BERT NLP - Text Classification Using BERT and Tensorflow.ipynb

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      "source": [
        "**BERT NLP - Text Classification Using BERT and Tensorflow**"
      ],
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    {
      "cell_type": "code",
      "source": [
        "%pip install -U -q tensorflow-text"
      ],
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        "id": "FTE3nUaNXc-x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text"
      ],
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        "id": "jg9cqIx3WhWx"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "X5OsvnAUXhOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df = pd.read_csv(\"/content/spam.csv\", encoding=\"ISO-8859-1\")"
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        "df.head()"
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              "  Category                                            Message    A    B    C\n",
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        "#Review Dataset\n",
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              "         Message                                                            \\\n",
              "           count unique                                                top   \n",
              "Category                                                                     \n",
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              "\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c0ab2119-1ff3-4ea3-98a1-dd660d1256a7 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-c0ab2119-1ff3-4ea3-98a1-dd660d1256a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Category\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zy8ecs0Li_B5",
        "outputId": "b0ebab33-77b2-4591-e234-aa3b59d54234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ham     4825\n",
              "spam     747\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Since we have unballanced data. We will have to balance it.\n",
        "df_spam = df[df['Category'] == \"spam\"]\n",
        "df_spam.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDAaYlrRw00d",
        "outputId": "6633c25f-ffa9-4675-ed28-38eeebb48036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(747, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ham = df[df[\"Category\"]==\"ham\"]\n",
        "df_ham.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clNIsHt8xKmo",
        "outputId": "e89ff6ba-0a61-40f5-a3d2-54247a76b00a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4825, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ham_downsample = df_ham.sample(df_spam.shape[0])\n",
        "df_ham_downsample.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnjpa4VzxTY8",
        "outputId": "c7ab8161-5c65-4a62-8268-04357f8783a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(747, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced = pd.concat([df_spam, df_ham_downsample])\n",
        "df_balanced.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9mOO6Vc0uan",
        "outputId": "88a99965-dabf-4b51-8fc5-134ac68c5668"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1494, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced[\"Category\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0op-E4207EA",
        "outputId": "15e2ea65-a836-49b3-eef9-077ae82bd816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "spam    747\n",
              "ham     747\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Apply boolean encoding on spam and ham\n",
        "df_balanced[\"spam\"] = df_balanced[\"Category\"].apply(lambda x: 1 if x==\"spam\" else 0)\n",
        "df_balanced.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "IhftYWva1EJ7",
        "outputId": "ac25b407-9a23-4fb2-ba7a-9dd252d19de8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Category                                            Message  spam\n",
              "1690     spam  Sunshine Quiz Wkly Q! Win a top Sony DVD playe...     1\n",
              "4868      ham  1. Tension face 2. Smiling face 3. Waste face ...     0\n",
              "1119     spam  Bored of speed dating? Try SPEEDCHAT, txt SPEE...     1\n",
              "3054     spam  EASTENDERS TV Quiz. What FLOWER does DOT compa...     1\n",
              "4340      ham                              ÌÏ all write or wat..     0\n",
              "929      spam  money!!! you r a lucky winner ! 2 claim your p...     1\n",
              "1698     spam  Free msg. Sorry, a service you ordered from 81...     1\n",
              "1779     spam  Loan for any purpose å£500 - å£75,000. Homeown...     1\n",
              "1063     spam  We have new local dates in your area - Lots of...     1\n",
              "5497     spam  SMS SERVICES. for your inclusive text credits,...     1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-372d43be-ffe4-4717-9073-ab2c3ccb39e9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Category</th>\n",
              "      <th>Message</th>\n",
              "      <th>spam</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1690</th>\n",
              "      <td>spam</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4868</th>\n",
              "      <td>ham</td>\n",
              "      <td>1. Tension face 2. Smiling face 3. Waste face ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1119</th>\n",
              "      <td>spam</td>\n",
              "      <td>Bored of speed dating? Try SPEEDCHAT, txt SPEE...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3054</th>\n",
              "      <td>spam</td>\n",
              "      <td>EASTENDERS TV Quiz. What FLOWER does DOT compa...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4340</th>\n",
              "      <td>ham</td>\n",
              "      <td>ÌÏ all write or wat..</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929</th>\n",
              "      <td>spam</td>\n",
              "      <td>money!!! you r a lucky winner ! 2 claim your p...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1698</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free msg. Sorry, a service you ordered from 81...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1779</th>\n",
              "      <td>spam</td>\n",
              "      <td>Loan for any purpose å£500 - å£75,000. Homeown...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1063</th>\n",
              "      <td>spam</td>\n",
              "      <td>We have new local dates in your area - Lots of...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5497</th>\n",
              "      <td>spam</td>\n",
              "      <td>SMS SERVICES. for your inclusive text credits,...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-372d43be-ffe4-4717-9073-ab2c3ccb39e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-372d43be-ffe4-4717-9073-ab2c3ccb39e9 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-372d43be-ffe4-4717-9073-ab2c3ccb39e9');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_text = train_test_split(df_balanced[\"Message\"], df_balanced[\"spam\"], stratify=df_balanced[\"spam\"])"
      ],
      "metadata": {
        "id": "DS0eVsy1160Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9x5Qq4Mz2biA",
        "outputId": "075b3930-f272-4ce7-8e54-75996f202b6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2320        This pain couldn't have come at a worse time.\n",
              "187     Please call our customer service representativ...\n",
              "4405    As one of our registered subscribers u can ent...\n",
              "2133    Spoke with uncle john today. He strongly feels...\n",
              "Name: Message, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Download pretrained process and encoder\n",
        "bert_preprocess = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")\n",
        "bert_encoder = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\")"
      ],
      "metadata": {
        "id": "0UDtRt1j23Gd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.io.xml import preprocess_data\n",
        "#Write a function to get the encoding out of pretrained model downloaded\n",
        "def get_sentence_embeding(sentences):\n",
        "  preprocess_text = bert_preprocess(sentences)\n",
        "  return bert_encoder(preprocess_text)[\"pooled_output\"]\n"
      ],
      "metadata": {
        "id": "w1XaJuIs4e2A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_sentence_embeding([\n",
        "                       \"500$ discount. hurry up!\",\n",
        "                       \"Are you up for a volleyball game tomorrow?\"\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Djl8jLkY604V",
        "outputId": "78c40238-3490-4ea4-9294-8f5761822bfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 768), dtype=float32, numpy=\n",
              "array([[-0.7995451 , -0.41840777, -0.80555654, ..., -0.5952304 ,\n",
              "        -0.70517206,  0.8829687 ],\n",
              "       [-0.9008625 , -0.47827342, -0.8823059 , ..., -0.7808709 ,\n",
              "        -0.72400814,  0.9222427 ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets try to see embeddings for some other words.\n",
        "e = get_sentence_embeding([\n",
        "                           \"banana\",\n",
        "                           \"grapes\",\n",
        "                           \"mango\",\n",
        "                           \"jeff bezos\",\n",
        "                           \"elon musk\",\n",
        "                           \"bill gates\"\n",
        "])\n",
        "e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2gsFP8S7EuJ",
        "outputId": "f350604f-6dc0-4956-cba3-8c530f9be1e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(6, 768), dtype=float32, numpy=\n",
              "array([[-0.7606919 , -0.14219381,  0.4960461 , ...,  0.42165333,\n",
              "        -0.532214  ,  0.80312175],\n",
              "       [-0.86023223, -0.21242939,  0.4915689 , ...,  0.3979803 ,\n",
              "        -0.60506296,  0.8447167 ],\n",
              "       [-0.7128864 , -0.15463923,  0.38401636, ...,  0.352787  ,\n",
              "        -0.5099134 ,  0.73474115],\n",
              "       [-0.8253345 , -0.3555054 , -0.5906963 , ..., -0.01613662,\n",
              "        -0.6141756 ,  0.87230295],\n",
              "       [-0.7504134 , -0.26812598, -0.26689604, ...,  0.02839445,\n",
              "        -0.5938098 ,  0.79749876],\n",
              "       [-0.78544384, -0.299497  ,  0.41027427, ...,  0.5222542 ,\n",
              "        -0.4957355 ,  0.81507534]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets look at the similarity using cosine\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# If the output is near to 1 they are similar. You can check for other words.\n",
        "cosine_similarity([e[0]], [e[1]]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVDyexa0CNR1",
        "outputId": "25fb0323-1c7c-49c1-942e-f2eb50017cfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9911088]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import dtype\n",
        "# There are 2 types of model. Sequential and Functional. Here we will use functional model.\n",
        "\n",
        "# BERT Layers\n",
        "text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name=\"text\")\n",
        "preprocessed_text = bert_preprocess(text_input)\n",
        "outputs = bert_encoder(preprocessed_text)\n",
        "\n",
        "# NNL\n",
        "nnl = tf.keras.layers.Dropout(0.1, name=\"dropout\")(outputs[\"pooled_output\"])\n",
        "nnl = tf.keras.layers.Dense(1, activation=\"sigmoid\", name=\"output\")(nnl)\n",
        "\n",
        "# Construct Final Model\n",
        "model = tf.keras.Model(inputs=[text_input], outputs=[nnl])"
      ],
      "metadata": {
        "id": "gphlezx7Gb1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCLcLUFoIL_c",
        "outputId": "6ec056b4-39a9-4015-e381-b7d4a82bb06c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " text (InputLayer)              [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)       {'input_mask': (Non  0           ['text[0][0]']                   \n",
            "                                e, 128),                                                          \n",
            "                                 'input_type_ids':                                                \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_word_ids':                                                \n",
            "                                (None, 128)}                                                      \n",
            "                                                                                                  \n",
            " keras_layer_1 (KerasLayer)     {'pooled_output': (  109482241   ['keras_layer[3][0]',            \n",
            "                                None, 768),                       'keras_layer[3][1]',            \n",
            "                                 'sequence_output':               'keras_layer[3][2]']            \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 'default': (None,                                                \n",
            "                                768),                                                             \n",
            "                                 'encoder_outputs':                                               \n",
            "                                 [(None, 128, 768),                                               \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768)]}                                               \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 768)          0           ['keras_layer_1[3][13]']         \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 1)            769         ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 109,483,010\n",
            "Trainable params: 769\n",
            "Non-trainable params: 109,482,241\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compile Model\n",
        "METRICS = [\n",
        "           tf.keras.metrics.BinaryAccuracy(name=\"accuracy\"),\n",
        "           tf.keras.metrics.Precision(name=\"precision\"),\n",
        "           tf.keras.metrics.Recall(name=\"recall\"),\n",
        "]\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), \n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=METRICS)"
      ],
      "metadata": {
        "id": "29SSUqy5I9TZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAbUlYfmJdi1",
        "outputId": "657cb9c4-4fe0-4b70-d22c-d528d99a2f38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "35/35 [==============================] - 449s 13s/step - loss: 0.7204 - accuracy: 0.5152 - precision: 0.5159 - recall: 0.4929\n",
            "Epoch 2/10\n",
            "35/35 [==============================] - 434s 12s/step - loss: 0.5571 - accuracy: 0.7875 - precision: 0.7692 - recall: 0.8214\n",
            "Epoch 3/10\n",
            "35/35 [==============================] - 433s 12s/step - loss: 0.4774 - accuracy: 0.8232 - precision: 0.8209 - recall: 0.8268\n",
            "Epoch 4/10\n",
            "35/35 [==============================] - 433s 12s/step - loss: 0.4249 - accuracy: 0.8518 - precision: 0.8373 - recall: 0.8732\n",
            "Epoch 5/10\n",
            "35/35 [==============================] - 433s 12s/step - loss: 0.3787 - accuracy: 0.8902 - precision: 0.8840 - recall: 0.8982\n",
            "Epoch 6/10\n",
            "35/35 [==============================] - 433s 12s/step - loss: 0.3656 - accuracy: 0.8884 - precision: 0.8705 - recall: 0.9125\n",
            "Epoch 7/10\n",
            "35/35 [==============================] - 435s 12s/step - loss: 0.3387 - accuracy: 0.8964 - precision: 0.8841 - recall: 0.9125\n",
            "Epoch 8/10\n",
            "35/35 [==============================] - 433s 12s/step - loss: 0.3272 - accuracy: 0.8955 - precision: 0.8866 - recall: 0.9071\n",
            "Epoch 9/10\n",
            "35/35 [==============================] - 435s 12s/step - loss: 0.3021 - accuracy: 0.9036 - precision: 0.8937 - recall: 0.9161\n",
            "Epoch 10/10\n",
            "35/35 [==============================] - 434s 12s/step - loss: 0.2938 - accuracy: 0.9062 - precision: 0.9027 - recall: 0.9107\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f19b579b710>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7XGs7ecclGK7",
        "outputId": "0cdbde51-0183-4607-f259-4cd28b2b5f56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 146s 12s/step - loss: 0.3077 - accuracy: 0.8877 - precision: 0.8436 - recall: 0.9519\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3077334463596344,\n",
              " 0.8877005577087402,\n",
              " 0.8436018824577332,\n",
              " 0.9518716335296631]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = model.predict(X_test)\n",
        "y_predicted = y_predicted.flatten()"
      ],
      "metadata": {
        "id": "joWJRfYJsYXB",
        "outputId": "4d530501-1518-415e-a830-6f5abb2314c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 146s 12s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Doing this beqacuse the y_predictyed has floating points and we need the output in 0 and 1. \n",
        "import numpy as np\n",
        "\n",
        "y_predicted = np.where(y_predicted > 0.5, 1, 0)\n",
        "y_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZKLPwBYx-ry",
        "outputId": "9952eeda-4b61-4f54-f710-9db8cb345fc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1,\n",
              "       0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "cm = confusion_matrix(y_text, y_predicted)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suuEf66EyTyY",
        "outputId": "db5b0c6a-8f58-424f-af15-f5fac6f27f0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[154,  33],\n",
              "       [  9, 178]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from matplotlib import pyplot as plt \n",
        "import seaborn as sn\n",
        "sn.heatmap(cm, annot=True, fmt=\"d\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"truth\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "m8gAQofCymkd",
        "outputId": "79753f9b-df32-48f9-fc64-ec83b9d94a36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'truth')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_text, y_predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9WFyAv6zPvq",
        "outputId": "c9e1f73c-4132-48eb-9ef6-55ebe297afb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.82      0.88       187\n",
            "           1       0.84      0.95      0.89       187\n",
            "\n",
            "    accuracy                           0.89       374\n",
            "   macro avg       0.89      0.89      0.89       374\n",
            "weighted avg       0.89      0.89      0.89       374\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zkZ21hswzXez"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}