notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQRsHSpuU15J"
      },
      "source": [
        "# 1. Install important dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKA1HcscK-tv",
        "outputId": "19547b84-b65a-49ac-a1b5-b99c91737a5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.27.4)\n",
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            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (1.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (3.7.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
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          ]
        }
      ],
      "source": [
        "# # Install necessary packages\n",
        "!pip install transformers nltk datasets numpy seaborn pandas scikit-learn matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eQPxlOmU15M"
      },
      "source": [
        "# 2. Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "VtQqaj5mKu0-"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# To import the Transformer Models\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
        "\n",
        "# to convert to Dataset datatype - the transformers library does not work well with pandas\n",
        "from datasets import Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rciOuXT4U15N"
      },
      "source": [
        "### 2.1 Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fq2939j3Ku1C",
        "outputId": "2be50d93-7a12-4f05-f4aa-24380fa22797"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   Class Index                                              Title  \\\n",
              "0            3  Wall St. Bears Claw Back Into the Black (Reuters)   \n",
              "1            3  Carlyle Looks Toward Commercial Aerospace (Reu...   \n",
              "2            3    Oil and Economy Cloud Stocks' Outlook (Reuters)   \n",
              "3            3  Iraq Halts Oil Exports from Main Southern Pipe...   \n",
              "4            3  Oil prices soar to all-time record, posing new...   \n",
              "\n",
              "                                         Description  \n",
              "0  Reuters - Short-sellers, Wall Street's dwindli...  \n",
              "1  Reuters - Private investment firm Carlyle Grou...  \n",
              "2  Reuters - Soaring crude prices plus worries\\ab...  \n",
              "3  Reuters - Authorities have halted oil export\\f...  \n",
              "4  AFP - Tearaway world oil prices, toppling reco...  "
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              "      <th>Class Index</th>\n",
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              "      <th>Description</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n",
              "      <td>Reuters - Short-sellers, Wall Street's dwindli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
              "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
              "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
              "      <td>Reuters - Authorities have halted oil export\\f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Oil prices soar to all-time record, posing new...</td>\n",
              "      <td>AFP - Tearaway world oil prices, toppling reco...</td>\n",
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              "                                                     [key], {});\n",
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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",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# The dataset does not contain class labels, so we need to explicitly provide it\n",
        "df=pd.read_csv(\"/content/train.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns={'Class Index':'label'})\n",
        "df.head()"
      ],
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        "colab": {
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          "height": 206
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        "id": "RoXrKL5NYmVg",
        "outputId": "f7d2a772-ce51-4841-fa3f-c81b1ccfa556"
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      "execution_count": 4,
      "outputs": [
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            "text/plain": [
              "   label                                              Title  \\\n",
              "0      3  Wall St. Bears Claw Back Into the Black (Reuters)   \n",
              "1      3  Carlyle Looks Toward Commercial Aerospace (Reu...   \n",
              "2      3    Oil and Economy Cloud Stocks' Outlook (Reuters)   \n",
              "3      3  Iraq Halts Oil Exports from Main Southern Pipe...   \n",
              "4      3  Oil prices soar to all-time record, posing new...   \n",
              "\n",
              "                                         Description  \n",
              "0  Reuters - Short-sellers, Wall Street's dwindli...  \n",
              "1  Reuters - Private investment firm Carlyle Grou...  \n",
              "2  Reuters - Soaring crude prices plus worries\\ab...  \n",
              "3  Reuters - Authorities have halted oil export\\f...  \n",
              "4  AFP - Tearaway world oil prices, toppling reco...  "
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              "      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n",
              "      <td>Reuters - Short-sellers, Wall Street's dwindli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
              "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
              "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
              "      <td>Reuters - Authorities have halted oil export\\f...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Oil prices soar to all-time record, posing new...</td>\n",
              "      <td>AFP - Tearaway world oil prices, toppling reco...</td>\n",
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              "    .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-8024b970-f313-47ac-9d59-eda309ad9f4d 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-8024b970-f313-47ac-9d59-eda309ad9f4d');\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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jn_ooUYAU15O"
      },
      "source": [
        "### 2.2 Data statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "fi6B9j6LKu1E",
        "outputId": "65e4fa03-66b0-4681-e54e-764f77171f8e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# checking for unbalanced dataset\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.figure(figsize=(8,4))\n",
        "sns.countplot(x=df['label'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUH8YgYHU15O"
      },
      "source": [
        "### 2.3 Check for null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqtmzkEgKu1E",
        "outputId": "8f303706-9770-4600-c63f-9bbe28a24c72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120000 entries, 0 to 119999\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count   Dtype \n",
            "---  ------       --------------   ----- \n",
            " 0   label        120000 non-null  int64 \n",
            " 1   Title        120000 non-null  object\n",
            " 2   Description  120000 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 2.7+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQhfrNPtU15O"
      },
      "source": [
        "# 3. Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "g8-WDIQBKu1F",
        "outputId": "b66073e9-75c0-4efe-f51f-a14a6e32f51b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                               text\n",
              "0      3  Wall St. Bears Claw Back Into the Black (Reute...\n",
              "1      3  Carlyle Looks Toward Commercial Aerospace (Reu...\n",
              "2      3  Oil and Economy Cloud Stocks' Outlook (Reuters...\n",
              "3      3  Iraq Halts Oil Exports from Main Southern Pipe...\n",
              "4      3  Oil prices soar to all-time record, posing new..."
            ],
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-01154961-d53b-4c78-bd1b-bb9d23e23480 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-01154961-d53b-4c78-bd1b-bb9d23e23480');\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": 7
        }
      ],
      "source": [
        "# concatinating the 'title' and 'description' column\n",
        "df['text']=(df['Title']+df['Description'])\n",
        "df.drop(columns=['Title','Description'],axis=1,inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "Sm-H47YiKu1G",
        "outputId": "ddd3263e-d231-403c-c24b-46b5fcbea74c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Carlyle Looks Toward Commercial Aerospace (Reuters)Reuters - Private investment firm Carlyle Group,\\\\which has a reputation for making well-timed and occasionally\\\\controversial plays in the defense industry, has quietly placed\\\\its bets on another part of the market.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Text before preprocessing - contains symbols like ()\\-,.' which is not useful\n",
        "df['text'][1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34N-E9H8U15P"
      },
      "source": [
        "### 3.1 Remove Punctuations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "LHLTgQsqKu1G"
      },
      "outputs": [],
      "source": [
        "import re   # regular expression can be used to remove any punctuation or unnecessary symbols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-iJ6AXygKu1H",
        "outputId": "5ae70f10-0855-446f-b7c9-4982aebb6471"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                               text\n",
              "0      3  Wall St Bears Claw Back Into the Black Reuters...\n",
              "1      3  Carlyle Looks Toward Commercial Aerospace Reut...\n",
              "2      3  Oil and Economy Cloud Stocks Outlook ReutersRe...\n",
              "3      3  Iraq Halts Oil Exports from Main Southern Pipe...\n",
              "4      3  Oil prices soar to all time record posing new ..."
            ],
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              "      <td>Oil prices soar to all time record posing new ...</td>\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5241edb0-cd7c-4d0c-8015-b58fe8afb585 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-5241edb0-cd7c-4d0c-8015-b58fe8afb585');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "def remove_punctuations(text):\n",
        "     text=re.sub(r'[\\\\-]',' ',text)\n",
        "     text=re.sub(r'[,.?;:\\'(){}!|0-9]','',text)\n",
        "     return text\n",
        "\n",
        "# the apply method applies a function along an axis of dataframe\n",
        "df['text']=df['text'].apply(remove_punctuations)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mulKS985ML0z",
        "outputId": "7e1333c0-131b-48bd-b64e-1d51f1911081"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NLTK Downloader\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> d\n",
            "\n",
            "Download which package (l=list; x=cancel)?\n",
            "  Identifier> all\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "    Downloading collection 'all'\n",
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            "       | Downloading package spanish_grammars to /root/nltk_data...\n",
            "       |   Unzipping grammars/spanish_grammars.zip.\n",
            "       | Downloading package state_union to /root/nltk_data...\n",
            "       |   Unzipping corpora/state_union.zip.\n",
            "       | Downloading package stopwords to /root/nltk_data...\n",
            "       |   Unzipping corpora/stopwords.zip.\n",
            "       | Downloading package subjectivity to /root/nltk_data...\n",
            "       |   Unzipping corpora/subjectivity.zip.\n",
            "       | Downloading package swadesh to /root/nltk_data...\n",
            "       |   Unzipping corpora/swadesh.zip.\n",
            "       | Downloading package switchboard to /root/nltk_data...\n",
            "       |   Unzipping corpora/switchboard.zip.\n",
            "       | Downloading package tagsets to /root/nltk_data...\n",
            "       |   Unzipping help/tagsets.zip.\n",
            "       | Downloading package timit to /root/nltk_data...\n",
            "       |   Unzipping corpora/timit.zip.\n",
            "       | Downloading package toolbox to /root/nltk_data...\n",
            "       |   Unzipping corpora/toolbox.zip.\n",
            "       | Downloading package treebank to /root/nltk_data...\n",
            "       |   Unzipping corpora/treebank.zip.\n",
            "       | Downloading package twitter_samples to /root/nltk_data...\n",
            "       |   Unzipping corpora/twitter_samples.zip.\n",
            "       | Downloading package udhr to /root/nltk_data...\n",
            "       |   Unzipping corpora/udhr.zip.\n",
            "       | Downloading package udhr2 to /root/nltk_data...\n",
            "       |   Unzipping corpora/udhr2.zip.\n",
            "       | Downloading package unicode_samples to /root/nltk_data...\n",
            "       |   Unzipping corpora/unicode_samples.zip.\n",
            "       | Downloading package universal_tagset to /root/nltk_data...\n",
            "       |   Unzipping taggers/universal_tagset.zip.\n",
            "       | Downloading package universal_treebanks_v20 to\n",
            "       |     /root/nltk_data...\n",
            "       | Downloading package vader_lexicon to /root/nltk_data...\n",
            "       | Downloading package verbnet to /root/nltk_data...\n",
            "       |   Unzipping corpora/verbnet.zip.\n",
            "       | Downloading package verbnet3 to /root/nltk_data...\n",
            "       |   Unzipping corpora/verbnet3.zip.\n",
            "       | Downloading package webtext to /root/nltk_data...\n",
            "       |   Unzipping corpora/webtext.zip.\n",
            "       | Downloading package wmt15_eval to /root/nltk_data...\n",
            "       |   Unzipping models/wmt15_eval.zip.\n",
            "       | Downloading package word2vec_sample to /root/nltk_data...\n",
            "       |   Unzipping models/word2vec_sample.zip.\n",
            "       | Downloading package wordnet to /root/nltk_data...\n",
            "       | Downloading package wordnet2021 to /root/nltk_data...\n",
            "       | Downloading package wordnet2022 to /root/nltk_data...\n",
            "       |   Unzipping corpora/wordnet2022.zip.\n",
            "       | Downloading package wordnet31 to /root/nltk_data...\n",
            "       | Downloading package wordnet_ic to /root/nltk_data...\n",
            "       |   Unzipping corpora/wordnet_ic.zip.\n",
            "       | Downloading package words to /root/nltk_data...\n",
            "       |   Unzipping corpora/words.zip.\n",
            "       | Downloading package ycoe to /root/nltk_data...\n",
            "       |   Unzipping corpora/ycoe.zip.\n",
            "       | \n",
            "     Done downloading collection all\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "---------------------------------------------------------------------------\n",
            "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
            "---------------------------------------------------------------------------\n",
            "Downloader> q\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "import nltk\n",
        "\n",
        "# downloading corpus only would work\n",
        "nltk.download()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WbZxkwPU15Q"
      },
      "source": [
        "### 3.2 Remove StopWords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHtaeEhMKu1I",
        "outputId": "8844f734-8197-44f3-c902-e9f1253ccd21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "from nltk.corpus import stopwords\n",
        "\n",
        "# english stopwords\n",
        "stopw=stopwords.words('english')\n",
        "stopw[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U_z3MkHQKu1J",
        "outputId": "1489d170-4f6e-4cd7-c7c9-35e425d50665"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label                                               text\n",
              "0      2  Wall St Bears Claw Back Into Black ReutersReut...\n",
              "1      2  Carlyle Looks Toward Commercial Aerospace Reut...\n",
              "2      2  Oil Economy Cloud Stocks Outlook ReutersReuter...\n",
              "3      2  Iraq Halts Oil Exports Main Southern Pipeline ...\n",
              "4      2  Oil prices soar time record posing new menace ..."
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Carlyle Looks Toward Commercial Aerospace Reut...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Oil Economy Cloud Stocks Outlook ReutersReuter...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Iraq Halts Oil Exports Main Southern Pipeline ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>Oil prices soar time record posing new menace ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3871786-9c20-4de0-b08d-98a19abbe6cd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\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-c3871786-9c20-4de0-b08d-98a19abbe6cd');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "def remove_stopwords(text):\n",
        "    clean_text=[]\n",
        "    for word in text.split(' '):\n",
        "        if word not in stopw:\n",
        "            clean_text.append(word)\n",
        "    return ' '.join(clean_text)\n",
        "\n",
        "# remove stopwords\n",
        "df['text']=df['text'].apply(remove_stopwords)\n",
        "\n",
        "# the class label in dataset contains labels as 1,2,3,4 but the model needs 0,1,2,3, so we subtract 1 from all\n",
        "df['label']=df['label'].apply(lambda x:x-1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "9rpIy2-MKu1K",
        "outputId": "ed30466e-c241-4ddf-d742-3778c433c7f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Carlyle Looks Toward Commercial Aerospace ReutersReuters   Private investment firm Carlyle Group reputation making well timed occasionally controversial plays defense industry quietly placed bets another part market'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df['text'][1]   # this is the final preprocessed text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U989qnbRU15Q"
      },
      "source": [
        "### 3.3 Split the data into training and testing sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Ew9DlapHKu1K"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# since training on the full dataset(120,000 samples) would be take so long, the train size is only taken to be 30%\n",
        "train_df,test_df=train_test_split(df[['text','label']],train_size=.3,shuffle=True)\n",
        "test_df=test_df[:10000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJuJ0V7gKu1L",
        "outputId": "d1066b8e-bbfb-4dc2-d863-34affa400465"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((36000, 2), (10000, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train_df.shape,test_df.shape    # training set has 36000 samples and testing set has 10000 samples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHM6NxePU15R"
      },
      "source": [
        "### 3.4 Load a pre-built tokenizer and convert to tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "W38jQpskKu1L",
        "outputId": "e4c34687-aec0-4c15-8f31-561c933f99ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "008f63a61092471d865b3572855b148b"
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      "source": [
        "# load tokenizer from bert base uncased model available from huggingface.co\n",
        "model_name='bert-base-uncased'\n",
        "tokenizer=AutoTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "KQ02imthKu1L"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    \"\"\"\n",
        "    Tokenizes the given text\n",
        "\n",
        "    input -> dataset (columns = text, label)\n",
        "    output -> tokenized dataset (columns = text, label, input, attention)\n",
        "    \"\"\"\n",
        "    return tokenizer(examples[\"text\"], truncation=True)\n",
        "\n",
        "def pipeline(dataframe):\n",
        "    \"\"\"\n",
        "    Prepares the dataframe so that it can be given to the transformer model\n",
        "    \n",
        "    input -> pandas dataframe\n",
        "    output -> tokenized dataset (columns = text, label, input, attention)\n",
        "    \"\"\"    \n",
        "    # This step isn't mentioned anywhere but is vital as Transformers library only seems to work with this Dataset data type\n",
        "    dataset = Dataset.from_pandas(dataframe, preserve_index=False)\n",
        "    tokenized_ds = dataset.map(preprocess_function, batched=True)\n",
        "    tokenized_ds = tokenized_ds.remove_columns('text')\n",
        "    return tokenized_ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
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            "1087a4fe56a045e0af877919fec0f6a7",
            "b22c8e0f65c446bcb1d556cec6f47cf6",
            "22b952a2b03b438a95a0aa5783b46f87"
          ]
        },
        "id": "tkElaBoNKu1M",
        "outputId": "60975e33-4f25-4eb6-d070-e96cfc9da4e5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/36000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d82811afafe44d978b93e234e0574656"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4b93033307d4903860c4af2c27d8520"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# create pipeline for training data and testing data\n",
        "tokenized_train = pipeline(train_df)\n",
        "tokenized_test = pipeline(test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KCWFEsPU15R"
      },
      "source": [
        "# 4. Load a pre-trained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmD63TTaU15R"
      },
      "source": [
        "### 4.1 Adjust Model training arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "fae1ada9ca134a31bba3f2c339e52393",
            "201425e2a0e142d39244b034d1b78c7c",
            "23524ec8602043cbb3d79fb870d132db",
            "434fdfca2ffa448a8d272cd3a8c71359",
            "7207cfa6124542dd99da07987dd6dc39",
            "d26b98f200ff45fa8d2e1149b9db50c0",
            "e275e3586cbe4415b8e5140845ccce5e",
            "4f57a585e6b24e8a8d20e8b1109c3e02",
            "3bcbc7c7affe4e138efbe3e09e1ce075",
            "01dd8e277522494aa2d06feff209b3c5",
            "383645fe88e047e289b9299b6ff19dc7"
          ]
        },
        "id": "zz7_lOFEU15R",
        "outputId": "f68a2817-ac0f-45c0-cc8e-fbe0d1f4f10d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fae1ada9ca134a31bba3f2c339e52393"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# load bert-based-uncased model for fine tuning\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=4)\n",
        "\n",
        "# adjust the training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    save_strategy = 'epoch',\n",
        "    optim='adamw_torch',\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "NDYIXq7xKu1M"
      },
      "outputs": [],
      "source": [
        "# create the trainer from Trainer class in transformer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdXX88plU15S"
      },
      "source": [
        "### 4.2 Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "f9edShL_Ku1N",
        "outputId": "60fbae5f-9a5c-4130-daa5-81618550cac4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6750' max='6750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6750/6750 23:15, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.403200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.312400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.289200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.259700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.233600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.195100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.178000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.189400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.179800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.126500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.130200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.124200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.120500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6750, training_loss=0.20764464230007595, metrics={'train_runtime': 1399.1534, 'train_samples_per_second': 77.19, 'train_steps_per_second': 4.824, 'total_flos': 3578785727712000.0, 'train_loss': 0.20764464230007595, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "IHJ_7ysKU15S",
        "outputId": "40e5acd7-288a-4b73-e0df-da668169f9f8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model_loss=pd.read_csv('loss.csv')\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.plot(model_loss['Step'],model_loss['Training Loss'],label='loss',marker='o')\n",
        "plt.xlabel('Step')\n",
        "plt.ylabel('Loss value')\n",
        "plt.legend()\n",
        "plt.title('Model Loss Trend')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4FgDG8PU15S"
      },
      "source": [
        "### 4.3 Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "38e57dc27af84c26926a4ba2dc950430",
            "7f5f37e0d4a5468693a9f1776e6d45fd",
            "ad98c01173a246d88418d2f7e7c8e161",
            "6bb4d214c02a41dca3ec6eae6e08983e",
            "f4caa97fdaae46919d180c200f35b731",
            "cb1c6a4aa5e145aa98e50cfb026acae0",
            "d7ca2716b0194fb2aa52ba4a52e76dde",
            "98958612fe644c0e8bd6ea5631418a78",
            "d80684d761dd4b7f877d53f937c7c528",
            "2ee2367afefa482ab0b8a03e210bf78e",
            "116d4a4026ee44bea9e822cb551ae1d5"
          ]
        },
        "id": "0SO-tvg-Ku1N",
        "outputId": "e04232f2-740d-4c6b-8503-433d1f377d03"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38e57dc27af84c26926a4ba2dc950430"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "# create tokenized text for test dataset\n",
        "tokenized_test = pipeline(test_df)\n",
        "tokenized_test = tokenized_test.remove_columns('label')\n",
        "\n",
        "# input the tokenized text to the trainer to get predictions\n",
        "preds = trainer.predict(tokenized_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "QIoeZDkHKu1O"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "\n",
        "# the maximum value in the prediction is the predicted class label\n",
        "preds_flat = [np.argmax(x) for x in preds[0]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChIseFEUKu1O",
        "outputId": "ea5e9754-3a1e-416c-9bc6-8193cb556148"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.92      0.93      2431\n",
            "           1       0.97      0.98      0.98      2570\n",
            "           2       0.91      0.87      0.89      2516\n",
            "           3       0.88      0.93      0.90      2483\n",
            "\n",
            "    accuracy                           0.93     10000\n",
            "   macro avg       0.93      0.93      0.93     10000\n",
            "weighted avg       0.93      0.93      0.93     10000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# The model got a precision of 96%, 97%, 90% and 89% on class labels 0,1,2,3 and similarly for recall, f1-score, support\n",
        "print(classification_report(test_df['label'], preds_flat))  # accuracy: 93%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "XE4om_N1U15T",
        "outputId": "ed5b51c8-b80c-462a-b111-4fd27e830345"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "# plot the heat map \n",
        "sns.heatmap(\n",
        "    confusion_matrix(test_df['label'], preds_flat),\n",
        "    annot=True,\n",
        "    xticklabels=['World','Sport','Business','Sci/Tech'],\n",
        "    yticklabels=['World','Sport','Business','Sci/Tech'],\n",
        "    cmap=plt.cm.magma_r\n",
        ")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qphnB-KrU15T"
      },
      "source": [
        "### 4.4 Test model on random predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Ie9SuIbbU15T"
      },
      "outputs": [],
      "source": [
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "2fa61776a4dc473491696043b38d6f99",
            "e6727ff60ea24b84b13f1b2cacec292e",
            "68523c13024e46f3ac01dcee3ac17b1b",
            "b2805342477146fb91bef31c607ef5e2",
            "927ac876f2b94f4b9a37b49b110cc8fa",
            "0eb78e81cdf840409c7752888f930236",
            "3e7b7aa770e747ed8dedb2189f74498f",
            "a3eb68f61e26474a86306802166aea8a",
            "48972f61d30342c7bff2ad86d3e83c96",
            "74fcfa7da43c4673ba13b9fed3f4595d",
            "1696e17df03244f8b86507876baf63a8"
          ]
        },
        "id": "Hd9Un4UNU15T",
        "outputId": "7f644582-900a-4540-a3ea-43fe08acf530"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fa61776a4dc473491696043b38d6f99"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "num=random.randint(0,len(test_df)-101)\n",
        "tokenized_test = pipeline(test_df[num:num+100]).remove_columns('label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "2FfzVLBQU15U",
        "outputId": "20c834ef-f755-423a-803c-077790a96eda"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.95      0.93        20\n",
            "           1       0.96      1.00      0.98        25\n",
            "           2       0.92      0.86      0.89        28\n",
            "           3       0.93      0.93      0.93        27\n",
            "\n",
            "    accuracy                           0.93       100\n",
            "   macro avg       0.93      0.93      0.93       100\n",
            "weighted avg       0.93      0.93      0.93       100\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# accuracy on random 100 samples from test dataset: 99% which is great!\n",
        "preds=trainer.predict(tokenized_test)\n",
        "preds_flat = [np.argmax(x) for x in preds[0]]\n",
        "print(classification_report(test_df['label'][num:num+100], preds_flat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5uF-Rz0U15U"
      },
      "source": [
        "### 4.5 Manually compare predictions on sample test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "ShuuBZVqU15U"
      },
      "outputs": [],
      "source": [
        "class_labels=['World','Sports','Business','Sci/Tech']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236,
          "referenced_widgets": [
            "2b1285fa4fca4b6b82dbbe56f9b5e6c5",
            "8a8d899e55af491dbef15791637d66d3",
            "3ca9526b95974ba7b4a7a6c66d42a968",
            "75346d9d4fea45df861de23dd5d3b9d7",
            "71a727d9f3f5479dbc5ac0304e486aac",
            "99ffd5cc78ee406f920876646605c334",
            "5658781ddaba41b497f7909c47ecf1c7",
            "607e5ed874844f5b9b2a7a962da4552e",
            "c0f653768c1c4ddf9e0de8a3a19ba7cf",
            "25d02d4130514dfcb7093562081c8365",
            "d9e5711b74524144ad33818c1f0311ce"
          ]
        },
        "id": "QJ_Cqwq_U15U",
        "outputId": "39af26ea-11d9-41fc-894e-caf0f01500a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b1285fa4fca4b6b82dbbe56f9b5e6c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction\tActual\n",
            "----------------------\n",
            "Sports  --->  Sports\n",
            "Business  --->  Business\n",
            "Sci/Tech  --->  Sci/Tech\n",
            "Business  --->  Business\n",
            "World  --->  World\n",
            "Sports  --->  Sports\n",
            "Business  --->  Business\n",
            "Business  --->  Sci/Tech\n",
            "World  --->  World\n",
            "Sci/Tech  --->  Sci/Tech\n"
          ]
        }
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        "num=random.randint(0,len(test_df)-1)\n",
        "tokenized_test = pipeline(test_df[num:num+10]).remove_columns('label')\n",
        "preds=trainer.predict(tokenized_test)\n",
        "preds_flat = [np.argmax(x) for x in preds[0]]\n",
        "\n",
        "# generating predicted class and actual class for 10 random samples from test dataset\n",
        "print('Prediction\\tActual\\n----------------------')\n",
        "for i in range(len(preds_flat)):\n",
        "    print(class_labels[preds_flat[i]],' ---> ',class_labels[test_df['label'].values[num+i]])"
      ]
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      "metadata": {
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      "source": [
        "# 5. Save Model"
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        "trainer.save_model('models')"
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      "metadata": {
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      "source": [
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      "execution_count": 40,
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        "# for loading model, we just need to specify the path of the folder for saved model\n",
        "model = AutoModelForSequenceClassification.from_pretrained('models')\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        ")"
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