Text_Classification_27_July_21.ipynb

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      "name": "Text Classification 27 July 21.ipynb",
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      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMjncJz8cjrn+tr+wTLzQER",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rajni-arora/Deep-Learning-Projects/blob/main/Text_Classification_27_July_21.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "8rS1xCvKlU4r",
        "outputId": "e39876b6-5519-459c-b5a4-29b9bd2c3813"
      },
      "source": [
        "!wget --header=\"Host: storage.googleapis.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.164 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\" --header=\"Accept-Language: en-US,en;q=0.9\" --header=\"Referer: https://www.kaggle.com/\" \"https://storage.googleapis.com/kaggle-data-sets/30764/533474/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210727%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210727T105623Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=2486a6d618126aee79cc2d035442a4a742010f36bca1a0e4f10333621967e59c4c0f71e63f4c13fd35a5b0f8f082c9dc56204f7454bd4201d2574cae35f54c3e3ed058cf3c569cfbd9905ed2c5233e370bbd4e56c4646f7d7e45152f8a0ba099908958517a5a1c51f8615fbf727e207007f36c79b43aea7788d8176d3a1c357ed878594c0aafbba0982a7c8aa8fc52017504b2a48298ef982876af1df23fc8e790ef96d2727acd2c4c249cf7bb33477f5ac24290bfdb0e6eb86c57be774307a47939135453225bbbfb7c9c5203bbf42d053532d36ad2c18db1dd9529debd30affa3f6e97d2b822305274918e7223ddf3ded47ab3a765de430c30fa4426de65d9\" -c -O 'archive.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-27 10:56:51--  https://storage.googleapis.com/kaggle-data-sets/30764/533474/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20210727%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210727T105623Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 74.125.195.128, 2607:f8b0:400e:c01::80, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
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            "Saving to: ‘archive.zip’\n",
            "\n",
            "\rarchive.zip           0%[                    ]       0  --.-KB/s               \rarchive.zip         100%[===================>]   3.30M  --.-KB/s    in 0.01s   \n",
            "\n",
            "2021-07-27 10:56:51 (220 MB/s) - ‘archive.zip’ saved [3460534/3460534]\n",
            "\n"
          ],
          "name": "stdout"
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        "id": "d7zZwfGbnDFX",
        "outputId": "b5cf4767-3471-4cd3-bb0a-353c1bf95199"
      },
      "source": [
        "!unzip archive.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  archive.zip\n",
            "  inflating: Sarcasm_Headlines_Dataset.json  \n",
            "  inflating: Sarcasm_Headlines_Dataset_v2.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "HqoFNgumnGHV",
        "outputId": "1d9d7ff9-57ec-4fff-d193-b92831bfac7b"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_json('Sarcasm_Headlines_Dataset.json', lines=True)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
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              "      <td>https://www.huffingtonpost.com/entry/roseanne-...</td>\n",
              "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://local.theonion.com/mom-starting-to-fea...</td>\n",
              "      <td>mom starting to fear son's web series closest ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://politics.theonion.com/boehner-just-wan...</td>\n",
              "      <td>boehner just wants wife to listen, not come up...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/jk-rowlin...</td>\n",
              "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
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              "                                        article_link  ... is_sarcastic\n",
              "0  https://www.huffingtonpost.com/entry/versace-b...  ...            0\n",
              "1  https://www.huffingtonpost.com/entry/roseanne-...  ...            0\n",
              "2  https://local.theonion.com/mom-starting-to-fea...  ...            1\n",
              "3  https://politics.theonion.com/boehner-just-wan...  ...            1\n",
              "4  https://www.huffingtonpost.com/entry/jk-rowlin...  ...            0\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 6
        }
      ]
    },
    {
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          "height": 200
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        "id": "ha2ICxiCoCWl",
        "outputId": "a5e13f63-af24-40b3-f7a0-0e84e2e6fe2d"
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      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>26704</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/american-...</td>\n",
              "      <td>american politics in moral free-fall</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26705</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/americas-...</td>\n",
              "      <td>america's best 20 hikes</td>\n",
              "      <td>0</td>\n",
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              "      <th>26706</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/reparatio...</td>\n",
              "      <td>reparations and obama</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26707</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/israeli-b...</td>\n",
              "      <td>israeli ban targeting boycott supporters raise...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>26708</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/gourmet-g...</td>\n",
              "      <td>gourmet gifts for the foodie 2014</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                            article_link  ... is_sarcastic\n",
              "26704  https://www.huffingtonpost.com/entry/american-...  ...            0\n",
              "26705  https://www.huffingtonpost.com/entry/americas-...  ...            0\n",
              "26706  https://www.huffingtonpost.com/entry/reparatio...  ...            0\n",
              "26707  https://www.huffingtonpost.com/entry/israeli-b...  ...            0\n",
              "26708  https://www.huffingtonpost.com/entry/gourmet-g...  ...            0\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_Ju_jfiorGV",
        "outputId": "ba93d331-06cc-4fa8-c2a2-966de541b0c9"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26709 entries, 0 to 26708\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   article_link  26709 non-null  object\n",
            " 1   headline      26709 non-null  object\n",
            " 2   is_sarcastic  26709 non-null  int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 626.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sVvBiHMo20b",
        "outputId": "7f9047bd-264a-4513-b51e-4b18e4825477"
      },
      "source": [
        " df['is_sarcastic'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14985\n",
              "1    11724\n",
              "Name: is_sarcastic, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "ied1RcgzpgoB",
        "outputId": "6d9a1ff7-0b19-4648-dd1e-519eb77f5bd1"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_json('Sarcasm_Headlines_Dataset_v2.json', lines=True)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\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>is_sarcastic</th>\n",
              "      <th>headline</th>\n",
              "      <th>article_link</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>thirtysomething scientists unveil doomsday clo...</td>\n",
              "      <td>https://www.theonion.com/thirtysomething-scien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>dem rep. totally nails why congress is falling...</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/donna-edw...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>eat your veggies: 9 deliciously different recipes</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/eat-your-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>inclement weather prevents liar from getting t...</td>\n",
              "      <td>https://local.theonion.com/inclement-weather-p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>mother comes pretty close to using word 'strea...</td>\n",
              "      <td>https://www.theonion.com/mother-comes-pretty-c...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   is_sarcastic  ...                                       article_link\n",
              "0             1  ...  https://www.theonion.com/thirtysomething-scien...\n",
              "1             0  ...  https://www.huffingtonpost.com/entry/donna-edw...\n",
              "2             0  ...  https://www.huffingtonpost.com/entry/eat-your-...\n",
              "3             1  ...  https://local.theonion.com/inclement-weather-p...\n",
              "4             1  ...  https://www.theonion.com/mother-comes-pretty-c...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLjiXL3kphph",
        "outputId": "2022eae3-433b-47a7-eaf9-500d17d206a6"
      },
      "source": [
        "df['is_sarcastic'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14985\n",
              "1    13634\n",
              "Name: is_sarcastic, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGIaBtURphjT",
        "outputId": "1f0a726e-393c-4988-ee92-81968ec2c662"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28619, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxBbS7s8phdF"
      },
      "source": [
        "df.drop('article_link', axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "kQX8suU1phYn",
        "outputId": "beb278bc-a036-4ee4-eb79-e5a69975fe06"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_sarcastic</th>\n",
              "      <th>headline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>thirtysomething scientists unveil doomsday clo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>dem rep. totally nails why congress is falling...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>eat your veggies: 9 deliciously different recipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>inclement weather prevents liar from getting t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>mother comes pretty close to using word 'strea...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   is_sarcastic                                           headline\n",
              "0             1  thirtysomething scientists unveil doomsday clo...\n",
              "1             0  dem rep. totally nails why congress is falling...\n",
              "2             0  eat your veggies: 9 deliciously different recipes\n",
              "3             1  inclement weather prevents liar from getting t...\n",
              "4             1  mother comes pretty close to using word 'strea..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-JADj7bphVi"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.optimizers import *\n",
        "from tensorflow.keras.callbacks import *\n",
        "from tensorflow.keras.models import *\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bABiu5Xjr4Wv"
      },
      "source": [
        "df[\"headline\"] = df[\"headline\"].str.lower()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "zfleZxc8syN1",
        "outputId": "69b8b2b5-32d0-4564-f1c0-04f6b6bdd9f7"
      },
      "source": [
        "df.head()"
      ],
      "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>is_sarcastic</th>\n",
              "      <th>headline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>thirtysomething scientists unveil doomsday clo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>dem rep. totally nails why congress is falling...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>eat your veggies: 9 deliciously different recipes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>inclement weather prevents liar from getting t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>mother comes pretty close to using word 'strea...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   is_sarcastic                                           headline\n",
              "0             1  thirtysomething scientists unveil doomsday clo...\n",
              "1             0  dem rep. totally nails why congress is falling...\n",
              "2             0  eat your veggies: 9 deliciously different recipes\n",
              "3             1  inclement weather prevents liar from getting t...\n",
              "4             1  mother comes pretty close to using word 'strea..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "hMIc4_jRs4D3",
        "outputId": "31323782-4c6d-459e-baf4-c12096177c11"
      },
      "source": [
        "df.tail()"
      ],
      "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>is_sarcastic</th>\n",
              "      <th>headline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>28614</th>\n",
              "      <td>1</td>\n",
              "      <td>jews to celebrate rosh hashasha or something</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28615</th>\n",
              "      <td>1</td>\n",
              "      <td>internal affairs investigator disappointed con...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28616</th>\n",
              "      <td>0</td>\n",
              "      <td>the most beautiful acceptance speech this week...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28617</th>\n",
              "      <td>1</td>\n",
              "      <td>mars probe destroyed by orbiting spielberg-gat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28618</th>\n",
              "      <td>1</td>\n",
              "      <td>dad clarifies this not a food stop</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       is_sarcastic                                           headline\n",
              "28614             1       jews to celebrate rosh hashasha or something\n",
              "28615             1  internal affairs investigator disappointed con...\n",
              "28616             0  the most beautiful acceptance speech this week...\n",
              "28617             1  mars probe destroyed by orbiting spielberg-gat...\n",
              "28618             1                 dad clarifies this not a food stop"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OpxFz-vms8T6",
        "outputId": "4670e8b1-b2bd-4a1d-a3c6-d95b4c2e2793"
      },
      "source": [
        "X = df[\"headline\"]\n",
        "y = df[\"is_sarcastic\"]\n",
        "# split into train test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(19174,) (9445,) (19174,) (9445,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8CxRSP9vC-J",
        "outputId": "f873f3f1-e51b-4a89-d344-5b1c1e6f97ef"
      },
      "source": [
        "X_train.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([\"ann coulter says 'every woman who has ever been employed by fox' has stories about roger ailes\",\n",
              "       'bob dole picked off by large hawk circling arena parking lot',\n",
              "       'all black athletes should be sitting for the national anthem',\n",
              "       ...,\n",
              "       'u.s. army deserter bowe bergdahl faces life in prison as sentencing hearing begins',\n",
              "       'pit bull lovers gather in washington',\n",
              "       \"security video pokes holes in robber's threat to shoot store workers\"],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSWSJPzfw13m"
      },
      "source": [
        "MAX_SEQUENCE_LENGTH = 15\n",
        "MAX_VOCAB_SIZE = 20000\n",
        "EMBEDDING_DIM = 300\n",
        "BATCH_SIZE = 128\n",
        "EPOCHS = 5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnOC4miEtsGI"
      },
      "source": [
        "# convert the sentences (strings) into integers\n",
        "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=MAX_VOCAB_SIZE)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "sequences_train = tokenizer.texts_to_sequences(X_train)\n",
        "sequences_test = tokenizer.texts_to_sequences(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQ2Dj_s1u7ro",
        "outputId": "62d9f5d7-3179-4633-f0ba-8822ab6f4d92"
      },
      "source": [
        "sequences_train[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2789,\n",
              "  4186,\n",
              "  52,\n",
              "  8801,\n",
              "  46,\n",
              "  39,\n",
              "  36,\n",
              "  202,\n",
              "  245,\n",
              "  8802,\n",
              "  17,\n",
              "  12656,\n",
              "  36,\n",
              "  1333,\n",
              "  16,\n",
              "  2083,\n",
              "  5615],\n",
              " [1178, 4786, 4787, 60, 17, 1490, 12657, 8803, 4788, 1491, 604],\n",
              " [35, 101, 3705, 120, 22, 1117, 5, 3, 304, 2236],\n",
              " [26, 32, 8804, 2582, 351, 171, 465],\n",
              " [537,\n",
              "  1066,\n",
              "  358,\n",
              "  1664,\n",
              "  26,\n",
              "  1,\n",
              "  589,\n",
              "  27,\n",
              "  1,\n",
              "  1572,\n",
              "  39,\n",
              "  203,\n",
              "  115,\n",
              "  217,\n",
              "  695,\n",
              "  29,\n",
              "  440,\n",
              "  134,\n",
              "  441]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoFXG22_xL6v"
      },
      "source": [
        "import seaborn as sns\n",
        "length = []\n",
        "\n",
        "for i in X_train.values:\n",
        "\n",
        "  length.append(len(i.split()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTRhLv01yDG4",
        "outputId": "7b6bd508-a0bf-4054-c662-5ee0b8105259"
      },
      "source": [
        "from statistics import mean\n",
        "mean(length)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.064931678314384"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "2GVqz2iUxi0B",
        "outputId": "debecd2b-b011-4d78-ca86-18afd670444a"
      },
      "source": [
        "sns.set()\n",
        "sns.distplot(length)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe177705790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mIg2LazLAzTffjCeffBL//d//jaamJrS3t2PVqlX4+te/jv379yMU4odlZHfN3Jw4us70Atc6IyLNinsyQE9PD/bv34/9+/dDp9Nh06ZNsFqteOONN9DR0YGXX35ZiTqzhscff9cZJwMQkZbF/Gn4xhtvYN++fTh37hzuvfdevPDCC7j99tuj99tsNvzt3/6tIkVmE68/fLGlOY4WjV7UMWiISLNi/jQ8fPgw/uEf/gH33HMPjEbjuPvNZjO2b9+e0uKyUWR3zXi7zjgZgIi0KuYxmjvvvBP33nvvuJC5+tqWZcuWpa6yLOXxByEKupi2CIgwiAJCITnu5XGIiLJBzJ+GO3bsmPD2V155JWXFaIHXLyHXpIdOp4v5MfrRUGKrhoi0aNr+nd///vcAwrtfvv/++5Dlz//qvnDhAvLy8pSrLgt5fIG4xmcARFs/HKchIi2a9hPxX//1XwGEF7LcvHlz9HadToeioiJ873vfU666LOT1S3GNzwBX7UnDoCEiDZr2E/Gdd94BAHz3u9/FCy+8oHhB2c7jT6JFw64zItKgmMdoGDKxiYzRxMMgsuuMiLRryk/Ee++9F7/+9a8BACtWrJh0gPu3v/1tygvLVsmM0XBPGiLSoik/Ebdu3Rr9+oc//KHixWhBMmM0bNEQkRZN+Ym4ZMmS6Nd33nmn4sVku6AUgj8Qf9BwjIaItCzmMZrdu3fjk08+AQB8/PHHuPvuu7Fy5UocPXpUseKyjW9kdPmZeMdo9Jx1RkTaFXPQ/PznP0d5eTkA4D/+4z/wjW98A9/85jfx/PPPK1Zctklk+RkAEAUddGDXGRFpU8xBMzw8jIKCArhcLpw+fRoPPvgg1q9fj+7ubiXryyqRBTXjDRqdTsfNz4hIs2L+RLRarfjDH/6AP//5z1iyZAlEUYTL5YIoikrWl1UiLZp4Z50B3PyMiLQr5k/E7373u9i0aROMRiNeeuklAMC7776LhQsXKlZctoluehbnGA3Azc+ISLti/kRcsWIFOjs7x9xWXV2N6urqlBeVrSJBk0iLRs/Nz4hIo+L6RBweHkZ3dzfcbveY25cuXZrSorKV15dEi4ZdZ0SkUTF/Iv7qV7/Cs88+i9zcXJhMpujtOp0Ov/nNbxQpLttEWzTGxLrO/N5AqksiIkq7mD8Rf/SjH+HFF1/EihUrlKwnq3n8QZiMIgQh9r1oIridMxFpVczTmyVJSmoHze7ubtTV1cFms6Gurg5nz56d8DWam5tRVVWFVatWobW1dcz9Bw8exNq1a1FTU4O1a9fi0qVLCdeTasEQMOwJwGQU4fYHo/9i3TQzPBmAQUNE2hNzi6ahoQGvvPIKHnvsMQhC7NsURzQ1NaG+vh61tbXYt28fGhsb8frrr4855sCBA+jp6UFHRwcGBwexbt06LF26FOXl5Thx4gRefvll/OIXv0BRURGGh4fHbSudTv5AEBcvhceuPvqkL3r7bTcVxfR4AycDEJFGxbUywCuvvIIvf/nLuPvuu8f8m47D4UBXVxdqamoAADU1Nejq6oLT6Rxz3MGDB7F+/XoIggCLxYKqqiq0t7dHX/+hhx5CUVH4g7ugoAA5OTmxlq+KQDAEgz6x64oMogApJEOKtQlERJQlYm7RJLN6s91uR0lJSfTiTlEUUVxcDLvdDovFMua4srKy6PdWqxW9vb0AgDNnzqC8vBxf//rX4fF4sGrVKnzzm9+cdOuCiRQW5idUf1FRwbTHyE4PJFlGrkmPgvzPJ0sYDGO/n+z2/LxwaOoEIabXi7e+dGJ9ycn0+oDMr5H1pVfMQZPu1ZslScLp06exe/dujIyM4JFHHkFZWRnWrVsX83M4HC6E4mwxFBUVYGBgeNrjPP4gfP4g8nL0GHb5orcHAsEx3092uxwKd5s5Bj0oyIm9VRRrfenC+pKT6fUBmV8j60ucIOgS/gN9zPPEeuDIyAh+9KMf4Z577sHixYsBAJ2dnWhpaZn2sVarFX19fZCk8FpgkiShv78fVqt13HEXL16Mfm+321FaWgoAKCsrQ3V1NYxGI/Lz83HPPffg+PHjsZaviqAUiq7EHK8cQzhcPKPX4hARaUXMn4rPP/88Pv30U/z7v/97tLvqxhtvxC9/+ctpH1tYWIjKykq0tbUBANra2lBZWTmm2wwIrzTQ2tqKUCgEp9OJQ4cOwWazAQiP63R2dkKWZQQCAbz//vu4+eabY/6PqiE8RpNY0BijQcNraYhIW2LuOjt06BA6OjqQm5sbnXVWUlKCvr6+aR4ZtmXLFjz11FPYuXMnZsyYgW3btgEIz2bbtGkTFi5ciNraWhw7dgyrV68GAGzcuBEVFRUAgDVr1uDkyZP46le/CkEQsGzZMnzta1+L6z+rJCkkIyjJSQRN+HGRiz6JiLQi5qAxGAzRrq8Ip9OJWbNmxfT4+fPnj7suBgB27doV/VoURTQ3N0/4eEEQ8PTTT+Ppp5+OtWRV+Uc3PUu268zNrjMi0piYPxWrq6vx5JNP4vz58wCA/v5+PPvss1izZo1ixWUT30g4IAxicl1nXgYNEWlMzJ+KTzzxBCoqKnDfffdhaGgINpsNRUVF2Lhxo5L1ZY1kWzSioINe1MHNMRoi0piYu856enrwV3/1V/jHf/xHSJKEqqoqLFiwQMnasoovGjSJbwRnNIjwcoyGiDRm2qCRZRmbN2/G3r17UVpaiuLiYvT19WHHjh2ora3F888/H9dFk1rlC4x2nekT/1kY9QLHaIhIc6YNmj179uDDDz/Enj17sGjRoujtx48fx7/8y7/gzTffxN/93d8pWmQ28PmTb9HkGESO0RCR5kw7oLBv3z5873vfGxMyALBo0SJs3rwZ+/btU6y4bBLtOktwMgAQ7jrjGA0Rac20n4pnzpzBHXfcMeF9d9xxB86cOZPyorJRspMBgNEWDcdoiEhjpv1UlCQJ+fkTr3WTn5+PUIhL2wNXTW9OImiMBo7REJH2TDtGEwwG8f7770OWJ16M8osXcV6rfCMSREGX0O6aETkGEYFgCIGglNRYDxFRJpk2aAoLC7F58+ZJ7//iemXXKt+IlFRrBvh8GRq3L4hZ+QwaItKGaYPmnXfeUaOOrOcbCaYgaD5fhmZWfmZt6kZElKjkPhkpyp+CFk0OV3AmIg1i0KSIb0RKamozcFWLxssJAUSkHQyaFPEFUtGiiYzRsEVDRNrBoEkRnz8FYzR6bhVARNrDoEkRfwpaNAaDAB04RkNE2sKgSRGfP/mgEXQ6mHP0cHkZNESkHQyaFAhKIQSkUEousswzGxg0RKQpDJoUSMWCmhH5ZgOGPQwaItIOBk0K+PzJr3MWkWdm1xkRaQuDJgV8KVi5OSKfXWdEpDEMmhTwpmDl5ohw19nIpIuYEhFlGwZNCnj9qWvR5JkNCEpytJVERJTtGDQpENmszJiiFg0Adp8RkWaoFjTd3d2oq6uDzWZDXV0dzp49O+4YSZLQ3NyMqqoqrFq1Cq2treOO+eyzz3Dbbbdh27ZtKlQdG08kaAzJT2+OBA1nnhGRVqgWNE1NTaivr8fbb7+N+vp6NDY2jjvmwIED6OnpQUdHB/bs2YPt27fjwoUL0fslSUJTUxOqqqrUKjsmkSv5U9V1BgAu70jSz0VElAlUCRqHw4Guri7U1NQAAGpqatDV1QWn0znmuIMHD2L9+vUQBAEWiwVVVVVob2+P3v/aa6/h7rvvxrx589QoO2YefxB6UQcxid01I9iiISKtUSVo7HY7SkpKIIrhriVRFFFcXAy73T7uuLKysuj3VqsVvb29AIBTp06hs7MT3/jGN9QoOS5eXxDmHD10OgYNEdEXTbvDZiYIBAJ45pln8P3vfz8aVokoLMxP6HFFRQVT3h+CDrkmAwryTePuMxj0cd1umZULvahDSKeb9nVjrS/dWF9yMr0+IPNrZH3ppUrQWK1W9PX1QZIkiKIISZLQ398Pq9U67riLFy9i0aJFAD5v4QwMDKCnpwePPvooAGBoaAiyLMPlcmHr1q0x1+FwuBAKxXd9SlFRAQYGhqc8xjnkhckoYtjlG3dfIBCM63avdwR5ZgP6Ha5pXzfW+tKJ9SUn0+sDMr9G1pc4QdAl/Af61VQJmsLCQlRWVqKtrQ21tbVoa2tDZWUlLBbLmOOqq6vR2tqK1atXY3BwEIcOHcIbb7yBsrIyfPDBB9Hjtm/fDo/HgyeffFKN8qfl9QVhNiY/4yyigOudEZGGqDbrbMuWLWhpaYHNZkNLSwuam5sBAA0NDThx4gQAoLa2FuXl5Vi9ejU2bNiAjRs3oqKiQq0SE+bxh8doUiXfbMAwr6MhIo1QbYxm/vz5E14Xs2vXrujXoihGA2gqjz/+eEprS5bHl9qgKcg14ny/K2XPR0SUTlwZIAVS3qLJDa93RkSkBVkx6yyTBYIhBIIhmE2p+VHqBB1yDCI8viCGvIHotTk5Bj1ScD0oEZHqGDRJiqxzlqoWjT8gwTnkgwzg/524CJMx/Lx3VJZAn8JWExGRWvg3cpIi65ylctZZzuhzcQVnItICBk2SPL7UtmgAIGd0cU4/g4aINIBBkySPPzwNOTeFQWNii4aINIRBkyQlWjSRoGGLhoi0gEGTpOhkgBTNOgOuGqMJMGiIKPsxaJL0+WSA1AWNKAgwiAJbNESkCQyaJHl8QQg6HYyG1P4oc4wifCPBlD4nEVE6MGiS5PEHkWtKzV40V8sxivCz64yINIBBkySvL5jSGWcRJqPIrjMi0gQGTZI8/mBKJwJE5BhETm8mIk1g0CTJ41ewRcOuMyLSAAZNktzeAHKVaNEYRQQlGUEplPLnJiJSE4MmSUPuEczIM6b8eU0Grg5ARNrAoElCUArB7QtiZm7qgyaHqwMQkUYwaJIw7Amvc6ZIiyYSNBynIaIsx6BJwpA7vAtmgRItGkN43IddZ0SU7Rg0SbgyGjQzlWzRMGiIKMsxaJIw7AkHzYw8Q8qf22gQoAMX1iSi7MegSUKk60yJMRqdThdehobrnRFRlmPQJOGKewRGgwBTClduvpo5Rx/d74aIKFsxaJIw5BnBDAUmAkTkmw1weQOKPT8RkRpUC5ru7m7U1dXBZrOhrq4OZ8+eHXeMJElobm5GVVUVVq1ahdbW1uh9O3bswJo1a7B27Vrcf//9eO+999QqfVJKXawZEQkaWZYVew0iIqUp0+czgaamJtTX16O2thb79u1DY2MjXn/99THHHDhwAD09Pejo6MDg4CDWrVuHpUuXory8HIsWLcJDDz0Es9mMU6dO4YEHHkBnZydMJpNa/4VxhtwBXDdTudfPM+sRlGReS0NEWU2VFo3D4UBXVxdqamoAADU1Nejq6oLT6Rxz3MGDB7F+/XoIggCLxYKqqiq0t7cDAO666y6YzWYAwIIFCyDLMgYHB9Uof1JDHuVbNADg8nKchoiylypBY7fbUVJSAlEMXxsiiiKKi4tht9vHHVdWVhb93mq1ore3d9zz7d27F3PmzEFpaamyhU8hFJIxrFLQuDlOQ0RZTLWus1T58MMP8eKLL+JnP/tZ3I8tLMxP6DWLigrG3TY47IcsA9eXFKCoqACy04OC/PHdaAaDPuHbjTnhoAlIMnJzc1BkyY25vkzC+pKT6fUBmV8j60svVYLGarWir68PkiRBFEVIkoT+/n5YrdZxx128eBGLFi0CML6Fc/ToUXznO9/Bzp07ccMNN8Rdh8PhQigU38B6UVEBBgaGx91+od8FANCFQhgYGIbHH8SwyzfuuEAguduNBgGOK154PH4MSOPHaiarL1OwvuRken1A5tfI+hInCLqE/0Af8zwpqGVahYWFqKysRFtbGwCgra0NlZWVsFgsY46rrq5Ga2srQqEQnE4nDh06BJvNBgA4fvw4nnjiCbz00ku45ZZb1Ch7SkMe5ZafuRqnOBNRtlOt62zLli146qmnsHPnTsyYMQPbtm0DADQ0NGDTpk1YuHAhamtrcezYMaxevRoAsHHjRlRUVAAAmpub4fP50NjYGH3OF154AQsWLFDrvzDGFZdyqwJcLd9siK6pRkSUjVQLmvnz54+5LiZi165d0a9FUURzc/OEj3/rrbcUqy0RjqFw95ZlhrLTq/PNBly85Oa1NESUtbgyQIKcQz7kmw3IGZe3kK8AAAplSURBVN0JUyl5ZgOCkszuMyLKWgyaBF0a8qFQwYs1IyJL3PQ6PIq/FhGREhg0CXJc8aFQ4W4zACicmQMA6Bmd5UZElG0YNAmQZRnOIb8qQWMy6pFn0qOnLzOnPxIRTYdBkwC3Lwh/QELhjBxVXq9wpgnn+9iiIaLsxKBJgONKeMaZGmM0AFA4w4SBQS/3piGirMSgSYBaU5sjIq9zjt1nRJSFGDQJiASNai2a0QkB53oZNESUfbJuUc10C4aAXqcHBr0AQdDB7Q93Z8W5hFpcTEY9LDNy0G0fUu5FiIgUwqCJkz8QxJm/XIE5R48jp/qjt992U5Gir3tD2Qx8en4QsixDp9Mp+lpERKnErrMEuH1B5JnUzei/Lp+FK+4R9Dp54SYRZRcGTQJcnkB0UzK13FQxCwBwuie9u4oSEcWLQRMnlzcAf0BSfHuAL7pupgmzC3Jwqueyqq9LRJQsjtHEqX+062pGvrpBI4gC5l8/E5+cuwyXLxAdpzF5uIUAEWU2Bk2c+i6Hg0btFo0/IMEg6jDsCeDQkfOYlR+e8rxi8RxwagARZTJ2ncWpz+mFIOiQp/IYDQCUFeUBAHq4HA0RZREGTZz6nB7MyDVASMMU4zyTAUWzzDjL62mIKIswaOLUd9mjerfZ1eaVFmDQNYJBlz9tNRARxYNBE4dAMATHFR9m5KuzavNE5pYWAOByNESUPRg0cegf9CIkqz8R4Gq5Jj1KZpvx2cUhyLKC694QEaUIgyYOvQ43gPQGDQDcNGcWhj0B/GXAndY6iIhiwaCJw58uXIFe1GGmytfQfNHckgKYc/T45Bwv3iSizMegicOJzxyYf/1M6MX0/tgEQYcFc2bB7vDgXC9noBFRZmPQxOjSFS/sDg++NM+S7lIAhNc+M+eIeG3vCXj93HmTiDIXgyZGJz9zAkDGBI3JKOKuRWXoc3rwk/1/hMsbSHdJREQTUi1ouru7UVdXB5vNhrq6Opw9e3bcMZIkobm5GVVVVVi1ahVaW1tjuk8NJz5zoHBGDkosZlVfdyqlhbmoX30z/tjtxDP/+wP8+oNzGOLaZ0SUYVRb66ypqQn19fWora3Fvn370NjYiNdff33MMQcOHEBPTw86OjowODiIdevWYenSpSgvL5/yPqX9+cIVnPjMgbtuK8u4TceW/6/rUX5dLt767Rm0vnsGew93Y+nCUiy9xYr5ZQVpH08iIlIlaBwOB7q6urB7924AQE1NDbZu3Qqn0wmL5fOuqIMHD2L9+vUQBAEWiwVVVVVob2/HI488MuV9sRKE+EPi0hUv/u9v/oQbK2bhayvmQxAF5JrGr3OmT9PtUiiEK+4RVN1RgTtvKcafzg/hdM9lnDp3GYJOh/xcAwpyjZiZZ0RBrmHa4NHpdBB04Z+VoNNBpwNEQXfV7fEFV16eA2535rayWF/yMr3Ga70+g17Al28qgskoxv3YRD4zJ6JK0NjtdpSUlEAUw/9RURRRXFwMu90+JmjsdjvKysqi31utVvT29k57X6xmz85LqP7t3/7KmO/LrTMnPO6G8tlpub2iZMaEtxMRZQL2qxARkaJUCRqr1Yq+vj5IkgQgPLDf398Pq9U67riLFy9Gv7fb7SgtLZ32PiIiylyqBE1hYSEqKyvR1tYGAGhra0NlZeWYbjMAqK6uRmtrK0KhEJxOJw4dOgSbzTbtfURElLl0skorM545cwZPPfUUhoaGMGPGDGzbtg033HADGhoasGnTJixcuBCSJOHZZ5/F7373OwBAQ0MD6urqAGDK+4iIKHOpFjRERHRt4mQAIiJSFIOGiIgUxaAhIiJFMWiIiEhRDJpJxLIIqJouX76MhoYG2Gw2rF27Fv/0T/8EpzO8ovTHH3+M++67DzabDQ899BAcDkdaa3355ZexYMECfPrppxlVn9/vR1NTE1avXo21a9fimWeeAZA57/W7776LdevWoba2Fvfddx86OjrSWt+2bduwcuXKMe/ldPWoWetE9U11ngDq/i5O9vOL+OJ5onZ9U9U42bkCJPgeyzShBx98UN67d68sy7K8d+9e+cEHH0xrPZcvX5bff//96Pc/+MEP5KefflqWJEmuqqqSP/roI1mWZXnHjh3yU089la4y5ZMnT8oPP/yw/JWvfEU+ffp0RtW3detW+bnnnpNDoZAsy7I8MDAgy3JmvNehUEhesmSJfPr0aVmWZfmTTz6Rb7/9dlmSpLTV99FHH8kXL16MvpcRU9WjZq0T1TfZeSLLsuq/i5P9/GR5/HmSjvqmqnGyc0WWE3uPGTQTuHTpkrx48WI5GAzKsizLwWBQXrx4sexwONJc2efa29vlv//7v5ePHTsmr1mzJnq7w+GQb7/99rTU5Pf75Q0bNsjnz5+P/uJmSn0ul0tevHix7HK5xtyeKe91KBSS77zzTvnIkSOyLMvyhx9+KK9evToj6rv6Q2iqetJV60Qf5BGR80SW5bT9Ln6xvonOk3TW98UaJztXZDnx80W1bQKySayLgKZLKBTCL3/5S6xcuXLcYqMWiwWhUAiDg4OYNWuWqnW9+OKLuO+++8Zs3ZAp9Z0/fx6zZs3Cyy+/jA8++AB5eXn453/+Z5hMpox4r3U6HX784x/jscceQ25uLtxuN1577bWM+12cqh5ZljOq1qvPk0jtmfC7ONF5kkn1TXauLFmyJOHfR47RZKGtW7ciNzcXDzzwQLpLiTp69ChOnjyJ+vr6dJcyIUmScP78eXzpS1/Cr371K3z729/G448/Do/Hk+7SAADBYBA/+clPsHPnTrz77rt45ZVX8K1vfStj6stGPE8SM9m54nK5En5OtmgmcPUioKIoTroIaDps27YN586dw6uvvgpBEMYtNup0OiEIguqtmY8++ghnzpzBPffcAwDo7e3Fww8/jAcffDAj6rNardDr9aipqQEA3HbbbZg9ezZMJlNGvNeffPIJ+vv7sXjxYgDA4sWLYTabkZOTkxH1RUx1bsiynDG1fvE8idSe7t/Fyc6T73//+xlRHzD5udLd3Y2ysrKE3mO2aCYQ6yKgavvP//xPnDx5Ejt27IDRaAQA3HrrrfD5fDhy5AgA4M0330R1dbXqtT366KPo7OzEO++8g3feeQelpaX46U9/ikceeSQj6rNYLPibv/mb6Fp53d3dcDgcmDdvXka816Wlpejt7cVnn30GILw2oMPhwNy5czOivoipzo1MOW8mOk+AzDhXJjtPli1blhH1AZOfK3Pnzk34PeZaZ5OYbBHQdPnTn/6EmpoazJs3DyaTCQBQXl6OHTt24A9/+AOamprg9/tx/fXX44c//CGuu+66tNUKACtXrsSrr76Km266KWPqO3/+PDZv3ozBwUHo9Xp861vfwooVKzLmvd6/fz927doV3S5806ZNqKqqSlt9//Zv/4aOjg5cunQJs2fPxqxZs/Bf//VfU9ajZq0T1ffjH/940vMEgKq/i5P9/K529Xmidn1T1TjZuQIk9h4zaIiISFHsOiMiIkUxaIiISFEMGiIiUhSDhoiIFMWgISIiRTFoiIhIUQwaIiJSFIOGiIgU9f8BSeyMHutBVLAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-8kMp78xjwq"
      },
      "source": [
        "data_train =pad_sequences(sequences_train, maxlen=MAX_SEQUENCE_LENGTH,padding=\"post\")\n",
        "data_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH,padding=\"post\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_y9Q5efzhUz",
        "outputId": "d314202c-ec83-4383-b24a-09f27bf7f98c"
      },
      "source": [
        "data_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   52,  8801,    46, ...,    16,  2083,  5615],\n",
              "       [ 1178,  4786,  4787, ...,     0,     0,     0],\n",
              "       [   35,   101,  3705, ...,     0,     0,     0],\n",
              "       ...,\n",
              "       [   53,    57,  1409, ...,     0,     0,     0],\n",
              "       [ 3102,  3414,  4777, ...,     0,     0,     0],\n",
              "       [  384,   121, 12244, ...,     0,     0,     0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9empMZorzkF4",
        "outputId": "a5967fd9-d244-4cf0-ab20-efe25f6e41e5"
      },
      "source": [
        "!wget https://nlp.stanford.edu/data/glove.42B.300d.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-27 11:52:04--  https://nlp.stanford.edu/data/glove.42B.300d.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/glove.42B.300d.zip [following]\n",
            "--2021-07-27 11:52:04--  http://downloads.cs.stanford.edu/nlp/data/glove.42B.300d.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1877800501 (1.7G) [application/zip]\n",
            "Saving to: ‘glove.42B.300d.zip’\n",
            "\n",
            "glove.42B.300d.zip  100%[===================>]   1.75G  5.17MB/s    in 5m 53s  \n",
            "\n",
            "2021-07-27 11:57:58 (5.07 MB/s) - ‘glove.42B.300d.zip’ saved [1877800501/1877800501]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOaYyFuvzr6y",
        "outputId": "845f3b51-5381-40aa-d823-af98f7b4b7ed"
      },
      "source": [
        "!unzip glove.42B.300d.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  glove.42B.300d.zip\n",
            "  inflating: glove.42B.300d.txt      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmsGK5Qb1xmM",
        "outputId": "8662042b-e0cd-4b75-ac69-4535a79dcff1"
      },
      "source": [
        "# get word -> integer mapping\n",
        "word2idx = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(word2idx))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 25369 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJOA6DIj3Aiw",
        "outputId": "e1789655-56b7-46e2-938d-fcab8c78370f"
      },
      "source": [
        "word2vec = {}\n",
        "with open(\"glove.42B.300d.txt\") as f:\n",
        "  # is just a space-separated text file in the format:\n",
        "  # word vec[0] vec[1] vec[2] ...\n",
        "  for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    vec = np.asarray(values[1:], dtype='float32')\n",
        "    word2vec[word] = vec\n",
        "print('Found %s word vectors.' % len(word2vec))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1917494 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1OzkKfr1xiY"
      },
      "source": [
        "num_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1)\n",
        "embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\n",
        "for word, i in word2idx.items():\n",
        "  if i < MAX_VOCAB_SIZE:\n",
        "    embedding_vector = word2vec.get(word)\n",
        "    if embedding_vector is not None:\n",
        "      # words not found in embedding index will be all zeros.\n",
        "      embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdZJPF9V1xej",
        "outputId": "aaa61970-07aa-4d89-852c-648eb4239798"
      },
      "source": [
        "embedding_matrix.shape # (num Words, Embedding Dimension)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 300)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hs0VAxpU3295"
      },
      "source": [
        "# load pre-trained word embeddings into an Embedding layer\n",
        "# note that we set trainable = False so as to keep the embeddings fixed\n",
        "embedding_layer = tf.keras.layers.Embedding(\n",
        "  num_words,\n",
        "  EMBEDDING_DIM,\n",
        "  weights=[embedding_matrix],\n",
        "  input_length=MAX_SEQUENCE_LENGTH,\n",
        "  trainable=False #True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxDW7fUr326E",
        "outputId": "59cab84c-9afe-4fd3-dfd0-43d038373326"
      },
      "source": [
        "print('Building model...')\n",
        "\n",
        "# create an LSTM network with a single LSTM\n",
        "input_ = Input(shape=(MAX_SEQUENCE_LENGTH,))\n",
        "x = embedding_layer(input_)\n",
        "# x = LSTM(15, return_sequences=True)(x)\n",
        "x = Bidirectional(LSTM(15, return_sequences=True))(x)\n",
        "x = GlobalMaxPool1D()(x)\n",
        "output = Dense(1, activation=\"sigmoid\")(x) \n",
        "\n",
        "model = tf.keras.models.Model(input_, output)\n",
        "model.compile(\n",
        "  loss='binary_crossentropy',\n",
        "  optimizer=tf.keras.optimizers.Adam(lr=0.01),\n",
        "  metrics=['accuracy']\n",
        ")\n",
        "\n",
        "\n",
        "print('Training model...')\n",
        "r = model.fit(\n",
        "  data_train,\n",
        " y_train,\n",
        "  batch_size=BATCH_SIZE,\n",
        "  epochs=EPOCHS,\n",
        "  validation_data=(data_test,y_test)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building model...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "Epoch 1/5\n",
            "150/150 [==============================] - 11s 13ms/step - loss: 0.4007 - accuracy: 0.8132 - val_loss: 0.3465 - val_accuracy: 0.8461\n",
            "Epoch 2/5\n",
            "150/150 [==============================] - 1s 10ms/step - loss: 0.2818 - accuracy: 0.8815 - val_loss: 0.3281 - val_accuracy: 0.8596\n",
            "Epoch 3/5\n",
            "150/150 [==============================] - 1s 9ms/step - loss: 0.2180 - accuracy: 0.9130 - val_loss: 0.3312 - val_accuracy: 0.8616\n",
            "Epoch 4/5\n",
            "150/150 [==============================] - 1s 9ms/step - loss: 0.1735 - accuracy: 0.9328 - val_loss: 0.3510 - val_accuracy: 0.8577\n",
            "Epoch 5/5\n",
            "150/150 [==============================] - 1s 10ms/step - loss: 0.1256 - accuracy: 0.9550 - val_loss: 0.3704 - val_accuracy: 0.8607\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "bsVocVH3322m",
        "outputId": "41eb1062-1753-4aa5-b85d-bde5e3e7fb65"
      },
      "source": [
        "# plot some data\n",
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.plot(r.history['val_loss'], label='val_loss')\n",
        "plt.legend()\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": 268
        },
        "id": "7Jwmzv7a32w5",
        "outputId": "6991a56e-1649-4060-909d-2fb67f4fbb73"
      },
      "source": [
        "# accuracies\n",
        "plt.plot(r.history['accuracy'], label='accuracy')\n",
        "plt.plot(r.history['val_accuracy'], label='val_accuracy')\n",
        "plt.legend()\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/"
        },
        "id": "8NwjNHnV32ss",
        "outputId": "fcc77d94-0cce-4420-e7e7-e649f9c8d057"
      },
      "source": [
        "input_string = [\"well, what a surprise.\"]\n",
        "text = tokenizer.texts_to_sequences(input_string)\n",
        "text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[624, 33, 6, 861]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-IdGQFY32ev"
      },
      "source": [
        "data_train = pad_sequences(text, maxlen=MAX_SEQUENCE_LENGTH,padding=\"post\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mY2yxzKo1xbA",
        "outputId": "13dd8665-a630-4a52-d140-45e836bbb46f"
      },
      "source": [
        "data_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[624,  33,   6, 861,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxYm3B7H1wZW"
      },
      "source": [
        "pred = model.predict(data_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_AI2-UL6n-s",
        "outputId": "75dbf017-5c00-49da-f0a7-766a51bdaf1c"
      },
      "source": [
        "np.argmax(pred,axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJ5lEc6c6rvS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}