NLP_Tweet_disaster.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Younes-Piro/Disaster_tweeter_classification/blob/main/NLP_Tweet_disaster.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USGS2FU1_7p6",
        "outputId": "ee3f98d0-8d16-420d-af2e-970379873570"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tensorflow-estimator, keras, flatbuffers, keras-preprocessing, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.12.0\n",
            "    Uninstalling tensorflow-estimator-2.12.0:\n",
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            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.12.0\n",
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            "    Found existing installation: flatbuffers 23.3.3\n",
            "    Uninstalling flatbuffers-23.3.3:\n",
            "      Successfully uninstalled flatbuffers-23.3.3\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.12.0\n",
            "    Uninstalling tensorflow-2.12.0:\n",
            "      Successfully uninstalled tensorflow-2.12.0\n",
            "Successfully installed flatbuffers-2.0.7 keras-2.7.0 keras-preprocessing-1.1.2 tensorflow-2.7.0 tensorflow-estimator-2.7.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow==2.7.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NaMniJk7VkIz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7sbLcDaoV2fN"
      },
      "outputs": [],
      "source": [
        "df_train = pd.read_csv('train.csv')"
      ]
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    {
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              "\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": 4
        }
      ],
      "source": [
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4hRmSWcWAP9",
        "outputId": "57b9d6d8-9aed-4a6e-f71e-d93235269325"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id             0\n",
              "keyword       61\n",
              "location    2533\n",
              "text           0\n",
              "target         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df_train.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "tGBmgt0cWU2N"
      },
      "outputs": [],
      "source": [
        "#All columns uppercase\n",
        "df_train.columns = [col.upper() for col in df_train.columns]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "fKJDmqKdWn3I"
      },
      "outputs": [],
      "source": [
        "df_train.drop(['KEYWORD', 'LOCATION'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLc-kHotWpKn",
        "outputId": "1bde1f65-a0b1-4f36-de1b-ea7ac6f26afa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4342\n",
              "1    3271\n",
              "Name: TARGET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df_train.TARGET.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2wYXPrWXCxM"
      },
      "source": [
        "# **DATA CLEANING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WwNFfZ2XALV",
        "outputId": "c4316b34-3992-4094-8758-c74b435124b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import string\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('wordnet')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('punkt')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "BiPGGak1YM-C"
      },
      "outputs": [],
      "source": [
        "en_stopword = nltk.corpus.stopwords.words('english')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JFiB1H9WW2Fh"
      },
      "outputs": [],
      "source": [
        "string.punctuation\n",
        "ps = nltk.PorterStemmer()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "KGFEz6NgaeP3"
      },
      "outputs": [],
      "source": [
        "transformer_regex = df_train.replace(regex={\n",
        "    ' AFAIK ':'As Far As I Know',\n",
        "    ' AFK ':' Away From Keyboard ',\n",
        "    ' ASAP ':' As Soon As Possible ',\n",
        "    ' ATK ':' At The Keyboard ',\n",
        "    ' ATM ':' At The Moment ',\n",
        "    ' A3 ':' Anytime, Anywhere, Anyplace ',\n",
        "    ' BAK ':' Back At Keyboard ',\n",
        "    ' BBL ':' Be Back Later ',\n",
        "    ' BBS ':' Be Back Soon ',\n",
        "    ' BFN ':' Bye For Now ',\n",
        "    ' B4N ':' Bye For Now ',\n",
        "    ' BRB ':' Be Right Back ',\n",
        "    ' BRT ':' Be Right There ',\n",
        "    ' BTW ':' By The Way ',\n",
        "    ' B4 ':' Before ',\n",
        "    ' B4N ':' Bye For Now ',\n",
        "    ' CU ':' See You ',\n",
        "    ' CUL8R ':' See You Later ',\n",
        "    ' CYA ':' See You ',\n",
        "    ' FAQ ':' Frequently Asked Questions ',\n",
        "    ' FC ':' Fingers Crossed ',\n",
        "    ' FWIW ':\" For What It's Worth \",\n",
        "    ' FYI ':' For Your Information ',\n",
        "    ' GAL ':' Get A Life ',\n",
        "    ' GG ':' Good Game ',\n",
        "    ' GN ':' Good Night ',\n",
        "    ' GMTA ':' Great Minds Think Alike ',\n",
        "    ' GR8 ':\" Great! \",\n",
        "    ' G9 ':\" Genius \",\n",
        "    ' IC ':\" I See \",\n",
        "    ' ICQ ':\" I Seek you \",\n",
        "    ' ILU ':\" I Love You \",\n",
        "    ' IMHO ':\" In My Honest \",\n",
        "    ' IMO ':\" In My Opinion \",\n",
        "    ' IOW ':\" In Other Words \",\n",
        "    ' IRL ':\" In Real Life \",\n",
        "    ' KISS ':\" Keep It Simple, Stupid \",\n",
        "    ' LDR ':\" Long Distance Relationship \",\n",
        "    ' LMAO ':\" Laugh My Ass \",\n",
        "    ' LOL ':\" Laughing Out Loud \",\n",
        "    ' LTNS ':\" Long Time No See \",\n",
        "    ' L8R ':\" Later \",\n",
        "    ' MTE ':\" My Thoughts Exactly \",\n",
        "    ' M8 ':\" Mate \",\n",
        "    ' NRN ':\" No Reply Necessary \",\n",
        "    ' OIC ':\" Oh I See \",\n",
        "    ' PITA ':\" Pain In The Ass \",\n",
        "    ' PRT ':\" Party \",\n",
        "    ' PRW ':\" Parents Are Watching \",\n",
        "    ' ROFL ':\" Rolling On The Floor Laughing \",\n",
        "    ' ROFLOL ':\" Rolling On The Floor Laughing Out Loud \",\n",
        "    ' ROTFLMAO ':\" Rolling On The Floor Laughing My Ass \",\n",
        "    ' SK8 ':\" Skate \",\n",
        "    ' STATS ':\" Your sex and age \",\n",
        "    ' ASL ':\" Age, Sex, Location \",\n",
        "    ' THX ':\" Thank You \",\n",
        "    ' TTFN ':\" Ta-Ta For Now! \",\n",
        "    ' TTYL ':\" Talk To You Later \",\n",
        "    ' U ':\" You \",\n",
        "    ' U2 ':\" You Too\",\n",
        "    ' U4E ':\" Yours For Ever \",\n",
        "    ' WB ':\" Welcome Back \",\n",
        "    ' WTF ':\" What The Fuck \",\n",
        "    ' WTG ':\" Way To Go! \",\n",
        "    ' WUF ':\" Where Are You From? \",\n",
        "    ' W8 ':\" Wait\",},inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "sk--CGlYalRA"
      },
      "outputs": [],
      "source": [
        "#remove stopwords\n",
        "def remove_stopwords(text) : \n",
        "    return \" \".join([word for word in text.split() if word not in (en_stopword)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "NCELs2BraxD-"
      },
      "outputs": [],
      "source": [
        "def remove_html(text) :\n",
        "    html = re.compile(r\"<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});\")\n",
        "    return re.sub(html, \"\", text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "wM0fFukTAe9-"
      },
      "outputs": [],
      "source": [
        "def remove_mentions(text):\n",
        "    mention = \"@[A-Za-z0-9_]+\"\n",
        "    return re.sub(mention,\"\", text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Qmm3AO4razq6"
      },
      "outputs": [],
      "source": [
        "def remove_emojis(text):\n",
        "    emoji_pattern = re.compile(\n",
        "        '['\n",
        "        u'\\U0001F600-\\U0001F64F'  # emoticons\n",
        "        u'\\U0001F300-\\U0001F5FF'  # symbols & pictographs\n",
        "        u'\\U0001F680-\\U0001F6FF'  # transport & map symbols\n",
        "        u'\\U0001F1E0-\\U0001F1FF'  # flags (iOS)\n",
        "        u'\\U00002702-\\U000027B0'\n",
        "        u'\\U000024C2-\\U0001F251'\n",
        "        ']+',\n",
        "        flags=re.UNICODE)\n",
        "    return emoji_pattern.sub(r'', text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "7Gg0-5vfa3EV"
      },
      "outputs": [],
      "source": [
        "def clean_text(text) :\n",
        "    text = str(text).lower()\n",
        "    text = remove_mentions(text)\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    text = remove_html(text)\n",
        "    text = remove_emojis(text)\n",
        "    text = remove_stopwords(text)\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "23tWtf1ya9RF"
      },
      "outputs": [],
      "source": [
        "df_train['CLEANED_TEXT'] = df_train['TEXT'].apply( lambda x:clean_text(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GmeKFt38bFlw",
        "outputId": "fee93e42-7fea-4e64-dc3c-ca7074db8d41"
      },
      "outputs": [
        {
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              "         ID                                               TEXT  TARGET  \\\n",
              "0         1  Our Deeds are the Reason of this #earthquake M...       1   \n",
              "1         4             Forest fire near La Ronge Sask. Canada       1   \n",
              "2         5  All residents asked to 'shelter in place' are ...       1   \n",
              "3         6  13,000 people receive #wildfires evacuation or...       1   \n",
              "4         7  Just got sent this photo from Ruby #Alaska as ...       1   \n",
              "...     ...                                                ...     ...   \n",
              "7608  10869  Two giant cranes holding a bridge collapse int...       1   \n",
              "7609  10870  @aria_ahrary @TheTawniest The out of control w...       1   \n",
              "7610  10871  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1   \n",
              "7611  10872  Police investigating after an e-bike collided ...       1   \n",
              "7612  10873  The Latest: More Homes Razed by Northern Calif...       1   \n",
              "\n",
              "                                           CLEANED_TEXT  \n",
              "0          deeds reason earthquake may allah forgive us  \n",
              "1                 forest fire near la ronge sask canada  \n",
              "2     residents asked shelter place notified officer...  \n",
              "3     people receive wildfires evacuation orders cal...  \n",
              "4     got sent photo ruby alaska smoke wildfires pou...  \n",
              "...                                                 ...  \n",
              "7608  two giant cranes holding bridge collapse nearb...  \n",
              "7609  control wild fires california even northern pa...  \n",
              "7610                                     volcano hawaii  \n",
              "7611  police investigating ebike collided car little...  \n",
              "7612  latest homes razed northern california wildfir...  \n",
              "\n",
              "[7613 rows x 4 columns]"
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<p>7613 rows × 4 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "eqN-WfNTbVE1"
      },
      "outputs": [],
      "source": [
        "# Tokenizing our cleaned text : \n",
        "df_train['clean_text_tokenized'.upper()] = df_train['CLEANED_TEXT'].apply(word_tokenize)"
      ]
    },
    {
      "cell_type": "code",
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              "         ID                                               TEXT  TARGET  \\\n",
              "0         1  Our Deeds are the Reason of this #earthquake M...       1   \n",
              "1         4             Forest fire near La Ronge Sask. Canada       1   \n",
              "2         5  All residents asked to 'shelter in place' are ...       1   \n",
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              "7609  10870  @aria_ahrary @TheTawniest The out of control w...       1   \n",
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              "7612  10873  The Latest: More Homes Razed by Northern Calif...       1   \n",
              "\n",
              "                                           CLEANED_TEXT  \\\n",
              "0          deeds reason earthquake may allah forgive us   \n",
              "1                 forest fire near la ronge sask canada   \n",
              "2     residents asked shelter place notified officer...   \n",
              "3     people receive wildfires evacuation orders cal...   \n",
              "4     got sent photo ruby alaska smoke wildfires pou...   \n",
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          "metadata": {},
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      "execution_count": 22,
      "metadata": {
        "id": "V8S6iWRJdxWC"
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      "source": [
        "lemma = WordNetLemmatizer()"
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    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "ZgQzL_Gcdy2j"
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      "source": [
        "for row in df_train['clean_text_tokenized'.upper()]:\n",
        "  for index, word in enumerate(row):\n",
        "    row[index] = lemma.lemmatize(word)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
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            "text/plain": [
              "7613"
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      "source": [
        "corpus = []\n",
        "for sentences in list(df_train['clean_text_tokenized'.upper()]) :\n",
        "    corpus.append(' '.join(sentences))\n",
        "len(corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "kqZmcklqeM78"
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      "outputs": [],
      "source": [
        "df_train['final_text'.upper()] = corpus"
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              "         ID                                               TEXT  TARGET  \\\n",
              "0         1  Our Deeds are the Reason of this #earthquake M...       1   \n",
              "1         4             Forest fire near La Ronge Sask. Canada       1   \n",
              "2         5  All residents asked to 'shelter in place' are ...       1   \n",
              "3         6  13,000 people receive #wildfires evacuation or...       1   \n",
              "4         7  Just got sent this photo from Ruby #Alaska as ...       1   \n",
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              "7608  10869  Two giant cranes holding a bridge collapse int...       1   \n",
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              "                                           CLEANED_TEXT  \\\n",
              "0          deeds reason earthquake may allah forgive us   \n",
              "1                 forest fire near la ronge sask canada   \n",
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              "3     people receive wildfires evacuation orders cal...   \n",
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              "3     people receive wildfire evacuation order calif...  \n",
              "4     got sent photo ruby alaska smoke wildfire pour...  \n",
              "...                                                 ...  \n",
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              "7610                                     volcano hawaii  \n",
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              "      <td>1</td>\n",
              "      <td>control wild fires california even northern pa...</td>\n",
              "      <td>[control, wild, fire, california, even, northe...</td>\n",
              "      <td>control wild fire california even northern par...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7610</th>\n",
              "      <td>10871</td>\n",
              "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...</td>\n",
              "      <td>1</td>\n",
              "      <td>volcano hawaii</td>\n",
              "      <td>[volcano, hawaii]</td>\n",
              "      <td>volcano hawaii</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7611</th>\n",
              "      <td>10872</td>\n",
              "      <td>Police investigating after an e-bike collided ...</td>\n",
              "      <td>1</td>\n",
              "      <td>police investigating ebike collided car little...</td>\n",
              "      <td>[police, investigating, ebike, collided, car, ...</td>\n",
              "      <td>police investigating ebike collided car little...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7612</th>\n",
              "      <td>10873</td>\n",
              "      <td>The Latest: More Homes Razed by Northern Calif...</td>\n",
              "      <td>1</td>\n",
              "      <td>latest homes razed northern california wildfir...</td>\n",
              "      <td>[latest, home, razed, northern, california, wi...</td>\n",
              "      <td>latest home razed northern california wildfire...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7613 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad1a9202-4e77-4bc2-b3f8-a27dcf1d6063')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad1a9202-4e77-4bc2-b3f8-a27dcf1d6063');\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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "fv8CEk8jelcA"
      },
      "outputs": [],
      "source": [
        "df_train.drop(['ID', 'TEXT', 'CLEANED_TEXT', 'CLEAN_TEXT_TOKENIZED'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2caVAQZJezJB",
        "outputId": "0f30ba6a-9c67-457c-adfc-1767ffab7c70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TARGET                                         FINAL_TEXT\n",
              "0       1         deed reason earthquake may allah forgive u\n",
              "1       1              forest fire near la ronge sask canada\n",
              "2       1  resident asked shelter place notified officer ...\n",
              "3       1  people receive wildfire evacuation order calif...\n",
              "4       1  got sent photo ruby alaska smoke wildfire pour..."
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TARGET</th>\n",
              "      <th>FINAL_TEXT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>deed reason earthquake may allah forgive u</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>forest fire near la ronge sask canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>resident asked shelter place notified officer ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>people receive wildfire evacuation order calif...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>got sent photo ruby alaska smoke wildfire pour...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af4a3e8b-bbaa-46c8-8ba9-5e919289d12e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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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",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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-af4a3e8b-bbaa-46c8-8ba9-5e919289d12e 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-af4a3e8b-bbaa-46c8-8ba9-5e919289d12e');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-uPDPQHnp00"
      },
      "source": [
        "# Modelisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVG-iDLdNcOz"
      },
      "source": [
        " ***build embedding layer***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "metadata": {
        "id": "HF2MT60Zqsc8"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout\n",
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.optimizers import Adam, Nadam\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.text import one_hot\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "yAitiwDK28br"
      },
      "outputs": [],
      "source": [
        "#one hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xr59D_LI3CiL",
        "outputId": "510b8dd0-bdd8-4ab5-84a4-d47bfba28284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7613"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "VOC_SIZE = 20000\n",
        "\n",
        "one_hot_represent = [one_hot(words,VOC_SIZE)for words in df_train['final_text'.upper()]]\n",
        "\n",
        "len(one_hot_represent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jfAr5bs3S9A",
        "outputId": "eaa0bcc6-90a2-4232-e536-d1e083140b00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[    0     0     0 ... 18060  1632  6993]\n",
            " [    0     0     0 ...  1042 14065  6399]\n",
            " [    0     0     0 ... 12699  3255   741]\n",
            " ...\n",
            " [    0     0     0 ...     0  5772  7681]\n",
            " [    0     0     0 ...  1353  3321 14787]\n",
            " [    0     0     0 ... 11940 11709 18218]]\n"
          ]
        }
      ],
      "source": [
        "sent_length = 128\n",
        "embedded_docs = pad_sequences(one_hot_represent,padding='pre',maxlen=sent_length)\n",
        "\n",
        "print(embedded_docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwN0ESp9OOmk"
      },
      "source": [
        "***split train and test***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SvegtUGu-aVb"
      },
      "outputs": [],
      "source": [
        "y = df_train['target'.upper()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jB5V2eU7wqOj"
      },
      "outputs": [],
      "source": [
        "X_final=np.array(embedded_docs)\n",
        "y_final=np.array(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aNjQ7S6ZvmVp"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.18, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mIVS_tkHzOyH",
        "outputId": "10fcb235-f529-44db-bc52-dd8299805929"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(6242,)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiTV1v7z8Mj3"
      },
      "source": [
        "# MODELISATION"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "   1 - ***LSTM***\n",
        "\n"
      ],
      "metadata": {
        "id": "odhE8vUKarQ9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 223,
      "metadata": {
        "id": "umodcNbZ_GZk"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM,Dense,Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 224,
      "metadata": {
        "id": "z6MXnErZMAz1"
      },
      "outputs": [],
      "source": [
        "embedding_vector_features = 50\n",
        "lstm_units = 64 # number of units in the LSTM layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 225,
      "metadata": {
        "id": "NA4OMyjQMAwy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baae2e49-812e-4f11-c4aa-3c2c96bd6534"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_6 (Embedding)     (None, 128, 50)           1000000   \n",
            "                                                                 \n",
            " dropout_162 (Dropout)       (None, 128, 50)           0         \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 64)                29440     \n",
            "                                                                 \n",
            " dropout_163 (Dropout)       (None, 64)                0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,029,505\n",
            "Trainable params: 1,029,505\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# create sequential model\n",
        "my_model = Sequential()\n",
        "\n",
        "my_model.add(Embedding(20000,embedding_vector_features,input_length=sent_length))\n",
        "my_model.add(Dropout(0.2))\n",
        "my_model.add(LSTM(lstm_units))\n",
        "my_model.add(Dropout(0.2))\n",
        "my_model.add(Dense(1,activation='sigmoid'))\n",
        "my_model.compile(loss = 'binary_crossentropy',optimizer = 'adam', metrics = ['accuracy'])\n",
        "my_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zzQUilo8MAuG"
      },
      "outputs": [],
      "source": [
        "history = my_model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    validation_data=(X_test,y_test),\n",
        "    epochs= 30,\n",
        "    batch_size=512\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***TEST data***"
      ],
      "metadata": {
        "id": "Rj6XiaxEbyYy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "HUp29F0vdQR9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jN4H-UcMArs"
      },
      "outputs": [],
      "source": [
        "df_test.drop(columns=['id','keyword','location'],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transformer_regex = df_test.replace(regex={\n",
        "    ' AFAIK ':'As Far As I Know',\n",
        "    ' AFK ':' Away From Keyboard ',\n",
        "    ' ASAP ':' As Soon As Possible ',\n",
        "    ' ATK ':' At The Keyboard ',\n",
        "    ' ATM ':' At The Moment ',\n",
        "    ' A3 ':' Anytime, Anywhere, Anyplace ',\n",
        "    ' BAK ':' Back At Keyboard ',\n",
        "    ' BBL ':' Be Back Later ',\n",
        "    ' BBS ':' Be Back Soon ',\n",
        "    ' BFN ':' Bye For Now ',\n",
        "    ' B4N ':' Bye For Now ',\n",
        "    ' BRB ':' Be Right Back ',\n",
        "    ' BRT ':' Be Right There ',\n",
        "    ' BTW ':' By The Way ',\n",
        "    ' B4 ':' Before ',\n",
        "    ' B4N ':' Bye For Now ',\n",
        "    ' CU ':' See You ',\n",
        "    ' CUL8R ':' See You Later ',\n",
        "    ' CYA ':' See You ',\n",
        "    ' FAQ ':' Frequently Asked Questions ',\n",
        "    ' FC ':' Fingers Crossed ',\n",
        "    ' FWIW ':\" For What It's Worth \",\n",
        "    ' FYI ':' For Your Information ',\n",
        "    ' GAL ':' Get A Life ',\n",
        "    ' GG ':' Good Game ',\n",
        "    ' GN ':' Good Night ',\n",
        "    ' GMTA ':' Great Minds Think Alike ',\n",
        "    ' GR8 ':\" Great! \",\n",
        "    ' G9 ':\" Genius \",\n",
        "    ' IC ':\" I See \",\n",
        "    ' ICQ ':\" I Seek you \",\n",
        "    ' ILU ':\" I Love You \",\n",
        "    ' IMHO ':\" In My Honest \",\n",
        "    ' IMO ':\" In My Opinion \",\n",
        "    ' IOW ':\" In Other Words \",\n",
        "    ' IRL ':\" In Real Life \",\n",
        "    ' KISS ':\" Keep It Simple, Stupid \",\n",
        "    ' LDR ':\" Long Distance Relationship \",\n",
        "    ' LMAO ':\" Laugh My Ass \",\n",
        "    ' LOL ':\" Laughing Out Loud \",\n",
        "    ' LTNS ':\" Long Time No See \",\n",
        "    ' L8R ':\" Later \",\n",
        "    ' MTE ':\" My Thoughts Exactly \",\n",
        "    ' M8 ':\" Mate \",\n",
        "    ' NRN ':\" No Reply Necessary \",\n",
        "    ' OIC ':\" Oh I See \",\n",
        "    ' PITA ':\" Pain In The Ass \",\n",
        "    ' PRT ':\" Party \",\n",
        "    ' PRW ':\" Parents Are Watching \",\n",
        "    ' ROFL ':\" Rolling On The Floor Laughing \",\n",
        "    ' ROFLOL ':\" Rolling On The Floor Laughing Out Loud \",\n",
        "    ' ROTFLMAO ':\" Rolling On The Floor Laughing My Ass \",\n",
        "    ' SK8 ':\" Skate \",\n",
        "    ' STATS ':\" Your sex and age \",\n",
        "    ' ASL ':\" Age, Sex, Location \",\n",
        "    ' THX ':\" Thank You \",\n",
        "    ' TTFN ':\" Ta-Ta For Now! \",\n",
        "    ' TTYL ':\" Talk To You Later \",\n",
        "    ' U ':\" You \",\n",
        "    ' U2 ':\" You Too\",\n",
        "    ' U4E ':\" Yours For Ever \",\n",
        "    ' WB ':\" Welcome Back \",\n",
        "    ' WTF ':\" What The Fuck \",\n",
        "    ' WTG ':\" Way To Go! \",\n",
        "    ' WUF ':\" Where Are You From? \",\n",
        "    ' W8 ':\" Wait\",},inplace=True)"
      ],
      "metadata": {
        "id": "qGPprVwrdjqR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['text'] = df_test['text'].apply( lambda x:clean_text(x))"
      ],
      "metadata": {
        "id": "QhYiJuvFdjnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['text'] = df_test['text'].apply(word_tokenize)"
      ],
      "metadata": {
        "id": "gueqbk4Mdjki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_corpus = []\n",
        "for sentences in list(df_test['text']) :\n",
        "    test_corpus.append(' '.join(sentences))\n",
        "len(test_corpus)"
      ],
      "metadata": {
        "id": "0LywSqoxdjdv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['final_text'] = test_corpus"
      ],
      "metadata": {
        "id": "WeXWFzAcdxNT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_test_represent = [one_hot(words,VOC_SIZE)for words in df_test['final_text']]\n",
        "\n",
        "embedded_test_docs = pad_sequences(one_hot_test_represent,padding='pre',maxlen=sent_length)"
      ],
      "metadata": {
        "id": "1mA7IxAXeBay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = my_model.predict(embedded_test_docs)"
      ],
      "metadata": {
        "id": "2JlQCPq6eVdD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_result = []\n",
        "\n",
        "for one_pred in y_pred :\n",
        "  if one_pred > 0.5 :\n",
        "    prediction_result.append(1)\n",
        "  else :\n",
        "    prediction_result.append(0)"
      ],
      "metadata": {
        "id": "xU0UcSpod3TT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Submission files**"
      ],
      "metadata": {
        "id": "Bsxp8tOYgdmf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submission_file = pd.read_csv('sample_submission.csv')"
      ],
      "metadata": {
        "id": "0Vd4vhW2d24f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission_file['target'] = prediction_result"
      ],
      "metadata": {
        "id": "q58KJkcSd214"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission_file.to_csv('MySubmission.csv', index=False)"
      ],
      "metadata": {
        "id": "AjzIgk0vgoAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vx0xTOitMBRk"
      },
      "source": [
        "  2  -  ***BERT*** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uazJxZ-1MrvJ",
        "outputId": "ab57cc1c-7652-40dc-df37-79e2f9780e1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m83.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5erqS22PLj4",
        "outputId": "fb0113fb-963b-43d5-a25c-3ff36e566eb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at bert-base-cased were not used when initializing TFBertModel: ['nsp___cls', 'mlm___cls']\n",
            "- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-cased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from transformers import TFAutoModel, AutoTokenizer\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "SEQ_LEN = 150\n",
        "\n",
        "# initialize model and tokenizer\n",
        "bert = TFAutoModel.from_pretrained(\"bert-base-cased\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "# define function to handle tokenization\n",
        "def tokenize(sentence):\n",
        "    tokens = tokenizer.encode_plus(sentence, max_length=SEQ_LEN,\n",
        "                                   truncation=True, padding='max_length',\n",
        "                                   add_special_tokens=True, return_attention_mask=True,\n",
        "                                   return_token_type_ids=False, return_tensors='tf')\n",
        "    return tokens['input_ids'], tokens['attention_mask']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "id": "CWyVn2PGTWOy"
      },
      "outputs": [],
      "source": [
        "# initialize two arrays for input tensors\n",
        "Xids = np.zeros((len(df_train), SEQ_LEN))\n",
        "Xmask = np.zeros((len(df_train), SEQ_LEN))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
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        },
        "id": "18kNairuTWMB",
        "outputId": "8b2e232c-1161-4f36-f02b-53a5188b906d"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7613, 150)"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ],
      "source": [
        "Xids.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3P_9CqzqTWIp",
        "outputId": "5f75fa58-b1da-4fe6-b962-9a146b48146d"
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              "(7613, 150)"
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          "metadata": {},
          "execution_count": 159
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        "Xmask.shape"
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    {
      "cell_type": "code",
      "execution_count": 160,
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              "0          1         deed reason earthquake may allah forgive u\n",
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              "2          1  resident asked shelter place notified officer ...\n",
              "3          1  people receive wildfire evacuation order calif...\n",
              "4          1  got sent photo ruby alaska smoke wildfire pour...\n",
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              "7608       1  two giant crane holding bridge collapse nearby...\n",
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              "7610       1                                     volcano hawaii\n",
              "7611       1  police investigating ebike collided car little...\n",
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              "          document.querySelector('#df-5cc79a02-067f-4448-99cf-a7a005181f24 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-5cc79a02-067f-4448-99cf-a7a005181f24');\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": 160
        }
      ],
      "source": [
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "id": "4v_Iu54BVAj0"
      },
      "outputs": [],
      "source": [
        "arr = df_train['TARGET'].values  # take sentiment column in df as array\n",
        "labels = np.zeros((arr.size, arr.max()+1))  # initialize empty (all zero) label array\n",
        "labels[np.arange(arr.size), arr] = 1  # add ones in indices where we have a value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "fS8XUwCwTWFa"
      },
      "outputs": [],
      "source": [
        "# loop through data and tokenize everything\n",
        "for i, sentence in enumerate(df_train['FINAL_TEXT']):\n",
        "    Xids[i, :], Xmask[i, :] = tokenize(sentence)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "ELsY2iRnU6bj"
      },
      "outputs": [],
      "source": [
        "# create tensorflow dataset object\n",
        "dataset = tf.data.Dataset.from_tensor_slices((Xids, Xmask, labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "7QyZZodoVPSk"
      },
      "outputs": [],
      "source": [
        "# restructure dataset format for BERT\n",
        "def map_func(input_ids, masks, labels):\n",
        "    return {'input_ids': input_ids, 'attention_mask': masks}, labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "LrpPpcKJVTC7"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.map(map_func)  # apply the mapping function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "IJ03iKACVVNm"
      },
      "outputs": [],
      "source": [
        "# shuffle and batch the dataset\n",
        "dataset = dataset.batch(32)\n",
        "\n",
        "DS_LEN = len(list(dataset))  # get dataset length\n",
        "\n",
        "SPLIT = 0.9  # we will create a 90-10 split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "211F_x-RVYY_"
      },
      "outputs": [],
      "source": [
        "# create training-validation sets\n",
        "train = dataset.take(round(DS_LEN*SPLIT))\n",
        "val = dataset.skip(round(DS_LEN*SPLIT))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "2d0oliN4Vl_D"
      },
      "outputs": [],
      "source": [
        "# free up space\n",
        "del dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "id": "LtCy1PeQVoTr"
      },
      "outputs": [],
      "source": [
        "# build the model\n",
        "input_ids = tf.keras.layers.Input(shape=(150,), name='input_ids', dtype='int32')\n",
        "mask = tf.keras.layers.Input(shape=(150,), name='attention_mask', dtype='int32')\n",
        "\n",
        "input_ids = tf.keras.layers.Input(shape=(150,), name='input_ids', dtype='int32')\n",
        "mask = tf.keras.layers.Input(shape=(150,), name='attention_mask', dtype='int32')\n",
        "\n",
        "embeddings = bert(input_ids, attention_mask=mask)[0]  # we only keep tensor 0 (last_hidden_state)\n",
        "\n",
        "X = tf.keras.layers.GlobalMaxPool1D()(embeddings)  # reduce tensor dimensionality\n",
        "X = tf.keras.layers.BatchNormalization()(X)\n",
        "X = tf.keras.layers.Dense(128, activation='relu')(X)\n",
        "X = tf.keras.layers.Dropout(0.1)(X)\n",
        "y = tf.keras.layers.Dense(2, activation='sigmoid', name='outputs')(X)  # adjust based on number of sentiment classes\n",
        "\n",
        "model = tf.keras.Model(inputs=[input_ids, mask], outputs=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "id": "ZUJp6z5JVt6_"
      },
      "outputs": [],
      "source": [
        "# freeze the BERT layer\n",
        "model.layers[2].trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "id": "YAsNRdneVyc8"
      },
      "outputs": [],
      "source": [
        "# compile the model\n",
        "optimizer = tf.keras.optimizers.Adam(0.01)\n",
        "loss = tf.keras.losses.BinaryCrossentropy()\n",
        "acc = tf.keras.metrics.Accuracy()\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZe9FbJ3V1jS",
        "outputId": "819799f3-2a8b-4169-a80b-7fb7daab2c15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "214/214 [==============================] - 121s 424ms/step - loss: 0.7575 - accuracy: 0.6301 - val_loss: 0.6446 - val_accuracy: 0.7294\n",
            "Epoch 2/5\n",
            "214/214 [==============================] - 90s 420ms/step - loss: 0.6370 - accuracy: 0.6733 - val_loss: 0.5637 - val_accuracy: 0.7190\n",
            "Epoch 3/5\n",
            "214/214 [==============================] - 90s 422ms/step - loss: 0.6175 - accuracy: 0.6812 - val_loss: 0.5239 - val_accuracy: 0.7673\n",
            "Epoch 4/5\n",
            "214/214 [==============================] - 90s 419ms/step - loss: 0.6144 - accuracy: 0.6726 - val_loss: 0.5633 - val_accuracy: 0.7477\n",
            "Epoch 5/5\n",
            "214/214 [==============================] - 90s 420ms/step - loss: 0.6009 - accuracy: 0.6866 - val_loss: 0.5153 - val_accuracy: 0.7582\n"
          ]
        }
      ],
      "source": [
        "# train model\n",
        "history = model.fit(\n",
        "    train, \n",
        "    validation_data=val\n",
        "    ,epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "id": "2xEWO_Foi9gO"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_1iylUFit0k"
      },
      "source": [
        "# Visualizing Model accuracy / loss :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 944
        },
        "id": "j6wioZE9i3c8",
        "outputId": "afb50a03-8788-4f14-927b-7764a00ebc19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model recall')\n",
        "plt.ylabel('recall')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlPB_ZVYlOsv"
      },
      "source": [
        "# TEST DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "id": "5WE_07_ylNv0"
      },
      "outputs": [],
      "source": [
        "df_test = pd.read_csv('test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "id": "D1OV_EFajliR"
      },
      "outputs": [],
      "source": [
        "df_test.drop(columns=['id','keyword','location'],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aG_1MdqQl4RK",
        "outputId": "3dd44b55-caa2-4068-bb7b-422450140e68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text\n",
              "0                    Just happened a terrible car crash\n",
              "1     Heard about #earthquake is different cities, s...\n",
              "2     there is a forest fire at spot pond, geese are...\n",
              "3              Apocalypse lighting. #Spokane #wildfires\n",
              "4         Typhoon Soudelor kills 28 in China and Taiwan\n",
              "...                                                 ...\n",
              "3258  EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...\n",
              "3259  Storm in RI worse than last hurricane. My city...\n",
              "3260  Green Line derailment in Chicago http://t.co/U...\n",
              "3261  MEG issues Hazardous Weather Outlook (HWO) htt...\n",
              "3262  #CityofCalgary has activated its Municipal Eme...\n",
              "\n",
              "[3263 rows x 1 columns]"
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              "      <td>Just happened a terrible car crash</td>\n",
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              "\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",
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              "\n",
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              "          document.querySelector('#df-d1e7297d-a903-424d-bbb6-9cc05b698441 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1e7297d-a903-424d-bbb6-9cc05b698441');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ],
      "source": [
        "df_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "id": "s2Fnx0Iel6Yc"
      },
      "outputs": [],
      "source": [
        "transformer_regex = df_test.replace(regex={\n",
        "    ' AFAIK ':'As Far As I Know',\n",
        "    ' AFK ':' Away From Keyboard ',\n",
        "    ' ASAP ':' As Soon As Possible ',\n",
        "    ' ATK ':' At The Keyboard ',\n",
        "    ' ATM ':' At The Moment ',\n",
        "    ' A3 ':' Anytime, Anywhere, Anyplace ',\n",
        "    ' BAK ':' Back At Keyboard ',\n",
        "    ' BBL ':' Be Back Later ',\n",
        "    ' BBS ':' Be Back Soon ',\n",
        "    ' BFN ':' Bye For Now ',\n",
        "    ' B4N ':' Bye For Now ',\n",
        "    ' BRB ':' Be Right Back ',\n",
        "    ' BRT ':' Be Right There ',\n",
        "    ' BTW ':' By The Way ',\n",
        "    ' B4 ':' Before ',\n",
        "    ' B4N ':' Bye For Now ',\n",
        "    ' CU ':' See You ',\n",
        "    ' CUL8R ':' See You Later ',\n",
        "    ' CYA ':' See You ',\n",
        "    ' FAQ ':' Frequently Asked Questions ',\n",
        "    ' FC ':' Fingers Crossed ',\n",
        "    ' FWIW ':\" For What It's Worth \",\n",
        "    ' FYI ':' For Your Information ',\n",
        "    ' GAL ':' Get A Life ',\n",
        "    ' GG ':' Good Game ',\n",
        "    ' GN ':' Good Night ',\n",
        "    ' GMTA ':' Great Minds Think Alike ',\n",
        "    ' GR8 ':\" Great! \",\n",
        "    ' G9 ':\" Genius \",\n",
        "    ' IC ':\" I See \",\n",
        "    ' ICQ ':\" I Seek you \",\n",
        "    ' ILU ':\" I Love You \",\n",
        "    ' IMHO ':\" In My Honest \",\n",
        "    ' IMO ':\" In My Opinion \",\n",
        "    ' IOW ':\" In Other Words \",\n",
        "    ' IRL ':\" In Real Life \",\n",
        "    ' KISS ':\" Keep It Simple, Stupid \",\n",
        "    ' LDR ':\" Long Distance Relationship \",\n",
        "    ' LMAO ':\" Laugh My Ass \",\n",
        "    ' LOL ':\" Laughing Out Loud \",\n",
        "    ' LTNS ':\" Long Time No See \",\n",
        "    ' L8R ':\" Later \",\n",
        "    ' MTE ':\" My Thoughts Exactly \",\n",
        "    ' M8 ':\" Mate \",\n",
        "    ' NRN ':\" No Reply Necessary \",\n",
        "    ' OIC ':\" Oh I See \",\n",
        "    ' PITA ':\" Pain In The Ass \",\n",
        "    ' PRT ':\" Party \",\n",
        "    ' PRW ':\" Parents Are Watching \",\n",
        "    ' ROFL ':\" Rolling On The Floor Laughing \",\n",
        "    ' ROFLOL ':\" Rolling On The Floor Laughing Out Loud \",\n",
        "    ' ROTFLMAO ':\" Rolling On The Floor Laughing My Ass \",\n",
        "    ' SK8 ':\" Skate \",\n",
        "    ' STATS ':\" Your sex and age \",\n",
        "    ' ASL ':\" Age, Sex, Location \",\n",
        "    ' THX ':\" Thank You \",\n",
        "    ' TTFN ':\" Ta-Ta For Now! \",\n",
        "    ' TTYL ':\" Talk To You Later \",\n",
        "    ' U ':\" You \",\n",
        "    ' U2 ':\" You Too\",\n",
        "    ' U4E ':\" Yours For Ever \",\n",
        "    ' WB ':\" Welcome Back \",\n",
        "    ' WTF ':\" What The Fuck \",\n",
        "    ' WTG ':\" Way To Go! \",\n",
        "    ' WUF ':\" Where Are You From? \",\n",
        "    ' W8 ':\" Wait\",},inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {
        "id": "i5jdSLMKmAny"
      },
      "outputs": [],
      "source": [
        "df_test['text'] = df_test['text'].apply( lambda x:clean_text(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "metadata": {
        "id": "FGh95xuumBi6"
      },
      "outputs": [],
      "source": [
        "df_test['text'] = df_test['text'].apply(word_tokenize)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {
        "id": "4474N1dpmTxK"
      },
      "outputs": [],
      "source": [
        "for row in df_test['text']:\n",
        "  for index, word in enumerate(row):\n",
        "    row[index] = lemma.lemmatize(word)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEtxBnvemVv6",
        "outputId": "e4eb911d-439a-46da-f2b6-e0111a0c6a99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3263"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ],
      "source": [
        "\n",
        "test_corpus = []\n",
        "for sentences in list(df_test['text']) :\n",
        "    test_corpus.append(' '.join(sentences))\n",
        "len(test_corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {
        "id": "U3F7rYf6mXpC"
      },
      "outputs": [],
      "source": [
        "df_test['final_text'] = test_corpus"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "odJU_q8imZuq",
        "outputId": "07f05c71-97af-4619-9ee3-a94f197a71c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text  \\\n",
              "0                      [happened, terrible, car, crash]   \n",
              "1     [heard, earthquake, different, city, stay, saf...   \n",
              "2     [forest, fire, spot, pond, goose, fleeing, acr...   \n",
              "3             [apocalypse, lighting, spokane, wildfire]   \n",
              "4              [typhoon, soudelor, kill, china, taiwan]   \n",
              "...                                                 ...   \n",
              "3258  [earthquake, safety, los, angeles, ûò, safety...   \n",
              "3259  [storm, ri, worse, last, hurricane, hardest, h...   \n",
              "3260                 [green, line, derailment, chicago]   \n",
              "3261     [meg, issue, hazardous, weather, outlook, hwo]   \n",
              "3262  [cityofcalgary, activated, municipal, emergenc...   \n",
              "\n",
              "                                             final_text  \n",
              "0                           happened terrible car crash  \n",
              "1     heard earthquake different city stay safe ever...  \n",
              "2     forest fire spot pond goose fleeing across str...  \n",
              "3                  apocalypse lighting spokane wildfire  \n",
              "4                    typhoon soudelor kill china taiwan  \n",
              "...                                                 ...  \n",
              "3258  earthquake safety los angeles ûò safety faste...  \n",
              "3259  storm ri worse last hurricane hardest hit yard...  \n",
              "3260                      green line derailment chicago  \n",
              "3261            meg issue hazardous weather outlook hwo  \n",
              "3262  cityofcalgary activated municipal emergency pl...  \n",
              "\n",
              "[3263 rows x 2 columns]"
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              "      <td>happened terrible car crash</td>\n",
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              "    <tr>\n",
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              "      <td>[apocalypse, lighting, spokane, wildfire]</td>\n",
              "      <td>apocalypse lighting spokane wildfire</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>3259</th>\n",
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              "      <td>storm ri worse last hurricane hardest hit yard...</td>\n",
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              "    <tr>\n",
              "      <th>3260</th>\n",
              "      <td>[green, line, derailment, chicago]</td>\n",
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              "      <th>3261</th>\n",
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              "      <td>meg issue hazardous weather outlook hwo</td>\n",
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              "    <tr>\n",
              "      <th>3262</th>\n",
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              "      <td>cityofcalgary activated municipal emergency pl...</td>\n",
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              "<p>3263 rows × 2 columns</p>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-6ab8ebe5-c0da-4543-9c38-4e44a5ff8115');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ],
      "source": [
        "df_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {
        "id": "fvmYqpvrnLTm"
      },
      "outputs": [],
      "source": [
        "labels = np.zeros((len(df_test), 2))  # initialize empty (all zero) label array\n",
        "\n",
        "# initialize two arrays for input tensors\n",
        "Xids = np.zeros((len(df_test), SEQ_LEN))\n",
        "Xmask = np.zeros((len(df_test), SEQ_LEN))\n",
        "\n",
        "# loop through data and tokenize everything\n",
        "for i, sentence in enumerate(df_test['final_text']):\n",
        "    Xids[i, :], Xmask[i, :] = tokenize(sentence)\n",
        "\n",
        "# create tensorflow dataset object\n",
        "dataset = tf.data.Dataset.from_tensor_slices((Xids, Xmask, labels))\n",
        "\n",
        "# restructure dataset format for BERT\n",
        "def map_func(input_ids, masks, labels):\n",
        "    return {'input_ids': input_ids, 'attention_mask': masks}, labels\n",
        "  \n",
        "dataset = dataset.map(map_func)  # apply the mapping function\n",
        "\n",
        "# shuffle and batch the dataset\n",
        "dataset = dataset.batch(32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uY3olxCitd3A",
        "outputId": "eb820064-7706-483a-ad86-04e436fc6a1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3263, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ],
      "source": [
        "labels.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "metadata": {
        "id": "bt8CAR6VrIfX"
      },
      "outputs": [],
      "source": [
        "y_pred = model.predict(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMQRvSo4rqdH",
        "outputId": "6517ff5d-d3b1-47ed-cd1d-110bfdf0bca5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.2716770e-02, 8.8915730e-01, 3.9029226e-01, 5.8270699e-01,\n",
              "       5.5269527e-01, 6.3473219e-04, 6.2702149e-01, 9.9729174e-01,\n",
              "       9.7868186e-01, 9.7434688e-01, 9.3367422e-01, 7.1859312e-01,\n",
              "       9.3148941e-01, 9.6568787e-01, 9.8559988e-01, 5.7949114e-01,\n",
              "       5.9817094e-01, 5.9836370e-01, 7.2203946e-01, 7.3112607e-01,\n",
              "       5.8708143e-01, 2.1666449e-01, 5.8017176e-01, 2.9646111e-01,\n",
              "       5.7674909e-01, 5.6079072e-01, 7.2891349e-01, 2.6100850e-01,\n",
              "       6.5738040e-01, 5.0104833e-01], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ],
      "source": [
        "y_pred[:, 0][:30]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMS2ccbbtbGO",
        "outputId": "0744180d-d195-4f3b-c43f-eaa565084163"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3263"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ],
      "source": [
        "len(y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 218,
      "metadata": {
        "id": "NZq6eIHWsImZ"
      },
      "outputs": [],
      "source": [
        "prediction_result = []\n",
        "\n",
        "for one_pred in y_pred[:,1] :\n",
        "  if one_pred > 0.5 :\n",
        "    prediction_result.append(1)\n",
        "  else :\n",
        "    prediction_result.append(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 219,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bVbzZ2D-sazc",
        "outputId": "572da1c8-bf71-4370-959e-8fc0f6cc6b67"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 0, 1, 0, 0, 1, 0, 0, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ],
      "source": [
        "prediction_result[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {
        "id": "Y0FYL_QYsx-d"
      },
      "outputs": [],
      "source": [
        "submission_file = pd.read_csv('sample_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ko_pnUfDtP0i",
        "outputId": "4be7aca5-19b4-4e56-8185-0e0bba9b57dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  target\n",
              "0         0       0\n",
              "1         2       0\n",
              "2         3       0\n",
              "3         9       0\n",
              "4        11       0\n",
              "...     ...     ...\n",
              "3258  10861       0\n",
              "3259  10865       0\n",
              "3260  10868       0\n",
              "3261  10874       0\n",
              "3262  10875       0\n",
              "\n",
              "[3263 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd052f48-df71-44e3-b912-87602544c2a5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>id</th>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3258</th>\n",
              "      <td>10861</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3259</th>\n",
              "      <td>10865</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3260</th>\n",
              "      <td>10868</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3261</th>\n",
              "      <td>10874</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3262</th>\n",
              "      <td>10875</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3263 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd052f48-df71-44e3-b912-87602544c2a5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd052f48-df71-44e3-b912-87602544c2a5 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-cd052f48-df71-44e3-b912-87602544c2a5');\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": 199
        }
      ],
      "source": [
        "submission_file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "id": "sntZufFJunsO"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 221,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "lElTxKzUumOP",
        "outputId": "d424cd8f-a7fc-4117-8e9d-2473d6a2a4c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='target', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 221
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(x='target',data=submission_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 220,
      "metadata": {
        "id": "gqKIgci3tUW5"
      },
      "outputs": [],
      "source": [
        "submission_file['target'] = prediction_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 222,
      "metadata": {
        "id": "2jr_TwzutVy7"
      },
      "outputs": [],
      "source": [
        "submission_file.to_csv('MySubmission.csv', index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNf1qvZOVgCudHm6ODYa5Su",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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