bert.ipynb

{
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      "execution_count": 1,
      "metadata": {
        "colab": {
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          "height": 320
        },
        "id": "xSFfrdgAXWU1",
        "outputId": "76f1e8d7-e2aa-4e08-bb8b-a79e36b64cb7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 COMMENT_ID           AUTHOR  \\\n",
              "1360  z12muzijetuwf3qgm04ce1nafxvtj535ao40k  Trollsif Stalin   \n",
              "1703  z12tclby1nuyflgbw04cdnyg3zfvefn5lrg0k     Technibility   \n",
              "1146    z13uu5dyinf2yplb504civzz5obby5baucg   Simply Terrell   \n",
              "1758    z13ighawfwy5y5t5a04ccnp5dozetzfpq0g       Snipe Last   \n",
              "374     z13ehf4g5z2rivxhm04cfjdyhl2kwljzsjs     Andrés Nuñez   \n",
              "\n",
              "                            DATE  \\\n",
              "1360  2015-05-21T01:05:19.225000   \n",
              "1703         2014-09-12T21:08:47   \n",
              "1146  2014-12-26T11:17:56.976000   \n",
              "1758         2014-09-22T19:34:46   \n",
              "374   2015-05-21T02:11:54.710000   \n",
              "\n",
              "                                                CONTENT  CLASS  \n",
              "1360  I believe that soccer promotes terrorism. Bad ...      0  \n",
              "1703  Great video by a great artist in Katy Perry! A...      1  \n",
              "1146  Subscribe to my channel <br />Tweet &amp; Foll...      1  \n",
              "1758               why the elephant have a broken horn﻿      0  \n",
              "374                                              super﻿      0  "
            ],
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              "      <th></th>\n",
              "      <th>COMMENT_ID</th>\n",
              "      <th>AUTHOR</th>\n",
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              "      <th>CLASS</th>\n",
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              "      <th>1360</th>\n",
              "      <td>z12muzijetuwf3qgm04ce1nafxvtj535ao40k</td>\n",
              "      <td>Trollsif Stalin</td>\n",
              "      <td>2015-05-21T01:05:19.225000</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1703</th>\n",
              "      <td>z12tclby1nuyflgbw04cdnyg3zfvefn5lrg0k</td>\n",
              "      <td>Technibility</td>\n",
              "      <td>2014-09-12T21:08:47</td>\n",
              "      <td>Great video by a great artist in Katy Perry! A...</td>\n",
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              "      <th>1146</th>\n",
              "      <td>z13uu5dyinf2yplb504civzz5obby5baucg</td>\n",
              "      <td>Simply Terrell</td>\n",
              "      <td>2014-12-26T11:17:56.976000</td>\n",
              "      <td>Subscribe to my channel &lt;br /&gt;Tweet &amp;amp; Foll...</td>\n",
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              "      <th>1758</th>\n",
              "      <td>z13ighawfwy5y5t5a04ccnp5dozetzfpq0g</td>\n",
              "      <td>Snipe Last</td>\n",
              "      <td>2014-09-22T19:34:46</td>\n",
              "      <td>why the elephant have a broken horn﻿</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>374</th>\n",
              "      <td>z13ehf4g5z2rivxhm04cfjdyhl2kwljzsjs</td>\n",
              "      <td>Andrés Nuñez</td>\n",
              "      <td>2015-05-21T02:11:54.710000</td>\n",
              "      <td>super﻿</td>\n",
              "      <td>0</td>\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
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              "\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-6609431e-d87d-4bf4-b8e7-b55a67b3f7e1 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-6609431e-d87d-4bf4-b8e7-b55a67b3f7e1');\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": 1
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns\n",
        "import os\n",
        "import glob\n",
        "\n",
        "path = \"/content/drive/MyDrive/Archive\"\n",
        "all_files = glob.glob(os.path.join(path, \"*.csv\"))\n",
        "dfs = []\n",
        "\n",
        "for filename in all_files:\n",
        "    frame = pd.read_csv(filename, index_col=None, header=0)\n",
        "    dfs.append(frame)\n",
        "\n",
        "df = pd.concat(dfs, axis=0, ignore_index=True)\n",
        "df = df.sample(frac=1, random_state=42)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ERWSp5PQeCcl"
      },
      "outputs": [],
      "source": [
        "df = df[[\"CONTENT\", \"CLASS\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_JOMM1WeY8j",
        "outputId": "a55f6931-0375-4327-ed51-177df99853fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1956, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyBBb1kmegPs",
        "outputId": "d8e3b14c-3ca7-4fbd-d55f-47e24af8fe6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1956 entries, 1360 to 1126\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   CONTENT  1956 non-null   object\n",
            " 1   CLASS    1956 non-null   int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 45.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxX4vLMme8hI",
        "outputId": "8921e1ea-1555-49c7-ce6c-debfcba6e35a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.513804\n",
              "0    0.486196\n",
              "Name: CLASS, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.CLASS.value_counts(\"0\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "qA0XBD4gfku2"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import  train_test_split\n",
        "train_data,test_data = train_test_split(df, test_size=0.2,stratify=df[\"CLASS\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eupb_P-i-q2",
        "outputId": "09305579-fc49-4f5b-cbec-1ca64ace006c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1564\n",
            "                                                CONTENT  CLASS\n",
            "611                          I found out this song now﻿      0\n",
            "1471                                                 :)      0\n",
            "10                         Help me get 50 subs please ﻿      1\n",
            "1256  5 years later i still love this song <br />~Ax...      0\n",
            "1557        Subscribe me, I will? subscribe you back!!!      1\n",
            "...                                                 ...    ...\n",
            "616   http://www.amazon.co.uk/gp/offer-listing/B00EC...      1\n",
            "1707                     I love KATY PERRY &lt;3 &lt;3﻿      0\n",
            "1010  Guys lmfaois going to have a reunion on June/2...      1\n",
            "593   I think this is now a place to promote channel...      0\n",
            "955                                   my favorite song﻿      0\n",
            "\n",
            "[1564 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "train_data.shape\n",
        "print(len(train_data))\n",
        "print(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-iwfrn2KjQsz",
        "outputId": "a608e596-0ed1-47f0-8d13-5acb12daa42e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(392, 2)\n",
            "392\n",
            "                                                CONTENT  CLASS\n",
            "1641  katy perry does remind me of a tiger,like as i...      0\n",
            "776   Haha its so funny to see the salt of westerner...      0\n",
            "1453                               Your a fucking bitch      0\n",
            "735   If I get 100 subscribers, I will summon Freddy...      1\n",
            "1497                                     She is perfect      0\n",
            "...                                                 ...    ...\n",
            "541   Does anyone here use gift cards like Amazon, i...      1\n",
            "1922  I love this song so much!:-D I've heard it so ...      0\n",
            "934                          you cant stop the shuffle﻿      0\n",
            "751   im sorry for the spam but My name is Jenny. I ...      1\n",
            "45    I personally have never been in a abusive rela...      1\n",
            "\n",
            "[392 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "print(test_data.shape)\n",
        "print(len(test_data))\n",
        "print(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjX7sVgljekX",
        "outputId": "c3fc883d-e6b7-48ee-ce1d-a06750b46ee0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.514066\n",
              "0    0.485934\n",
              "Name: CLASS, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train_data.CLASS.value_counts(\"0\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_eo1usnjvWP",
        "outputId": "8eceecbc-f815-45c9-c782-cfad512952ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.512755\n",
              "0    0.487245\n",
              "Name: CLASS, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "test_data.CLASS.value_counts(\"0\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QyuQEP_kXYQ",
        "outputId": "db850068-6c64-41c5-8f69-9c66a479e58c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m62.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting datasets\n",
            "  Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m44.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: tokenizers, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.4 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "8NHWpySXlFg6"
      },
      "outputs": [],
      "source": [
        "# Modeling\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.optim import AdamW\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, get_scheduler\n",
        "\n",
        "# Progress bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Hugging Face Dataset\n",
        "from datasets import Dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "6f7KJ_6vjxfQ"
      },
      "outputs": [],
      "source": [
        "hg_train_data = Dataset.from_pandas(train_data)\n",
        "hg_test_data = Dataset.from_pandas(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwjZTTaDmm6c",
        "outputId": "ad288886-227a-4dae-dfa8-9719781c8c94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1564\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CONTENT': 'I found out this song now\\ufeff',\n",
              " 'CLASS': 0,\n",
              " '__index_level_0__': 611}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "print(len(hg_train_data))\n",
        "hg_train_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDMtc69YnI5W",
        "outputId": "549a7403-9625-443c-a082-225edc46f4e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "392\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CONTENT': 'katy perry does remind me of a tiger,like as if its her spirit animal :3 &lt;3\\ufeff',\n",
              " 'CLASS': 0,\n",
              " '__index_level_0__': 1641}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "print(len(hg_test_data))\n",
        "hg_test_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "4-g8TF4CnSph",
        "outputId": "e2e40355-326d-4afe-d069-ed8201cb6f8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              CONTENT  CLASS\n",
              "42  Check out this video on YouTube<br /><br /><br...      1"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-43f4e55a-8226-481e-860d-d09154c112e0\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Check out this video on YouTube&lt;br /&gt;&lt;br /&gt;&lt;br...</td>\n",
              "      <td>1</td>\n",
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              "    background-color: #E8F0FE;\n",
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              "    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",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43f4e55a-8226-481e-860d-d09154c112e0 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-43f4e55a-8226-481e-860d-d09154c112e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.iloc[[1092]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
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            "bc5848114e514891b3bd1337201764f4",
            "4fcd036a9a0c465ca13fe6d26e21fcc7",
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            "ecad65a5df94462788b49e85d56b82e6",
            "01da23692eda46bb9e5bedc0561564c2",
            "6294b7b3b6cd4a61afa046f2c22891aa",
            "aa5481db163246f6b12404ca45e11b19",
            "db167c3f7059466d9704944f75b7b6a5",
            "dcebf5388ecf472a888c432780a7d458",
            "4e1f8f63d81743abbe046d2fe1d6164b",
            "1bede00c2c044ed7ae5ddbec7563b2e8",
            "6de0b7b6e54e4806afa8ba5ac478e22b",
            "d1c70c02634741c7b2107c00a81b801a"
          ]
        },
        "id": "nIwecOFEnSsT",
        "outputId": "d4189d7a-e245-4caa-8c5d-1032ab80e8e2"
      },
      "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,
              "version_minor": 0,
              "model_id": "0009ca11b5a14a6d89a5bdcb93570767"
            }
          },
          "metadata": {}
        },
        {
          "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": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "edd9f0176f24439898b58fba5328f4e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8832dea53d204d7ab3ea237ff4ca3d78"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b99fdc34b3843948c6ffe9b6d985677"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertTokenizerFast(name_or_path='bert-base-uncased', vocab_size=30522, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#Tokenizer from a pretrained model\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "# Take a look at the tokenizer\n",
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "3WxkbiwUomtY",
        "outputId": "0920f864-90a5-49bb-b2e8-f80e7c52826a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1200\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(df.CONTENT.apply(len).max())\n",
        "\n",
        "lengths = df.CONTENT.apply(len)\n",
        "plt.hist(lengths, bins=10, edgecolor='black')\n",
        "plt.xlabel('String Length')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of String Lengths')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "724eda1e627a4bba94f97a79cec89b27",
            "d77bfe804c2941b2974f5dd5ec8bb09a",
            "bf196540bf914749bebd5f39df58ab3e",
            "efbc11146d8240b380ada5a274d194cc",
            "d21dbeae63b24d3495be9dc00fce7810",
            "dfab53269cbd4deabf7687761a49af25",
            "60d92a1675f449009e30a3437c5f2a91",
            "3fdbdc3e21c84686ba3499363ec21406",
            "166fc1b73e8a48549387100feb67632c",
            "67a3b16829a647a0a68185e1b9533ea1",
            "5d80f63d87d74a3d9f43b2c0d9e9d6ba",
            "9d28cc99386340c8ab3d2a3975747924",
            "6e0e23ba1a5145dfa9b3e0cd1e5607b8",
            "428f5c4e330c4da1bfce9cf1f099d743",
            "785be0822bca4f8483390bafb9796e7a",
            "4fd2b9c94a2d48f19080070df6d5fc93",
            "805e5512a4f946a8ac5cd686efc06fdd",
            "75a48a3ae0874b5dbe54f84f02fb8da2",
            "e0ea40cfb6ae4ada8b7ff4e242da4497",
            "f834e67c39064672a8c09da6f57681c9",
            "ff73d4c0da2b4e7482fe093c6ea833b6",
            "2a81d1f0a47448fb9762679420e05ab6"
          ]
        },
        "id": "CBa4OlsKogLp",
        "outputId": "cadff007-5f8c-4f01-befe-322f5975619b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1564 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "724eda1e627a4bba94f97a79cec89b27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/392 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d28cc99386340c8ab3d2a3975747924"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "length = 512\n",
        "\n",
        "def tokenize_dataset(data):\n",
        "    return tokenizer(data[\"CONTENT\"],\n",
        "                     max_length=length,\n",
        "                     truncation=True,\n",
        "                     pad_to_max_length=True,\n",
        "                     padding=\"max_length\")\n",
        "\n",
        "# Tokenize the dataset\n",
        "dataset_train = hg_train_data.map(tokenize_dataset)\n",
        "dataset_test = hg_test_data.map(tokenize_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bP6lnxFfrRs1",
        "outputId": "361ceb31-6c33-47b3-cf2d-aef3b8705a5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['CONTENT', 'CLASS', '__index_level_0__', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 1564\n",
            "})\n",
            "Dataset({\n",
            "    features: ['CONTENT', 'CLASS', '__index_level_0__', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 392\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(dataset_train)\n",
        "print(dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60gGeGFDrlll",
        "outputId": "d08be1b2-fa0d-421e-d332-16c7e7cdb31a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'datasets.arrow_dataset.Dataset'>\n"
          ]
        }
      ],
      "source": [
        "print(type(dataset_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "JXvFIkGmreKd"
      },
      "outputs": [],
      "source": [
        "dataset_train = dataset_train.remove_columns([\"CONTENT\", \"__index_level_0__\"])\n",
        "dataset_test = dataset_test.remove_columns([\"CONTENT\", \"__index_level_0__\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "DRjMGVj7My9U"
      },
      "outputs": [],
      "source": [
        "dataset_train = dataset_train.rename_column(\"CLASS\", \"labels\")\n",
        "dataset_test = dataset_test.rename_column(\"CLASS\", \"labels\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztXja5jkMzAV",
        "outputId": "956106c4-f8b5-44c8-fec6-f73ac921ea13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 1564\n",
            "})\n",
            "Dataset({\n",
            "    features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 392\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Change the format to PyTorch tensors\n",
        "dataset_train.set_format(\"torch\")\n",
        "dataset_test.set_format(\"torch\")\n",
        "\n",
        "# Take a look at the data\n",
        "print(dataset_train)\n",
        "print(dataset_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ieTvAJdEOO4z"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()\n",
        "\n",
        "# DataLoader\n",
        "train_dataloader = DataLoader(dataset=dataset_train, shuffle=True, batch_size=4)\n",
        "eval_dataloader = DataLoader(dataset=dataset_test, batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "426631686a604816be9aedc7502c8a2b",
            "6769cefbfe734e5ea2ef129da5663f3e",
            "7ae9761e4f634c45ba7cd2df781219bc",
            "0e1001e5d9404ffbb888386a50ee88e1",
            "759a617e3b61425cac370f14d2937b1f",
            "25959ee34a4a4314bf1e1c3936588632",
            "e1c50ecc253b44f6b8f9f9b0218af949",
            "367dfe9dbcd5461886517d4967f21d5d",
            "a8db12d09a37412f8c562dca642a03e7",
            "4cfcc7d152e44427a102cfd17c39f8fd",
            "075a148ef0ba43df951fbd2ab5eba4c5"
          ]
        },
        "id": "0OJGOPSAOO7f",
        "outputId": "79613e43-42c6-46f7-ec94-518580763536"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "426631686a604816be9aedc7502c8a2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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 model\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-4i1lfDPj-c",
        "outputId": "2e5e7ef2-655b-42e9-deae-a76db8f0c735"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using GPU: Tesla T4\n"
          ]
        }
      ],
      "source": [
        "# Number of epochs\n",
        "num_epochs = 10\n",
        "\n",
        "# Number of training steps\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "\n",
        "# Optimizer\n",
        "optimizer = AdamW(params=model.parameters(), lr=5e-6)\n",
        "\n",
        "# Set up the learning rate scheduler\n",
        "lr_scheduler = get_scheduler(name=\"linear\",\n",
        "                             optimizer=optimizer,\n",
        "                             num_warmup_steps=0,\n",
        "                             num_training_steps=num_training_steps)\n",
        "\n",
        "# Use GPU if it is available\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    gpu_name = torch.cuda.get_device_name(0)\n",
        "    print(f\"Using GPU: {gpu_name}\")\n",
        "else:\n",
        "    print(\"No GPU available\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0OEZj6LPkLz",
        "outputId": "785fdac3-12a7-4b21-dd47-6c1fef358855"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3910"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "num_training_steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678,
          "referenced_widgets": [
            "6d0adf15b0ad4919b7e8cf5f47692f76",
            "fe38a0a6cb4042f2ae9c2b99cbbb5fd1",
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            "5a97e62614e443e1bd61a14caac6fde9",
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            "e217119306404a879bea03ba46d79e1a",
            "3491e747ace44f5eac487698dfc97def",
            "b0750072dd6d47ccb0d1707dd6a362ed",
            "012d1a09f9c342a88d008f2199310bbc",
            "71e244c1486e4580aa45906272fb4d1e",
            "37ba199c9b8248b19ab010bc62721f0b"
          ]
        },
        "id": "B-h3DWSEPkrH",
        "outputId": "3de2787f-a733-4353-c9f1-e061ac616255"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training Progress:   0%|          | 0/3910 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d0adf15b0ad4919b7e8cf5f47692f76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10 - Training Loss: 0.3314\n",
            "Epoch 2/10 - Training Loss: 0.0987\n",
            "Epoch 3/10 - Training Loss: 0.0440\n",
            "Epoch 4/10 - Training Loss: 0.0264\n",
            "Epoch 5/10 - Training Loss: 0.0121\n",
            "Epoch 6/10 - Training Loss: 0.0056\n",
            "Epoch 7/10 - Training Loss: 0.0045\n",
            "Epoch 8/10 - Training Loss: 0.0027\n",
            "Epoch 9/10 - Training Loss: 0.0022\n",
            "Epoch 10/10 - Training Loss: 0.0025\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Set the progress bar\n",
        "progress_bar = tqdm(range(num_training_steps),desc = \"Training Progress\")\n",
        "\n",
        "training_losses = []  # To store losses for each batch\n",
        "# Tells the model that we are training the model\n",
        "model.train()\n",
        "# Loop through the epochs\n",
        "for epoch in range(num_epochs):\n",
        "    # Loop through the batches\n",
        "    epoch_loss = 0.0  # Initialize epoch loss\n",
        "    for batch in train_dataloader:\n",
        "        # Get the batch\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        # Compute the model output for the batch\n",
        "        outputs = model(**batch)\n",
        "        # Loss computed by the model\n",
        "\n",
        "        loss = outputs.loss\n",
        "\n",
        "        epoch_loss += loss.item()\n",
        "        # backpropagates the error to calculate gradients\n",
        "        loss.backward()\n",
        "        # Update the model weights\n",
        "        optimizer.step()\n",
        "        # Learning rate scheduler\n",
        "        lr_scheduler.step()\n",
        "        # Clear the gradients\n",
        "        optimizer.zero_grad()\n",
        "        # Update the progress bar\n",
        "        progress_bar.update(1)\n",
        "\n",
        "  # Calculate average loss for the epoch\n",
        "    epoch_loss /= len(train_dataloader)\n",
        "    training_losses.append(epoch_loss)\n",
        "    print(f\"Epoch {epoch + 1}/{num_epochs} - Training Loss: {epoch_loss:.4f}\")\n",
        "\n",
        "\n",
        "progress_bar.close()\n",
        "\n",
        "# Plot the training loss over epochs if desired\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(range(1, num_epochs + 1), training_losses, marker='o')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Training Loss')\n",
        "plt.title('Training Loss Over Epochs')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "mhnb2T9OOO-_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "576c84d88bfa472784fac9bd26e5ddad",
            "6d1551de20134f1395fb3a2a013b6f3d",
            "6670314710b140a58a7f27e4cdd305d7",
            "03dabd8891564afd979e9c2e01228e67",
            "d745a626223e4fb2afdf5c32c3e186d0",
            "4483d32fb59244878d3ef26b51331448",
            "f9b0e4eb6a9d466180bf2076d7727392",
            "4b884db9b6374e2aa50baeb24d09b724",
            "1f3829a788d042498d35182428db55cb",
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            "c9000a3e26d44dc7ab23e319dfa93f84",
            "dff1e0ffbeb344c788c566df0bbd5ccb"
          ]
        },
        "outputId": "79d6d707-e8c7-4113-82bc-d76f34da3789"
      },
      "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": "576c84d88bfa472784fac9bd26e5ddad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5102c0e731d74bfd84add9953f47c9e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc2b821b28da4fb584095485956b4da9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Model performance evaluation\n",
        "import evaluate\n",
        "\n",
        "# Load the evaluation metric\n",
        "metric1 = evaluate.load(\"accuracy\")\n",
        "metric2 = evaluate.load(\"f1\")\n",
        "metric3 = evaluate.load(\"recall\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "SczG1XRIXE9s"
      },
      "outputs": [],
      "source": [
        "# Tells the model that we are evaluting the model performance\n",
        "model.eval()\n",
        "\n",
        "#  A list for all logits\n",
        "logits_all = []\n",
        "\n",
        "# A list for all predicted probabilities\n",
        "predicted_prob_all = []\n",
        "\n",
        "# A list for all predicted labels\n",
        "predictions_all = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "iJ6QxWhCXFGS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e9facd9-be0f-4353-831c-8f7d7403da23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy': 0.9719387755102041}\n",
            "{'f1': 0.972568578553616}\n",
            "{'recall': 0.9701492537313433}\n"
          ]
        }
      ],
      "source": [
        "for batch in eval_dataloader:\n",
        "    # Get the batch\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    # Disable the gradient calculation\n",
        "    with torch.no_grad():\n",
        "        # Compute the model output\n",
        "        outputs = model(**batch)\n",
        "    # Get the logits\n",
        "    logits = outputs.logits\n",
        "    # Append the logits batch to the list\n",
        "    logits_all.append(logits)\n",
        "    # Get the predicted probabilities for the batch\n",
        "    predicted_prob = torch.softmax(logits, dim=1)\n",
        "    # Append the predicted probabilities for the batch to all the predicted probabilities\n",
        "    predicted_prob_all.append(predicted_prob)\n",
        "    # Get the predicted labels for the batch\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "    # Append the predicted labels for the batch to all the predictions\n",
        "    predictions_all.append(predictions)\n",
        "    # Add the prediction batch to the evaluation metric\n",
        "    metric1.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    metric2.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    metric3.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "\n",
        "# Compute the metric\n",
        "print(metric1.compute())\n",
        "print(metric2.compute())\n",
        "print(metric3.compute())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "vKpfHWxVXY9W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7e764bf-6d97-4949-d112-2b5b78afbf80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([[ 3.9079, -3.1044],\n",
              "         [ 2.5765, -2.4495],\n",
              "         [ 3.5727, -3.2288],\n",
              "         [-3.4387,  3.3489],\n",
              "         [ 3.8341, -3.2221],\n",
              "         [ 2.6190, -2.2009],\n",
              "         [ 3.6122, -3.1459],\n",
              "         [ 3.8269, -3.3460],\n",
              "         [ 3.6812, -3.2144],\n",
              "         [ 3.4286, -2.9174],\n",
              "         [ 3.6081, -3.0233],\n",
              "         [-3.6813,  3.4982],\n",
              "         [-3.9738,  3.5799],\n",
              "         [-4.0414,  3.6492],\n",
              "         [ 2.9541, -2.4313],\n",
              "         [ 3.6917, -3.1860],\n",
              "         [-3.6507,  3.5435],\n",
              "         [-3.7879,  3.5010],\n",
              "         [-3.7890,  3.6080],\n",
              "         [-3.7946,  3.5995],\n",
              "         [ 3.3880, -2.8260],\n",
              "         [ 3.5304, -3.0758],\n",
              "         [-3.8624,  3.3243],\n",
              "         [ 3.7446, -3.1889],\n",
              "         [ 3.7123, -3.2039],\n",
              "         [ 3.6743, -2.8575],\n",
              "         [ 3.8217, -3.2317],\n",
              "         [-4.0093,  3.4727],\n",
              "         [-2.9726,  2.7656],\n",
              "         [ 3.8018, -3.3599],\n",
              "         [ 3.8396, -3.3210],\n",
              "         [ 3.5829, -3.1261]], device='cuda:0'),\n",
              " tensor([[-3.9042,  3.5533],\n",
              "         [ 3.5730, -3.1191],\n",
              "         [-3.9263,  3.6581],\n",
              "         [ 3.4023, -2.9515],\n",
              "         [-3.7464,  3.5233],\n",
              "         [-3.9242,  3.6398],\n",
              "         [ 3.6857, -3.1512],\n",
              "         [-3.6199,  3.5590],\n",
              "         [-3.6069,  3.4380],\n",
              "         [-3.9606,  3.3220],\n",
              "         [-4.0667,  3.5468],\n",
              "         [-3.8142,  3.5892],\n",
              "         [ 3.3943, -3.0864],\n",
              "         [ 3.6364, -3.2287],\n",
              "         [ 3.5356, -3.1810],\n",
              "         [-4.0405,  3.5739],\n",
              "         [-2.8054,  2.9354],\n",
              "         [-3.9242,  3.6398],\n",
              "         [-3.7451,  3.5556],\n",
              "         [ 3.5776, -2.9867],\n",
              "         [ 3.7125, -3.2043],\n",
              "         [-3.8740,  3.6590],\n",
              "         [-3.2857,  3.1761],\n",
              "         [ 3.4534, -3.0382],\n",
              "         [ 3.7097, -3.2875],\n",
              "         [-3.8204,  3.5618],\n",
              "         [ 3.3578, -3.0537],\n",
              "         [ 3.5360, -2.9967],\n",
              "         [-3.9242,  3.6398],\n",
              "         [ 3.4376, -2.7995],\n",
              "         [-3.6710,  3.4857],\n",
              "         [-3.9242,  3.6398]], device='cuda:0'),\n",
              " tensor([[-3.9242,  3.6398],\n",
              "         [ 3.5384, -3.1336],\n",
              "         [-3.9728,  3.6508],\n",
              "         [ 3.1636, -2.7405],\n",
              "         [-3.7985,  3.6521],\n",
              "         [-3.9609,  3.5757],\n",
              "         [-1.2853,  1.2091],\n",
              "         [-3.7837,  3.6396],\n",
              "         [ 3.7312, -3.3298],\n",
              "         [ 3.8713, -3.2856],\n",
              "         [-3.7928,  3.5955],\n",
              "         [ 3.8168, -3.3360],\n",
              "         [-3.7655,  3.5114],\n",
              "         [ 3.7980, -3.0879],\n",
              "         [ 3.8416, -3.2901],\n",
              "         [ 3.8421, -3.2642],\n",
              "         [-3.4288,  3.2199],\n",
              "         [-3.4553,  3.2416],\n",
              "         [-3.9380,  3.6365],\n",
              "         [ 3.5376, -3.0060],\n",
              "         [ 3.8024, -3.3416],\n",
              "         [-3.9287,  3.6088],\n",
              "         [-3.8908,  3.6290],\n",
              "         [-3.8104,  3.5693],\n",
              "         [-3.9547,  3.6248],\n",
              "         [ 3.0919, -2.7564],\n",
              "         [-2.3593,  1.8605],\n",
              "         [ 3.6147, -3.1359],\n",
              "         [ 3.7407, -3.3527],\n",
              "         [ 3.7627, -3.2432],\n",
              "         [ 3.5859, -2.8238],\n",
              "         [-3.7879,  3.5551]], device='cuda:0'),\n",
              " tensor([[-3.9078,  3.6533],\n",
              "         [-3.6753,  3.5754],\n",
              "         [ 3.5140, -2.7194],\n",
              "         [-3.6727,  3.5384],\n",
              "         [ 3.6136, -3.1724],\n",
              "         [ 3.6572, -3.2668],\n",
              "         [-3.5563,  3.0821],\n",
              "         [-3.8514,  3.5161],\n",
              "         [ 3.0708, -2.7347],\n",
              "         [-3.8102,  3.6942],\n",
              "         [ 3.3277, -2.7070],\n",
              "         [-3.8966,  3.6407],\n",
              "         [-3.5701,  3.4580],\n",
              "         [-3.4018,  2.6227],\n",
              "         [-3.8546,  3.6014],\n",
              "         [-3.9322,  3.5642],\n",
              "         [-3.7249,  3.5398],\n",
              "         [-3.9837,  3.4833],\n",
              "         [-3.9362,  3.6574],\n",
              "         [-3.8865,  3.6291],\n",
              "         [-3.8221,  3.5879],\n",
              "         [-3.7694,  3.6012],\n",
              "         [-3.9560,  3.3078],\n",
              "         [ 3.4302, -3.1053],\n",
              "         [ 1.5678, -0.9279],\n",
              "         [ 2.4990, -2.0738],\n",
              "         [-3.8003,  3.5185],\n",
              "         [-3.7204,  3.5271],\n",
              "         [-3.9242,  3.6398],\n",
              "         [ 3.6945, -3.1799],\n",
              "         [-3.8144,  3.5948],\n",
              "         [ 3.4552, -2.8529]], device='cuda:0'),\n",
              " tensor([[-3.8139,  3.5789],\n",
              "         [-3.9170,  3.6100],\n",
              "         [ 3.6756, -3.2720],\n",
              "         [ 3.8284, -3.1644],\n",
              "         [-3.7033,  3.4994],\n",
              "         [-3.8184,  3.5748],\n",
              "         [ 3.5820, -3.1777],\n",
              "         [-3.8747,  3.5956],\n",
              "         [-3.9242,  3.6398],\n",
              "         [-3.8958,  3.6232],\n",
              "         [-3.8771,  3.6042],\n",
              "         [ 3.6953, -3.2482],\n",
              "         [ 3.7227, -3.2633],\n",
              "         [ 3.5400, -2.9968],\n",
              "         [-4.0041,  3.6307],\n",
              "         [-3.7470,  3.5219],\n",
              "         [-3.8958,  3.6232],\n",
              "         [ 3.1780, -2.5707],\n",
              "         [ 3.2018, -2.6142],\n",
              "         [-3.9242,  3.6398],\n",
              "         [-3.6688,  3.3869],\n",
              "         [ 3.7874, -3.3236],\n",
              "         [-3.9292,  3.6286],\n",
              "         [ 3.8643, -3.3146],\n",
              "         [ 3.1309, -2.5073],\n",
              "         [ 3.7088, -2.8457],\n",
              "         [-2.7286,  2.8384],\n",
              "         [-3.6101,  3.4164],\n",
              "         [-3.7236,  3.5683],\n",
              "         [-3.4866,  2.7700],\n",
              "         [-3.7044,  3.4648],\n",
              "         [ 3.6684, -3.0433]], device='cuda:0')]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Take a look at the logits\n",
        "logits_all[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "4hA8t5ePXZIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f529870d-bffa-4279-bc45-07816fd0a0e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([[9.9910e-01, 8.9994e-04],\n",
              "         [9.9348e-01, 6.5216e-03],\n",
              "         [9.9889e-01, 1.1109e-03],\n",
              "         [1.1264e-03, 9.9887e-01],\n",
              "         [9.9914e-01, 8.6126e-04],\n",
              "         [9.9200e-01, 8.0028e-03],\n",
              "         [9.9884e-01, 1.1601e-03],\n",
              "         [9.9923e-01, 7.6644e-04],\n",
              "         [9.9899e-01, 1.0112e-03],\n",
              "         [9.9825e-01, 1.7507e-03],\n",
              "         [9.9868e-01, 1.3166e-03],\n",
              "         [7.6143e-04, 9.9924e-01],\n",
              "         [5.2391e-04, 9.9948e-01],\n",
              "         [4.5692e-04, 9.9954e-01],\n",
              "         [9.9544e-01, 4.5620e-03],\n",
              "         [9.9897e-01, 1.0294e-03],\n",
              "         [7.5041e-04, 9.9925e-01],\n",
              "         [6.8262e-04, 9.9932e-01],\n",
              "         [6.1275e-04, 9.9939e-01],\n",
              "         [6.1447e-04, 9.9939e-01],\n",
              "         [9.9800e-01, 1.9973e-03],\n",
              "         [9.9865e-01, 1.3502e-03],\n",
              "         [7.5603e-04, 9.9924e-01],\n",
              "         [9.9903e-01, 9.7362e-04],\n",
              "         [9.9901e-01, 9.9059e-04],\n",
              "         [9.9855e-01, 1.4543e-03],\n",
              "         [9.9914e-01, 8.6371e-04],\n",
              "         [5.6276e-04, 9.9944e-01],\n",
              "         [3.2102e-03, 9.9679e-01],\n",
              "         [9.9922e-01, 7.7511e-04],\n",
              "         [9.9922e-01, 7.7599e-04],\n",
              "         [9.9878e-01, 1.2185e-03]], device='cuda:0'),\n",
              " tensor([[5.7671e-04, 9.9942e-01],\n",
              "         [9.9876e-01, 1.2391e-03],\n",
              "         [5.0809e-04, 9.9949e-01],\n",
              "         [9.9826e-01, 1.7369e-03],\n",
              "         [6.9579e-04, 9.9930e-01],\n",
              "         [5.1853e-04, 9.9948e-01],\n",
              "         [9.9893e-01, 1.0722e-03],\n",
              "         [7.6195e-04, 9.9924e-01],\n",
              "         [8.7106e-04, 9.9913e-01],\n",
              "         [6.8693e-04, 9.9931e-01],\n",
              "         [4.9351e-04, 9.9951e-01],\n",
              "         [6.0882e-04, 9.9939e-01],\n",
              "         [9.9847e-01, 1.5304e-03],\n",
              "         [9.9896e-01, 1.0425e-03],\n",
              "         [9.9879e-01, 1.2092e-03],\n",
              "         [4.9305e-04, 9.9951e-01],\n",
              "         [3.2020e-03, 9.9680e-01],\n",
              "         [5.1853e-04, 9.9948e-01],\n",
              "         [6.7460e-04, 9.9933e-01],\n",
              "         [9.9859e-01, 1.4078e-03],\n",
              "         [9.9901e-01, 9.8999e-04],\n",
              "         [5.3484e-04, 9.9947e-01],\n",
              "         [1.5596e-03, 9.9844e-01],\n",
              "         [9.9849e-01, 1.5138e-03],\n",
              "         [9.9909e-01, 9.1364e-04],\n",
              "         [6.2184e-04, 9.9938e-01],\n",
              "         [9.9836e-01, 1.6398e-03],\n",
              "         [9.9855e-01, 1.4529e-03],\n",
              "         [5.1853e-04, 9.9948e-01],\n",
              "         [9.9805e-01, 1.9517e-03],\n",
              "         [7.7903e-04, 9.9922e-01],\n",
              "         [5.1853e-04, 9.9948e-01]], device='cuda:0'),\n",
              " tensor([[5.1853e-04, 9.9948e-01],\n",
              "         [9.9874e-01, 1.2642e-03],\n",
              "         [4.8857e-04, 9.9951e-01],\n",
              "         [9.9728e-01, 2.7208e-03],\n",
              "         [5.8074e-04, 9.9942e-01],\n",
              "         [5.3297e-04, 9.9947e-01],\n",
              "         [7.6245e-02, 9.2376e-01],\n",
              "         [5.9683e-04, 9.9940e-01],\n",
              "         [9.9914e-01, 8.5713e-04],\n",
              "         [9.9922e-01, 7.7893e-04],\n",
              "         [6.1802e-04, 9.9938e-01],\n",
              "         [9.9922e-01, 7.8207e-04],\n",
              "         [6.9087e-04, 9.9931e-01],\n",
              "         [9.9898e-01, 1.0210e-03],\n",
              "         [9.9920e-01, 7.9869e-04],\n",
              "         [9.9918e-01, 8.1928e-04],\n",
              "         [1.2939e-03, 9.9871e-01],\n",
              "         [1.2332e-03, 9.9877e-01],\n",
              "         [5.1309e-04, 9.9949e-01],\n",
              "         [9.9856e-01, 1.4372e-03],\n",
              "         [9.9921e-01, 7.8897e-04],\n",
              "         [5.3244e-04, 9.9947e-01],\n",
              "         [5.4195e-04, 9.9946e-01],\n",
              "         [6.2340e-04, 9.9938e-01],\n",
              "         [5.1053e-04, 9.9949e-01],\n",
              "         [9.9712e-01, 2.8765e-03],\n",
              "         [1.4487e-02, 9.8551e-01],\n",
              "         [9.9883e-01, 1.1689e-03],\n",
              "         [9.9917e-01, 8.2986e-04],\n",
              "         [9.9909e-01, 9.0571e-04],\n",
              "         [9.9836e-01, 1.6428e-03],\n",
              "         [6.4663e-04, 9.9935e-01]], device='cuda:0'),\n",
              " tensor([[5.2006e-04, 9.9948e-01],\n",
              "         [7.0918e-04, 9.9929e-01],\n",
              "         [9.9804e-01, 1.9589e-03],\n",
              "         [7.3782e-04, 9.9926e-01],\n",
              "         [9.9887e-01, 1.1281e-03],\n",
              "         [9.9902e-01, 9.8290e-04],\n",
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          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "predicted_prob_all[:5]"
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          "output_type": "execute_result",
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              "[tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
              "         0, 0, 0, 1, 1, 0, 0, 0], device='cuda:0'),\n",
              " tensor([1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n",
              "         0, 1, 0, 0, 1, 0, 1, 1], device='cuda:0'),\n",
              " tensor([1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1,\n",
              "         1, 0, 1, 0, 0, 0, 0, 1], device='cuda:0'),\n",
              " tensor([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "         0, 0, 1, 1, 1, 0, 1, 0], device='cuda:0'),\n",
              " tensor([1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0,\n",
              "         0, 0, 1, 1, 1, 1, 1, 0], device='cuda:0')]"
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        "predictions_all[:5]"
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      "cell_type": "code",
      "execution_count": 36,
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        "id": "t21uyb7sXq9P"
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      "source": [
        "tokenizer.save_pretrained('./sentiment_transfer_learning_pytorch/')\n",
        "\n",
        "# Save model\n",
        "model.save_pretrained('./sentiment_transfer_learning_pytorch/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "hYS0GJsoX0qW"
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      "source": [
        "# Load tokenizer\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"./sentiment_transfer_learning_pytorch/\")\n",
        "\n",
        "# Load model\n",
        "loaded_model = AutoModelForSequenceClassification.from_pretrained('./sentiment_transfer_learning_pytorch/')\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "oeDZOjIYX0tt",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "f27792cc-8bb8-47d7-c273-b0cb6affee43"
      },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted class index: 0\n"
          ]
        }
      ],
      "source": [
        "# Input text for inference\n",
        "input_text = \"This is a negative review.\"\n",
        "\n",
        "# Tokenize the input text\n",
        "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
        "\n",
        "# Perform inference\n",
        "with torch.no_grad():\n",
        "    outputs = loaded_model(**inputs)\n",
        "\n",
        "# Get the predicted class (label) index\n",
        "predicted_class_idx = torch.argmax(outputs.logits).item()\n",
        "\n",
        "# Print the predicted class\n",
        "print(\"Predicted class index:\", predicted_class_idx)"
      ]
    }
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