f83dcf82-42cb-4288-ccf3-1c59ac267f29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fba53afff3bb4e538f1e4b915aa53284"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4481f4b53824e3f94c9066dd30c807f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/714M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "708fc3cd83cb47faaa2582a646f41cda"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bi-Directional LSTM**"
      ],
      "metadata": {
        "id": "s9wYVUB10DwD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, Dropout\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Assuming 'df' is your dataframe containing text and label columns\n",
        "# Tokenize the text data to get the vocabulary size\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(df['text'])  # Assuming 'text' is the column containing your text data\n",
        "num_words = len(tokenizer.word_index) + 1  # Add 1 for the padding token\n",
        "\n",
        "# Convert text to sequences\n",
        "sequences = tokenizer.texts_to_sequences(df['text'])\n",
        "\n",
        "# Pad sequences to ensure uniform length\n",
        "max_seq_length = 259  # Maximum sequence length based on your requirements\n",
        "X_padded = pad_sequences(sequences, maxlen=max_seq_length, padding='post')\n",
        "\n",
        "# Define LSTM model with Bidirectional layer and additional layers\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=num_words, output_dim=128, input_length=max_seq_length))\n",
        "model.add(Bidirectional(LSTM(units=128, return_sequences=True)))\n",
        "model.add(Dropout(0.5))  # Adding dropout for regularization\n",
        "model.add(Bidirectional(LSTM(units=64)))\n",
        "model.add(Dense(units=32, activation='relu'))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Assuming 'y' contains your labels\n",
        "y = df['label']\n",
        "\n",
        "# Train the model on the entire dataset\n",
        "model.fit(X_padded, y, epochs=10, batch_size=32, validation_split=0.2)  # Assuming you want to use 20% of data for validation\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlha9C5ruE0R",
        "outputId": "25b21cb9-b599-4027-ed0c-d37b5578e0b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "10/10 [==============================] - 25s 2s/step - loss: 0.6940 - accuracy: 0.5110 - val_loss: 0.6857 - val_accuracy: 0.5375\n",
            "Epoch 2/10\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.6175 - accuracy: 0.8370 - val_loss: 0.6291 - val_accuracy: 0.6125\n",
            "Epoch 3/10\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.1823 - accuracy: 0.9530 - val_loss: 0.4261 - val_accuracy: 0.8375\n",
            "Epoch 4/10\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.7663 - val_accuracy: 0.8000\n",
            "Epoch 5/10\n",
            "10/10 [==============================] - 15s 2s/step - loss: 0.0279 - accuracy: 0.9906 - val_loss: 0.4256 - val_accuracy: 0.8250\n",
            "Epoch 6/10\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.1618 - val_accuracy: 0.8000\n",
            "Epoch 7/10\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.1282 - val_accuracy: 0.8250\n",
            "Epoch 8/10\n",
            "10/10 [==============================] - 17s 2s/step - loss: 3.8352e-04 - accuracy: 1.0000 - val_loss: 0.6302 - val_accuracy: 0.9000\n",
            "Epoch 9/10\n",
            "10/10 [==============================] - 17s 2s/step - loss: 2.6406e-04 - accuracy: 1.0000 - val_loss: 1.1785 - val_accuracy: 0.8250\n",
            "Epoch 10/10\n",
            "10/10 [==============================] - 16s 2s/step - loss: 2.3274e-04 - accuracy: 1.0000 - val_loss: 1.3734 - val_accuracy: 0.8000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x796bf8fca830>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LSTM**"
      ],
      "metadata": {
        "id": "eqrUayPT0RDW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/df_with_embeddings.csv')"
      ],
      "metadata": {
        "id": "QEwhnUusmbYL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, Dropout\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Tokenize the text data to get the vocabulary size\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(df['text'])  # Assuming 'text' is the column containing your text data\n",
        "num_words = len(tokenizer.word_index) + 1  # Add 1 for the padding token\n",
        "\n",
        "# Convert text to sequences\n",
        "sequences = tokenizer.texts_to_sequences(df['text'])\n",
        "\n",
        "# Pad sequences to ensure uniform length\n",
        "max_seq_length = 259\n",
        "X_padded = pad_sequences(sequences, maxlen=max_seq_length, padding='post')\n",
        "\n",
        "# Define LSTM model with Bidirectional layer and reduced layers\n",
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=num_words, output_dim=128, input_length=max_seq_length))\n",
        "model.add(LSTM(units=64))\n",
        "model.add(Dense(units=32, activation='relu'))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Assuming 'y' contains your labels\n",
        "y = df['label']\n",
        "\n",
        "# Train the model on the entire dataset\n",
        "model.fit(X_padded, y, epochs=20, batch_size=32, validation_split=0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCBZP5ITxFAm",
        "outputId": "e63f1d0b-8cc2-4427-bcda-880f457f5b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "10/10 [==============================] - 5s 280ms/step - loss: 0.6951 - accuracy: 0.4734 - val_loss: 0.6930 - val_accuracy: 0.5250\n",
            "Epoch 2/20\n",
            "10/10 [==============================] - 2s 193ms/step - loss: 0.6934 - accuracy: 0.4984 - val_loss: 0.6935 - val_accuracy: 0.4750\n",
            "Epoch 3/20\n",
            "10/10 [==============================] - 2s 215ms/step - loss: 0.6930 - accuracy: 0.5110 - val_loss: 0.6936 - val_accuracy: 0.4750\n",
            "Epoch 4/20\n",
            "10/10 [==============================] - 3s 293ms/step - loss: 0.6935 - accuracy: 0.4859 - val_loss: 0.6931 - val_accuracy: 0.5250\n",
            "Epoch 5/20\n",
            "10/10 [==============================] - 2s 193ms/step - loss: 0.6931 - accuracy: 0.5110 - val_loss: 0.6947 - val_accuracy: 0.4750\n",
            "Epoch 6/20\n",
            "10/10 [==============================] - 2s 192ms/step - loss: 0.6938 - accuracy: 0.5110 - val_loss: 0.6956 - val_accuracy: 0.4750\n",
            "Epoch 7/20\n",
            "10/10 [==============================] - 2s 194ms/step - loss: 0.6927 - accuracy: 0.5110 - val_loss: 0.6942 - val_accuracy: 0.4750\n",
            "Epoch 8/20\n",
            "10/10 [==============================] - 2s 190ms/step - loss: 0.6928 - accuracy: 0.5110 - val_loss: 0.6939 - val_accuracy: 0.4750\n",
            "Epoch 9/20\n",
            "10/10 [==============================] - 2s 190ms/step - loss: 0.6928 - accuracy: 0.5110 - val_loss: 0.6938 - val_accuracy: 0.4750\n",
            "Epoch 10/20\n",
            "10/10 [==============================] - 3s 303ms/step - loss: 0.6931 - accuracy: 0.4577 - val_loss: 0.6934 - val_accuracy: 0.4750\n",
            "Epoch 11/20\n",
            "10/10 [==============================] - 2s 196ms/step - loss: 0.6929 - accuracy: 0.5110 - val_loss: 0.6944 - val_accuracy: 0.4750\n",
            "Epoch 12/20\n",
            "10/10 [==============================] - 2s 199ms/step - loss: 0.6925 - accuracy: 0.5110 - val_loss: 0.6936 - val_accuracy: 0.4750\n",
            "Epoch 13/20\n",
            "10/10 [==============================] - 2s 194ms/step - loss: 0.6919 - accuracy: 0.5110 - val_loss: 0.6940 - val_accuracy: 0.4750\n",
            "Epoch 14/20\n",
            "10/10 [==============================] - 2s 193ms/step - loss: 0.6916 - accuracy: 0.5141 - val_loss: 0.8787 - val_accuracy: 0.5375\n",
            "Epoch 15/20\n",
            "10/10 [==============================] - 2s 188ms/step - loss: 0.7205 - accuracy: 0.5172 - val_loss: 0.6938 - val_accuracy: 0.4750\n",
            "Epoch 16/20\n",
            "10/10 [==============================] - 3s 288ms/step - loss: 0.6918 - accuracy: 0.5110 - val_loss: 0.6944 - val_accuracy: 0.4750\n",
            "Epoch 17/20\n",
            "10/10 [==============================] - 2s 214ms/step - loss: 0.6921 - accuracy: 0.5110 - val_loss: 0.6952 - val_accuracy: 0.4750\n",
            "Epoch 18/20\n",
            "10/10 [==============================] - 2s 187ms/step - loss: 0.6917 - accuracy: 0.5110 - val_loss: 0.6946 - val_accuracy: 0.4750\n",
            "Epoch 19/20\n",
            "10/10 [==============================] - 2s 192ms/step - loss: 0.6918 - accuracy: 0.5110 - val_loss: 0.6942 - val_accuracy: 0.4750\n",
            "Epoch 20/20\n",
            "10/10 [==============================] - 2s 190ms/step - loss: 0.6917 - accuracy: 0.5110 - val_loss: 0.6940 - val_accuracy: 0.4750\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7cf4f5ab3730>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import numpy as np\n",
        "\n",
        "# Define the new Telugu sentence\n",
        "#new_sentence = \"సోనీ బ్రావియా అనేది అన్ని సంస్థలలోని అత్యంత అందమైన మరియు అత్యుత్తమ బ్రాండ్లలో ఒకటి సోనీ బ్రావియా సంస్థ యొక్క పిక్చర్  నాణ్యత మరియు సౌండ్ ప్రభావాలు నాకు ఇష్టం ఇది చాలా అందమైన TV  హోమ్ థియేటర్లా ఉంటుంది  సోనీ కంపెనీ బ్రాండ్లు నేను చాలా ఇష్టపడతాను ఎందుకంటే ఈ సంస్థ సరిఅయిన కోంట్రాస్ట్ మరియు అధిక నాణ్యతగల పిక్చర్ను ఇస్తుంది\"\n",
        "new_sentence = \"ఈ Nokia మొబైల్ ఫోన్ గురించి నా అనుభవాన్ని చెప్పండి. ఈ ఫోన్ దరఖాస్తు అద్భుతం! ఇది కనుగొన్నానా మరియు మీరు నిరాశలు పట్టకుండా ఉండగలరు. మీరు ఇచ్చిన ధర కి అద్భుత ఫీచర్లు మరియు కన్నా బాగా పని చేస్తుంది.\"\n",
        "# Tokenize the new sentence\n",
        "new_sequence = tokenizer.texts_to_sequences([new_sentence])\n",
        "\n",
        "# Pad the sequence\n",
        "padded_sequence = pad_sequences(new_sequence, maxlen=max_seq_length, padding='post')\n",
        "\n",
        "# Make predictions using the model\n",
        "predictions = model.predict(padded_sequence)\n",
        "\n",
        "# Convert predictions to labels\n",
        "predicted_labels = (predictions > 0.5).astype(int)\n",
        "\n",
        "# Print the predicted label\n",
        "print(\"Predicted label:\", predicted_labels[0][0])\n"
      ],
      "metadata": {
        "id": "paPyLZddzSLG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf9ceaf5-44ca-4dd4-dea2-060ae9a15a37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 81ms/step\n",
            "Predicted label: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VbT1LdU1p6y_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}