Emotion_Analysis.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>Emotion Analysis with Hugging Face </div></b>\n",
        "\n"
      ],
      "metadata": {
        "id": "DU7V20qdHtJN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>1. Dataset Loading  </div></b>"
      ],
      "metadata": {
        "id": "IxVJXtJNHwUV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ryvG23iG4A0",
        "outputId": "4bea771d-4067-40e0-921c-5c5bea2575f2"
      },
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            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m184.3/194.1 kB\u001b[0m \u001b[31m50.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets"
      ]
    },
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      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "emotions = load_dataset(\"dair-ai/emotion\")"
      ],
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        "outputId": "1b39d27b-2d34-4054-ad72-eada31b5ed9e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/9.05k [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/1.03M [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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              "validation-00000-of-00001.parquet:   0%|          | 0.00/127k [00:00<?, ?B/s]"
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              "version_major": 2,
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        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7485dce451ef413bb033829cda83bc66"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "087740af070e40aebaa72f55960070cc"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>2. Understanding the Dataset </div></b>"
      ],
      "metadata": {
        "id": "Oydat0tjIdc9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emotions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiXd-edaIQt_",
        "outputId": "577825d8-7d8f-43af-d0fd-ef844d6db0e3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = emotions[\"train\"]\n",
        "train_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnpHn2OEIhC7",
        "outputId": "9c98f211-448a-4dc8-cc25-0ece28a61260"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLYv68W1IrKt",
        "outputId": "4fdec0de-0ff3-4240-df31-cbdea9925200"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj4KSH2JI1O4",
        "outputId": "a75cf117-a01f-4dcd-edf3-13f5a486cb27"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
              " 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds.column_names"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "VKvjAl54I9Ru",
        "outputId": "af158d9d-4468-4c1e-a082-65006fa2ce6b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTGTv7viJHqa",
        "outputId": "03541ced-2f62-40d5-c64b-8fb2eaa7521a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eb7P04qbJZpV",
        "outputId": "f3e20d1c-23d8-4040-c497-321b962a09f0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': ['i didnt feel humiliated',\n",
              "  'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
              "  'im grabbing a minute to post i feel greedy wrong',\n",
              "  'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n",
              "  'i am feeling grouchy'],\n",
              " 'label': [0, 0, 3, 2, 3]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[\"text\"][:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCHtkx14KcK7",
        "outputId": "2473be1b-1d46-4750-a967-c3eb74c216a6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i didnt feel humiliated',\n",
              " 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
              " 'im grabbing a minute to post i feel greedy wrong',\n",
              " 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n",
              " 'i am feeling grouchy']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>3. From Dataset to Pandas  </div></b>"
      ],
      "metadata": {
        "id": "WBOzYcAAKfxY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "emotions.set_format(type= \"pandas\")\n",
        "df=emotions[\"train\"][:]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1XT27anaKewr",
        "outputId": "33db1136-fe17-4564-b094-211e941fb96b"
      },
      "execution_count": 11,
      "outputs": [
        {
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          },
          "metadata": {},
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    {
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        "def label_int2str(row):\n",
        "    return emotions[\"train\"].features[\"label\"].int2str(row)\n",
        "\n",
        "df[\"label_name\"] = df[\"label\"].apply(label_int2str)\n",
        "df.head()"
      ],
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      "execution_count": 12,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                                text  label label_name\n",
              "0                            i didnt feel humiliated      0    sadness\n",
              "1  i can go from feeling so hopeless to so damned...      0    sadness\n",
              "2   im grabbing a minute to post i feel greedy wrong      3      anger\n",
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              "      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-52b62176-3eca-46b5-a0ea-250688b8a0e0 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-52b62176-3eca-46b5-a0ea-250688b8a0e0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-060aa89d-f8f9-4225-a0f6-4e74fe77224b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-060aa89d-f8f9-4225-a0f6-4e74fe77224b')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-060aa89d-f8f9-4225-a0f6-4e74fe77224b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 16000,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15969,\n        \"samples\": [\n          \"i feel rather imbicilic or at least complacent\",\n          \"i was in the bathroom i had sat down to pee it was to make me feel submissive again per instructions\",\n          \"i am thrilled with the way my skin and hair feel if you are like me you are skeptical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"sadness\",\n          \"anger\",\n          \"joy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>4. Data Visualization  </div></b>"
      ],
      "metadata": {
        "id": "NCwmlgLGLxkM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df[\"label_name\"].value_counts(ascending = True).plot.barh()\n",
        "plt.title(\"Frequency of Classes\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "R7BDMX8OLgQW",
        "outputId": "d138d8bd-e37e-4511-9cf4-a1c3333a4664"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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YgJAFAABgAUIWAACABQhZAAAAFuAF0QVAzVFL5eUI8HQZAAAUGcmx7T1dAkeyAAAArEDIAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAALELIAAAAsQMgCAACwACHrIn369FGXLl08XQYAACgCeHfhRSZMmCBjjKfLAAAARQAh6yLBwcGeLgEAABQRnC68yMWnC9PT0zVo0CCVKVNGfn5+atq0qbZs2SJJMsaoSpUqevvtt93Wj4+Pl81m0+7duy/Zf3p6utLS0twmAABQNBGyLuOFF17Q/Pnz9fHHH2vr1q2qUqWKoqOj9eeff8pms6lfv36aOXOm2zozZ85U8+bNVaVKlUv2OW7cOAUHB7umsLCwG7ErAADAAwhZl3DmzBlNmTJFb731ltq2basaNWpo+vTp8vf314cffijp/FGvpKQk/fjjj5Kkc+fOac6cOerXr99l+x0xYoRSU1Nd0/79+2/I/gAAgBuPkHUJe/bs0blz59SkSRPXPF9fXzVo0ECJiYmSpPLly6t9+/b66KOPJEnffPON0tPT9eCDD162X4fDIafT6TYBAICiiZB1HQYMGKB58+bpr7/+0syZM9W9e3cFBAR4uiwAAFAAELIuoXLlyrLb7Vq/fr1r3rlz57RlyxbVqFHDNa9du3YqVqyYpkyZoiVLllzxVCEAALi58AiHSyhWrJj+9a9/adiwYSpRooQqVKigN998U2fPnlX//v1d7by9vdWnTx+NGDFCERERatSokQerBgAABQlHsi4jNjZWDzzwgB577DHVrVtXu3fv1tKlS1W8eHG3dv3791dGRob69u3roUoBAEBBxJGsi6SnpyswMFCS5Ofnp4kTJ2rixIlXXOfAgQPy9fVVr169bkSJAACgkOBIlqTMzEzt3LlTGzduVGRkZK7WSU9P1++//67Ro0frwQcfVNmyZS2uEgAAFCaELEk7duxQ/fr1FRkZqSeffDJX68ydO1fh4eE6efKk3nzzTYsrBAAAhY3N8EZkj0lLSzv/5Pchn8nLwaMfAADIL8mx7S3r+8Lf79TU1Cs+85IjWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAW4GGkBcCOmOgr3p0AAAAKH45kAQAAWICQBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABH08XAKnmqKXycgR4ugwAQBGVHNve0yXclDiSBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABggSIVsmw2mxYuXOjpMgAAAIpWyAIAACgoCFkAAAAW8GjI+uKLL1SrVi35+/urZMmSioqK0pkzZ7Rlyxa1atVKpUqVUnBwsFq0aKGtW7e6rbtr1y41b95cfn5+qlGjhpYtW+a2PDk5WTabTV9++aXuvfdeBQQEqE6dOtq4caNbu3Xr1qlZs2by9/dXWFiYBg0apDNnzriWT548WREREfLz81PZsmXVrVu3q9YPAADgsZB16NAh9ejRQ/369VNiYqJWrVqlrl27yhijU6dOqXfv3lq3bp02bdqkiIgItWvXTqdOnZIkZWdnq2vXrrLb7dq8ebM++OADvfjii5fczsiRIzV06FDFx8eratWq6tGjhzIzMyVJe/bsUZs2bfTAAw9o27Zt+vTTT7Vu3To988wzkqSffvpJgwYN0pgxY5SUlKQlS5aoefPmV63/ctLT05WWluY2AQCAoslmrpQKLLR161bVq1dPycnJCg8Pv2Lb7OxshYSEaM6cOerQoYO+//57tW/fXr/99pvKly8vSVqyZInatm2rBQsWqEuXLkpOTlbFihU1Y8YM9e/fX5K0c+dORUZGKjExUbfffrsGDBggb29vTZ061bWtdevWqUWLFjpz5oy+++479e3bV7///ruCgoLyXP8Fo0ePVkxMTI75YUM+k5cjIFd9AABwrZJj23u6hCIlLS1NwcHBSk1NldPpvGw7jx3JqlOnjlq2bKlatWrpwQcf1PTp03XixAlJ0uHDh/X4448rIiJCwcHBcjqdOn36tFJSUiRJiYmJCgsLcwUsSWrUqNElt1O7dm3Xz6GhoZKkI0eOSJISEhI0a9YsBQYGuqbo6GhlZ2dr3759atWqlcLDw1WpUiU99thjmj17ts6ePXvV+i9nxIgRSk1NdU379+/P4+gBAICCzmMhy9vbW8uWLdPixYtVo0YNTZo0SdWqVdO+ffvUu3dvxcfHa8KECdqwYYPi4+NVsmRJZWRkXPN2fH19XT/bbDZJ54+MSdLp06f1xBNPKD4+3jUlJCRo165dqly5soKCgrR161bNnTtXoaGheuWVV1SnTh2dPHnyivVfjsPhkNPpdJsAAEDR5NEL3202m5o0aaKYmBjFxcXJbrdrwYIFWr9+vQYNGqR27dopMjJSDodDx44dc61XvXp17d+/X4cOHXLN27Rp0zVvv27dutq5c6eqVKmSY7Lb7ZIkHx8fRUVF6c0339S2bduUnJysFStWXLF+AAAAH09tePPmzVq+fLlat26tMmXKaPPmzTp69KiqV6+uiIgI/ec//1H9+vWVlpamYcOGyd/f37VuVFSUqlatqt69e+utt95SWlqaRo4cec01vPjii7r77rv1zDPPaMCAASpWrJh27typZcuW6d///rcWLVqkvXv3qnnz5ipevLi+++47ZWdnq1q1alesHwAAwGMhy+l0as2aNXrvvfeUlpam8PBwvfPOO2rbtq3KlSungQMHqm7dugoLC9Prr7+uoUOHutb18vLSggUL1L9/fzVo0EC33XabJk6cqDZt2lxTDbVr19bq1as1cuRINWvWTMYYVa5cWd27d5ckhYSE6Msvv9To0aP1999/KyIiQnPnznVdPH+5+gEAADx2dyH+/90J3F0IALASdxfmrwJ/dyEAAEBRRsgCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAKELAAAAAt47Inv+P92xETzsmgAAIoYjmQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAEfTxcAqeaopfJyBHi6DOCGSY5t7+kSAMByHMkCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALBAnkNWZmamfvjhB02dOlWnTp2SJB08eFCnT5/Ot+IAAAAKqzy9Vue3335TmzZtlJKSovT0dLVq1UpBQUF64403lJ6erg8++CC/6wQAAChU8nQka/Dgwapfv75OnDghf39/1/z7779fy5cvz7fiAAAACqs8Hclau3atNmzYILvd7jb/tttu04EDB/KlsMLs3Llz8vX19XQZAADAg/J0JCs7O1tZWVk55v/+++8KCgq67qJya8mSJWratKlCQkJUsmRJdejQQXv27JEkJScny2az6csvv9S9996rgIAA1alTRxs3bnTrY/r06QoLC1NAQIDuv/9+jR8/XiEhIW5tvvrqK9WtW1d+fn6qVKmSYmJilJmZ6Vpus9k0ZcoUderUScWKFdPYsWMt33cAAFCw5SlktW7dWu+9957rs81m0+nTpzVq1Ci1a9cuv2q7qjNnzui5557TTz/9pOXLl8vLy0v333+/srOzXW1GjhypoUOHKj4+XlWrVlWPHj1cAWn9+vV68sknNXjwYMXHx6tVq1Y5AtLatWvVq1cvDR48WDt37tTUqVM1a9asHO1Gjx6t+++/X9u3b1e/fv0uWW96errS0tLcJgAAUDTZjDHmWlf6/fffFR0dLWOMdu3apfr162vXrl0qVaqU1qxZozJlylhR61UdO3ZMpUuX1vbt2xUYGKiKFStqxowZ6t+/vyRp586dioyMVGJiom6//XY9/PDDOn36tBYtWuTq49FHH9WiRYt08uRJSVJUVJRatmypESNGuNp88skneuGFF3Tw4EFJ50PmkCFD9O67716xvtGjRysmJibH/LAhn8nLEXC9uw8UGsmx7T1dAgDkWVpamoKDg5Wamiqn03nZdnk6knXrrbcqISFBL730kp599lndeeedio2NVVxc3A0NWLt27VKPHj1UqVIlOZ1O3XbbbZKklJQUV5vatWu7fg4NDZUkHTlyRJKUlJSkBg0auPX5z88JCQkaM2aMAgMDXdPjjz+uQ4cO6ezZs6529evXv2q9I0aMUGpqqmvav3//te0wAAAoNPJ04bsk+fj46NFHH83PWq5Zx44dFR4erunTp6t8+fLKzs5WzZo1lZGR4Wpz8QXoNptNktxOJ17N6dOnFRMTo65du+ZY5ufn5/q5WLFiV+3L4XDI4XDketsAAKDwynPIOnjwoNatW6cjR47kCC2DBg267sKu5vjx40pKStL06dPVrFkzSdK6deuuqY9q1appy5YtbvP++blu3bpKSkpSlSpVrq9gAABwU8lTyJo1a5aeeOIJ2e12lSxZ0nWESDp/tOhGhKzixYurZMmSmjZtmkJDQ5WSkqLhw4dfUx//8z//o+bNm2v8+PHq2LGjVqxYocWLF7vtzyuvvKIOHTqoQoUK6tatm7y8vJSQkKAdO3botddey+/dAgAARUSersl6+eWX9corryg1NVXJycnat2+fa9q7d29+13hJXl5emjdvnn7++WfVrFlTzz77rN56661r6qNJkyb64IMPNH78eNWpU0dLlizRs88+63YaMDo6WosWLdL333+vu+66S3fffbfeffddhYeH5/cuAQCAIiRPdxeWLFlSP/74oypXrmxFTR71+OOP69dff9XatWst39aFuxO4uxA3G+4uBFCYWXp3Yf/+/fX555/nubiC5O2331ZCQoJ2796tSZMm6eOPP1bv3r09XRYAACjk8nRN1rhx49ShQwctWbJEtWrVyvEKmfHjx+dLcTfCjz/+qDfffFOnTp1SpUqVNHHiRA0YMMDTZQEAgEIuzyFr6dKlqlatmiTluPC9MPnss888XQIAACiC8hSy3nnnHX300Ufq06dPPpcDAABQNOTpmiyHw6EmTZrkdy0AAABFRp5C1uDBgzVp0qT8rgUAAKDIyNPpwh9//FErVqzQokWLFBkZmePC9y+//DJfigMAACis8hSyQkJCLvkuPwAAAJyXp4eRIn/k9mFmAACg4LD0YaQAAAC4sjydLpSkL774Qp999plSUlKUkZHhtmzr1q3XXRgAAEBhlqcjWRMnTlTfvn1VtmxZxcXFqUGDBipZsqT27t2rtm3b5neNAAAAhU6eQtbkyZM1bdo0TZo0SXa7XS+88IKWLVumQYMGKTU1Nb9rBAAAKHTyFLJSUlLUuHFjSZK/v79OnTolSXrsscc0d+7c/KsOAACgkMpTyCpXrpz+/PNPSVKFChW0adMmSdK+ffvEzYoAAAB5DFn33Xefvv76a0lS37599eyzz6pVq1bq3r277r///nwtEAAAoDDK03OysrOzlZ2dLR+f8zcnzps3Txs2bFBERISeeOIJ2e32fC+0KOI5WQAAFD65/fvNw0g9iJAFAEDhk9u/33l+TtbJkyf1448/6siRI8rOznZb1qtXr7x2CwAAUCTkKWR988036tmzp06fPi2n0ymbzeZaZrPZCFkAAOCml6cL359//nn169dPp0+f1smTJ3XixAnXdOGuQwAAgJtZnkLWgQMHNGjQIAUEBOR3PQAAAEVCnkJWdHS0fvrpp/yuBQAAoMjI0zVZ7du317Bhw7Rz507VqlVLvr6+bss7deqUL8UBAAAUVnl6hIOX1+UPgNlsNmVlZV1XUTcLHuEAAEDhY+kjHP75yAYAAAC4y9M1WblVq1Yt7d+/38pNAAAAFEiWhqzk5GSdO3fOyk0AAAAUSJaGLAAAgJsVIQsAAMAChCwAAAALELIAAAAsQMgCAACwgKUha+rUqSpbtqyVmwAAACiQcv0w0okTJ+a600GDBkmSHnnkkWuvCAAAoAjI9Wt1KlasmLsObTbt3bv3uoq6WfBaHQAACp98f63Ovn378qUwAACAm8F1XZOVkZGhpKQkZWZm5lc9AAAARUKeQtbZs2fVv39/BQQEKDIyUikpKZKk//mf/1FsbGy+FggAAFAY5fp04cVGjBihhIQErVq1Sm3atHHNj4qK0ujRozV8+PB8K/BmUHPUUnk5AjxdBnBZybHtPV0CABQ6eQpZCxcu1Keffqq7775bNpvNNT8yMlJ79uzJt+IAAAAKqzydLjx69KjKlCmTY/6ZM2fcQhcAAMDNKk8hq379+vr2229dny8EqxkzZqhRo0b5UxkAAEAhlqfTha+//rratm2rnTt3KjMzUxMmTNDOnTu1YcMGrV69Or9rBAAAKHTydCSradOmio+PV2ZmpmrVqqXvv/9eZcqU0caNG1WvXr38rhEAAKDQydORLEmqXLmypk+fnp+1AAAAFBl5DllZWVlasGCBEhMTJUk1atRQ586d5eOT5y4BAACKjDwlol9++UWdOnXSH3/8oWrVqkmS3njjDZUuXVrffPONatasma9FAgAAFDZ5uiZrwIABioyM1O+//66tW7dq69at2r9/v2rXrq2BAwfmd40AAACFTp5CVnx8vMaNG6fixYu75hUvXlxjx45VXFxcvhWX34wxGjhwoEqUKCGbzab4+HhPlwQAAIqoPIWsqlWr6vDhwznmHzlyRFWqVLnuoqyyZMkSzZo1S4sWLdKhQ4c4rQkAACyT62uy0tLSXD+PGzdOgwYN0ujRo3X33XdLkjZt2qQxY8bojTfeyP8q88mePXsUGhqqxo0bW7aNjIwM2e12y/oHAACFQ65DVkhIiNsrc4wxeuihh1zzjDGSpI4dOyorKyufy7x+ffr00ccffyzp/BPqw8PDtXfvXr3xxhuaNm2a/vjjD1WtWlUvv/yyunXrJun8HZQDBw7UihUr9Mcff6hChQp66qmnNHjwYLd+T548qbvuukvvv/++HA6H9u3b55F9BAAABUeuQ9bKlSutrMNyEyZMUOXKlTVt2jRt2bJF3t7eGjdunD755BN98MEHioiI0Jo1a/Too4+qdOnSatGihbKzs3Xrrbfq888/V8mSJbVhwwYNHDhQoaGheuihh1x9L1++XE6nU8uWLbtiDenp6UpPT3d9vvjoIAAAKFpyHbJatGhhZR2WCw4OVlBQkLy9vVWuXDmlp6fr9ddf1w8//OB632KlSpW0bt06TZ06VS1atJCvr69iYmJcfVSsWFEbN27UZ5995hayihUrphkzZlz1NOG4cePc+gMAAEXXdT059OzZs0pJSVFGRobb/Nq1a19XUTfC7t27dfbsWbVq1cptfkZGhu68807X5/fff18fffSRUlJS9NdffykjI0N33HGH2zq1atXK1XVYI0aM0HPPPef6nJaWprCwsOvbEQAAUCDlKWQdPXpUffv21eLFiy+5vCBek/VPp0+fliR9++23uuWWW9yWORwOSdK8efM0dOhQvfPOO2rUqJGCgoL01ltvafPmzW7tixUrlqttOhwOV98AAKBoy1PIGjJkiE6ePKnNmzfrnnvu0YIFC3T48GG99tpreuedd/K7RkvUqFFDDodDKSkplz0Vun79ejVu3FhPPfWUa96ePXtuVIkAAKAQy1PIWrFihb766ivVr19fXl5eCg8PV6tWreR0OjVu3Di1b98+v+vMd0FBQRo6dKieffZZZWdnq2nTpkpNTdX69evldDrVu3dvRURE6P/+7/+0dOlSVaxYUf/5z3+0ZcsWVaxY0dPlAwCAAi5PIevMmTMqU6aMpPNPej969KiqVq2qWrVqaevWrflaoJVeffVVlS5dWuPGjdPevXsVEhKiunXr6qWXXpIkPfHEE4qLi1P37t1ls9nUo0cPPfXUU5c9TQoAAHCBzVx4wNU1uOuuu/Taa68pOjpanTp1UkhIiMaNG6eJEyfqiy++4JRaLqWlpSk4OFhhQz6TlyPA0+UAl5UcW/CPTgPAjXLh73dqaqqcTudl2+XpSNbgwYN16NAhSdKoUaPUpk0bffLJJ7Lb7a4HfgIAANzM8hSyHn30UdfP9erV02+//aZff/1VFSpUUKlSpfKtOAAAgMIq1yHr4uc7Xc348ePzVAwAAEBRkeuQFRcXl6t2F7/fEAAA4GZ107y7EAAA4Eby8nQBAAAARREhCwAAwAKELAAAAAsQsgAAACyQp+dkIX/tiIm+4hNjAQBA4cORLAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAALELIAAAAsQMgCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAALELIAAAAsQMgCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAAL+Hi6AEg1Ry2VlyPA02XgOiTHtvd0CQCAAoYjWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWOCmCVn33HOPhgwZ4ukyAADATeKmCVkAAAA3EiELAADAAjdlyDpx4oR69eql4sWLKyAgQG3bttWuXbskSWlpafL399fixYvd1lmwYIGCgoJ09uxZSdL+/fv10EMPKSQkRCVKlFDnzp2VnJx8o3cFAAAUUDdlyOrTp49++uknff3119q4caOMMWrXrp3OnTsnp9OpDh06aM6cOW7rzJ49W126dFFAQIDOnTun6OhoBQUFae3atVq/fr0CAwPVpk0bZWRkXHa76enpSktLc5sAAEDRdNOFrF27dunrr7/WjBkz1KxZM9WpU0ezZ8/WgQMHtHDhQklSz549tXDhQtdRq7S0NH377bfq2bOnJOnTTz9Vdna2ZsyYoVq1aql69eqaOXOmUlJStGrVqstue9y4cQoODnZNYWFhVu8uAADwkJsuZCUmJsrHx0cNGzZ0zStZsqSqVaumxMRESVK7du3k6+urr7/+WpI0f/58OZ1ORUVFSZISEhK0e/duBQUFKTAwUIGBgSpRooT+/vtv7dmz57LbHjFihFJTU13T/v37LdxTAADgST6eLqAgstvt6tatm+bMmaOHH35Yc+bMUffu3eXjc364Tp8+rXr16mn27Nk51i1duvRl+3U4HHI4HJbVDQAACo6bLmRVr15dmZmZ2rx5sxo3bixJOn78uJKSklSjRg1Xu549e6pVq1b65ZdftGLFCr322muuZXXr1tWnn36qMmXKyOl03vB9AAAABd9Nd7owIiJCnTt31uOPP65169YpISFBjz76qG655RZ17tzZ1a558+YqV66cevbsqYoVK7qdXuzZs6dKlSqlzp07a+3atdq3b59WrVqlQYMG6ffff/fEbgEAgALmpgtZkjRz5kzVq1dPHTp0UKNGjWSM0XfffSdfX19XG5vNph49eighIcF1wfsFAQEBWrNmjSpUqKCuXbuqevXq6t+/v/7++2+ObAEAAEmSzRhjPF3EzSotLe38XYZDPpOXI8DT5eA6JMe293QJAIAb5MLf79TU1CseXLkpj2QBAABYjZAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABa46d5dWBDtiInmSfEAABQxHMkCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAALELIAAAAsQMgCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAALELIAAAAsQMgCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsAAhCwAAwAI+ni4AUs1RS+XlCPB0GdclOba9p0sAAKBA4UgWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABYgZAEAAFiAkAUAAGABQhYAAIAFCFkAAAAWIGRdZPTo0brjjjs8XQYAACgCCFkXGTp0qJYvX+7pMgAAQBFQpF4QnZGRIbvdfs3rGWOUlZWlwMBABQYGWlAZAAC42Xj8SNYXX3yhWrVqyd/fXyVLllRUVJTOnDmje+65R0OGDHFr26VLF/Xp08f1+bbbbtOrr76qXr16yel0auDAgUpOTpbNZtO8efPUuHFj+fn5qWbNmlq9erVrvVWrVslms2nx4sWqV6+eHA6H1q1bl+N04apVq9SgQQMVK1ZMISEhatKkiX777TfX8q+++kp169aVn5+fKlWqpJiYGGVmZl52X9PT05WWluY2AQCAosmjIevQoUPq0aOH+vXrp8TERK1atUpdu3aVMSbXfbz99tuqU6eO4uLi9PLLL7vmDxs2TM8//7zi4uLUqFEjdezYUcePH3dbd/jw4YqNjVViYqJq167ttiwzM1NdunRRixYttG3bNm3cuFEDBw6UzWaTJK1du1a9evXS4MGDtXPnTk2dOlWzZs3S2LFjL1vruHHjFBwc7JrCwsJyvZ8AAKBw8ejpwkOHDikzM1Ndu3ZVeHi4JKlWrVrX1Md9992n559/3vU5OTlZkvTMM8/ogQcekCRNmTJFS5Ys0YcffqgXXnjB1XbMmDFq1arVJftNS0tTamqqOnTooMqVK0uSqlev7loeExOj4cOHq3fv3pKkSpUq6dVXX9ULL7ygUaNGXbLPESNG6LnnnnPbBkELAICiyaMhq06dOmrZsqVq1aql6OhotW7dWt26dVPx4sVz3Uf9+vUvOb9Ro0aun318fFS/fn0lJibmal1JKlGihPr06aPo6Gi1atVKUVFReuihhxQaGipJSkhI0Pr1692OXGVlZenvv//W2bNnFRAQkKNPh8Mhh8OR630DAACFl0dPF3p7e2vZsmVavHixatSooUmTJqlatWrat2+fvLy8cpw2PHfuXI4+ihUrluftX23dmTNnauPGjWrcuLE+/fRTVa1aVZs2bZIknT59WjExMYqPj3dN27dv165du+Tn55fnmgAAQNHg8QvfbTabmjRpopiYGMXFxclut2vBggUqXbq0Dh065GqXlZWlHTt25LrfC2FIOn991c8//+x2ui+37rzzTo0YMUIbNmxQzZo1NWfOHElS3bp1lZSUpCpVquSYvLw8PqwAAMDDPHq6cPPmzVq+fLlat26tMmXKaPPmzTp69KiqV6+uYsWK6bnnntO3336rypUra/z48Tp58mSu+37//fcVERGh6tWr691339WJEyfUr1+/XK+/b98+TZs2TZ06dVL58uWVlJSkXbt2qVevXpKkV155RR06dFCFChXUrVs3eXl5KSEhQTt27NBrr712rUMBAACKGI+GLKfTqTVr1ui9995TWlqawsPD9c4776ht27Y6d+6cEhIS1KtXL/n4+OjZZ5/Vvffem+u+Y2NjFRsbq/j4eFWpUkVff/21SpUqlev1AwIC9Ouvv+rjjz/W8ePHFRoaqqefflpPPPGEJCk6OlqLFi3SmDFj9MYbb8jX11e33367BgwYcM3jAAAAih6buZbnJRQCycnJqlixouLi4gr8K3LS0tLOP8phyGfycuS8UL4wSY5t7+kSAAC4IS78/U5NTZXT6bxsOy4eAgAAsAAhCwAAwAJF6t2F0vlX7RSxM6AAAKAQ4kgWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAWK3N2FhdGOmOgrPswMAAAUPhzJAgAAsAAhCwAAwAKELAAAAAsQsgAAACxAyAIAALAAIQsAAMAChCwAAAALELIAAAAsQMgCAACwACELAADAAoQsAAAACxCyAAAALEDIAgAAsICPpwu4mRljJElpaWkergQAAOTWhb/bF/6OXw4hy4OOHz8uSQoLC/NwJQAA4FqdOnVKwcHBl11OyPKgEiVKSJJSUlKu+CUhb9LS0hQWFqb9+/fL6XR6upwih/G1FuNrLcbXWkV9fI0xOnXqlMqXL3/FdoQsD/LyOn9JXHBwcJH8JSwonE4n42shxtdajK+1GF9rFeXxzc3BES58BwAAsAAhCwAAwAKELA9yOBwaNWqUHA6Hp0spkhhfazG+1mJ8rcX4WovxPc9mrnb/IQAAAK4ZR7IAAAAsQMgCAACwACELAADAAoQsAAAACxCyAAAALEDI8pD3339ft912m/z8/NSwYUP9+OOPni6pQFqzZo06duyo8uXLy2azaeHChW7LjTF65ZVXFBoaKn9/f0VFRWnXrl1ubf7880/17NlTTqdTISEh6t+/v06fPu3WZtu2bWrWrJn8/PwUFhamN9980+pd87hx48bprrvuUlBQkMqUKaMuXbooKSnJrc3ff/+tp59+WiVLllRgYKAeeOABHT582K1NSkqK2rdvr4CAAJUpU0bDhg1TZmamW5tVq1apbt26cjgcqlKlimbNmmX17nnclClTVLt2bdcTrxs1aqTFixe7ljO2+Ss2NlY2m01DhgxxzWOMr8/o0aNls9ncpttvv921nPHNBYMbbt68ecZut5uPPvrI/PLLL+bxxx83ISEh5vDhw54urcD57rvvzMiRI82XX35pJJkFCxa4LY+NjTXBwcFm4cKFJiEhwXTq1MlUrFjR/PXXX642bdq0MXXq1DGbNm0ya9euNVWqVDE9evRwLU9NTTVly5Y1PXv2NDt27DBz5841/v7+ZurUqTdqNz0iOjrazJw50+zYscPEx8ebdu3amQoVKpjTp0+72jz55JMmLCzMLF++3Pz000/m7rvvNo0bN3Ytz8zMNDVr1jRRUVEmLi7OfPfdd6ZUqVJmxIgRrjZ79+41AQEB5rnnnjM7d+40kyZNMt7e3mbJkiU3dH9vtK+//tp8++235r///a9JSkoyL730kvH19TU7duwwxjC2+enHH380t912m6ldu7YZPHiwaz5jfH1GjRplIiMjzaFDh1zT0aNHXcsZ36sjZHlAgwYNzNNPP+36nJWVZcqXL2/GjRvnwaoKvn+GrOzsbFOuXDnz1ltvueadPHnSOBwOM3fuXGOMMTt37jSSzJYtW1xtFi9ebGw2mzlw4IAxxpjJkyeb4sWLm/T0dFebF1980VSrVs3iPSpYjhw5YiSZ1atXG2POj6Wvr6/5/PPPXW0SExONJLNx40ZjzPkQ7OXlZf744w9XmylTphin0+kazxdeeMFERka6bat79+4mOjra6l0qcIoXL25mzJjB2OajU6dOmYiICLNs2TLTokULV8hijK/fqFGjTJ06dS65jPHNHU4X3mAZGRn6+eefFRUV5Zrn5eWlqKgobdy40YOVFT779u3TH3/84TaWwcHBatiwoWssN27cqJCQENWvX9/VJioqSl5eXtq8ebOrTfPmzWW3211toqOjlZSUpBMnTtygvfG81NRUSVKJEiUkST///LPOnTvnNr633367KlSo4Da+tWrVUtmyZV1toqOjlZaWpl9++cXV5uI+LrS5mX7fs7KyNG/ePJ05c0aNGjVibPPR008/rfbt2+cYB8Y4f+zatUvly5dXpUqV1LNnT6WkpEhifHOLkHWDHTt2TFlZWW6/dJJUtmxZ/fHHHx6qqnC6MF5XGss//vhDZcqUcVvu4+OjEiVKuLW5VB8Xb6Ooy87O1pAhQ9SkSRPVrFlT0vl9t9vtCgkJcWv7z/G92thdrk1aWpr++usvK3anwNi+fbsCAwPlcDj05JNPasGCBapRowZjm0/mzZunrVu3aty4cTmWMcbXr2HDhpo1a5aWLFmiKVOmaN++fWrWrJlOnTrF+OaSj6cLAOB5Tz/9tHbs2KF169Z5upQipVq1aoqPj1dqaqq++OIL9e7dW6tXr/Z0WUXC/v37NXjwYC1btkx+fn6eLqdIatu2revn2rVrq2HDhgoPD9dnn30mf39/D1ZWeHAk6wYrVaqUvL29c9yBcfjwYZUrV85DVRVOF8brSmNZrlw5HTlyxG15Zmam/vzzT7c2l+rj4m0UZc8884wWLVqklStX6tZbb3XNL1eunDIyMnTy5Em39v8c36uN3eXaOJ3OIv8Ptd1uV5UqVVSvXj2NGzdOderU0YQJExjbfPDzzz/ryJEjqlu3rnx8fOTj46PVq1dr4sSJ8vHxUdmyZRnjfBYSEqKqVatq9+7d/A7nEiHrBrPb7apXr56WL1/umpedna3ly5erUaNGHqys8KlYsaLKlSvnNpZpaWnavHmzaywbNWqkkydP6ueff3a1WbFihbKzs9WwYUNXmzVr1ujcuXOuNsuWLVO1atVUvHjxG7Q3N54xRs8884wWLFigFStWqGLFim7L69WrJ19fX7fxTUpKUkpKitv4bt++3S3ILlu2TE6nUzVq1HC1ubiPC21uxt/37OxspaenM7b5oGXLltq+fbvi4+NdU/369dWzZ0/Xz4xx/jp9+rT27Nmj0NBQfodzy9NX3t+M5s2bZxwOh5k1a5bZuXOnGThwoAkJCXG7AwPnnTp1ysTFxZm4uDgjyYwfP97ExcWZ3377zRhz/hEOISEh5quvvjLbtm0znTt3vuQjHO68806zefNms27dOhMREeH2CIeTJ0+asmXLmscee8zs2LHDzJs3zwQEBBT5Rzj861//MsHBwWbVqlVut2ifPXvW1ebJJ580FSpUMCtWrDA//fSTadSokWnUqJFr+YVbtFu3bm3i4+PNkiVLTOnSpS95i/awYcNMYmKief/994vULdqXM3z4cLN69Wqzb98+s23bNjN8+HBjs9nM999/b4xhbK1w8d2FxjDG1+v55583q1atMvv27TPr1683UVFRplSpUubIkSPGGMY3NwhZHjJp0iRToUIFY7fbTYMGDcymTZs8XVKBtHLlSiMpx9S7d29jzPnHOLz88sumbNmyxuFwmJYtW5qkpCS3Po4fP2569OhhAgMDjdPpNH379jWnTp1ya5OQkGCaNm1qHA6HueWWW0xsbOyN2kWPudS4SjIzZ850tfnrr7/MU089ZYoXL24CAgLM/fffbw4dOuTWT3Jysmnbtq3x9/c3pUqVMs8//7w5d+6cW5uVK1eaO+64w9jtdlOpUiW3bRRV/fr1M+Hh4cZut5vSpUubli1bugKWMYytFf4Zshjj69O9e3cTGhpq7Ha7ueWWW0z37t3N7t27XcsZ36uzGWOMZ46hAQAAFF1ckwUAAGABQhYAAIAFCFkAAAAWIGQBAABYgJAFAABgAUIWAACABQhZAAAAFiBkAQAAWICQBQAAYAFCFgAAgAUIWQAAABb4f5UvkCzXKenCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Words per Tweet\"] = df[\"text\"].str.split().apply(len)\n",
        "df.boxplot(\"Words per Tweet\", by= \"label_name\", grid = False, showfliers = False, color = \"green\")\n",
        "plt.suptitle(\"\")\n",
        "plt.xlabel(\"\")\n",
        "plt.ylabel(\"\")\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "p0esg1V4MWdT",
        "outputId": "78ee83a1-ec9b-4b88-a5bb-824efaaf4df5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "block : bool, optional\n",
              "    Whether to wait for all figures to be closed before returning.\n",
              "\n",
              "    If `True` block and run the GUI main loop until all figure windows\n",
              "    are closed.\n",
              "\n",
              "    If `False` ensure that all figure windows are displayed and return\n",
              "    immediately.  In this case, you are responsible for ensuring\n",
              "    that the event loop is running to have responsive figures.\n",
              "\n",
              "    Defaults to True in non-interactive mode and to False in interactive\n",
              "    mode (see `.pyplot.isinteractive`).\n",
              "\n",
              "See Also\n",
              "--------\n",
              "ion : Enable interactive mode, which shows / updates the figure after\n",
              "      every plotting command, so that calling ``show()`` is not necessary.\n",
              "ioff : Disable interactive mode.\n",
              "savefig : Save the figure to an image file instead of showing it on screen.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "**Saving figures to file and showing a window at the same time**\n",
              "\n",
              "If you want an image file as well as a user interface window, use\n",
              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
              "limitation of command order does not apply if the show is non-blocking or\n",
              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
              "\n",
              "**Auto-show in jupyter notebooks**\n",
              "\n",
              "The jupyter backends (activated via ``%matplotlib inline``,\n",
              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
              "explicitly there.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
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              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 401);\n",
              "          };\n",
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              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
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              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions.reset_format()"
      ],
      "metadata": {
        "id": "Wo3JvILsNQVc"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>5. Data Preprocessing   </div></b>"
      ],
      "metadata": {
        "id": "lER4UbZpNi1R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Character Tokenization\n"
      ],
      "metadata": {
        "id": "VJu7Ye0hNsBE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"It's fun to work with NLP using HuggingFace\"\n",
        "tokenized_text = list(text)\n",
        "print(tokenized_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNH2pNmUNhzN",
        "outputId": "76540b06-5e4b-4fcf-82e4-7a7ba10e0ca8"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['I', 't', \"'\", 's', ' ', 'f', 'u', 'n', ' ', 't', 'o', ' ', 'w', 'o', 'r', 'k', ' ', 'w', 'i', 't', 'h', ' ', 'N', 'L', 'P', ' ', 'u', 's', 'i', 'n', 'g', ' ', 'H', 'u', 'g', 'g', 'i', 'n', 'g', 'F', 'a', 'c', 'e']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token2idx = {ch: idx for idx, ch in enumerate(sorted(set(tokenized_text)))}\n",
        "print(token2idx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DBEPdjZODsN",
        "outputId": "e8379ac0-6bd8-46e4-c8c2-54ac3f015a0a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{' ': 0, \"'\": 1, 'F': 2, 'H': 3, 'I': 4, 'L': 5, 'N': 6, 'P': 7, 'a': 8, 'c': 9, 'e': 10, 'f': 11, 'g': 12, 'h': 13, 'i': 14, 'k': 15, 'n': 16, 'o': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'w': 22}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids =[token2idx[token] for token in tokenized_text]\n",
        "print(input_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85CyRaYkOg_t",
        "outputId": "499af10a-e890-4a9f-cba5-97ce24f8d541"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4, 20, 1, 19, 0, 11, 21, 16, 0, 20, 17, 0, 22, 17, 18, 15, 0, 22, 14, 20, 13, 0, 6, 5, 7, 0, 21, 19, 14, 16, 12, 0, 3, 21, 12, 12, 14, 16, 12, 2, 8, 9, 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How to work one-hot encoding?\n"
      ],
      "metadata": {
        "id": "YaJtevbxSDAN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\"name\": [\"ahmet\", \"didar\", \"merve\"],\n",
        "                   \"label\":[0,1,2]})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "EwWSI3WPSCCw",
        "outputId": "7b7a269d-5fa8-441e-b192-a5e94f94c2b2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    name  label\n",
              "0  ahmet      0\n",
              "1  didar      1\n",
              "2  merve      2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf2bd780-34a6-4dc5-8df1-582c3f01ce4e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ahmet</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>didar</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>merve</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf2bd780-34a6-4dc5-8df1-582c3f01ce4e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bf2bd780-34a6-4dc5-8df1-582c3f01ce4e 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-bf2bd780-34a6-4dc5-8df1-582c3f01ce4e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-83126099-2292-4181-9438-e4dc8e98decd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83126099-2292-4181-9438-e4dc8e98decd')\"\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-83126099-2292-4181-9438-e4dc8e98decd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_85e5f8f7-7a1c-4b55-84df-d9c5a7cdf7c9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_85e5f8f7-7a1c-4b55-84df-d9c5a7cdf7c9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"ahmet\",\n          \"didar\",\n          \"merve\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df, dtype=int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "83Hlgt9MSu37",
        "outputId": "547df86d-2a29-485c-d996-8624aa2f7597"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label  name_ahmet  name_didar  name_merve\n",
              "0      0           1           0           0\n",
              "1      1           0           1           0\n",
              "2      2           0           0           1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9862a712-e0a9-4723-8a1a-374211b3d0d3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>name_ahmet</th>\n",
              "      <th>name_didar</th>\n",
              "      <th>name_merve</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9862a712-e0a9-4723-8a1a-374211b3d0d3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9862a712-e0a9-4723-8a1a-374211b3d0d3 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-9862a712-e0a9-4723-8a1a-374211b3d0d3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-c18c4b16-b25b-4b54-bdba-977a29a70ab8\">\n",
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              "            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",
              "     width=\"24px\">\n",
              "    <g>\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-c18c4b16-b25b-4b54-bdba-977a29a70ab8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_ahmet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_didar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_merve\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One-hot encoding with Torch\n"
      ],
      "metadata": {
        "id": "vQpx1sVxTB-q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# Converting inputs into tensor\n",
        "input_ids = torch.tensor(input_ids)\n",
        "\n",
        "one_hot_encodings = F.one_hot(input_ids, num_classes=len(token2idx))\n",
        "one_hot_encodings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DECz8gWS_WA",
        "outputId": "d3d5acef-b959-4d2f-e741-31b6761c6d96"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([43, 23])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Token: {tokenized_text[0]}\")\n",
        "print(f\"Tensor index: {input_ids[0]}\")\n",
        "print(f\"One-hot: {one_hot_encodings[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3_JOocRTvqN",
        "outputId": "99958016-68f8-472b-bb6e-0aa10281677d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token: I\n",
            "Tensor index: 4\n",
            "One-hot: tensor([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Word Tokenization\n"
      ],
      "metadata": {
        "id": "5-ehUy5tUUrh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_text = text.split()\n",
        "print(tokenized_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DufxNzQuUMiV",
        "outputId": "4c89797a-a0bb-4384-e822-442824b4899a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"It's\", 'fun', 'to', 'work', 'with', 'NLP', 'using', 'HuggingFace']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Subword Tokenization\n"
      ],
      "metadata": {
        "id": "nHKmCmNNUzde"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
          "referenced_widgets": [
            "e7a00de1b519469da410c81cc90e6653",
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        "id": "mM1kCBiWUvj6",
        "outputId": "0ec3149a-db1e-4fa0-88d6-719e657c0f5e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7a00de1b519469da410c81cc90e6653"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ebe0506a1a042cbb9de70eecdb272a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17d4aa85670d4a8784e97ac22e9874ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b1f69a82a2242dab457a26cfb88d71e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7a8609aac6f4a0ca5ef446954970416"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Custom Tokenization\n"
      ],
      "metadata": {
        "id": "dSwIyQUUVA3e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertTokenizer\n",
        "\n",
        "distbert_tokenize=DistilBertTokenizer.from_pretrained(model_ckpt)"
      ],
      "metadata": {
        "id": "JSg0sSX6U_iU"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How to work with Tokenizer\n"
      ],
      "metadata": {
        "id": "s8d5ZfZEVIds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_text = tokenizer(text)\n",
        "print(encoded_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2hCq0DLVGLa",
        "outputId": "62d3de52-7ccd-4320-c2a4-935c0468a7b6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 2009, 1005, 1055, 4569, 2000, 2147, 2007, 17953, 2361, 2478, 17662, 12172, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(encoded_text.input_ids)\n",
        "print(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtOFA9vJVWCy",
        "outputId": "d3fef0a4-6a96-403b-ecdc-7596d561f502"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'it', \"'\", 's', 'fun', 'to', 'work', 'with', 'nl', '##p', 'using', 'hugging', '##face', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_tokens_to_string(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "8fzgsI00VozM",
        "outputId": "40ef3718-cfe4-461d-ca16-5425ee17b7d8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"[CLS] it ' s fun to work with nlp using huggingface [SEP]\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Attributes of Tokenizer\n"
      ],
      "metadata": {
        "id": "j6rjbkUcVtYm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.vocab_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bES3j5S3Vr7e",
        "outputId": "b0a8dc27-6a9b-4fb6-d286-1e7697298ffc"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30522"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.model_max_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CSPOFcaV1GW",
        "outputId": "0b1cc111-8ad4-44ad-d0ed-5513df3408a3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenizing the entire dataset\n"
      ],
      "metadata": {
        "id": "LGy_GaAQWAzu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(batch):\n",
        "  return tokenizer(batch[\"text\"], truncation=True)"
      ],
      "metadata": {
        "id": "zEF4qm7jV6Yg"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# How to work tokenizer on our some data:\n",
        "print(tokenize(emotions[\"train\"][:2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBcbwAeYH3Ne",
        "outputId": "fd8973bf-04f0-49f1-d97f-56ce476f1d9b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[101, 1045, 2134, 2102, 2514, 26608, 102], [101, 1045, 2064, 2175, 2013, 3110, 2061, 20625, 2000, 2061, 9636, 17772, 2074, 2013, 2108, 2105, 2619, 2040, 14977, 1998, 2003, 8300, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "f3d2a2d594ee48fda6fd057c389573de",
            "3fe12cbfc7e54cbca6aa06a70f5c9423",
            "c401c0fda2eb4d5285f8f36922e80bed",
            "3a28922633494f8eb10d43f319291f7f",
            "dfa2e5db967c4b67bfdf1359c269fa60",
            "7553a8f6cd3b43489f6b505af98213bd",
            "58a47db7b6b34e649ff08a3f10d1ee3f",
            "07ae55f232b841bc9a00c67e1444c21f",
            "86600f1f05854b68ae82722040022500",
            "cc5aee8bf8014dc68a3cbe516e567ad2",
            "efc3739372f1497c818854c586387d86",
            "cc71628c61b64d4cbcbf5dbf3ef4c61d",
            "1b2e68101cb243bda5a42259ead20cce",
            "5fd2cf7e314a46e4aa8661329d735a0b",
            "eba96240cfca4923b4bc410aef9f1015",
            "bb1d55aaf65342aeb8b0bf62e7212cf4",
            "ba2b314ced014dc7bfe1abf4bb874d77"
          ]
        },
        "id": "LzZxEfYKIHNU",
        "outputId": "c7ed4fec-1847-4f43-b4ee-13fbcce2fb07"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "558055bf56504d749148007cd1da0d53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42034b12d510469384a001bbd8fc70b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58a47db7b6b34e649ff08a3f10d1ee3f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Padding\n"
      ],
      "metadata": {
        "id": "cgr74KdZIWqZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer = tokenizer)"
      ],
      "metadata": {
        "id": "rBxb-CQuITGA"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's take a look at the columns of the dataset\n",
        "emotions_encoded[\"train\"].column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZH6JRi0qIsGV",
        "outputId": "164f52bb-cc9b-44e0-dd79-4c4227aed5ba"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label', 'input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>6. Model Training   </div></b>"
      ],
      "metadata": {
        "id": "O6LgnfAlJNFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = 6\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_ckpt, num_labels = num_labels).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "a2db1b7c976540daa78606f7d30483d1",
            "f2ded7e325084df895ebe00b966c4cd8",
            "4f8ab5b98d3d4a66955dedb7a914b51e",
            "29095ca71bcd4f7e9e8a4306d8d93c50",
            "594989cb07c8452188b1ad4f46265d62",
            "65e520aebe62425ea8097a7e6ee26567",
            "8b2a7b9f923c4d45ba4e6956a27ea084",
            "7892689cef9a4d2a8054a3cdf445eec4",
            "30a284107ae645aaa4b64e9c2121ec49",
            "0da0337032474ec993263dd7b6cb12af",
            "19232e8535f64c52a404ad77143f8df2"
          ]
        },
        "id": "KZhcBI0SJKXV",
        "outputId": "673c4e1a-f922-4286-a8b3-e297c9b6edbc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2db1b7c976540daa78606f7d30483d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "DjPEhI5lKPQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qU evaluate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C97b7SZEKLi0",
        "outputId": "e7fe861e-301f-4301-f46d-271cc855e8f4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "import numpy as np\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")\n",
        "\n",
        "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9d2b3fb8812d44b998b57bfad1d8ff3c",
            "f06cfaae4cf94a63a8b45179cc406749",
            "c796782e89e2452b871d41a5feae7076",
            "59181089f88d43f98b4782a90dcc6bfc",
            "53a98dea7c2543d88c30d7550f244cb4",
            "e78b702cdf534f289b6bb556584a3837",
            "8d299b07184f475e8c11d0f2af1f4825",
            "03aa50cec47c49d2a12973df241d54ee",
            "fdc589412f1944c0a82e2371b48b6ea1",
            "69b7a774b78e42a886f3aa097d47c14f",
            "6950bdc99eb441299c15b981ce7d0a27"
          ]
        },
        "id": "WIqhL8unKScn",
        "outputId": "008640a6-9de9-41d3-892d-23d3ae93a2f6"
      },
      "execution_count": 40,
      "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": "9d2b3fb8812d44b998b57bfad1d8ff3c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logging to HuggingFace\n"
      ],
      "metadata": {
        "id": "l7zX_3nHLGVJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "e4a30109e3c1496ab685a68c86aca816",
            "f22a8c3be4ea4568985f2cb01a0d09fe",
            "d3ddc99bd1f544f4a26b994f3dfdd31a",
            "ec2459f41d5d4331b8d82d907f1418d6",
            "f1327015f81c47639ac726750b67920a",
            "e309633f4bd1474a87c4342bc0d41709",
            "19bcc600dc894f4aa831f54555c1fc40",
            "8b2c95b7ab46455185b778ded891267d",
            "7ba615e5ca7d47948c5a705534b81142",
            "1d36548aa7bf4f46a3bb544a0bd91a36",
            "31f7957eac784a4fb3e217d401984ca0",
            "ef37f5dfa9c8434fb7997f002b28c007",
            "d562950cdae841639353d56aa2d94edf",
            "81c77a718f2c4466b6037b459d786d12",
            "dcde2582e0b849be8772126def0358a6",
            "cec4c1741e354fcda0ba300ca68054d8",
            "e311b49d56e841e19c4167a3ec65160f",
            "19f37fbeb57146039b04fc2d9e0370f9",
            "eb87e028d7b2480bb01c6dbe64191e54",
            "e3043e15cdb2483ab7e86f5c814f8a15",
            "e611afcfb5e44e7eab8bbc89be03d65a",
            "d0c9997a09ed4470837b82f4e5222d81",
            "288691070688473ca24e3a33c33a5f91",
            "87f835da161e40649d1276daa7e9e6b8",
            "b4367cde54344505b9450e16a115125a",
            "8d9b5a2db6a5476ab2da794ff655bfa8",
            "f502e11ddd89415eae8ea0f5c6b2e14b",
            "6d25ced0f43245ababa1c486fffa2bca",
            "c7a261b2685549ada22886b0d6fd9dbf",
            "b8ebe24147ac4befa76a696117753e9c",
            "01ee7d6503704b99ab20b936d55ace70",
            "ae1356ee7b754d38be48ac589e8fe75b"
          ]
        },
        "id": "WAtNnC1NLDHU",
        "outputId": "24ea811f-1974-4c90-8d70-a39908b465eb"
      },
      "execution_count": 41,
      "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": "e4a30109e3c1496ab685a68c86aca816"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setting Training Arguments\n"
      ],
      "metadata": {
        "id": "is9SHPNsMDys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "        output_dir=\"distilbert-emotion\",\n",
        "        num_train_epochs=2,\n",
        "        per_device_train_batch_size=64,\n",
        "        per_device_eval_batch_size=64,\n",
        "        weight_decay=0.01,\n",
        "        evaluation_strategy=\"epoch\",\n",
        "        save_strategy=\"epoch\",\n",
        "        load_best_model_at_end=True,\n",
        "        push_to_hub = True,\n",
        "        report_to = \"none\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHAH4wyPLLSx",
        "outputId": "62f0d60c-bef0-45cf-bf09-38e046f69a21"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=emotions_encoded[\"train\"],\n",
        "    eval_dataset=emotions_encoded[\"validation\"],\n",
        "    tokenizer=tokenizer,\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "wVvAVA3kNAaO"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "Bfdv03WxN162",
        "outputId": "18b22b31-e16c-40ab-93c7-bae9e7771a34"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 02:49, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.180811</td>\n",
              "      <td>0.927000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.334200</td>\n",
              "      <td>0.134757</td>\n",
              "      <td>0.941000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=0.33420654296875, metrics={'train_runtime': 172.4966, 'train_samples_per_second': 185.511, 'train_steps_per_second': 2.899, 'total_flos': 467179604923392.0, 'train_loss': 0.33420654296875, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>7. Model Evaluation   </div></b>"
      ],
      "metadata": {
        "id": "IDFIWyfXOFIs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting the validation dataset\n"
      ],
      "metadata": {
        "id": "gcsh-1A0OQSU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "preds_output = trainer.predict(emotions_encoded[\"validation\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "FlbpBL6oN5GJ",
        "outputId": "e0837c42-d2cd-4796-a94d-c07b9b572478"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_output.metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ygul69mmOot9",
        "outputId": "afa394ce-28b5-496e-e683-a081fbb35852"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.13475705683231354,\n",
              " 'test_accuracy': 0.941,\n",
              " 'test_runtime': 3.227,\n",
              " 'test_samples_per_second': 619.778,\n",
              " 'test_steps_per_second': 9.916}"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matrix\n"
      ],
      "metadata": {
        "id": "FggT6cG6Ot6z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "\n",
        "y_preds = np.argmax(preds_output.predictions, axis=1)\n",
        "\n",
        "def plot_confusion_matrix(y_preds, y_true, labels):\n",
        "    cm = confusion_matrix(y_true, y_preds, normalize = \"true\")\n",
        "    fig, ax = plt.subplots(figsize=(6,6))\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix = cm,\n",
        "                                 display_labels=labels)\n",
        "    disp.plot(cmap=\"Blues\", values_format = \".2f\", ax = ax,\n",
        "             colorbar=False)\n",
        "    plt.title(\"Normalized confusion matrix\")\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "OX7RzWqtOqDr"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_valid = np.array(emotions_encoded[\"validation\"][\"label\"])\n",
        "labels = emotions[\"train\"].features[\"label\"].names\n"
      ],
      "metadata": {
        "id": "sSSQQ8iCQLv_"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "702v7bg0QiI_",
        "outputId": "db62c016-6207-415b-bb3e-a5f02fbb1b90"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(commit_message=\"Training completed!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "_L1nofNpQqDa",
        "outputId": "c4350e77-c5ee-4c24-e927-4b7eeac3a893"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/RidaDogrul/distilbert-emotion/commit/97d5f4d0b71739d3fff57ec46e1154043a520796', commit_message='Training completed!', commit_description='', oid='97d5f4d0b71739d3fff57ec46e1154043a520796', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <b><div style='padding:15px;background-color:#850E35;color:white;border-radius:2px;font-size:110%;text-align: center'>8. Model Prediction   </div></b>"
      ],
      "metadata": {
        "id": "h_lS3sJrRJ3q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "model_id =  \"RidaDogrul/distilbert-emotion\"\n",
        "classifier = pipeline(\"text-classification\", model= model_id)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226,
          "referenced_widgets": [
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            "b2003e42997e42639ab92cca8fdbf45f",
            "dcc3fadc926744e889aea6b8c48a8691"
          ]
        },
        "id": "gs-HIH6mREUb",
        "outputId": "da0c6c0e-10a9-4ea0-e312-bea5efe824c8"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/883 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8aa0c52f4ab41e5a5006ecca595bc84"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "284c4c7e5edb4df6a4430dc7cf156f17"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da1a36ff324f434892a8aad5046fbdef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c20775384cf44719588cf8c28b86ab6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9e63068937343dd830a9ceb73674f6d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6180332012224efb952cfb725c4f46f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "custom_text=\"I watched a movie yesterday. It was really good.\""
      ],
      "metadata": {
        "id": "ywmcfMsDRfgH"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds=classifier(custom_text, return_all_scores=True)\n",
        "preds_df = pd.DataFrame(preds[0])\n",
        "plt.bar(labels, 100*preds_df[\"score\"])\n",
        "plt.title(f'\"{custom_text}\"')\n",
        "plt.ylabel(\"Class probability (%)\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "MgD_2OeGRm08",
        "outputId": "71540ef4-adc8-4eb1-ec1f-3278ad9b9f21"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:104: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qHuR3AfyTmEJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}