ML4HC_Project_2_Part_3.ipynb

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      "cell_type": "code",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: bert-tensorflow in /usr/local/lib/python3.10/dist-packages (1.0.4)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install bert-tensorflow\n",
        "!pip install transformers accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import string\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import re \n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "import random\n",
        "from sklearn import metrics, model_selection, preprocessing\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torch.nn import BCEWithLogitsLoss\n",
        "\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from transformers import BertTokenizerFast, TFBertModel, BertForSequenceClassification, TrainingArguments, Trainer\n",
        "import transformers\n",
        "from transformers import AdamW, get_linear_schedule_with_warmup\n",
        "\n",
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import average_precision_score\n",
        "\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "OR1HrE_50VZx"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download(\"punkt\")\n",
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))"
      ],
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        "colab": {
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        "outputId": "6dc1e2e0-e9cd-4620-eee8-76f9ef9f69d9"
      },
      "execution_count": 28,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "df = pd.read_csv(\"TweetsCOV19.csv\")\n",
        "df.head()\n",
        "df.info()"
      ],
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      "execution_count": 29,
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          "text": [
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            " 13  TweetText     213713 non-null  object\n",
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            "dtypes: int64(6), object(9)\n",
            "memory usage: 24.5+ MB\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sample(frac = 0.025)"
      ],
      "metadata": {
        "id": "tWYnM3m51Lim"
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      "execution_count": 30,
      "outputs": []
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5343 entries, 133425 to 12084\n",
            "Data columns (total 15 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
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            " 1   TweetId       5343 non-null   int64 \n",
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            " 4   NoFollowers   5343 non-null   int64 \n",
            " 5   NoFriends     5343 non-null   int64 \n",
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            " 12  URLs          5343 non-null   object\n",
            " 13  TweetText     5343 non-null   object\n",
            " 14  UserLocation  4099 non-null   object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 667.9+ KB\n"
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      "cell_type": "code",
      "source": [
        "df.head()"
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      "execution_count": 32,
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              "\n",
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              "      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-41f8c697-106d-48a0-8e6b-630f6e2fc29e 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-41f8c697-106d-48a0-8e6b-630f6e2fc29e');\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYAA3rjK30Am",
        "outputId": "c34d22cc-122e-440a-d44b-860fe3dc3962"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unnamed: 0         0\n",
            "TweetId            0\n",
            "Username           0\n",
            "Timestamp          0\n",
            "NoFollowers        0\n",
            "NoFriends          0\n",
            "NoRetweets         0\n",
            "NoFavorites        0\n",
            "Entities           0\n",
            "Sentiment          0\n",
            "Mentions          14\n",
            "Hashtags           3\n",
            "URLs               0\n",
            "TweetText          0\n",
            "UserLocation    1244\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['UserLocation'], inplace=True)"
      ],
      "metadata": {
        "id": "cqp4b-jG-tw5"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "87_cE-xeBHQU",
        "outputId": "46907b78-f129-4166-e7bf-44119240fc42"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
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              "11644                           @laurachina00 Hope so😭😭🙏🙏   \n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3ab7041-1d92-4331-8b79-30066e1e6e5a')\"\n",
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              "      </button>\n",
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              "    .colab-df-convert:hover {\n",
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              "      <script>\n",
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              "          document.querySelector('#df-d3ab7041-1d92-4331-8b79-30066e1e6e5a button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-d3ab7041-1d92-4331-8b79-30066e1e6e5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5xcImCu_xZa",
        "outputId": "39b54834-05ca-41fc-b459-7a641e9ab68d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4099 entries, 133425 to 12084\n",
            "Data columns (total 15 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Unnamed: 0    4099 non-null   int64 \n",
            " 1   TweetId       4099 non-null   int64 \n",
            " 2   Username      4099 non-null   object\n",
            " 3   Timestamp     4099 non-null   object\n",
            " 4   NoFollowers   4099 non-null   int64 \n",
            " 5   NoFriends     4099 non-null   int64 \n",
            " 6   NoRetweets    4099 non-null   int64 \n",
            " 7   NoFavorites   4099 non-null   int64 \n",
            " 8   Entities      4099 non-null   object\n",
            " 9   Sentiment     4099 non-null   object\n",
            " 10  Mentions      4086 non-null   object\n",
            " 11  Hashtags      4097 non-null   object\n",
            " 12  URLs          4099 non-null   object\n",
            " 13  TweetText     4099 non-null   object\n",
            " 14  UserLocation  4099 non-null   object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 512.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We drop all columns other than Sentiment and TweetText since they are irrelevant to this task\n",
        "\n",
        "df = df.drop([\"Unnamed: 0\", \"TweetId\", \"Username\", \"Timestamp\", \"NoFollowers\", \"NoFriends\", \"NoRetweets\", \"NoFavorites\", \"Entities\", \"Mentions\", \"Hashtags\", \"URLs\"], axis = 1)"
      ],
      "metadata": {
        "id": "nOtxXMfMAldN"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(df[\"Sentiment\"].iloc[0]).split(\" \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYpnBrejBrNH",
        "outputId": "6b3bf86d-b395-4f2e-f0ff-28c33f6e0a8c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1', '-1']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_sentm(col):\n",
        "  sent_list = (col).split(\" \")\n",
        "  # print(sent_list)\n",
        "  sum = np.sum([int(sent_list[0]), int(sent_list[1])])\n",
        "\n",
        "  if sum > 0:\n",
        "    temp = \"positive\"\n",
        "  elif sum < 0:\n",
        "    temp = \"negative\"\n",
        "  else:\n",
        "    temp = \"neutral\"\n",
        "  \n",
        "  return temp"
      ],
      "metadata": {
        "id": "tlmjeSimBtDn"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Sentiment\"] = df[\"Sentiment\"].map(calc_sentm)"
      ],
      "metadata": {
        "id": "XytK7ei7C9R5"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9wI4nx_9EnaD",
        "outputId": "b98b647f-2d85-4315-f727-6843bd0e61cd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sentiment                                          TweetText  \\\n",
              "133425   neutral  You can rely on a flow between thought and act...   \n",
              "117717  positive  hi, everything is pointless and only looks to ...   \n",
              "17209   positive  Remember the Munch It tasks that happened in t...   \n",
              "11644   positive                          @laurachina00 Hope so😭😭🙏🙏   \n",
              "49231    neutral  Teachers - planning 2020 scope &amp; sequences...   \n",
              "\n",
              "                    UserLocation  \n",
              "133425       Fort Lauderdale, FL  \n",
              "117717                   forest   \n",
              "17209                    Nigeria  \n",
              "11644                  12/17/22   \n",
              "49231   NSW, QLD, ACT & Victoria  "
            ],
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>TweetText</th>\n",
              "      <th>UserLocation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>133425</th>\n",
              "      <td>neutral</td>\n",
              "      <td>You can rely on a flow between thought and act...</td>\n",
              "      <td>Fort Lauderdale, FL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117717</th>\n",
              "      <td>positive</td>\n",
              "      <td>hi, everything is pointless and only looks to ...</td>\n",
              "      <td>forest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17209</th>\n",
              "      <td>positive</td>\n",
              "      <td>Remember the Munch It tasks that happened in t...</td>\n",
              "      <td>Nigeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11644</th>\n",
              "      <td>positive</td>\n",
              "      <td>@laurachina00 Hope so😭😭🙏🙏</td>\n",
              "      <td>12/17/22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49231</th>\n",
              "      <td>neutral</td>\n",
              "      <td>Teachers - planning 2020 scope &amp;amp; sequences...</td>\n",
              "      <td>NSW, QLD, ACT &amp; Victoria</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21066d3a-652b-4600-af47-21c4b58d8614')\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-21066d3a-652b-4600-af47-21c4b58d8614 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-21066d3a-652b-4600-af47-21c4b58d8614');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_tweets(tweet):\n",
        "  # Convert all words to lower case\n",
        "  tweet = tweet.lower()\n",
        "\n",
        "  # Remove all punctuations\n",
        "  tweet = tweet.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "  # Remove all stop words\n",
        "  # Tokenize the sentence\n",
        "  tokens = nltk.word_tokenize(tweet)\n",
        "  # Remove stop words\n",
        "  clean_tokens = [token for token in tokens if not token.lower() in stop_words]\n",
        "  # Join the tokens back into a sentence\n",
        "  tweet = ' '.join(clean_tokens)\n",
        "\n",
        "  # Removing HTML Tags, URLs and Website Links\n",
        "  # Removing Website Links\n",
        "  temp_text = tweet.split()\n",
        "\n",
        "  for i in temp_text:\n",
        "    if i[-4:] == \".com\" or i[:4] == \"www.\":\n",
        "      temp_text.remove(i)\n",
        "\n",
        "  tweet = ' '.join(temp_text)\n",
        "\n",
        "  # Remove HTML tags\n",
        "  clean_text = re.sub('<[^<]+?>', '', tweet)\n",
        "\n",
        "  # Remove URLs\n",
        "  clean_text = re.sub(r'http\\S+', '', clean_text)\n",
        "\n",
        "  tweet = clean_text\n",
        "\n",
        "  # Remove numbers\n",
        "  tweet = re.sub(r'\\d+', '', tweet)\n",
        "  \n",
        "  # Remove mentions\n",
        "  tweet = re.sub(r'@\\w+', '', tweet)\n",
        "\n",
        "  return tweet  \n"
      ],
      "metadata": {
        "id": "yyjXQYwD_1i2"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"TweetText\"] = df[\"TweetText\"].map(preprocess_tweets)"
      ],
      "metadata": {
        "id": "KxdpW4WPErvw"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import label encoder\n",
        "from sklearn import preprocessing\n",
        "  \n",
        "# label_encoder object knows \n",
        "# how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'species'.\n",
        "df['Sentiment']= label_encoder.fit_transform(df['Sentiment'])\n",
        "  \n",
        "df['Sentiment'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVueLuFUGVLI",
        "outputId": "f9410143-9b51-4262-cc74-802a4c8ff845"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tC4qOugAN1t9",
        "outputId": "081ba7de-8aea-4256-f74b-9c9bb0fe0ff4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Sentiment                                          TweetText  \\\n",
              "133425          1              rely flow thought action appears leo    \n",
              "117717          2  hi everything pointless looks leave impression...   \n",
              "17209           2  remember munch tasks happened bbnaija house im...   \n",
              "11644           2                             laurachina hope so😭😭🙏🙏   \n",
              "49231           1  teachers planning  scope amp sequences use cur...   \n",
              "\n",
              "                    UserLocation  \n",
              "133425       Fort Lauderdale, FL  \n",
              "117717                   forest   \n",
              "17209                    Nigeria  \n",
              "11644                  12/17/22   \n",
              "49231   NSW, QLD, ACT & Victoria  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0418d5fe-8ce4-4df9-a060-4a8198f698c2\">\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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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentiment</th>\n",
              "      <th>TweetText</th>\n",
              "      <th>UserLocation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>133425</th>\n",
              "      <td>1</td>\n",
              "      <td>rely flow thought action appears leo</td>\n",
              "      <td>Fort Lauderdale, FL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117717</th>\n",
              "      <td>2</td>\n",
              "      <td>hi everything pointless looks leave impression...</td>\n",
              "      <td>forest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17209</th>\n",
              "      <td>2</td>\n",
              "      <td>remember munch tasks happened bbnaija house im...</td>\n",
              "      <td>Nigeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11644</th>\n",
              "      <td>2</td>\n",
              "      <td>laurachina hope so😭😭🙏🙏</td>\n",
              "      <td>12/17/22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49231</th>\n",
              "      <td>1</td>\n",
              "      <td>teachers planning  scope amp sequences use cur...</td>\n",
              "      <td>NSW, QLD, ACT &amp; Victoria</td>\n",
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              "  </tbody>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0418d5fe-8ce4-4df9-a060-4a8198f698c2 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0418d5fe-8ce4-4df9-a060-4a8198f698c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into train and validation sets\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, test = train_test_split(df, test_size=0.2, random_state=42)\n",
        "train, val = train_test_split(train, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "vd0RqL3TFuVl"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "LKM6fruuMtTj"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "from transformers import InputExample, InputFeatures\n",
        "\n",
        "model = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels = 3)\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pU_BmaRyOPya",
        "outputId": "0e5b217c-6384-4fa9-8785-17e621ff763e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the data\n",
        "# train_data and validation_data are pandas dataframes with columns 'text' and 'label'\n",
        "\n",
        "# Convert data into InputExample format\n",
        "train_examples = train.apply(lambda x: InputExample(guid=None, text_a = x['TweetText'], label = x['Sentiment']), axis = 1)\n",
        "val_examples = val.apply(lambda x: InputExample(guid=None, text_a = x['TweetText'], label = x['Sentiment']), axis = 1)\n",
        "\n",
        "def convert_examples_to_tf_dataset(examples, tokenizer, max_length=128):\n",
        "    features = [] # -> will hold InputFeatures to be converted later\n",
        "\n",
        "    for e in examples:\n",
        "        # Documentation is really strong for this method, so please take a look at it\n",
        "        input_dict = tokenizer.encode_plus(\n",
        "            e.text_a,\n",
        "            add_special_tokens=True,\n",
        "            max_length=max_length, # truncates if len(s) > max_length\n",
        "            return_token_type_ids=True,\n",
        "            return_attention_mask=True,\n",
        "            pad_to_max_length=True, # pads to the right by default\n",
        "            truncation=True\n",
        "        )\n",
        "\n",
        "        input_ids, token_type_ids, attention_mask = (input_dict[\"input_ids\"],\n",
        "            input_dict[\"token_type_ids\"], input_dict['attention_mask'])\n",
        "\n",
        "        features.append(\n",
        "            InputFeatures(\n",
        "                input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, label=e.label\n",
        "            )\n",
        "        )\n",
        "\n",
        "    def gen():\n",
        "        for f in features:\n",
        "            yield (\n",
        "                {\n",
        "                    \"input_ids\": f.input_ids,\n",
        "                    \"attention_mask\": f.attention_mask,\n",
        "                    \"token_type_ids\": f.token_type_ids,\n",
        "                },\n",
        "                f.label,\n",
        "            )\n",
        "\n",
        "    return tf.data.Dataset.from_generator(gen, ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n",
        "        (\n",
        "            {\n",
        "                \"input_ids\": tf.TensorShape([None]),\n",
        "                \"attention_mask\": tf.TensorShape([None]),\n",
        "                \"token_type_ids\": tf.TensorShape([None]),\n",
        "            },\n",
        "            tf.TensorShape([]),\n",
        "        ),\n",
        "    )\n",
        "\n",
        "# Convert examples into tensorflow dataset\n",
        "train_dataset = convert_examples_to_tf_dataset(list(train_examples), tokenizer).shuffle(100).batch(32).repeat(2)\n",
        "validation_dataset = convert_examples_to_tf_dataset(list(val_examples), tokenizer).batch(64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PewK1jpfE_n2",
        "outputId": "a4a75660-5fd1-4bd0-a980-25e5052a64f6"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2364: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5, epsilon=1e-08, clipnorm=1.0), \n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n",
        "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy('accuracy')])\n",
        "\n",
        "# Train the model\n",
        "model.fit(train_dataset, epochs=5, validation_data=validation_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVVBoE8sMoZe",
        "outputId": "a940179f-9caf-4975-eafe-a36b9bf8e8e5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "164/164 [==============================] - 239s 933ms/step - loss: 0.8136 - accuracy: 0.6258 - val_loss: 0.6425 - val_accuracy: 0.7576\n",
            "Epoch 2/5\n",
            "164/164 [==============================] - 140s 851ms/step - loss: 0.3187 - accuracy: 0.8912 - val_loss: 0.9522 - val_accuracy: 0.7165\n",
            "Epoch 3/5\n",
            "164/164 [==============================] - 140s 851ms/step - loss: 0.1121 - accuracy: 0.9644 - val_loss: 1.2224 - val_accuracy: 0.7180\n",
            "Epoch 4/5\n",
            "164/164 [==============================] - 138s 840ms/step - loss: 0.0573 - accuracy: 0.9825 - val_loss: 1.1992 - val_accuracy: 0.7409\n",
            "Epoch 5/5\n",
            "164/164 [==============================] - 137s 833ms/step - loss: 0.0408 - accuracy: 0.9899 - val_loss: 1.2299 - val_accuracy: 0.7424\n"
          ]
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          "metadata": {},
          "execution_count": 50
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      "source": [
        "test_examples = test.apply(lambda x: InputExample(guid=None, text_a = x['TweetText'], label = x['Sentiment']), axis = 1)\n",
        "test_dataset = convert_examples_to_tf_dataset(list(test_examples), tokenizer).batch(64)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gJm-cvnXysI",
        "outputId": "e8fc9d33-63b6-400f-c4a1-7d488d803bcb"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2364: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  warnings.warn(\n"
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    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(test_dataset)"
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "8_SqU1qAbLaP",
        "outputId": "9760a496-b09e-40e7-93f1-0e81d4832d57"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 7s 552ms/step - loss: 1.3847 - accuracy: 0.7159\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_dataset)"
      ],
      "metadata": {
        "id": "zP2kvia-NIXP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a2ab4c8-4560-4152-f7c5-c7c907d7fa21"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 15s 529ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "predictions"
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      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TFSequenceClassifierOutput(loss=None, logits=array([[-3.0706558,  3.5679653, -0.1300736],\n",
              "       [-3.2201633,  1.2292706,  1.994171 ],\n",
              "       [-3.1255264, -1.9361776,  5.1832542],\n",
              "       ...,\n",
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              "       [-2.9857552, -2.1015275,  5.2899265]], dtype=float32), hidden_states=None, attentions=None)"
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        "# The logits are the first item in this object\n",
        "logits = predictions[0]\n",
        "\n",
        "# Convert logits to probabilities via softmax\n",
        "probabilities = tf.nn.softmax(logits, axis=-1)\n",
        "\n",
        "# Get the class with the highest probability\n",
        "integer_predictions = tf.argmax(probabilities, axis=-1)\n",
        "\n",
        "# Convert tensor to numpy array if needed\n",
        "integer_predictions = integer_predictions.numpy()"
      ],
      "metadata": {
        "id": "vKdU_9lXcu4i"
      },
      "execution_count": 62,
      "outputs": []
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        "integer_predictions[:200]"
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        "outputId": "0b8fefdc-8c5a-463a-de39-f93a40c633f2"
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              "        Sentiment                                          TweetText  \\\n",
              "133425          1              rely flow thought action appears leo    \n",
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              "17209           2  remember munch tasks happened bbnaija house im...   \n",
              "11644           2                             laurachina hope so😭😭🙏🙏   \n",
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              "        Sentiment                                          TweetText  \\\n",
              "8967            0  know antivirus software wont protect biggest t...   \n",
              "180774          2  public believe happened national capital impul...   \n",
              "175608          2                       anf act like nothing happen    \n",
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              "172959          2  primark sells harry potter cushions £ shoppers...   \n",
              "188666          2  authors franwilde molder marthawells curtiscch...   \n",
              "91609           1  tune  secs countdown happening experience mass...   \n",
              "90295           2  alx really understand see real coming commente...   \n",
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              "                   UserLocation  Predicted Sentiments  \n",
              "8967           Winona, MN 55987                     1  \n",
              "180774                New Delhi                     2  \n",
              "175608       Since 2018. Rules:                     2  \n",
              "124857                     Moon                     2  \n",
              "130885            New York, USA                     1  \n",
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              "39253                    181227                     2  \n",
              "172959                       UK                     2  \n",
              "188666                      NYC                     1  \n",
              "91609   James' ❤ (@tellemjaye)                      1  \n",
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              "      <td>1</td>\n",
              "      <td>tune  secs countdown happening experience mass...</td>\n",
              "      <td>James' ❤ (@tellemjaye)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90295</th>\n",
              "      <td>2</td>\n",
              "      <td>alx really understand see real coming commente...</td>\n",
              "      <td>Houston, TX</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>820 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c12a584-e46f-425c-86ed-59e53b186b44')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-7c12a584-e46f-425c-86ed-59e53b186b44 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-7c12a584-e46f-425c-86ed-59e53b186b44');\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": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lmMwnuFzdyrS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}