Spam_Filter_using_Transformers.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4ZZhN8BtFaW",
        "outputId": "29f1477d-883c-4fa2-f34b-d1adbbff7c27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMzw25SrsuSz",
        "outputId": "349adf26-73d8-4ee0-f9ba-431d38ca2d2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.34.0-py3-none-any.whl (7.7 MB)\n",
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            "Collecting tokenizers<0.15,>=0.14 (from transformers)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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            "Installing collected packages: xxhash, safetensors, dill, responses, multiprocess, huggingface-hub, tokenizers, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.1 huggingface-hub-0.17.3 multiprocess-0.70.15 responses-0.18.0 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.34.0 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VtZP0ahttD0i"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import DataCollatorWithPadding\n",
        "from transformers import create_optimizer\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "from transformers.keras_callbacks import KerasMetricCallback\n",
        "from transformers import AutoModel\n",
        "from transformers import pipeline\n",
        "import datasets\n",
        "import evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ekKANOf1tl57",
        "outputId": "8f2557a8-d2f5-4b89-c980-d6c25093b70e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Unnamed: 0 label                                               text  \\\n",
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              "3        4685  spam  Subject: photoshop , windows , office . cheap ...   \n",
              "4        2030   ham  Subject: re : indian springs\\r\\nthis deal is t...   \n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1ec27169-ecf3-4dd6-9956-c5f7e52c236e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/CSDF Mini Project/spam_dataset.csv\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a78gy9k3ttBu",
        "outputId": "e7ac3c59-8983-4b8c-bfd7-f03879de33d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5171 entries, 0 to 5170\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Unnamed: 0  5171 non-null   int64 \n",
            " 1   label       5171 non-null   object\n",
            " 2   text        5171 non-null   object\n",
            " 3   label_num   5171 non-null   int64 \n",
            "dtypes: int64(2), object(2)\n",
            "memory usage: 161.7+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gA5GKuujtwBi"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=['Unnamed: 0', 'label'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LkH3sazzwoag"
      },
      "outputs": [],
      "source": [
        "data.columns = ['text', 'label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bmYf32GSt_El",
        "outputId": "a80c4cb8-5b63-4410-cbe1-3ecc037e5e25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label\n",
              "0  Subject: enron methanol ; meter # : 988291\\r\\n...      0\n",
              "1  Subject: hpl nom for january 9 , 2001\\r\\n( see...      0\n",
              "2  Subject: neon retreat\\r\\nho ho ho , we ' re ar...      0\n",
              "3  Subject: photoshop , windows , office . cheap ...      1\n",
              "4  Subject: re : indian springs\\r\\nthis deal is t...      0"
            ],
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b08f99fa-776a-4768-8504-7d149a7b6676 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-b08f99fa-776a-4768-8504-7d149a7b6676');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a6a37ed-c923-4d72-a260-d0127c7ea7f7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a6a37ed-c923-4d72-a260-d0127c7ea7f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gaWHoLrQ0Teb"
      },
      "outputs": [],
      "source": [
        "X = data['text']\n",
        "y = data['label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bw3TweAM0j7c"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "74KiBj3e0s-r",
        "outputId": "d7cc4b2e-f714-42b4-d2a9-62ebbe9a774f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3619,), (1552,), (3619,), (1552,))"
            ]
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          "metadata": {},
          "execution_count": 11
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      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
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    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/684 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/760k [00:00<?, ?B/s]"
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        }
      ],
      "source": [
        "model_checkpoint = 'albert-base-v2'\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qqngB05YxQHl"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples, truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jFYadJsV6RAE"
      },
      "outputs": [],
      "source": [
        "X_train_tokenized = preprocess_function(X_train.to_list())\n",
        "X_test_tokenized = preprocess_function(X_test.to_list())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o_4-lqnY63EC"
      },
      "outputs": [],
      "source": [
        "tokenized_train_data = {}\n",
        "tokenized_train_data['input_ids'] = X_train_tokenized['input_ids']\n",
        "tokenized_train_data['attention_mask'] = X_train_tokenized['attention_mask']\n",
        "tokenized_train_data['labels'] = y_train\n",
        "\n",
        "tokenized_test_data = {}\n",
        "tokenized_test_data['input_ids'] = X_test_tokenized['input_ids']\n",
        "tokenized_test_data['attention_mask'] = X_test_tokenized['attention_mask']\n",
        "tokenized_test_data['labels'] = y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YNvn8iRD7UO6"
      },
      "outputs": [],
      "source": [
        "train_data_df = pd.DataFrame(data=dict(tokenized_train_data))\n",
        "test_data_df = pd.DataFrame(data=dict(tokenized_test_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kEpuuoGg7jk_"
      },
      "outputs": [],
      "source": [
        "tokenized_train_data = datasets.Dataset.from_pandas(train_data_df)\n",
        "tokenized_test_data = datasets.Dataset.from_pandas(test_data_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rW06JFhfyaeV"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aSFI0WYqyg78"
      },
      "outputs": [],
      "source": [
        "id2label = {0: \"Not Spam\", 1: \"Spam\"}\n",
        "label2id = {\"Not Spam\": 0, \"Spam\": 1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xb3HRZ3vzwT1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bf92d63143e348d793f80659f61ebd72",
            "6341871acdd14e61a8ee6ae34ba674e9",
            "d8d7d8ae49b1443cb1d0dbf828d00539",
            "2c17ff486b1c424083a7bd5781432232",
            "8be46605859a4d6482b1a4515fecfdfd",
            "aeb5db66ea954c4488ced8ed908cd41a",
            "21cb38a278de43e38d88ee24645458b6",
            "992a22dcbfdc484dbd18c9f6d29caccd",
            "b66e91c940814381b768f35309f1a405",
            "1fcdb1d5fca24b2583fb137a4ffcee85",
            "cd538d75f53d4f42b83ee15864120dff"
          ]
        },
        "outputId": "e4a3de80-1fc7-4db9-98dc-fbf4f0dc107e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf92d63143e348d793f80659f61ebd72"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "accuracy = evaluate.load(\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jSzrHQa4z4-v"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29MWzDj0z_fu"
      },
      "outputs": [],
      "source": [
        "batch_size = 16\n",
        "num_epochs = 5\n",
        "batches_per_epoch = len(X_train) // batch_size\n",
        "total_train_steps = int(batches_per_epoch * num_epochs)\n",
        "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "a083ba37afe24ec095f79f4337d7e58b",
            "712ab8de01064dbda8f4c8ace131e157",
            "9578722c3aef4f93b8689a252a23d5d0",
            "03e4bc16e9a54eb489ca15463a1f2e2d",
            "5ea05890508a4a94b33e736b17d32203",
            "9bba6e0500a244718f1d29c8c764f38f",
            "8fe7b8f39e374de78b8629f5e9ac4fc3",
            "dad6fbda6af0449babd56546e237c7da",
            "b98c22bccdb44f328dc79625ab84e190",
            "189c408df4f94736945529f1343351af",
            "ff987cb66a2a447bad43270ff8c111f3"
          ]
        },
        "id": "jQhKmFrf0HE1",
        "outputId": "18949c33-a596-4593-dddc-1bd2ba6f069d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/47.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a083ba37afe24ec095f79f4337d7e58b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFAlbertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFAlbertForSequenceClassification were not initialized from the PyTorch model 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": [
        "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
        "    model_checkpoint, num_labels=2, id2label=id2label, label2id=label2id\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eotfRCMm4KHO",
        "outputId": "1298ef6d-506c-4da6-f817-7bb9cdd5062f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a AlbertTokenizerFast 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"
          ]
        }
      ],
      "source": [
        "tf_train_set = model.prepare_tf_dataset(\n",
        "    tokenized_train_data,\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "tf_validation_set = model.prepare_tf_dataset(\n",
        "    tokenized_test_data,\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CQLbxarY4Tni"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=optimizer, metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vjmcwbup8qAm"
      },
      "outputs": [],
      "source": [
        "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pao52Aq483ff",
        "outputId": "6d4a3a1a-19c6-4635-805e-ad09d9235b9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "226/226 [==============================] - 578s 2s/step - loss: 0.1608 - accuracy: 0.9800 - val_loss: 0.0555 - val_accuracy: 0.9800\n",
            "Epoch 2/3\n",
            "226/226 [==============================] - 568s 3s/step - loss: 0.0512 - accuracy: 0.9710 - val_loss: 0.0945 - val_accuracy: 0.9710\n",
            "Epoch 3/3\n",
            "226/226 [==============================] - 565s 3s/step - loss: 0.0136 - accuracy: 0.9858 - val_loss: 0.0487 - val_accuracy: 0.9858\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ebac5781600>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "model.fit(tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=[metric_callback])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mb8DNBjf9i0W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a821d7d4-db8e-4fda-e2cd-b214300bb9e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97/97 [==============================] - 69s 714ms/step - loss: 0.0487 - accuracy: 0.9858\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.04866095632314682, 0.9858247637748718]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "model.evaluate(tf_validation_set)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login"
      ],
      "metadata": {
        "id": "QiqCK58aNyVm"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "c4c28dd9f71b4eb7806b3ce93fee895c",
            "28c2c1902740407d800f0aa07e6f87dd",
            "856a73cd90e7470bbbf5bedddbeea522",
            "4056128522084b749d2c55b6bcd57daa",
            "3d9ee0b07ac5449d821bdb5a47293d70",
            "915ce2357ff74f809aea438400c5d942",
            "ae11a502a79d4466845563558d4b2538",
            "e835ea25dcb0485eaa1c477e70910843",
            "6130b6e12eb14c05a1dafdfb7624f774",
            "37c825f0447c48348f0837c51b130ad0",
            "ab8c3891b2484bf6aa1228defc2bcfab",
            "a6281d75002e469391d7276a4ce12f8e",
            "f1b9df409942422a9e3cd20f3632fad2",
            "02ea2940ed984210b1efa266ce27849f",
            "5c27c9a36f784afa93a040c63fdb0722",
            "fd4c74d59b5c4ea1ac330a17b6b0353e",
            "8e51c3e81fd34be682b621ad0ac1fdb2",
            "da0632eb75c84d72b82703d52a352246",
            "9183b3a56f224ede915a45669ce98228",
            "3cac1653efd84082aef966b744f4afbd",
            "4d055036c10e485bbf49e322850b5bc8",
            "4c2c8530e77346bbb383c8f4f92c3e63",
            "aaa5d170bc704687ab2cc4339a41e5d6",
            "2565afda4e0f415b96649f0d0f22f038",
            "06ee157a768e42bebd6488f416a8b0d8",
            "9f1e202934e54433b137e5206f69c30a",
            "fb9fb2963aa04fa0b037a7333632fd42",
            "aa44ec7cc2cf49cd9a6c412f7c499557",
            "c048bea5c20449af8864591d906eb22a",
            "d3674d7b2acd4a3aa6788a3a484eb6e8",
            "3aa7e14a0b0a4a5cb8a51276f97d60d9",
            "b8708124959847918cc29d93e4532b02"
          ]
        },
        "id": "dFHuG0uZN0Yh",
        "outputId": "4d2d08ef-0a9b-4763-eca9-59271b7325ce"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c4c28dd9f71b4eb7806b3ce93fee895c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('albert_spam_filter.keras')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXoQcDyIG49j",
        "outputId": "1fb2806f-f18b-432a-a634-f9e7942827c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/tf_utils.py:465: UserWarning: `seed_generator` is deprecated and will be removed in a future version.\n",
            "  warnings.warn(\"`seed_generator` is deprecated and will be removed in a future version.\", UserWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp \"/content/albert_spam_filter.keras\" \"/content/drive/MyDrive/CSDF Mini Project/\""
      ],
      "metadata": {
        "id": "gL42auN4Hq3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"NotShrirang/albert-spam-filter\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "584a4b41759d4867b3d2d77d234f27fd",
            "4857f030314e4d9f89d81bf7844214d2",
            "2b88bd47e11b4674b877e31dd9944883",
            "109c81b079314542866fdc8653271126",
            "e9cbf26bfcb84027946777d23b32ebdb",
            "675f37aca7c249cda3d79830448f1b02",
            "e323bd2635f749d1a1826cb4b80289c1",
            "b49feefdf16349729dbfa6d286e045ee",
            "3fb6b11149e24690aef61f2a7bc566db",
            "f6e25426b297422389e595041426660e",
            "d670080e747d46dc89c71d8bfe5e9635"
          ]
        },
        "id": "61D2XOH8LxGK",
        "outputId": "3a88140b-db77-4246-e331-8352026a3a48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tf_model.h5:   0%|          | 0.00/46.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "584a4b41759d4867b3d2d77d234f27fd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model is available at https://huggingface.co/NotShrirang/albert-spam-filter"
      ],
      "metadata": {
        "id": "uuv4bWEDOkuG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_email = \"\"\"director CRD <directorcrd@mmcoe.edu.in>\n",
        "\n",
        "12:20 PM (10 minutes ago)\n",
        "\n",
        "to beetc1_2024, beetc2_2024, becomp1_2024, becomp2_2024, BEIT_2024, beelect_2024, BE, BE, TPO, Rahul\n",
        "HSBC :\n",
        "\n",
        "Kindly finish the registration process on  20th Oct  by 10:00am\n",
        "Please follow the instructions mentioned below.\n",
        "\n",
        "1.       Login to career page - https://mycareer.hsbc.com/en_GB/external\n",
        "\n",
        "2.       Scroll down the page, go to Recently viewed jobs and enter into the text field “Keywords” the correct job number i.e.  0000JGTO and then click Search\n",
        "\n",
        "3.       Click on “Campus Pipeline Requisition” it takes you to job page then click “Apply”( Pls note this is for the assessment purpose only)\n",
        "\n",
        "4.       On Privacy statement click “Accept”\n",
        "\n",
        "5.       On the next page scroll down and click on “New User”\n",
        "\n",
        "6.       Next page fill up the mandatory fields marked in * and click Register, follow the steps further and complete the registration\n",
        "\n",
        " The registration window will be open on 13th Oct , please ensure that they complete the above steps within the stipulated timeline.\n",
        "\n",
        " Post that we shall trigger the HSBC Values assessment test on their registered email ID, which is a scenario based test and they have to choose the best and worst possible answers for the scenario provided. It is imperative that they clear HSBC Values assessment before commencement of further rounds of assessment.\n",
        "\n",
        " Important test instructions (Mandatory for each applicant to follow):\n",
        "\n",
        " ·       Use latest Google Chrome or MS Edge browser only\n",
        "\n",
        "·       Test should be taken on laptop and not mobile phone\n",
        "\n",
        "·       Good and uninterrupted internet connectivity for seamless assessment experience\n",
        "\n",
        "·       Register on career site using same email ID as mentioned in the application list (usage of different email ID will disqualify them from the application process)\n",
        "\n",
        "·       Any candidate who fails to register on career site during the below stipulated timeline will stand disqualified from further process\n",
        "\n",
        "·      Kindly complete the assessment before 21st Oct 2023  .\n",
        "Eligibility: 10th: 65%, 12th: 65%, UG: 6.5 CGPA\n",
        "\n",
        "Link :  https://mycareer.hsbc.com/en_GB/external/PipelineDetail/Campus-Pipeline-Requisition/205865\n",
        "Best Of Luck! May you all get the job.\n",
        "Stay Blessed!\"\"\""
      ],
      "metadata": {
        "id": "r4mMiL4Wq1qF"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0: \"Not Spam\", 1: \"Spam\"}\n",
        "label2id = {\"Not Spam\": 0, \"Spam\": 1}"
      ],
      "metadata": {
        "id": "dxluofh3eVqR"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = TFAutoModelForSequenceClassification.from_pretrained(\"NotShrirang/albert-spam-filter\", num_labels=2, id2label=id2label, label2id=label2id)\n",
        "loaded_tokenizer = AutoTokenizer.from_pretrained(\"albert-base-v2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LulUoPv3pNKf",
        "outputId": "db6edf09-2c78-4602-dee2-0e579f7d1ec1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at NotShrirang/albert-spam-filter were not used when initializing TFAlbertForSequenceClassification: ['dropout_4']\n",
            "- This IS expected if you are initializing TFAlbertForSequenceClassification 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 TFAlbertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFAlbertForSequenceClassification were initialized from the model checkpoint at NotShrirang/albert-spam-filter.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFAlbertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_tokenizer.push_to_hub(\"NotShrirang/albert-spam-filter\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8ObiDW9fN0y",
        "outputId": "e5c315c2-3f42-45b1-f152-48488caa3a48"
      },
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              "CommitInfo(commit_url='https://huggingface.co/NotShrirang/albert-spam-filter/commit/a48e5425b2cfcf579a53e035f16e06f83d5c79b6', commit_message='Upload tokenizer', commit_description='', oid='a48e5425b2cfcf579a53e035f16e06f83d5c79b6', pr_url=None, pr_revision=None, pr_num=None)"
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        "classifier = pipeline('sentiment-analysis',\n",
        "                      model=loaded_model,\n",
        "                      tokenizer=loaded_tokenizer\n",
        "             )"
      ],
      "metadata": {
        "id": "GQBGwquylopX"
      },
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      "outputs": []
    },
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        "prediction = classifier(test_email)[0]"
      ],
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        },
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      "execution_count": 18,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (541 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "print(f\"Prediction: {prediction['label']}, confidence: {prediction['score']}\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8y9yTsRqZtM",
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      },
      "execution_count": 19,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Prediction: Not Spam, confidence: 0.9925408363342285\n"
          ]
        }
      ]
    }
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