nepali_sentiment_classifier.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import confusion_matrix"
      ]
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      "source": [
        "! git clone https://github.com/rockerritesh/NepaliSentiment"
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          "text": [
            "Cloning into 'NepaliSentiment'...\n",
            "remote: Enumerating objects: 72, done.\u001b[K\n",
            "remote: Counting objects: 100% (72/72), done.\u001b[K\n",
            "remote: Compressing objects: 100% (65/65), done.\u001b[K\n",
            "remote: Total 72 (delta 36), reused 14 (delta 5), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (72/72), done.\n"
          ]
        }
      ]
    },
    {
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        "# Reading the train, test\n",
        "\n",
        "df_train = pd.read_csv('/content/NepaliSentiment/train.csv',encoding='utf-8')\n",
        "df_test = pd.read_csv('/content/NepaliSentiment/test.csv',encoding='utf-8')"
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              "            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",
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              "  "
            ]
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          "metadata": {},
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    {
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        "df_test"
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          "height": 424
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        "id": "GbpoZl-PTv8E",
        "outputId": "86973a0e-fca1-4994-d27b-b78b31124938"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                                   text label\n",
              "0     असाध्यै राम्रो कार्यक्रम आयोजना गरिएको छ हजुरह...     1\n",
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              "4     बहसको सुरुवात भएको छ, अझै जोडदार रुपमा गर्नुपर...     1\n",
              "...                                                 ...   ...\n",
              "1991        कस्ता कस्ता पागल memory king छन यार नेपालमा     0\n",
              "1992                          दोस्रो मूर्ख बिजय साही हो     0\n",
              "1993  बिजय शाहीलाई किन निरुत्साहित गरेको त पुण्य गौत...     0\n",
              "1994  यस्ता बिदेशी महादलालीहरु कहाँ गएर यो प्रश्नको ...     0\n",
              "1995   बुढा खालहरूमा बाँकि रहेको नेताहरुमा बाबुराम ज...     0\n",
              "\n",
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              "          const element = document.querySelector('#df-b12190ab-0c01-44e5-9409-50aef8d251da');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df_train['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iW5adhHVv1Um",
        "outputId": "5ad86c4e-2569-45f4-f416-bbc6346d58f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     2378\n",
              "0     2377\n",
              "2     1236\n",
              "-        5\n",
              "20       1\n",
              "11       1\n",
              "o        1\n",
              "--       1\n",
              "Name: label, dtype: int64"
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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    {
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              "1    888\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,4))\n",
        "df_train['label'].value_counts().plot(kind='bar')\n",
        "plt.axis()\n",
        "plt.show()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "y9K-UZxpvFqq",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x288 with 1 Axes>"
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    {
      "cell_type": "code",
      "source": [
        "df_train['label'].value_counts().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpAWfCVMvFu2",
        "outputId": "50ed9d48-f9a4-4c0e-d6c6-07f53f5bdc36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JHP03sTSczy0",
        "outputId": "0751602b-fae2-4b36-abc7-10ab1e1c6427"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                                   text label\n",
              "0     बजार ले जसरी ट्रेन्ड चेन्ज गर्यो यो हेर्दा तत्...     2\n",
              "1     1000 अंकले घटेको नेप्से 200 अंकले बढ्नु ठूलो क...     1\n",
              "2     होइन यो सानिमा बैंक ले bonus घोसणा गरेको २ महि...     2\n",
              "3     खैँ MBJC प्रति कित्तामा रू,10/-ले बढेर आज रू,1...     2\n",
              "4                                          राम्रो भयो️️     1\n",
              "...                                                 ...   ...\n",
              "5995  समाज परिवर्तन गराउन लाई अरु को मुख हेर्ने भन्द...     1\n",
              "5996  Filmy क्षेत्रमा धेरै गर्नु भयो,,अब समाज र देश ...     2\n",
              "5997  यस्तै यस्तै कार्यक्रम अझ बढी हुन जरुरी छ कुना ...     2\n",
              "5998                                 बधाई र सुभकामना ।।     1\n",
              "5999      कार्यक्रम एकदमै सकारात्मक छ। निरन्तरता पाओस्।     1\n",
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              "          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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tRpy4P__U6ek",
        "outputId": "de319902-cd6a-471a-cf79-831d8a540d1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text label\n",
              "0     असाध्यै राम्रो कार्यक्रम आयोजना गरिएको छ हजुरह...     1\n",
              "1     राम्रो कार्यक्रम, पहिलो सिजनले समेटेको कार्यक्...     1\n",
              "2     महानायक राजेश हमाल तपाई साँच्चै धन्यवादको पात्...     1\n",
              "3     जातको प्रष्न बाट सबै जनालाई सकरात्मक सन्देश मि...     1\n",
              "4     बहसको सुरुवात भएको छ, अझै जोडदार रुपमा गर्नुपर...     1\n",
              "...                                                 ...   ...\n",
              "1991        कस्ता कस्ता पागल memory king छन यार नेपालमा     0\n",
              "1992                          दोस्रो मूर्ख बिजय साही हो     0\n",
              "1993  बिजय शाहीलाई किन निरुत्साहित गरेको त पुण्य गौत...     0\n",
              "1994  यस्ता बिदेशी महादलालीहरु कहाँ गएर यो प्रश्नको ...     0\n",
              "1995   बुढा खालहरूमा बाँकि रहेको नेताहरुमा बाबुराम ज...     0\n",
              "\n",
              "[1995 rows x 2 columns]"
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              "      <td>राम्रो कार्यक्रम, पहिलो सिजनले समेटेको कार्यक्...</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1993</th>\n",
              "      <td>बिजय शाहीलाई किन निरुत्साहित गरेको त पुण्य गौत...</td>\n",
              "      <td>0</td>\n",
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              "      <th>1994</th>\n",
              "      <td>यस्ता बिदेशी महादलालीहरु कहाँ गएर यो प्रश्नको ...</td>\n",
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              "      <th>1995</th>\n",
              "      <td>बुढा खालहरूमा बाँकि रहेको नेताहरुमा बाबुराम ज...</td>\n",
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              "<p>1995 rows × 2 columns</p>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c845602-fe9c-4a00-9eff-02639c0395a7');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OYhTmMjqWdWA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.drop(df_train[df_train['label'] == '-'].index, inplace = True)\n",
        "df_train.drop(df_train[df_train['label'] == '20'].index, inplace = True)\n",
        "df_train.drop(df_train[df_train['label'] == '11'].index, inplace = True)\n",
        "df_train.drop(df_train[df_train['label'] == 'o'].index, inplace = True)\n",
        "df_train.drop(df_train[df_train['label'] == '--'].index, inplace = True)"
      ],
      "metadata": {
        "id": "Ktz2SOcNvFx2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.drop(df_test[df_test['label'] == '-'].index, inplace = True)\n",
        "df_test.drop(df_test[df_test['label'] == 'o'].index, inplace = True)"
      ],
      "metadata": {
        "id": "b226PLKTT6s4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_whrFMJPUc46",
        "outputId": "ded0a1b5-46c4-45fd-ace1-877d91390e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5991"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df_train.drop(df_train.index[[850,3869]],inplace=True)"
      ],
      "metadata": {
        "id": "yaSFt_uVS1s9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uktoAQ8V6Gj",
        "outputId": "316c81c6-7ae2-4bba-8274-38ba4f7d612e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5991"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML"
      ],
      "metadata": {
        "id": "kHCC9oTKgrXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['label']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6i4h89WhN1F",
        "outputId": "f317cd3d-fe12-497e-b07a-a93459ff8007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2\n",
              "1       1\n",
              "2       2\n",
              "3       2\n",
              "4       1\n",
              "       ..\n",
              "5995    1\n",
              "5996    2\n",
              "5997    2\n",
              "5998    1\n",
              "5999    1\n",
              "Name: label, Length: 5991, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dh9eb_UXlLzM",
        "outputId": "9766190e-808b-433c-9e44-e350d801854e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
              "            ...\n",
              "            5990, 5991, 5992, 5993, 5994, 5995, 5996, 5997, 5998, 5999],\n",
              "           dtype='int64', length=5991)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPJ-WkOSUIeQ",
        "outputId": "fb11c2b1-8e0c-42e6-ba45-350907d1fb37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    888\n",
              "0    610\n",
              "2    496\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dIMjMaGvF09",
        "outputId": "ed53bab6-32c7-475a-8084-4420e7ee58a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2378\n",
              "0    2377\n",
              "2    1236\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,4))\n",
        "df_train['label'].value_counts().plot(kind='bar')\n",
        "plt.axis()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "c_HjjPehvF38",
        "outputId": "7ffd1a51-1993-4e20-cf4a-deccf1e3ba9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x288 with 1 Axes>"
            ],
            "image/png": 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    {
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      "source": [
        "df_train"
      ],
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "eZTeJGDQ1ETe",
        "outputId": "005b63f3-b453-4fff-d8af-ab1f603e407c"
      },
      "execution_count": null,
      "outputs": [
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              "                                                   text label\n",
              "0     बजार ले जसरी ट्रेन्ड चेन्ज गर्यो यो हेर्दा तत्...     2\n",
              "1     1000 अंकले घटेको नेप्से 200 अंकले बढ्नु ठूलो क...     1\n",
              "2     होइन यो सानिमा बैंक ले bonus घोसणा गरेको २ महि...     2\n",
              "3     खैँ MBJC प्रति कित्तामा रू,10/-ले बढेर आज रू,1...     2\n",
              "4                                          राम्रो भयो️️     1\n",
              "...                                                 ...   ...\n",
              "5995  समाज परिवर्तन गराउन लाई अरु को मुख हेर्ने भन्द...     1\n",
              "5996  Filmy क्षेत्रमा धेरै गर्नु भयो,,अब समाज र देश ...     2\n",
              "5997  यस्तै यस्तै कार्यक्रम अझ बढी हुन जरुरी छ कुना ...     2\n",
              "5998                                 बधाई र सुभकामना ।।     1\n",
              "5999      कार्यक्रम एकदमै सकारात्मक छ। निरन्तरता पाओस्।     1\n",
              "\n",
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              "      <td>Filmy क्षेत्रमा धेरै गर्नु भयो,,अब समाज र देश ...</td>\n",
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              "          document.querySelector('#df-fe37f6a5-1aad-40f1-bb37-ccba91f77f6b button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-fe37f6a5-1aad-40f1-bb37-ccba91f77f6b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          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",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = df_train['text'],df_train['label']\n",
        "X_test, y_test = df_test['text'],df_test['label']"
      ],
      "metadata": {
        "id": "7G5It2jFvF7B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.index[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSi0PinVS_Y5",
        "outputId": "4fe233aa-479e-48b7-cac1-528613ad8e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UZYSpXNhS_mg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WwJvXnMsvF9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P9nPG76X3oto"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## emoji"
      ],
      "metadata": {
        "id": "aCJaaVjq3yzS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install emot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYx1jIMuFzqw",
        "outputId": "507fa65c-8021-4b7a-9a4c-a3028810d292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: emot in /usr/local/lib/python3.7/dist-packages (3.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import pickle\n",
        "from emot.emo_unicode import *"
      ],
      "metadata": {
        "id": "C9WsQWtGGEG1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_TRAIN=[]"
      ],
      "metadata": {
        "id": "72vdHbphHAXe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 'Emoji_Dict.p'- download link https://drive.google.com/open?id=1G1vIkkbqPBYPKHcQ8qy0G2zkoab2Qv4v\n",
        "with open('Emoji_Dict.p', 'rb') as fp:\n",
        "    Emoji_Dict = pickle.load(fp)\n",
        "Emoji_Dict = {v: k for k, v in Emoji_Dict.items()}"
      ],
      "metadata": {
        "id": "_juFrHCeH2Vl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_emojis_to_word(text):\n",
        "    for emot in Emoji_Dict:\n",
        "        text = re.sub(r'('+emot+')', \"_\".join(Emoji_Dict[emot].replace(\",\",\"\").replace(\":\",\"\").split()), text)\n",
        "    return text"
      ],
      "metadata": {
        "id": "hhwgUUbzFzoN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(X_train)):\n",
        "  test=convert_emojis_to_word(X_train[df_train.index[i]])\n",
        "  X_TRAIN.append(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "1SfOjHNHIShD",
        "outputId": "194f8538-1fb6-4435-eabb-43610fe998b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-418-40e285c30693>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_emojis_to_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mX_TRAIN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-417-97514322994f>\u001b[0m in \u001b[0;36mconvert_emojis_to_word\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_emojis_to_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0memot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mEmoji_Dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m         \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'('\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0memot\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m')'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEmoji_Dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0memot\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\":\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/re.py\u001b[0m in \u001b[0;36msub\u001b[0;34m(pattern, repl, string, count, flags)\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0ma\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mMatch\u001b[0m \u001b[0mobject\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmust\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     a replacement string to be used.\"\"\"\n\u001b[0;32m--> 194\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msubn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: expected string or bytes-like object"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_TRAIN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qR7-sXs92eZH",
        "outputId": "c573d80a-c90a-436a-c34c-2ce0dab9de4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3869"
            ]
          },
          "metadata": {},
          "execution_count": 419
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cQXTcrV2n26",
        "outputId": "3daa0fa7-2cc9-4c70-c084-b5f26e28febf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5991"
            ]
          },
          "metadata": {},
          "execution_count": 420
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X-NZO2aSPf7o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_TEST=[]\n",
        "Y_TEST=[]\n",
        "\n",
        "\n",
        "for i in range(len(X_test)):\n",
        "  if(i==972):\n",
        "    print(i)\n",
        "  else:\n",
        "    test=convert_emojis_to_word(X_test[df_test.index[i]])\n",
        "    X_TEST.append(test)\n",
        "    testy= y_test[df_test.index[i]]\n",
        "    Y_TEST.append(testy)"
      ],
      "metadata": {
        "id": "sLMOV-SDpmF_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OhgviSxKt1x3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2FPMQlvxtXpq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "id": "A8qOl1fHFzmO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_TEST)"
      ],
      "metadata": {
        "id": "QvhtckrGIA3x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FX9AZmWUFzjR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[3871]"
      ],
      "metadata": {
        "id": "YZbCu3gNFzg-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_TRAIN[3868]"
      ],
      "metadata": {
        "id": "0gPXOkioFzfC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nDAVhteEFza3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "id": "hK5y_pdF2Tix",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f84722b2-6ed8-497d-a121-ba5c6de160dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /root/nltk_data/corpora/stopwords"
      ],
      "metadata": {
        "id": "zQBzZm5r58a2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6E57m_6ovuo8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#X_train=X_train.values.astype('U')\n",
        "#X_test=X_test.values.astype('U')"
      ],
      "metadata": {
        "id": "GK2OhEQX8erE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "id": "pp_CUppn8k4r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "#stopwords_list = stopwords.words('english') + stopwords.words('nepali')\n",
        "\n",
        "stopWords = set(nltk.corpus.stopwords.words('/content/nepali'))\n",
        "\n",
        "cv_doc = CountVectorizer(max_features=20000,stop_words=stopWords ,ngram_range=(1,1), min_df=3,encoding='utf-8')\n",
        "cv_vector = cv_doc.fit(X_train)"
      ],
      "metadata": {
        "id": "_7rmG3b1vGAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import stopwords\n",
        "#from nltk import TweetTokenizer\n",
        "#tokenizer = TweetTokenizer()"
      ],
      "metadata": {
        "id": "L_Wkao4XNcos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stopWords = nltk.corpus.stopwords.words('/content/NepaliSentiment/nepali') + stopwords.words('english') + nltk.corpus.stopwords.words('/content/NepaliSentiment/hindi')\n",
        "\n",
        "tfidfVectorizer = TfidfVectorizer(max_features=50000,tokenizer=lambda x: x.split(\" \") ,sublinear_tf=True, encoding='utf-8',decode_error='ignore',ngram_range=(1, 1),stop_words=stopWords)\n",
        "\n",
        "vectorised = tfidfVectorizer.fit(X_TRAIN)"
      ],
      "metadata": {
        "id": "Zlg2jpCcgkC6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# FOR EMOJII\n",
        "X_train = vectorised.transform(X_TRAIN)\n",
        "X_test= vectorised.transform(X_TEST)"
      ],
      "metadata": {
        "id": "N-daB8bowD78"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = vectorised.transform(X_train)\n",
        "X_test= vectorised.transform(X_test)"
      ],
      "metadata": {
        "id": "3B5_ddJOgj3I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import Word2Vec, KeyedVectors\n",
        "#model = Word2Vec(X_train, size=1000, min_count=5, window=10, sg=0, workers=10)\n",
        "#print(\"Vocabulary dimension\", model.vector_size)\n",
        "#print(\"Number of words in vocab\", len(model.wv.vocab))"
      ],
      "metadata": {
        "id": "r9ysU6BdGDeI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model.wv.most_similar('न', topn=10)"
      ],
      "metadata": {
        "id": "FnR-6_OBGXMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#X_train = cv_vector.transform(X_train)\n",
        "#X_test= cv_vector.transform(X_test)"
      ],
      "metadata": {
        "id": "k3ynYdINvGIv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "id": "8I_TBxYUvGL_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(Y_TEST)"
      ],
      "metadata": {
        "id": "w8wM0rckxm67"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "id": "iKVGjq9mxz9x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# FOR EMOJI\n",
        "label_encoder = LabelEncoder().fit(y_train)\n",
        "\n",
        "y_train = label_encoder.transform(y_train)\n",
        "y_test = label_encoder.transform(Y_TEST)"
      ],
      "metadata": {
        "id": "Gn0F74LkwQzo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = LabelEncoder().fit(y_train)\n",
        "\n",
        "y_train = label_encoder.transform(y_train)\n",
        "y_test = label_encoder.transform(y_test)"
      ],
      "metadata": {
        "id": "HNGEgDiA8Ljg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#FOR EMOJI\n",
        "from sklearn.svm import LinearSVC\n",
        "clf=LinearSVC(loss='hinge',random_state=0,max_iter=10000,penalty='l2')\n",
        "clf.fit(X_train, y_train)    # training model on train data\n",
        "\n",
        "clfval = clf.predict(X_test)   # predicting test data\n",
        "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "clf=SVC(kernel='linear',random_state=0,max_iter=10000)\n",
        "clf.fit(X_train, y_train)    # training model on train data\n",
        "\n",
        "clfval = clf.predict(X_test)   # predicting test data\n",
        "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score\n",
        "\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "clf=SVC(kernel='linear',random_state=0,max_iter=10000)\n",
        "clf.fit(X_train, y_train)    # training model on train data\n",
        "\n",
        "clfval = clf.predict(X_test)   # predicting test data\n",
        "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score\n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "nb=MultinomialNB(alpha=1.5,fit_prior=False)\n",
        "nb.fit(X_train,y_train)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "y_pred = nb.predict(X_test)\n",
        "\n",
        "print('accuracy %s' % f1_score(y_test, y_pred,average='micro'))"
      ],
      "metadata": {
        "id": "JqZYlfBQwcBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "clf=LinearSVC(loss='hinge',random_state=0,max_iter=10000,penalty='l2')\n",
        "clf.fit(X_train, y_train)    # training model on train data\n",
        "\n",
        "clfval = clf.predict(X_test)   # predicting test data\n",
        "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score\n"
      ],
      "metadata": {
        "id": "gaev856598LT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yfLH5q2jwltf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "clf=SVC(kernel='linear',random_state=0,max_iter=10000)\n",
        "clf.fit(X_train, y_train)    # training model on train data\n",
        "\n",
        "clfval = clf.predict(X_test)   # predicting test data\n",
        "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))  # printing F1 score\n"
      ],
      "metadata": {
        "id": "lit69RfI98IQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "nb=MultinomialNB(alpha=1.5,fit_prior=False)\n",
        "nb.fit(X_train,y_train)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "y_pred = nb.predict(X_test)\n",
        "\n",
        "print('accuracy %s' % f1_score(y_test, y_pred,average='micro'))"
      ],
      "metadata": {
        "id": "CvuVYiqGsgE3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lrr=LogisticRegression(solver='saga', n_jobs=1, C=1e5)\n",
        "lrr.fit(X_train, y_train)\n",
        "lrrval = lrr.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, lrrval, average='micro')))"
      ],
      "metadata": {
        "id": "FAp6Gzs598Fx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import BernoulliNB\n",
        "clf = BernoulliNB(alpha=0.9,fit_prior=False)\n",
        "clf.fit(X_train, y_train)\n",
        "clfval = clf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, clfval, average='micro')))"
      ],
      "metadata": {
        "id": "lse2rsYu98DD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import ComplementNB\n",
        "cllf = ComplementNB(alpha=1.0, fit_prior=False,norm=True)\n",
        "cllf.fit(X_train, y_train)\n",
        "cllfval = clf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, cllfval, average='micro')))"
      ],
      "metadata": {
        "id": "Bp0aJZwN98Ad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import PassiveAggressiveClassifier\n",
        "cclf = PassiveAggressiveClassifier(max_iter=2500, random_state=43,tol=1e-5)\n",
        "cclf.fit(X_train, y_train)\n",
        "\n",
        "cllfval = cclf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, cllfval, average='micro')))"
      ],
      "metadata": {
        "id": "-tu2ghVd9794"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C17lU2xO977L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.2,max_depth=1, random_state=0).fit(X_train, y_train)\n",
        "\n",
        "gbpred=clf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, gbpred, average='micro')))"
      ],
      "metadata": {
        "id": "I9xpNFkyayA-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.linear_model import Perceptron\n",
        "\n",
        "percept = Perceptron(alpha=0.00001, tol=1e-6, penalty='l1', random_state=1, max_iter=3000)\n",
        "percept = percept.fit(X_train, y_train)\n",
        "\n",
        "cpfval = percept.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, cpfval, average='micro')))"
      ],
      "metadata": {
        "id": "_vqv5sTrax-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "\n",
        "clf = BaggingClassifier(base_estimator=SVC(kernel='linear',random_state=0,max_iter=10000),n_estimators=10, random_state=0).fit(X_train, y_train)\n",
        "cpfval = clf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, cpfval, average='micro')))"
      ],
      "metadata": {
        "id": "4alh95jvfOQ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "\n",
        "clf = BaggingClassifier(base_estimator=MultinomialNB(alpha=1.20,fit_prior=False),n_estimators=10, random_state=0).fit(X_train, y_train)\n",
        "cpfval = clf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, cpfval, average='micro')))"
      ],
      "metadata": {
        "id": "HHUsQdFcfOMf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "neigh = KNeighborsClassifier(weights='distance',algorithm='brute' ,n_neighbors=5)\n",
        "neigh.fit(X_train, y_train)\n",
        "\n",
        "ngval = clf.predict(X_test)\n",
        "print('F1 Score : {}'.format(f1_score(y_test, ngval, average='micro')))"
      ],
      "metadata": {
        "id": "UTGfMrHAfOIE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o5nNju7MfOEw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NdkbBoLufN77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "shlLKSP3ax2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "#countvector\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(cf_matrix, linewidths=1, annot=True, ax=ax, fmt='g')"
      ],
      "metadata": {
        "id": "bfghz32IaxzY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U0SDNNQJqRU0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yVHlRVmlw20_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1gq0bI_zw2yE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zE9RCiR7xN7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "834Cx1FRw2vf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GmcoMvxkw2tG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jdL5EzRRw2qS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_F4B8wqdw2ny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KMDz8PQbw2lI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ibIbPYtyw2hs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sWTGOp9Bw2fS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X0h3e8auw2cn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k1nhPeqYw2aN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ShJHZCpEw2Xd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XJmSncPgw2Ur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5SzxDlafw2SS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cYTH74nZw2QP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AjOfDPX1w2NH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JsrTaa4tw2KP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRansformer"
      ],
      "metadata": {
        "id": "DAhxCn55eJGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train"
      ],
      "metadata": {
        "id": "3oYSkTm7w2Hu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "6054c918-0014-438b-be0b-8eb36401dd13"
      },
      "execution_count": null,
      "outputs": [
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              "                                                   text label\n",
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              "1     1000 अंकले घटेको नेप्से 200 अंकले बढ्नु ठूलो क...     1\n",
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              "5995  समाज परिवर्तन गराउन लाई अरु को मुख हेर्ने भन्द...     1\n",
              "5996  Filmy क्षेत्रमा धेरै गर्नु भयो,,अब समाज र देश ...     2\n",
              "5997  यस्तै यस्तै कार्यक्रम अझ बढी हुन जरुरी छ कुना ...     2\n",
              "5998                                 बधाई र सुभकामना ।।     1\n",
              "5999      कार्यक्रम एकदमै सकारात्मक छ। निरन्तरता पाओस्।     1\n",
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              "            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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = pd.get_dummies(df.label, prefix='label')\n",
        "print(y.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGUxkTQee3sw",
        "outputId": "74138cfe-4372-4cfe-8606-f795d6574b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   label_0  label_1  label_2\n",
            "0        0        0        1\n",
            "1        0        1        0\n",
            "2        0        0        1\n",
            "3        0        0        1\n",
            "4        0        1        0\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQ-gcqL7fzA3",
        "outputId": "f7241320-5f0d-43d3-ec9b-b377f08fd823"
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      "outputs": [
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          "metadata": {},
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        "df['label_0'] = y['label_0']\n",
        "df['label_1'] = y['label_1']\n",
        "df['label_2'] = y['label_2']"
      ],
      "metadata": {
        "id": "UBSwQB7Df2oC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "Tl_n6zlLgD4B",
        "outputId": "9f4ff4ec-3896-4f0c-9fed-c6ec490df09c"
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      "execution_count": null,
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    {
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        "df = df.drop(columns='label')\n",
        "df"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "A5_1NfnYgEss",
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      "execution_count": null,
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7972</th>\n",
              "      <td>बिजय शाहीलाई किन निरुत्साहित गरेको त पुण्य गौत...</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7973</th>\n",
              "      <td>यस्ता बिदेशी महादलालीहरु कहाँ गएर यो प्रश्नको ...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7974</th>\n",
              "      <td>बुढा खालहरूमा बाँकि रहेको नेताहरुमा बाबुराम ज...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "<p>7974 rows × 4 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08d5e187-0b30-42da-a4af-a56fa3b75f6e');\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": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install transformers -q"
      ],
      "metadata": {
        "id": "wifpGKDxg2Qo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import re\n",
        "import string\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "\n",
        "import transformers\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from transformers import BertTokenizer, AutoTokenizer, BertModel, BertConfig, AutoModel, AdamW,get_linear_schedule_with_warmup\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "pd.set_option(\"display.max_columns\", None)"
      ],
      "metadata": {
        "id": "GyjOpse2gK8v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "df_train, df_dev = train_test_split(df, test_size=0.2, random_state=42)\n",
        "print(f'Training examples: {len(df_train)}, validation examples: {len(df_dev)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjbhWWsXgWZU",
        "outputId": "9400fd03-dec1-4300-c898-c7f383c8b743"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training examples: 6379, validation examples: 1595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train =  df_train.reset_index()\n",
        "df_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LlJE0Es6hEnP",
        "outputId": "059de2c6-05d3-45c3-f4d4-fc6735e52984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "          const element = document.querySelector('#df-66a34d9a-f326-44cf-890b-1b387acf4f11');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dev =  df_dev.reset_index()\n",
        "df_dev.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "S9LdYfJXhGQU",
        "outputId": "686fc417-11c4-44e3-c5a7-6b5878327776"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\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-2bf050e7-a381-46be-b31d-ee391b3663bd 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-2bf050e7-a381-46be-b31d-ee391b3663bd');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train.shape)\n",
        "print(df_dev.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5JcPbfHhIgP",
        "outputId": "da83a10b-5eaa-4cae-b5dd-b16f6ef99ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(6379, 5)\n",
            "(1595, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "zctEjvjEhK33"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sections of config\n",
        "\n",
        "# Defining some key variables that will be used later on in the training\n",
        "MAX_LEN = 168\n",
        "TRAIN_BATCH_SIZE = 16\n",
        "VALID_BATCH_SIZE = 16\n",
        "EPOCHS = 3\n",
        "LEARNING_RATE = 5e-5\n",
        "tokenizer = AutoTokenizer.from_pretrained('Sakonii/deberta-base-nepali')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          "output_type": "display_data",
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            "text/plain": [
              "Downloading:   0%|          | 0.00/413 [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "target_cols = ['label_0', 'label_1', 'label_2']\n",
        "target_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vUSJ7hSGhPl_",
        "outputId": "83d0fdd7-b27f-481e-a6f7-b4381d5c5f26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['label_0', 'label_1', 'label_2']"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, df, tokenizer, max_len):\n",
        "        self.df = df\n",
        "        self.max_len = max_len\n",
        "        self.text = df.text\n",
        "        self.tokenizer = tokenizer\n",
        "        self.targets = df[target_cols].values\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        text = self.text[index]\n",
        "        inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            truncation=True,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            padding='max_length',\n",
        "            return_token_type_ids=True\n",
        "        )\n",
        "        ids = inputs['input_ids']\n",
        "        mask = inputs['attention_mask']\n",
        "        token_type_ids = inputs[\"token_type_ids\"]\n",
        "\n",
        "        return {\n",
        "            'ids': torch.tensor(ids, dtype=torch.long),\n",
        "            'mask': torch.tensor(mask, dtype=torch.long),\n",
        "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n",
        "            'targets': torch.tensor(self.targets[index], dtype=torch.float)\n",
        "        }"
      ],
      "metadata": {
        "id": "1q5qbxg6hXZj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = BERTDataset(df_train, tokenizer, MAX_LEN)\n",
        "valid_dataset = BERTDataset(df_dev, tokenizer, MAX_LEN)"
      ],
      "metadata": {
        "id": "DVzSrIczhaRO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = DataLoader(train_dataset, batch_size=TRAIN_BATCH_SIZE,\n",
        "                          num_workers=4, shuffle=True, pin_memory=True)\n",
        "valid_loader = DataLoader(valid_dataset, batch_size=VALID_BATCH_SIZE,\n",
        "                          num_workers=4, shuffle=False, pin_memory=True)"
      ],
      "metadata": {
        "id": "S3WoreXTheUB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shushant/nepaliBERT"
      ],
      "metadata": {
        "id": "AxpKuMk4sNmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the customized model, by adding a drop out and a dense layer on top of distil bert to get the final output for the model.\n",
        "from transformers import AutoModelForMaskedLM\n",
        "class BERTClass(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(BERTClass, self).__init__()\n",
        "        self.roberta = AutoModel.from_pretrained('NepBERTa/NepBERTa', from_tf = True)  #for bert-base-model\n",
        "        #self.roberta = AutoModelForMaskedLM.from_pretrained('Sakonii/deberta-base-nepali')\n",
        "        self.l2 = torch.nn.Dropout(0.4)\n",
        "        #self.fc = torch.nn.RNN(10,20,1,8)\n",
        "        self.fc = torch.nn.Linear(768,3)\n",
        "\n",
        "    def forward(self, ids, mask, token_type_ids):\n",
        "        _, features = self.roberta(ids, attention_mask = mask, token_type_ids = token_type_ids, return_dict=False)\n",
        "        output_2 = self.l2(features)\n",
        "        output = self.fc(output_2)\n",
        "        return output\n",
        "\n",
        "model = BERTClass()\n",
        "model.to(device);"
      ],
      "metadata": {
        "id": "Qr9eO9xwhgBF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_fn(outputs, targets):\n",
        "    return torch.nn.BCEWithLogitsLoss()(outputs, targets)"
      ],
      "metadata": {
        "id": "aDlXzHIohihc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(params =  model.parameters(), lr=LEARNING_RATE, weight_decay=1e-6)"
      ],
      "metadata": {
        "id": "mn4VKkNWhlv7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler = get_linear_schedule_with_warmup(\n",
        "                optimizer,\n",
        "                num_warmup_steps=0,\n",
        "                num_training_steps=len(train_loader) * EPOCHS\n",
        "            )"
      ],
      "metadata": {
        "id": "to4QT_oqhnSY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation():\n",
        "    model.eval()\n",
        "    fin_targets=[]\n",
        "    fin_outputs=[]\n",
        "    with torch.no_grad():\n",
        "        for _, data in enumerate(valid_loader, 0):\n",
        "            ids = data['ids'].to(device, dtype = torch.long)\n",
        "            mask = data['mask'].to(device, dtype = torch.long)\n",
        "            token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "            targets = data['targets'].to(device, dtype = torch.float)\n",
        "            outputs = model(ids, mask, token_type_ids)\n",
        "            fin_targets.extend(targets.cpu().detach().numpy().tolist())\n",
        "            fin_outputs.extend(torch.sigmoid(outputs).cpu().detach().numpy().tolist())\n",
        "    return fin_outputs, fin_targets"
      ],
      "metadata": {
        "id": "dQ9DMk9ohpIk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(epoch):\n",
        "    model.train()\n",
        "    for _,data in enumerate(train_loader, 0):\n",
        "        ids = data['ids'].to(device, dtype = torch.long)\n",
        "        mask = data['mask'].to(device, dtype = torch.long)\n",
        "        token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "        targets = data['targets'].to(device, dtype = torch.float)\n",
        "        outputs = model(ids, mask, token_type_ids)\n",
        "        loss = loss_fn(outputs, targets)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "    print(f'Epoch: {epoch}, Loss:  {loss.item()}')\n",
        "    outputs, targets = validation()\n",
        "    outputs = np.array(outputs) >= 0.5\n",
        "    accuracy = metrics.accuracy_score(targets, outputs)\n",
        "    f1_score_micro = metrics.f1_score(targets, outputs, average='micro')\n",
        "    f1_score_macro = metrics.f1_score(targets, outputs, average='macro')\n",
        "    print(f\"Accuracy Score = {accuracy}\")\n",
        "    print(f\"F1 Score (Micro) = {f1_score_micro}\")\n",
        "    print(f\"F1 Score (Macro) = {f1_score_macro}\")\n",
        "    print('\\n')"
      ],
      "metadata": {
        "id": "OrErg4p6htpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(EPOCHS):\n",
        "    train(epoch)"
      ],
      "metadata": {
        "id": "mCkDNI8ih016"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs, targets = validation()\n",
        "outputs = np.array(outputs) >= 0.5\n",
        "accuracy = metrics.accuracy_score(targets, outputs)\n",
        "f1_score_micro = metrics.f1_score(targets, outputs, average='micro')\n",
        "f1_score_macro = metrics.f1_score(targets, outputs, average='macro')\n",
        "f1_score_weighted = metrics.f1_score(targets, outputs, average='weighted')\n",
        "print(f\"Accuracy Score = {accuracy}\")\n",
        "print(f\"F1 Score (Micro) = {f1_score_micro}\")\n",
        "print(f\"F1 Score (Macro) = {f1_score_macro}\")\n",
        "print(f\"F1 Score (Weighted) = {f1_score_weighted}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kx7V4q3Dh3rJ",
        "outputId": "218b960c-7c56-40e6-d542-c11a9bdba0e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score = 0.7410658307210032\n",
            "F1 Score (Micro) = 0.7583572110792741\n",
            "F1 Score (Macro) = 0.7368247617145977\n",
            "F1 Score (Weighted) = 0.7571653780070924\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.f1_score(outputs[:,0],np.array(targets)[:,0]),\n",
        "metrics.f1_score(outputs[:,1],np.array(targets)[:,1]),\n",
        "metrics.f1_score(outputs[:,2],np.array(targets)[:,2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dE2bJ-QXj-St",
        "outputId": "90df28b8-b428-4857-c478-e1fe7d57d3fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7497773820124667 0.8409785932721713 0.6197183098591549\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOgwx7cqkE1i",
        "outputId": "afcc6572-5877-4fa9-ed07-f432873087bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False, False, False],\n",
              "       [ True, False, False],\n",
              "       [False,  True, False],\n",
              "       ...,\n",
              "       [False,  True, False],\n",
              "       [False,  True, False],\n",
              "       [False,  True, False]])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(targets, outputs))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwiY8rCbko6r",
        "outputId": "57f3b445-769f-4bf9-c986-ff58ebd7de7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.75      0.76       573\n",
            "           1       0.83      0.85      0.84       654\n",
            "           2       0.65      0.58      0.61       368\n",
            "\n",
            "   micro avg       0.77      0.75      0.76      1595\n",
            "   macro avg       0.75      0.73      0.74      1595\n",
            "weighted avg       0.77      0.75      0.76      1595\n",
            " samples avg       0.75      0.75      0.75      1595\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cf_matrix = confusion_matrix(np.argmax(targets, axis = 1), np.argmax(outputs, axis = 1))\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(cf_matrix, linewidths=1, annot=True, ax=ax, fmt='g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "0A07NwrmkroG",
        "outputId": "0ee8f2f1-c65c-4bc6-dbad-db57acaf9aaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ae77c34f0>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#np.argmax(targets, axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9W5DFcblYrv",
        "outputId": "ca8b0924-caa6-4600-b83f-20093da9ee52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 1, ..., 1, 2, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "77WiO0kkluE9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# test deploy"
      ],
      "metadata": {
        "id": "ddxbNtT-txdb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS, TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "8jMR6S5ktxAc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = df_train['text'],df_train['label']\n",
        "X_test, y_test = df_test['text'],df_test['label']"
      ],
      "metadata": {
        "id": "XUXVkwtUtw9W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=X_train.values.astype('U')\n",
        "X_test=X_test.values.astype('U')"
      ],
      "metadata": {
        "id": "jyXPgQvPvgVK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "stopWords = nltk.corpus.stopwords.words('/content/NepaliSentiment/nepali') + stopwords.words('english') + nltk.corpus.stopwords.words('/content/NepaliSentiment/hindi')\n",
        "\n",
        "# create a TF-IDF vectorizer object\n",
        "\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features=1000,tokenizer=lambda x: x.split(\" \") ,sublinear_tf=True, encoding='utf-8',decode_error='ignore',ngram_range=(1, 1),stop_words=stopWords)\n",
        "\n",
        "# fit the object with the training data tweets\n",
        "tfidf_vectorizer.fit(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cad7HW6rtw6P",
        "outputId": "80a2cda9-85c8-4129-a9f7-dadeca5aad23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/feature_extraction/text.py:516: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TfidfVectorizer(decode_error='ignore', max_features=1000,\n",
              "                stop_words=['छ', 'र', 'पनि', 'छन्', 'लागि', 'भएको', 'गरेको',\n",
              "                            'भने', 'गर्न', 'गर्ने', 'हो', 'तथा', 'यो', 'रहेको',\n",
              "                            'उनले', 'थियो', 'हुने', 'गरेका', 'थिए', 'गर्दै',\n",
              "                            'तर', 'नै', 'को', 'मा', 'हुन्', 'भन्ने', 'हुन',\n",
              "                            'गरी', 'त', 'हुन्छ', ...],\n",
              "                sublinear_tf=True,\n",
              "                tokenizer=<function <lambda> at 0x7fbe19a52040>)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_idf = tfidf_vectorizer.transform(X_train)\n",
        "test_idf  = tfidf_vectorizer.transform(X_test)"
      ],
      "metadata": {
        "id": "te5tuvzmtw12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create the object of LinearRegression Model\n",
        "model_LR = LogisticRegression()\n",
        "\n",
        "# fit the model with the training data\n",
        "model_LR.fit(train_idf, y_train)\n",
        "\n",
        "# predict the label on the traning data\n",
        "predict_train = model_LR.predict(train_idf)\n",
        "\n",
        "# predict the model on the test data\n",
        "predict_test = model_LR.predict(test_idf)\n",
        "\n",
        "# f1 score on train data\n",
        "f1_score(y_true= y_train, y_pred= predict_train, average = 'macro'), f1_score(y_true= y_test, y_pred= predict_test, average='macro')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfJbF73bvsKs",
        "outputId": "5cb3baf8-f20e-4593-cef4-52a42c52062d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.7389616479410014, 0.5065948653205213)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define the stages of the pipeline\n",
        "pipeline = Pipeline(steps= [('tfidf', TfidfVectorizer(max_features=1000,\n",
        "                                                      tokenizer=lambda x: x.split(\" \") ,\n",
        "                                                      sublinear_tf=True,\n",
        "                                                      encoding='utf-8',\n",
        "                                                      decode_error='ignore',\n",
        "                                                      ngram_range=(1, 1),\n",
        "                                                      stop_words=stopWords)),\n",
        "                            ('model', LogisticRegression())])\n",
        "\n",
        "# fit the pipeline model with the training data\n",
        "pipeline.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVTpLxiRwAo-",
        "outputId": "c44b5359-04ea-498b-810e-aca98b34bdc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tfidf',\n",
              "                 TfidfVectorizer(decode_error='ignore', max_features=1000,\n",
              "                                 stop_words=['छ', 'र', 'पनि', 'छन्', 'लागि',\n",
              "                                             'भएको', 'गरेको', 'भने', 'गर्न',\n",
              "                                             'गर्ने', 'हो', 'तथा', 'यो',\n",
              "                                             'रहेको', 'उनले', 'थियो', 'हुने',\n",
              "                                             'गरेका', 'थिए', 'गर्दै', 'तर',\n",
              "                                             'नै', 'को', 'मा', 'हुन्', 'भन्ने',\n",
              "                                             'हुन', 'गरी', 'त', 'हुन्छ', ...],\n",
              "                                 sublinear_tf=True,\n",
              "                                 tokenizer=<function <lambda> at 0x7fbe1969d040>)),\n",
              "                ('model', LogisticRegression())])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sample tweet\n",
        "text = [\"बधाई र सुभकामना ।।\"]\n",
        "\n",
        "# predict the label using the pipeline\n",
        "pipeline.predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWSDLcTBw_R8",
        "outputId": "c966251e-b538-4d0d-d80c-3c73b480c9d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import dill\n",
        "\n",
        "# Pickle the pipeline using dill\n",
        "with open(\"pipeline.pkl\", \"wb\") as f:\n",
        "    dill.dump(pipeline, f)"
      ],
      "metadata": {
        "id": "t_1Hs5KpzA9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import joblib\n",
        "#from joblib import dump\n",
        "\n",
        "# dump the pipeline model\n",
        "#dump(pipeline, filename=\"text_sentiment_nepali.joblib\")"
      ],
      "metadata": {
        "id": "DFA1-d7JxiDU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import joblib\n",
        "from joblib import load\n",
        "\n",
        "# sample tweet text\n",
        "text = [\"बधाई र सुभकामना ।।\"]\n",
        "\n",
        "# load the saved pipleine model\n",
        "# Pickle the pipeline using dill\n",
        "with open(\"pipeline.pkl\", \"wb\") as f:\n",
        "    dill.dump(pipeline, f)\n",
        "\n",
        "# predict on the sample tweet text\n",
        "pipeline.predict(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUopUMALxMB2",
        "outputId": "bcd3d2ea-9c0a-4dac-d5d8-15d937385fb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FyAekX6tzTQ3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}