Spam_bert.ipynb

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  "cells": [
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      "metadata": {
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      "source": [
        "##**Spam Classification with BERT**\n",
        "\n",
        "The aim of this project is a spam detection to get a model to predict if the messages is spam or not. This approach we have used a BERT (Bidirectional Encoder Representations from Transformers) model to gain our model. In our analysis we have used a Huggingface Transformers library as well.\n",
        "\n",
        "**Dataset**\n",
        "\n",
        "The dataset comes from SMS Spam Collection and that can be found at https://www.kaggle.com/uciml/sms-spam-collection-dataset.\n",
        "\n",
        "This SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research. It comprises one set of SMS messages in English of 5,574 messages, which is tagged acording being ham (legitimate) or spam."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWq1S4cnRVpi"
      },
      "source": [
        "#!pip install transformers"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oYTd470qa0I"
      },
      "source": [
        "**Importing the required libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFm61Fi6GHVC"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras \n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.models import Model\n",
        "\n",
        "import transformers\n",
        "from transformers import BertTokenizer, TFBertModel\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDQ7eC2SgUzp",
        "outputId": "51f7df87-9273-4699-afe6-63242cf0eee9"
      },
      "source": [
        "nltk.download('stopwords')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uc7zSBWLGWOH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bc82321-b8da-4ff9-fc73-bdb1cdc68b7e"
      },
      "source": [
        "cd '/content/drive/My Drive/moje pliki/data'"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/moje pliki/data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
        "id": "0kRqjC0bT7cf"
      },
      "source": [
        "**First observations:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJyT0HAQGWRh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0d4d467a-179b-4098-9f68-658aeae23b76"
      },
      "source": [
        "df = pd.read_csv('spam.csv', encoding='latin-1')\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>v1</th>\n",
              "      <th>v2</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     v1  ... Unnamed: 4\n",
              "0   ham  ...        NaN\n",
              "1   ham  ...        NaN\n",
              "2  spam  ...        NaN\n",
              "3   ham  ...        NaN\n",
              "4   ham  ...        NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi9n38VKGWUv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d32e831-a242-4f14-dab8-5a8c778ac1bf"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0qbCQ7hGWXo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ebd9ef4-2629-42a6-a246-798b04a6f6b1"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   v1          5572 non-null   object\n",
            " 1   v2          5572 non-null   object\n",
            " 2   Unnamed: 2  50 non-null     object\n",
            " 3   Unnamed: 3  12 non-null     object\n",
            " 4   Unnamed: 4  6 non-null      object\n",
            "dtypes: object(5)\n",
            "memory usage: 217.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrpkGG4BUqRN"
      },
      "source": [
        "\n",
        "### **Data preparation**\n",
        "\n",
        "Remove unnecessary variables:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vTOUTu2Ukg8"
      },
      "source": [
        "df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3MDp7rxVmuL"
      },
      "source": [
        "Rename columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kt5eSS9JUlrN"
      },
      "source": [
        "df.rename(columns={'v1': 'Class', 'v2': 'Text'}, inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "B2Dy8vYuV0L0",
        "outputId": "36c9e98b-6841-4760-e1d9-98353367bc06"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Class                                               Text\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSdCUG50Vfrt"
      },
      "source": [
        "We change column \"Class\" to 0 and 1:\n",
        "\n",
        "- spam = 1\n",
        "- ham = 0\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1c0y-m8cUluT",
        "outputId": "7476bdab-bf73-45ad-8f6a-14d5d53e31f4"
      },
      "source": [
        "df['Class'] = df['Class'].map({'ham':0, 'spam':1})\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                               Text\n",
              "0      0  Go until jurong point, crazy.. Available only ...\n",
              "1      0                      Ok lar... Joking wif u oni...\n",
              "2      1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      0  U dun say so early hor... U c already then say...\n",
              "4      0  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "yiMi0qPmUlx3",
        "outputId": "eb10f1b4-c4c0-4568-bfeb-9097b9a3205e"
      },
      "source": [
        "df['Text'][2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDgICyMJWy5D",
        "outputId": "76c6d279-e8f2-4394-d1d1-9ec8d6fd4141"
      },
      "source": [
        "# Checking for any missing values\n",
        "df.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class    0\n",
              "Text     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0J0EEz_WjFo"
      },
      "source": [
        "### **EDA and Data Visualization**\n",
        "\n",
        "We check proportion Class variable:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pg01LWs3Ul0n",
        "outputId": "509ccc72-e321-44c1-e7d0-e1cd6e901be3"
      },
      "source": [
        "df['Class'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4825\n",
              "1     747\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "B6NireTwUl3C",
        "outputId": "51b56f1f-2251-430a-b6d1-bc6b3ed4138b"
      },
      "source": [
        "sns.set(style = \"darkgrid\" , font_scale = 1.2)\n",
        "sns.countplot(df.Class).set_title(\"Number of ham and spam messages\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "X082B8PSWwaq",
        "outputId": "23263e64-e790-4564-fcc6-bd7efd1b4205"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5572.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.134063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.340751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Class\n",
              "count  5572.000000\n",
              "mean      0.134063\n",
              "std       0.340751\n",
              "min       0.000000\n",
              "25%       0.000000\n",
              "50%       0.000000\n",
              "75%       0.000000\n",
              "max       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uz0zyDsBYWOW"
      },
      "source": [
        "The target class variable is imbalanced, where \"ham\" values are more dominating than \"spam\" ones."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IycH0pdYYr19"
      },
      "source": [
        "**SMS Distribution**\n",
        "\n",
        "Now we check the percentage of spam SMS and ham SMS messages:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "_BAmRT-wWwds",
        "outputId": "55d5efac-02b2-4c7a-9863-d89c9d078e94"
      },
      "source": [
        "sms = pd.value_counts(df[\"Class\"], sort=True)\n",
        "sms.plot(kind=\"pie\", labels=[\"ham\", \"spam\"], autopct=\"%1.0f%%\")\n",
        "\n",
        "plt.title(\"SMS messages Distribution\")\n",
        "plt.ylabel(\"\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4zjqnUgZ3Xx"
      },
      "source": [
        "Above 87% of these SMS  messages are ham (legitimate) and 13% of them are spam.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmyXc2LaairR"
      },
      "source": [
        "Length of text messages:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RSMZfBSsWwgO",
        "outputId": "ba18964b-f753-4ba2-fd90-f94a047cf861"
      },
      "source": [
        "df['length'] = df.Text.apply(len)\n",
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>Text</th>\n",
              "      <th>length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                               Text  length\n",
              "0      0  Go until jurong point, crazy.. Available only ...     111\n",
              "1      0                      Ok lar... Joking wif u oni...      29\n",
              "2      1  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
              "3      0  U dun say so early hor... U c already then say...      49\n",
              "4      0  Nah I don't think he goes to usf, he lives aro...      61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "OGWKaImxZ4ZB",
        "outputId": "38e155e9-cce6-4797-de8f-eef4216f91a2"
      },
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "df[df.Class == 0].length.plot(bins=35, kind='hist', color='blue', label='Ham', alpha=0.6)\n",
        "df[df.Class == 1].length.plot(kind='hist', color='red', label='Spam', alpha=0.6)\n",
        "plt.legend()\n",
        "plt.xlabel(\"Messages Length\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiHE-b1NbR3P"
      },
      "source": [
        "Now let's see if the length has an influence on messages spam or ham:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "vnkDB9DfZ4b1",
        "outputId": "ce76d3f7-5ff0-4f43-f474-0ff4137e139a"
      },
      "source": [
        "_, ax = plt.subplots(figsize=(10, 4))\n",
        "sns.kdeplot(df.loc[df.Class == 0, \"length\"], shade=True, label=\"Ham\", clip=(-50, 250),)\n",
        "sns.kdeplot(df.loc[df.Class == 1, \"length\"], shade=True, label=\"Spam\")\n",
        "ax.set(\n",
        "    xlabel=\"Length\",\n",
        "    ylabel=\"Density\",\n",
        "    title=\"Length of messages.\",\n",
        ")\n",
        "ax.legend(loc=\"upper right\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHgKS88Ucm2R"
      },
      "source": [
        "As we can notice the spam messages are longer than ham ones (that is normal due to the number of words) and  have around 150 characters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwD6QTGObDzB"
      },
      "source": [
        "### **Text Pre-processing**\n",
        "\n",
        "In the next step we clean text, remove stop words and apply stemming operation for each line of text:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSeLeoJsZ4ir",
        "outputId": "8cdd2567-f799-48d8-e3a9-0fda1fc7b85d"
      },
      "source": [
        "stop_words = stopwords.words('english')\n",
        "print(stop_words[::10])\n",
        "\n",
        "porter = PorterStemmer()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['i', \"you've\", 'himself', 'they', 'that', 'been', 'a', 'while', 'through', 'in', 'here', 'few', 'own', 'just', 're', 'doesn', 'ma', \"shouldn't\"]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sxZZ2XmZ4fT"
      },
      "source": [
        "def clean_text(words):\n",
        "    \"\"\"The function to clean text\"\"\"\n",
        "    words = re.sub(\"[^a-zA-Z]\",\" \", words)\n",
        "    text = words.lower().split()                   \n",
        "    return \" \".join(text)\n",
        "\n",
        "def remove_stopwords(text):\n",
        "    \"\"\"The function to removing stopwords\"\"\"\n",
        "    text = [word.lower() for word in text.split() if word.lower() not in stop_words]\n",
        "    return \" \".join(text)\n",
        "\n",
        "def stemmer(stem_text):\n",
        "    \"\"\"The function to apply stemming\"\"\"\n",
        "    stem_text = [porter.stem(word) for word in stem_text.split()]\n",
        "    return \" \".join(stem_text)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxyWlogbWwil"
      },
      "source": [
        "df['Text'] = df['Text'].apply(clean_text)\n",
        "df['Text'] = df['Text'].apply(remove_stopwords)\n",
        "df['Text'] = df['Text'].apply(stemmer)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Y0SYb-EtbFHF",
        "outputId": "14e51761-493a-4af6-b7ab-fae3dcd96a79"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>Text</th>\n",
              "      <th>length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>go jurong point crazi avail bugi n great world...</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>ok lar joke wif u oni</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>free entri wkli comp win fa cup final tkt st m...</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>u dun say earli hor u c alreadi say</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>nah think goe usf live around though</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class                                               Text  length\n",
              "0      0  go jurong point crazi avail bugi n great world...     111\n",
              "1      0                              ok lar joke wif u oni      29\n",
              "2      1  free entri wkli comp win fa cup final tkt st m...     155\n",
              "3      0                u dun say earli hor u c alreadi say      49\n",
              "4      0               nah think goe usf live around though      61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dF8OV70bFKs",
        "outputId": "e732ea54-aada-4920-bbbf-95f5b73f023a"
      },
      "source": [
        "print(df['Text'].apply(lambda x: len(x.split(' '))).sum())"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "49940\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtgcpPXog4Z1"
      },
      "source": [
        "### **BERT model**\n",
        "\n",
        "BERT (Bidirectional Encoder Representations from Transformers) is a pretrained NLP algorithm devolped by google AI. BERT is a bidirectionally trained of Transformer a popular attention model, use to language modelling. So it can have a deeper sense of language context and flow compared to the single-direction language models. BERT model instead of predicting the next word in a sequence makes use of a novel technique called Masked LM (MLM). It relies on randomly masks words in the sentence and then it tries to predict them. Unlike the previous language models, it takes both the previous and next tokens into account at the same time. This is a contrast to previous LSTM based models combined a text sequence left-to-right and right-to-left.\n",
        "\n",
        "In the first step we have to make tokenization on our dataset. Tokenization will allow us to feed batches of sequences into the model at the same time. \n",
        "\n",
        "To do the tokenization of our datasets we have to choose  a pre-trained model. We load the basic model (`bert-large-uncased`) from the Huggingface Transformers library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206,
          "referenced_widgets": [
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            "321264fb33374b35a1c67d941e7ce009"
          ]
        },
        "id": "KmbjO_W-g3pD",
        "outputId": "d5ae2e79-8b81-4367-9a08-82aa4fc4d2a8"
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')\n",
        "tokenizer"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5f258aba9bf453899cffe8e5588acfd",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91b88f22eadf4c24ae298deeb09f9f86",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9811aa1ae4ae47b59f3a7e6c0fd92fa6",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreTrainedTokenizer(name_or_path='bert-large-uncased', vocab_size=30522, model_max_len=512, is_fast=False, padding_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NroWYm7RjJ-b"
      },
      "source": [
        "Now we have to load BERT model. In the Transformers library is avaliable many different BERT models. We use the „TFBertModel”  model (bert-base-uncased) from the library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230,
          "referenced_widgets": [
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        },
        "id": "_nuEF0S7g3sd",
        "outputId": "80e20649-9d6a-48e0-aa60-2e4c96d3928c"
      },
      "source": [
        "bert_model = TFBertModel.from_pretrained('bert-base-uncased')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "faa354ee828e4e0f864237cc8a8ac45e",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=570.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a567d0e08f7c4476ac0bf6f130b1fc5d",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=536063208.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n",
            "- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wWT1byYxT8wd"
      },
      "source": [
        "Spliting the data into train and test sets:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KFKoiduheF9"
      },
      "source": [
        "X = df['Text']\n",
        "y = df['Class']"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfN_rXrmGKpj"
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state = 0)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USy25R7uk6XY"
      },
      "source": [
        "The function which allows to encode our dataset with BERT tokenizer. We have decided on a maximum sentence length is 64 (maxlen).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYBJLPa_g3wB"
      },
      "source": [
        "def encode(text, maxlen):\n",
        "  input_ids=[]\n",
        "  attention_masks=[]\n",
        "\n",
        "  for row in text:\n",
        "    encoded = tokenizer.encode_plus(\n",
        "        row,\n",
        "        add_special_tokens=True,\n",
        "        max_length=maxlen,\n",
        "        pad_to_max_length=True,\n",
        "        return_attention_mask=True,\n",
        "    )\n",
        "    input_ids.append(encoded['input_ids'])\n",
        "    attention_masks.append(encoded['attention_mask'])\n",
        "\n",
        "  return np.array(input_ids),np.array(attention_masks)\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XX_P9VC7mCkd"
      },
      "source": [
        "Based on this  encodings for our training and testing datasets are generated as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqv6__lNKGi1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad18e980-aa40-4a6e-af8c-0ada904f2257"
      },
      "source": [
        "X_train_input_ids, X_train_attention_masks = encode(X_train.values, maxlen=64)\n",
        "X_test_input_ids, X_test_attention_masks = encode(X_test.values, maxlen=64)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ssSgebHoCaM"
      },
      "source": [
        "#### **Build the model**\n",
        "\n",
        "We create a model using BERT model and then add two Dense layers with Dropout layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QsLuPYpK5E8"
      },
      "source": [
        "def build_model(bert_model):\n",
        "   input_word_ids = tf.keras.Input(shape=(64,),dtype='int32')\n",
        "   attention_masks = tf.keras.Input(shape=(64,),dtype='int32')\n",
        "\n",
        "   sequence_output = bert_model([input_word_ids,attention_masks])\n",
        "   output = sequence_output[1]\n",
        "   output = tf.keras.layers.Dense(32,activation='relu')(output)\n",
        "   output = tf.keras.layers.Dropout(0.2)(output)\n",
        "   output = tf.keras.layers.Dense(1,activation='sigmoid')(output)\n",
        "\n",
        "   model = tf.keras.models.Model(inputs = [input_word_ids,attention_masks], outputs = output)\n",
        "   model.compile(Adam(lr=1e-5), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "   return model"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vaxP6BLi6b8q",
        "outputId": "053efccc-6615-45b0-b5a8-7dea485425c2"
      },
      "source": [
        "model = build_model(bert_model)\n",
        "model.summary()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x7f582f624de0>> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.sugar.socket.Socket object at 0x7f582f624de0>> and will run it as-is.\n",
            "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
            "Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING:tensorflow:AutoGraph could not transform <function wrap at 0x7f584aecddd0> and will run it as-is.\n",
            "Cause: while/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING: AutoGraph could not transform <function wrap at 0x7f584aecddd0> and will run it as-is.\n",
            "Cause: while/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 64)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 64)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "tf_bert_model (TFBertModel)     TFBaseModelOutputWit 109482240   input_1[0][0]                    \n",
            "                                                                 input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 32)           24608       tf_bert_model[0][1]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_37 (Dropout)            (None, 32)           0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 1)            33          dropout_37[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 109,506,881\n",
            "Trainable params: 109,506,881\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tm7uCsxoo-Vk"
      },
      "source": [
        "We set class weights for the loss function to adjust for class imbalance. 'Spam' variable is set to weight 8x more.\n",
        "\n",
        "We train the model for 5 epoch:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMNDAM24Y988"
      },
      "source": [
        "class_weight = {0: 1, 1: 8}"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gs1yZXqd6cBb",
        "outputId": "f94665dd-736f-4278-c99d-f7c451eec03c"
      },
      "source": [
        "history = model.fit(\n",
        "    [X_train_input_ids, X_train_attention_masks],\n",
        "    y_train,\n",
        "    batch_size=32,\n",
        "    epochs=5,\n",
        "    validation_data=([X_test_input_ids, X_test_attention_masks], y_test),\n",
        "    class_weight=class_weight)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "140/140 [==============================] - ETA: 0s - loss: 0.6121 - accuracy: 0.8822WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n",
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "140/140 [==============================] - 81s 448ms/step - loss: 0.6121 - accuracy: 0.8822 - val_loss: 0.2755 - val_accuracy: 0.9193\n",
            "Epoch 2/5\n",
            "140/140 [==============================] - 61s 432ms/step - loss: 0.2890 - accuracy: 0.9596 - val_loss: 0.1546 - val_accuracy: 0.9516\n",
            "Epoch 3/5\n",
            "140/140 [==============================] - 60s 430ms/step - loss: 0.1994 - accuracy: 0.9746 - val_loss: 0.0827 - val_accuracy: 0.9758\n",
            "Epoch 4/5\n",
            "140/140 [==============================] - 60s 429ms/step - loss: 0.1279 - accuracy: 0.9852 - val_loss: 0.0781 - val_accuracy: 0.9704\n",
            "Epoch 5/5\n",
            "140/140 [==============================] - 60s 428ms/step - loss: 0.0811 - accuracy: 0.9906 - val_loss: 0.0548 - val_accuracy: 0.9830\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XN9YA-czpEbR"
      },
      "source": [
        "Visualization of training:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "h7zUrJZdN4j4",
        "outputId": "aa38626a-21d3-4a71-ad36-45acf3be8dcc"
      },
      "source": [
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7e-JZWjppB2n"
      },
      "source": [
        "Predictions on a test set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zw0H64ll-xf4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f31ecc41-c06d-4bee-d98b-f3bc26c5a148"
      },
      "source": [
        "loss, accuracy = model.evaluate([X_test_input_ids, X_test_attention_masks], y_test)\n",
        "print('Test accuracy :', accuracy)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "35/35 [==============================] - 5s 140ms/step - loss: 0.0548 - accuracy: 0.9830\n",
            "Test accuracy : 0.9829596281051636\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvSOTTKYN4pO"
      },
      "source": [
        "#save model\n",
        "model.save_weights('bert_model')"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-va0YOO5piAD"
      },
      "source": [
        "### **Summary**\n",
        "\n",
        "For our analysis we used a pretrained BERT model to resolve our classification problem.  After trained model we achieved an accuracy on the test set equal to 98 % and it is a very good result in comparison to previous Machine Learning models that we have used (e.g. Logistic Regression).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1J8L9WiUhrwD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}