Data_Preprocessing(translation).ipynb

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        "<a href=\"https://colab.research.google.com/github/anujsahani01/English-Marathi-Translation/blob/main/Data_Preprocessing(translation).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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        "!pip install kaggle\n",
        "!pip install datasets\n",
        "!pip install --upgrade accelerate\n",
        "!pip install wandb\n",
        "!pip uninstall transformers\n",
        "!pip install --no-cache-dir transformers sentencepiece\n",
        "!pip install evaluate\n",
        "!pip install rouge_score\n",
        "!pip install sacremoses\n",
        "!pip install sacrebleu\n",
        "!pip install langid"
      ]
    },
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      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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        "colab": {
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        },
        "id": "1vVUb77EByiv",
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import langid\n",
        "import pandas as pd\n",
        "import datasets\n",
        "import statistics"
      ],
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        "id": "oJI4zA7GI_Cq"
      },
      "execution_count": null,
      "outputs": []
    },
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        "dataset"
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              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3f9e29d45b1427b90f707df1fca8080"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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        {
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            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
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              "model_id": "31f5387b447046b5a44c850aec8ca407"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['english', 'marathi'],\n",
              "        num_rows: 2637962\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['english', 'marathi'],\n",
              "        num_rows: 879321\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def filter(data):\n",
        "  data = data.replace('*', '').replace(':', '').replace(';', '').replace('$', '').replace('#', '').replace('@', '').replace('%', '').replace('&', '').replace('-', '').replace('_', '').replace('+', '').replace('~', '').replace('`', '').replace('>', '').replace('<', '').replace(\"|\", '').replace('^', '').replace('(', '').replace(')', '').replace('{', '').replace('}', '').replace('!', '').replace('\"', '').replace('[', '').replace(']', '').replace('删', '').replace('=', '').replace(\"'\", '').replace('/', '').replace(\"⚡\", \" \").replace(\".\", '').replace('🐰', '').replace(\"【\", '').replace('?', '')\n",
        "  return data"
      ],
      "metadata": {
        "id": "8sATDTFDFrgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def lang(word):\n",
        "  try:\n",
        "    language = langid.classify(word)[0]\n",
        "  except:\n",
        "    language = None\n",
        "  return language"
      ],
      "metadata": {
        "id": "RtICo3aAJIZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()\n",
        "#  working with half of the data as the dataset is too large\n",
        "df['en'] = dataset['train']['english'][:int(len(dataset['train'])/2.5)]\n",
        "df['mr'] = dataset['train']['marathi'][:int(len(dataset['train'])/2.5)]\n",
        "\n",
        "df1 = pd.DataFrame()\n",
        "df1['en'] = dataset['test']['english'][:int(len(dataset['test'])/2.5)]\n",
        "df1['mr'] = dataset['test']['marathi'][:int(len(dataset['test'])/2.5)]"
      ],
      "metadata": {
        "id": "msCFoTYS_sPj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process(df):\n",
        "  # removing special characters from english text data\n",
        "  df['en_new'] = list(map(lambda x: filter(x) , df['en']))\n",
        "  df['mr_new'] = list(map(lambda x: x , df['mr']))\n",
        "  # drop the previous english and marathi columns\n",
        "  df.drop(['en', 'mr'], axis = 1, inplace = True)\n",
        "  # extracting the first 5 letters for langauge detection( masking it a bit faster)\n",
        "  df['en_wrd'] = list(map(lambda x: x[:5] , df['en_new']))\n",
        "  df['mr_wrd'] = list(map(lambda x: x[:5] , df['mr_new']))\n",
        "  # performing language detection using langid function made above\n",
        "  return df\n",
        "\n",
        "def filter_lang(df):\n",
        "  df['inp_lang'] = df['en_wrd'].map(lang)\n",
        "  df['out_lang']  = df['mr_wrd'].map(lang)\n",
        "  # dropping coulmns in no language is detected\n",
        "  df.dropna(subset = ['inp_lang', 'out_lang'], inplace = True)\n",
        "  # dropping other languages if present in the dataset\n",
        "  if df['inp_lang'].value_counts()['en'] != len(df['inp_lang']):\n",
        "    df.drop(df[df['inp_lang'] != 'en'].index, inplace = True)\n",
        "  # Not do language detection for maratahi as it can be confused with hindi\n",
        "  df.reset_index(inplace = True)\n",
        "  df.drop(['en_wrd', 'mr_wrd', 'index', 'inp_lang'm 'out_lang'], axis = 1, inplace = True)\n",
        "  # dropping duplicate line to make dataset more effective\n",
        "  df.drop_duplicates(inplace = True)\n",
        "  # finding out the length of english data\n",
        "  df['en_len'] = df['en_new'].apply(lambda row: len(row.split(' ')))\n",
        "  return df"
      ],
      "metadata": {
        "id": "Jda0Y-o_Byng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_1 = process(df)\n",
        "df_train = filter_lang(df_1)"
      ],
      "metadata": {
        "id": "rGr--4woCqbm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_train))\n",
        "df_train.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "1xnNQ0vgFBeH",
        "outputId": "1717e52a-9115-4e85-87b0-f5d5106e27f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "608385\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              en_new  \\\n",
              "0          Next few months are really crucial for us   \n",
              "1  Kanwal Singh Chauhan, the president of Sonepat...   \n",
              "2  He has won several competitions at the state a...   \n",
              "3                        This is a worrying increase   \n",
              "4                    Most of the victims were Dalits   \n",
              "\n",
              "                                              mr_new  en_len  \n",
              "0  पुढील तीन महिने आमच्यासाठी खूप महत्त्वपूर्ण आहेत.       8  \n",
              "1  वृत्तसंस्थेने दिलेल्या माहितीनुसार, विरोध करण्...      15  \n",
              "2  राज्य व राष्ट्रीय पातळीवरील अनेक मल्ल त्यांनी ...      11  \n",
              "3                           ही वाढ चिंताजनक तर आहेच.       5  \n",
              "4  त्यातील बहुतांशी घटनांत पीडित व्यक्ती या दलित ...       6  "
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              "      <td>पुढील तीन महिने आमच्यासाठी खूप महत्त्वपूर्ण आहेत.</td>\n",
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              "      <th>3</th>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
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              "    domScope.querySelector('#df-75397123-6f4a-40e8-8c25-191c60420083 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\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-8b3b88b8-d1d4-48f6-aa1d-6a2df43a8b51 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-8b3b88b8-d1d4-48f6-aa1d-6a2df43a8b51');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_2 = process(df1)\n",
        "df_test = filter_lang(df_2)"
      ],
      "metadata": {
        "id": "9Ijggvuj5qlI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "c3TVhphK5J86",
        "outputId": "5a6c5f12-d773-4068-fcd2-10d8e9327c1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              en_new  \\\n",
              "0  This name is derived from two Sanskrit words K...   \n",
              "1  Speaking on the occasion, Minister of State of...   \n",
              "2          Two accused of raping minor girl arrested   \n",
              "3  There was a man of the hill country of Ephraim...   \n",
              "\n",
              "                                              mr_new  en_len  \n",
              "0  हे नाव दोन संस्कृत शब्दांकडून प्राप्त झाले आहे...      11  \n",
              "1  रेल्वे राज्यमंत्री सुरेश अंगडी, भारतीय उद्योग ...      21  \n",
              "2  अल्पवयीन मुलीवर अत्याचार करणाऱ्या दोन नराधमांन...       7  \n",
              "3  एफ्राइमच्या डोंगराळ प्रदेशात मीखा नामक एक माणू...      14  "
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              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Speaking on the occasion, Minister of State of...</td>\n",
              "      <td>रेल्वे राज्यमंत्री सुरेश अंगडी, भारतीय उद्योग ...</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Two accused of raping minor girl arrested</td>\n",
              "      <td>अल्पवयीन मुलीवर अत्याचार करणाऱ्या दोन नराधमांन...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>There was a man of the hill country of Ephraim...</td>\n",
              "      <td>एफ्राइमच्या डोंगराळ प्रदेशात मीखा नामक एक माणू...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4cf27c80-c67f-4b20-b918-b4453df762a6')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
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              "    <div id=\"df-ed1596dd-0ade-46ec-81a4-fda1c80ba0a6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed1596dd-0ade-46ec-81a4-fda1c80ba0a6')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "  .colab-df-quickchart: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",
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              "\n",
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              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "  }\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "    domScope.querySelector('#df-ed1596dd-0ade-46ec-81a4-fda1c80ba0a6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-4cf27c80-c67f-4b20-b918-b4453df762a6 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-4cf27c80-c67f-4b20-b918-b4453df762a6');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuMk19I4PJh7",
        "outputId": "07504e41-4a2f-419e-c635-1026a90fa2c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "327569"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# combining the two datasets\n",
        "frames  = [df_train, df_test]\n",
        "df_final = pd.concat(frames)\n",
        "df_final.head(10)"
      ],
      "metadata": {
        "id": "SmAfCWEgB0OI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "f09f2025-5420-4566-c949-6b63d0ebb04d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              en_new  \\\n",
              "0          Next few months are really crucial for us   \n",
              "1  Kanwal Singh Chauhan, the president of Sonepat...   \n",
              "2  He has won several competitions at the state a...   \n",
              "3                        This is a worrying increase   \n",
              "4                    Most of the victims were Dalits   \n",
              "5                      This has shocked the Congress   \n",
              "6   The funds collected are used for social purposes   \n",
              "7  Similarly, there is a distance restriction for...   \n",
              "8  She said, I needed to have my counsellor on th...   \n",
              "9                                      Dont remember   \n",
              "\n",
              "                                              mr_new  en_len  \n",
              "0  पुढील तीन महिने आमच्यासाठी खूप महत्त्वपूर्ण आहेत.       8  \n",
              "1  वृत्तसंस्थेने दिलेल्या माहितीनुसार, विरोध करण्...      15  \n",
              "2  राज्य व राष्ट्रीय पातळीवरील अनेक मल्ल त्यांनी ...      11  \n",
              "3                           ही वाढ चिंताजनक तर आहेच.       5  \n",
              "4  त्यातील बहुतांशी घटनांत पीडित व्यक्ती या दलित ...       6  \n",
              "5               त्यामुळे काँग्रेसला मोठा धक्का बसला.       5  \n",
              "6          त्या निधीचा वापर सामाजिक कार्यासाठी करतो.       8  \n",
              "7  त्याचप्रमाणे रॅक्स साठी लोडींग पॉईंट्स म्हणजे ...      34  \n",
              "8  'चित्रपटाच्या सेटवर मला माझ्या समुपदेशकांची गर...      16  \n",
              "9                                         आठवले, ना.       2  "
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kanwal Singh Chauhan, the president of Sonepat...</td>\n",
              "      <td>वृत्तसंस्थेने दिलेल्या माहितीनुसार, विरोध करण्...</td>\n",
              "      <td>15</td>\n",
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              "      <th>2</th>\n",
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              "      <td>त्यामुळे काँग्रेसला मोठा धक्का बसला.</td>\n",
              "      <td>5</td>\n",
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              "      <td>The funds collected are used for social purposes</td>\n",
              "      <td>त्या निधीचा वापर सामाजिक कार्यासाठी करतो.</td>\n",
              "      <td>8</td>\n",
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              "      <td>34</td>\n",
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              "      <th>9</th>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZH8AEiKqKxMs",
        "outputId": "63a8a89f-32b2-4bca-da5c-53aba9e790fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "935954"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Minimum:', df_final['en_len'].min(), 'Maximum: ' , df_final['en_len'].max(), 'Mean: ', df_final['en_len'].mean(), 'Mode:' , statistics.mode(df_final['en_len']))"
      ],
      "metadata": {
        "id": "h5vuhPmQB0Qx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38df55de-9e18-4ba9-a772-25138106ec04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum: 1 Maximum:  2411 Mean:  10.481850603768988 Mode: 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# so we will be lossing this many data points\n",
        "count = 0\n",
        "for i in range(4):\n",
        "  if i in list(df_final['en_len']):\n",
        "    count = count + df_final['en_len'].value_counts()[i]\n",
        "\n",
        "print(count)"
      ],
      "metadata": {
        "id": "SkmYBnkwB0TR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e469ccb-9097-4a80-a760-c89d647875db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "81673\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_final.drop(df_final[df_final['en_len'] <= 4].index , inplace =True, axis = 0)\n",
        "df_final.reset_index(inplace = True)\n",
        "df_final.drop(['index', 'en_len'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "fppnIhitB0V1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C17hfLW9TQKT",
        "outputId": "7baf1094-785b-40f0-a8c2-cb239bfff78c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "671781"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observing the length after filtering the sentences"
      ],
      "metadata": {
        "id": "CaP4uTbrcXgC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_final['en_len'] = df_final['en_new'].apply(lambda row: len(row.split(' ')))\n",
        "print('Minimum:', df_final['en_len'].min(), 'Maximum: ' , df_final['en_len'].max(), 'Mean: ', df_final['en_len'].mean(), 'Mode:' , statistics.mode(df_final['en_len']))\n",
        "df_final.drop(['en_len'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "PuDLcLiGB0Yq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "191b97ec-8b94-42e1-aebe-7dff313b3a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum: 5 Maximum:  2411 Mean:  12.03523767418251 Mode: 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split = int(0.80*len(df_final))\n",
        "\n",
        "df_train = df_final[:split]\n",
        "df_test = df_final[split:]"
      ],
      "metadata": {
        "id": "TygrUWWLykFc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.sample(frac = 1)\n",
        "df_train.reset_index(inplace = True)\n",
        "df_test = df_test.sample(frac = 1)\n",
        "df_test.reset_index(inplace= True)"
      ],
      "metadata": {
        "id": "n7lwp4k3Vioo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.drop(['index'], inplace = True, axis = 1)\n",
        "df_test.drop(['index'], inplace = True, axis = 1)"
      ],
      "metadata": {
        "id": "nGv6VRpNWWtE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.to_csv('train.csv' , index = False , encoding = 'utf-8')\n",
        "df_test.to_csv('test.csv' , index = False , encoding = 'utf-8')"
      ],
      "metadata": {
        "id": "oTnOFPLUB0eV"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DBW2I5TqaleM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}