Text_Classification_with_transformers.ipynb

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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alaeddinehamroun/Natural-Language-Processing/blob/main/Text_Classification_with_transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets transformers[torch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrYAwhehZJ5p",
        "outputId": "6bdde08b-d82a-4d94-ec3e-7abda2d27a73"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.13.1)\n",
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.6)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.27.1)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.65.0)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.2.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.14)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.4.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.15.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.12.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.3.1)\n",
            "Requirement already satisfied: torch!=1.12.0,>=1.9 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.0.1+cu118)\n",
            "Requirement already satisfied: accelerate>=0.20.2 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.20.3)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.2->transformers[torch]) (5.9.5)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.0.12)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.12.0,>=1.9->transformers[torch]) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch!=1.12.0,>=1.9->transformers[torch]) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch!=1.12.0,>=1.9->transformers[torch]) (16.0.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.9->transformers[torch]) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.9->transformers[torch]) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBXxQb4eYSdw",
        "outputId": "ce083830-8689-4001-b26a-c8bdf8bfe4f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 44516 datasets currently available on the Hub\n",
            "The first 10 are: ['acronym_identification', 'ade_corpus_v2', 'adversarial_qa', 'aeslc', 'afrikaans_ner_corpus', 'ag_news', 'ai2_arc', 'air_dialogue', 'ajgt_twitter_ar', 'allegro_reviews']\n"
          ]
        }
      ],
      "source": [
        "from datasets import list_datasets\n",
        "\n",
        "all_datasets = list_datasets()\n",
        "\n",
        "print(f\"There are {len(all_datasets)} datasets currently available on the Hub\")\n",
        "print(f\"The first 10 are: {all_datasets[:10]}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "emotions = load_dataset(\"emotion\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "ddf3f40c8c184d07a46472a9569f6038",
            "ed9f53ed87d44b6c9592833b991e215f",
            "9f7e689d91d145e7aa957aa3f7a8ed9e",
            "0045ac011379497eb168aec0459e96f7",
            "baeebf034a7b464eb9d34f363fb0b607",
            "3f907f4be7d94119b23088c1564c1acb",
            "751214d3e5d0497f8d5afede5f94874d",
            "a2ce876dfc4147afa07b75e681edac49",
            "476c7a7d4c014386a042165a4880e5c3",
            "755bb0e5e8e04c3b99bd60ce63d9edbf",
            "4df989e9dbf541f49db4183bf55afe01"
          ]
        },
        "id": "oGxE_NpPZTQf",
        "outputId": "9b6a2c5c-3c90-46e1-8aba-df4e3fa7f6d7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n",
            "WARNING:datasets.builder:Found cached dataset emotion (/root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddf3f40c8c184d07a46472a9569f6038"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSn-5uFTZdIi",
        "outputId": "c9e0ee53-5215-4119-c2b5-fc02e0ad7278"
      },
      "execution_count": 10,
      "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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = emotions[\"train\"]\n",
        "train_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uiEFAU-2Ze7p",
        "outputId": "784753ad-54c6-44be-99e4-2569dc00da66"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrAHJFqjZmGb",
        "outputId": "8590d9f3-2e8b-4537-a8fc-4395fa9a0af5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfiRCHD3ZsF-",
        "outputId": "97219b16-22db-4737-bcc2-4adc9d8c50a8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'i didnt feel humiliated', 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds.column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3QkQiumZw_U",
        "outputId": "475c8aec-e7dc-4d23-ae73-e2e28d138cf2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_ds.features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gg-GYtk5Z-HX",
        "outputId": "86d180d4-7356-4501-c95f-66d802cd5d58"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_ds[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4rV56-1aQzE",
        "outputId": "628f39bd-a619-4b56-ee5e-d0af5f3a55ef"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'text': ['i didnt feel humiliated', 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake', 'im grabbing a minute to post i feel greedy wrong', 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property', 'i am feeling grouchy'], 'label': [0, 0, 3, 2, 3]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_ds[\"text\"][:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHzDDMGoaTxa",
        "outputId": "3af21351-33b0-4de8-bbe4-a9993a03c737"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i didnt feel humiliated', 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake', 'im grabbing a minute to post i feel greedy wrong', 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property', 'i am feeling grouchy']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# From Dataset to DataFrame\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "emotions.set_format(type=\"pandas\")\n",
        "df = emotions[\"train\"][:]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pwF3nFWLbTGZ",
        "outputId": "dc3c203a-e122-4128-d521-cec6804bc3a9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                                text  label\n",
              "0                            i didnt feel humiliated      0\n",
              "1  i can go from feeling so hopeless to so damned...      0\n",
              "2   im grabbing a minute to post i feel greedy wrong      3\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1a3e2dc8-6ebd-4b5c-a9b1-80004cb2ada9 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-1a3e2dc8-6ebd-4b5c-a9b1-80004cb2ada9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LA1-Q3okbiCy",
        "outputId": "2eb1a468-775c-419f-dd6c-ccdd97bdec26"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3  i am ever feeling nostalgic about the fireplac...      2       love\n",
              "4                               i am feeling grouchy      3      anger"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cd942ac-32a9-4d19-a949-f83e60aee239\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>label_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>i didnt feel humiliated</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>i can go from feeling so hopeless to so damned...</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
              "      <td>3</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
              "      <td>2</td>\n",
              "      <td>love</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i am feeling grouchy</td>\n",
              "      <td>3</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cd942ac-32a9-4d19-a949-f83e60aee239')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3cd942ac-32a9-4d19-a949-f83e60aee239 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-3cd942ac-32a9-4d19-a949-f83e60aee239');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visaulizing the Class Distribution\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\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": "L7O9wGIhcBO5",
        "outputId": "3c75fe2c-8544-4913-d39d-2b548df75a78"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset is heavily imbalanced; the joy and sadness classes appear frequently, whereas love and surprise are about 5-10 times rarers.\n",
        "There are several ways to deal with imbalanced data, including:\n",
        "* Randomly oversample the minority class\n",
        "* Randomly undersample the majority class\n",
        "* Gather more labeled data from the underrepresented classes\n",
        "\n"
      ],
      "metadata": {
        "id": "aKnMtPkvcYIT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tweet lengths per emotion\n",
        "df[\"Words Per Tweet\"] = df[\"text\"].str.split().apply(len)\n",
        "df.boxplot(\"Words Per Tweet\", by=\"label_name\", grid=False, showfliers=False, color=\"black\")\n",
        "plt.suptitle(\"\")\n",
        "plt.xlabel(\"\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "TgsEloipdAgs",
        "outputId": "4e22453f-6d7e-431f-85e2-57481b536ec4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot we see that for each emotion, most tweets are around 15 words long\n",
        "and the longest tweets are well below DistilBERT’s maximum context size. Texts that\n",
        "are longer than a model’s context size need to be truncated, which can lead to a loss in\n",
        "performance if the truncated text contains crucial information; in this case, it looks\n",
        "like that won’t be an issue."
      ],
      "metadata": {
        "id": "o-UM0T1gd31u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reset the output format of dataset\n",
        "emotions.reset_format()"
      ],
      "metadata": {
        "id": "1RpkHE0RdvqA"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transformer models like DistilBERT cannot receive raw strings as input; instead, they\n",
        "assume the text has been tokenized and encoded as numerical vectors."
      ],
      "metadata": {
        "id": "W-uae-ZZeLTa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenization\n",
        "\n",
        "# Technique 1: Character tokenization\n",
        "text = \"Tokenizing text is a core task of NLP.\"\n",
        "tokenized_text = list(text)\n",
        "print(tokenized_text)\n",
        "  # numericalization: This gives us a mapping from each character in our vocabulary to a unique integer\n",
        "token2idx = {ch: idx for idx, ch in enumerate(sorted(set(tokenized_text)))}\n",
        "print(token2idx)\n",
        "# We can now use token2idx to transform the tokenized text to a list of integers:\n",
        "input_ids = [token2idx[token] for token in tokenized_text]\n",
        "print(input_ids)\n",
        "# The last step is to convert input_ids to a 2D tensor of one-hot vectors. REASON: TO AVOID ORDERING\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "input_ids = torch.tensor(input_ids)\n",
        "one_hot_encodings = F.one_hot(input_ids, num_classes=len(token2idx))\n",
        "one_hot_encodings.shape\n",
        "\n",
        "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": "1acZTCHHeDtJ",
        "outputId": "796ad4f1-16a2-4a6a-c9a7-8227741aff05"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['T', 'o', 'k', 'e', 'n', 'i', 'z', 'i', 'n', 'g', ' ', 't', 'e', 'x', 't', ' ', 'i', 's', ' ', 'a', ' ', 'c', 'o', 'r', 'e', ' ', 't', 'a', 's', 'k', ' ', 'o', 'f', ' ', 'N', 'L', 'P', '.']\n",
            "{' ': 0, '.': 1, 'L': 2, 'N': 3, 'P': 4, 'T': 5, 'a': 6, 'c': 7, 'e': 8, 'f': 9, 'g': 10, 'i': 11, 'k': 12, 'n': 13, 'o': 14, 'r': 15, 's': 16, 't': 17, 'x': 18, 'z': 19}\n",
            "[5, 14, 12, 8, 13, 11, 19, 11, 13, 10, 0, 17, 8, 18, 17, 0, 11, 16, 0, 6, 0, 7, 14, 15, 8, 0, 17, 6, 16, 12, 0, 14, 9, 0, 3, 2, 4, 1]\n",
            "Token: T\n",
            "Tensor index: 5\n",
            "One-hot: tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Technique 2: Word Tokenization\n",
        "tokenized_text = text.split()\n",
        "print(tokenized_text)\n",
        "# we can already see one potential problem with this\n",
        "# tokenization scheme: punctuation is not accounted for, so NLP. is treated as a single\n",
        "# token. Given that words can include declinations, conjugations, or misspellings, the\n",
        "# size of the vocabulary can easily grow into the millions!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvMlU9BJevW2",
        "outputId": "9e060399-f924-4e88-c9db-a88b91894f70"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Tokenizing', 'text', 'is', 'a', 'core', 'task', 'of', 'NLP.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Technique 3: subword tokenization\n"
      ],
      "metadata": {
        "id": "Sg3Wb6w7h3Eh"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The basic idea behind subword tokenization is to combine the best aspects of charac‐\n",
        "ter and word tokenization. On the one hand, we want to split rare words into smaller\n",
        "units to allow the model to deal with complex words and misspellings. On the other\n",
        "hand, we want to keep frequent words as unique entities so that we can keep the\n",
        "length of our inputs to a manageable size. The main distinguishing feature of\n",
        "subword tokenization (as well as word tokenization) is that it is learned from the pre‐\n",
        "training corpus using a mix of statistical rules and algorithms.\n",
        "There are several subword tokenization algorithms that are commonly used in NLP, but let’s start with WordPiece,5 which is used by the BERT and DistilBERT tokenizers."
      ],
      "metadata": {
        "id": "MRWL_TigiGgl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the tokenizer for DistilBERT\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ],
      "metadata": {
        "id": "Lt1x45OQiHLy"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_text = tokenizer(text)\n",
        "print(encoded_text)\n",
        "# Convert back into tokens\n",
        "tokens = tokenizer.convert_ids_to_tokens(encoded_text.input_ids)\n",
        "print(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R90HAWUxi-gM",
        "outputId": "8bffd2d1-3ec5-404f-b7bb-4cf0ffa8e991"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 19204, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
            "['[CLS]', 'token', '##izing', 'text', 'is', 'a', 'core', 'task', 'of', 'nl', '##p', '.', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can observe three things here. First, some special [CLS] and [SEP] tokens have\n",
        "# been added to the start and end of the sequence. These tokens differ from model to\n",
        "# model, but their main role is to indicate the start and end of a sequence. Second, the\n",
        "# tokens have each been lowercased, which is a feature of this particular checkpoint.\n",
        "# Finally, we can see that “tokenizing” and “NLP” have been split into two tokens,\n",
        "# which makes sense since they are not common words. The ## prefix in ##izing and\n",
        "# ##p means that the preceding string is not whitespace; any token with this prefix\n",
        "# should be merged with the previous token when you convert the tokens back to a\n",
        "# string."
      ],
      "metadata": {
        "id": "3UslKIEFjYO-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting tokens to string\n",
        "print(tokenizer.convert_tokens_to_string(tokens))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AJo73xVkIb8",
        "outputId": "e942b736-44cf-4be2-8e0e-58be1fc48033"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] tokenizing text is a core task of nlp. [SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Other infos about the tokenizer\n",
        "print(tokenizer.vocab_size)\n",
        "print(tokenizer.model_max_length)\n",
        "print(tokenizer.model_input_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8qB9_LDnkVD_",
        "outputId": "f15ec9af-fb1e-4048-b1b9-3e1ec9064bef"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30522\n",
            "512\n",
            "['input_ids', 'attention_mask']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When using pretrained models, it is really important to make sure\n",
        "that you use the same tokenizer that the model was trained with.\n",
        "From the model’s perspective, switching the tokenizer is like shuf‐\n",
        "fling the vocabulary. If everyone around you started swapping\n",
        "random words like “house” for “cat,” you’d have a hard time under‐\n",
        "standing what was going on too!"
      ],
      "metadata": {
        "id": "BGiivROIksyN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizing thie whole dataset\n",
        "def tokenize(batch):\n",
        "  return tokenizer(batch[\"text\"], padding=True, truncation=True)"
      ],
      "metadata": {
        "id": "_Kdj5kQ_ktcg"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Passing a batch of two examples from the training set:\n",
        "print(tokenize(emotions[\"train\"][:2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxD3evCulANI",
        "outputId": "b1bc5734-48fa-4b38-8b50-f09de4c7c273"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[101, 1045, 2134, 2102, 2514, 26608, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "The attention mask allows\n",
        "the model to ignore the padded parts of the input."
      ],
      "metadata": {
        "id": "-sQHhobdlW4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizing the whole dataset\n",
        "emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LF1PWLTOlg_-",
        "outputId": "01a452db-a4c5-48bd-b484-0b45220892f4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-16d106b5ce4bd091.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-41aa898cbdc8c4e8.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-a77e7ff5386e972f.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(emotions_encoded[\"train\"].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DAF8mMMl3xP",
        "outputId": "2a74535a-1edc-495d-fe5d-1f927f3770ac"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['text', 'label', 'input_ids', 'attention_mask']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training a Text Classifier"
      ],
      "metadata": {
        "id": "DpjuP3GYo3rb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two options:\n",
        "* Feature extraction: We use the hidden states as features and just train a classifier on them, without modifying the pretrained model.\n",
        "* Fine-tuning: We train the whole model end-to-end, which also updates the parameters of the pretrained model."
      ],
      "metadata": {
        "id": "qp9Kt1vopUaV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Option one: using pretrained models\n",
        "from transformers import AutoModel\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = AutoModel.from_pretrained(model_ckpt).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTL_TUyFmCIh",
        "outputId": "13d64f30-22b9-4196-f64a-2616cf1bff51"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting the last hidden states\n",
        "text = \"this is a test\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\")\n",
        "print(f\"Input tensor shape: {inputs['input_ids'].size()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUWxzsrFI9Sx",
        "outputId": "3428f644-304f-450b-a693-db8b966fa3d6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input tensor shape: torch.Size([1, 6])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = {k:v.to(device) for k, v in inputs.items()}\n",
        "with torch.no_grad(): # to disable the automatic calculation of the gradient\n",
        "  outputs = model(**inputs)\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4DuXjQJJVWp",
        "outputId": "6adf62aa-ff75-4002-9b75-3f3fd615722d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BaseModelOutput(last_hidden_state=tensor([[[-0.1565, -0.1862,  0.0528,  ..., -0.1188,  0.0662,  0.5470],\n",
            "         [-0.3575, -0.6484, -0.0618,  ..., -0.3040,  0.3508,  0.5221],\n",
            "         [-0.2772, -0.4459,  0.1818,  ..., -0.0948, -0.0076,  0.9958],\n",
            "         [-0.2841, -0.3917,  0.3753,  ..., -0.2151, -0.1173,  1.0526],\n",
            "         [ 0.2661, -0.5094, -0.3180,  ..., -0.4203,  0.0144, -0.2149],\n",
            "         [ 0.9441,  0.0112, -0.4714,  ...,  0.1439, -0.7288, -0.1619]]],\n",
            "       device='cuda:0'), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs.last_hidden_state.size()\n",
        "# returns [batch_size, n_tokens, hidden_dim]: 768-dimensional vector is\n",
        "# returned for each of the 6 input tokens."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zg2m4LkfKNwO",
        "outputId": "e7a3c322-e08e-46d8-df27-07a182381b21"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 6, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For classification tasks, it is common practice to just use the hidden state associated with the [CLS] token\n",
        "# as the input feature\n",
        "outputs.last_hidden_state[:,0].size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6IKJcEYKSyS",
        "outputId": "4f855511-ba53-4fb4-c442-fce402597bcf"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting the last hidden state for each string, in batches\n",
        "def extract_hidden_states(batch):\n",
        "  # Place model inputs on the GPU\n",
        "  inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names}\n",
        "\n",
        "  # Extract last hidden states\n",
        "  with torch.no_grad():\n",
        "    last_hidden_state = model(**inputs).last_hidden_state\n",
        "\n",
        "  # Return vector for [CLS] token\n",
        "  return {\"hidden_state\": last_hidden_state[:, 0].cpu().numpy()}\n"
      ],
      "metadata": {
        "id": "LB5Lq3SpK2lS"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])\n",
        "emotions_hidden = emotions_encoded.map(extract_hidden_states, batched=True)\n",
        "emotions_hidden[\"train\"].column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "d9d92d33005741458696f96fb02276bc",
            "40cc9effda8048e2a00e09b04c8c7291",
            "6b80fcceb4634740a662d508cc358293",
            "e16fd7b8072547908c6cb874646cc8e5",
            "278ed0fdfb1c4547a0c64c4c4a5cf358",
            "905ae1737f8d47b797d8852645e9f0dc",
            "e2ecbe94ec284178b8eec9ee1bc40829",
            "5c1b1fb44b22476685a1375510bc2ac6",
            "46fc83987c8c4646b9d049d2b6bb1be7",
            "b060105dd3474396b77ad83f19e0f028",
            "4bc1f289931f4bf2a82400b41471c912",
            "f62fd8c3de41436581418c8da4a27c25",
            "60b35ac1a82343b2adebfb89b25c0522",
            "494bd55c3aa24ee8accf651a8581992f",
            "9e9e54b5d76a4a4e901485aa10aa34a3",
            "61b4604a61a94ac0a8e154a9d98acafa",
            "84f7e7a92f5e4bc1a2f725fe1a6f6dc4",
            "0494be9b734a46f3b8246dea7572e446",
            "99497c2aa02242b48c89d4ef90b5b9f0",
            "2f195cbe5b424cdc8b4863f611c4d02d",
            "c97692b7742d410d836c37c12ea6fee0",
            "a904f774d6d04885924d3585dc790d51",
            "7d91285c5ed3463ba266e2137459b7ee",
            "38f2623899694626a729df8ae358e8b4",
            "7c0eaf073849417c9dcbc99457dde06c",
            "6d60c466747644b5a09bb8a09cd5fe58",
            "c091f158f3654b1f9caa99b5b2c2b687",
            "4b5bafc8f96d44808fd5b4f339bf82c8",
            "c5774ca8abfd42d990f2dc37c298302c",
            "f0e4447e68f44a00acb36df7d55e5b3b",
            "e328fffd8f2245ba9234708090e644df",
            "dc77f779e3264cd485216c63d4f7ce95",
            "f0490910ad034504952b7668f5c6e780"
          ]
        },
        "id": "yeQApPS1Lhyx",
        "outputId": "f5c7532e-3acf-4c5e-f541-81ef462a79a2"
      },
      "execution_count": 39,
      "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": "d9d92d33005741458696f96fb02276bc"
            }
          },
          "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": "f62fd8c3de41436581418c8da4a27c25"
            }
          },
          "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": "7d91285c5ed3463ba266e2137459b7ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label', 'input_ids', 'attention_mask', 'hidden_state']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have the hidden states associated with each tweet, the next step is to\n",
        "train a classifier on them."
      ],
      "metadata": {
        "id": "yyl3wap4OsdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using the hidden states as input features and the labels as targets\n",
        "import numpy as np\n",
        "\n",
        "X_train = np.array(emotions_hidden[\"train\"][\"hidden_state\"])\n",
        "X_valid = np.array(emotions_hidden[\"validation\"][\"hidden_state\"])\n",
        "y_train = np.array(emotions_hidden[\"train\"][\"label\"])\n",
        "y_valid = np.array(emotions_hidden[\"validation\"][\"label\"])\n",
        "\n",
        "X_train.shape, X_valid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWQOxww4MDSu",
        "outputId": "55a59cd9-96cc-4805-90e1-9e4df607d9b9"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16000, 768), (2000, 768))"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install umap-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iFrb6-ARCI-",
        "outputId": "f55dabf5-5617-4062-8a09-c81a14a8f843"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: umap-learn in /usr/local/lib/python3.10/dist-packages (0.5.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.10.1)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (0.56.4)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (0.5.10)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn) (4.65.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.49->umap-learn) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.49->umap-learn) (67.7.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pynndescent>=0.5->umap-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the training set\n",
        "# We'll use the UMAP algorithm to project the vectors down to 2D.\n",
        "# Since UMAP works best when the features are scaled to lie in the [0,1] interval,\n",
        "# we'll first apply a MinMaxScaler\n",
        "from umap import UMAP\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Scale features to [0,1] range\n",
        "X_scaled = MinMaxScaler().fit_transform(X_train)\n",
        "# Initialize and fit UMAP\n",
        "mapper = UMAP(n_components=2, metric=\"cosine\").fit(X_scaled)\n",
        "# Create a DataFrame of 2D embeddings\n",
        "df_emb = pd.DataFrame(mapper.embedding_, columns=[\"X\", \"Y\"])\n",
        "df_emb[\"label\"] = y_train\n",
        "df_emb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QmJ4-2cuPa9-",
        "outputId": "0b23583b-b262-4dbb-ed75-f87af06d836e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          X         Y  label\n",
              "0  4.579472  6.403979      0\n",
              "1 -2.730236  5.972702      0\n",
              "2  5.473777  2.760597      3\n",
              "3 -2.366809  3.704432      2\n",
              "4 -3.167409  4.033630      3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-531b8864-87dc-478a-a36a-1c8d48c94182\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.579472</td>\n",
              "      <td>6.403979</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.730236</td>\n",
              "      <td>5.972702</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.473777</td>\n",
              "      <td>2.760597</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.366809</td>\n",
              "      <td>3.704432</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-3.167409</td>\n",
              "      <td>4.033630</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-531b8864-87dc-478a-a36a-1c8d48c94182')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-531b8864-87dc-478a-a36a-1c8d48c94182 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-531b8864-87dc-478a-a36a-1c8d48c94182');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 3, figsize=(7, 5))\n",
        "axes = axes.flatten()\n",
        "cmaps = [\"Greys\", \"Blues\", \"Oranges\", \"Reds\", \"Purples\", \"Greens\"]\n",
        "labels = emotions[\"train\"].features[\"label\"].names\n",
        "\n",
        "for i, (label, cmap) in enumerate(zip(labels, cmaps)):\n",
        "  df_emb_sub = df_emb.query(f\"label == {i}\")\n",
        "  axes[i].hexbin(df_emb_sub[\"X\"], df_emb_sub[\"Y\"], cmap=cmap, gridsize=20, linewidths=(0,))\n",
        "  axes[i].set_title(label)\n",
        "  axes[i].set_xticks([]), axes[i].set_yticks([])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "J8eNS0G4RYi1",
        "outputId": "d9eddc75-35ea-4af3-b3b4-1fad1d894c64"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this plot we can see some clear patterns: the negative feelings such as sadness,\n",
        "anger, and fear all occupy similar regions with slightly varying distributions. On the\n",
        "other hand, joy and love are well separated from the negative emotions and also\n",
        "share a similar space. Finally, surprise is scattered all over the place. Although we\n",
        "may have hoped for some separation, this is in no way guaranteed since the model\n",
        "was not trained to know the difference between these emotions. It only learned them\n",
        "implicitly by guessing the masked words in texts."
      ],
      "metadata": {
        "id": "kYrqYb2HS5bt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# We increase 'max_iter' to guarantee convergence\n",
        "lr_clf = LogisticRegression(max_iter=3000)\n",
        "lr_clf.fit(X_train, y_train)\n",
        "lr_clf.score(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpPb--r9S5Ux",
        "outputId": "d133f5a7-ab08-4cd9-af93-8f6da2c59d74"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.633"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the accuracy, it might appear that our model is just a bit better than ran‐\n",
        "dom—but since we are dealing with an unbalanced multiclass dataset, it’s actually sig‐\n",
        "nificantly better. We can examine whether our model is any good by comparing it\n",
        "against a simple baseline. In Scikit-learn there is a DummyClassifier that can be used\n",
        "to build a classifier with simple heuristics such as always choosing the majority class\n",
        "or always drawing a random class. In this case the best-performing heuristic is to\n",
        "always choose the most frequent class, which yields an accuracy of about 35%:"
      ],
      "metadata": {
        "id": "z31JE27_Tgkx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
        "dummy_clf.fit(X_train, y_train)\n",
        "dummy_clf.score(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "keIKGFoYThMi",
        "outputId": "b688e14a-1954-4fbc-fb7b-16b3c513f71d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.352"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, our simple classifier with DistilBERT embeddings is significantly better than our\n",
        "baseline. We can further investigate the performance of the model by looking at the\n",
        "confusion matrix of the classifier, which tells us the relationship between the true and\n",
        "predicted labels:"
      ],
      "metadata": {
        "id": "GL9xhUyuTzkS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "\n",
        "def plot_confusion_matrix(y_preds, y_true, labels):\n",
        "  cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
        "  fix, ax = plt.subplots(figsize=(6, 6))\n",
        "  disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "  disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
        "  plt.title(\"Normalized confusion matrix\")\n",
        "  plt.show()\n",
        "\n",
        "y_preds = lr_clf.predict(X_valid)\n",
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "SJ1u6vUqT0Gs",
        "outputId": "7d6250c6-5009-4137-ed86-ac316d3628e8"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that anger and fear are most often confused with sadness, which agrees\n",
        "with the observation we made when visualizing the embeddings. Also, love and\n",
        "surprise are frequently mistaken for joy."
      ],
      "metadata": {
        "id": "9zxtwZZyUwVu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next section we will explore the fine-tuning approach, which leads to superior\n",
        "classification performance. It is, however, important to note that doing this requires\n",
        "more computational resources, such as GPUs, that might not be available in your\n",
        "organization. In cases like these, a feature-based approach can be a good compromise\n",
        "between doing traditional machine learning and deep learning."
      ],
      "metadata": {
        "id": "rnXoX6RoU99k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Option two: Fine-Tuning Transformers\n",
        "# With the fine-tuning approach we do not use the hidden states as fixed features, but instead train them\n",
        "\n",
        "# Loading a pretrained model\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = 6\n",
        "model = (AutoModelForSequenceClassification\n",
        "         .from_pretrained(model_ckpt, num_labels=num_labels)\n",
        "         .to(device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrE20xy-U9FK",
        "outputId": "e9b07220-370c-498d-910e-1818c8562b03"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', '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": "code",
      "source": [
        "# Defining the performance metrics\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "def compute_metrics(pred):\n",
        "  labels = pred.label_ids\n",
        "  preds = pred.predictions.argmax(-1)\n",
        "  f1 = f1_score(labels, preds, average=\"weighted\")\n",
        "  acc = accuracy_score(labels, preds)\n",
        "\n",
        "  return {\"accuracy\": acc, \"f1\": f1}"
      ],
      "metadata": {
        "id": "1jHppjhhVrWA"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "# 1. Login to Hugging Face Hub. This will allow us to push our fine-tuned model to our account\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "5940f7c848154665963b13340cfca644",
            "1db8128a9d90490c9a49dbd3f2048d27",
            "a62c8749a9f14737bd80ee46d674e11a",
            "f20da09937394164b4d9b1aa89bff05a",
            "84a44c629f824e529b56548831789343",
            "2270de06e9fd47a9b70000b36474556a",
            "fd8cd0ee65a74376815d1203800e0ee0",
            "841410a1ddda43389cc321674d12236f",
            "2395e9cfe125451aa180c5c24208c2a4",
            "ff0778d5c9374279b5a8e732ec404a68",
            "491354273902498f9159107d85cca4ec",
            "efd8b3fcf9294a228df0571727de0c7c",
            "35d6ae68d3b14fdca858e33dd176029f",
            "dbf03a0cf9b94e59847bcfeae0020c06",
            "228be94099ff462fb81565c4cdb14e71",
            "28ab3868fbf64fbeac021be946079ef4",
            "782b5f67312c4d298fff026b6e612922",
            "5d8367631ebd46f3af0498caff93eac3",
            "be19fee92f9141ac81684254453f1920",
            "8e56f6f9066147a5bccce78f3e8e08fe",
            "63b2688a1ad449bfb1ec9d69303d746c",
            "189b54d789e444b19c0df0749566eb31",
            "979654b620f14faf8eb9b45902c154f7",
            "dabb1736c41b4aae95e29ad0cc77ad94",
            "8fe4e7442b114786b9f4fee8b4e8b011",
            "440102d0637d40939ca352bf0efc2714",
            "d3440c9a486e4e5e9d9b1fabf773eecc",
            "3b9d851572fa4458985ee58414c67a71",
            "28a004edd13c4583a7369509989ecbf7",
            "5dc0789fcb3d442aaea6c8599d513fcc",
            "c3a8ed833dce479e89356d743cb6c112",
            "3aa165da52a5443d868deda7623e5d98"
          ]
        },
        "id": "Oi6hR7DFWXT_",
        "outputId": "02b66639-bac1-4227-cec1-8e2b5aa89003"
      },
      "execution_count": 56,
      "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": "5940f7c848154665963b13340cfca644"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Define the training params\n",
        "# We use the TrainingArguments class. This class\n",
        "# stores a lot of information and gives you fine-grained control over the training and\n",
        "# evaluation. The most important argument to specify is output_dir, which is where\n",
        "# all the artifacts from training are stored.\n",
        "\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "batch_size = 64\n",
        "logging_steps = len(emotions_encoded[\"train\"]) // batch_size\n",
        "model_name = f\"{model_ckpt}-finetunde-emotion\"\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=model_name,\n",
        "    num_train_epochs=2,\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    disable_tqdm=False,\n",
        "    logging_steps=logging_steps,\n",
        "    push_to_hub=True,\n",
        "    log_level=\"error\")\n",
        "# Here we also set the batch size, learning rate, and number of epochs, and specify to\n",
        "# load the best model at the end of the training run."
      ],
      "metadata": {
        "id": "k6zTFR1QXKqz"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate and fine_tune our model with the Trainer\n",
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(model=model, args=training_args,\n",
        "compute_metrics=compute_metrics,\n",
        "train_dataset=emotions_encoded[\"train\"],\n",
        "eval_dataset=emotions_encoded[\"validation\"],\n",
        "tokenizer=tokenizer)\n",
        "trainer.train();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "UI3Krn26YZp-",
        "outputId": "4475660e-c2f1-48c1-c022-52c80375b57d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion into local empty directory.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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 04:01, 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",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.830400</td>\n",
              "      <td>0.326606</td>\n",
              "      <td>0.900500</td>\n",
              "      <td>0.897007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.253800</td>\n",
              "      <td>0.225067</td>\n",
              "      <td>0.921500</td>\n",
              "      <td>0.921737</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_output = trainer.predict(emotions_encoded[\"validation\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "QD9McRS1g9rY",
        "outputId": "15f3211a-8c9f-47e8-acfb-b25159c8c829"
      },
      "execution_count": 59,
      "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": "-pCtjc1jhEWI",
        "outputId": "8fcdf231-11e2-4a84-d7fc-0743005cd307"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.22506652772426605,\n",
              " 'test_accuracy': 0.9215,\n",
              " 'test_f1': 0.9217368692620141,\n",
              " 'test_runtime': 4.4127,\n",
              " 'test_samples_per_second': 453.232,\n",
              " 'test_steps_per_second': 7.252}"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = np.argmax(preds_output.predictions, axis=1)"
      ],
      "metadata": {
        "id": "AuT8K2cThG4c"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zsh2kjaxhMmG",
        "outputId": "3905a922-8f58-445f-8e43-5fa701d0024e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is much closer to the ideal diagonal confusion matrix. The love category is still\n",
        "often confused with joy, which seems natural. surprise is also frequently mistaken\n",
        "for joy, or confused with fear."
      ],
      "metadata": {
        "id": "qxazXQ8MhYHL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Error analysis\n",
        "\n",
        "from torch.nn.functional import cross_entropy\n",
        "\n",
        "def forward_pass_with_label(batch):\n",
        "  # Place all input tensors on the same device as the model\n",
        "  inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names}\n",
        "\n",
        "  with torch.no_grad():\n",
        "    output = model(**inputs)\n",
        "    pred_label = torch.argmax(output.logits, axis=-1)\n",
        "    loss = cross_entropy(output.logits, batch[\"label\"].to(device), reduction=\"none\")\n",
        "  # Place outputs on CPU for compatibility with other dataset columns\n",
        "  return {\"loss\": loss.cpu().numpy(),\n",
        "          \"predicted_label\": pred_label.cpu().numpy()}"
      ],
      "metadata": {
        "id": "SnpjsKe9h0dz"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the losses for all the samples\n",
        "\n",
        "# Convert our dataset back to PyTorch tensors\n",
        "emotions_encoded.set_format(\"torch\",\n",
        "columns=[\"input_ids\", \"attention_mask\", \"label\"])\n",
        "# Compute loss values\n",
        "emotions_encoded[\"validation\"] = emotions_encoded[\"validation\"].map(\n",
        "forward_pass_with_label, batched=True, batch_size=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "0f3ca457-3834-401e-aa17-b235371d21f0"
      },
      "execution_count": 64,
      "outputs": [
        {
          "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": "8224d55cb91440bba5acbd3f9a045246"
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    {
      "cell_type": "code",
      "source": [
        "emotions_encoded.set_format(\"pandas\")\n",
        "cols = [\"text\", \"label\", \"predicted_label\", \"loss\"]\n",
        "df_test = emotions_encoded[\"validation\"][:][cols]\n",
        "df_test[\"label\"] = df_test[\"label\"].apply(label_int2str)\n",
        "df_test[\"predicted_label\"] = (df_test[\"predicted_label\"]\n",
        ".apply(label_int2str))"
      ],
      "metadata": {
        "id": "SpkFzF3CjE9v"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Look at the data samples with the highest losses\n",
        "df_test.sort_values(\"loss\", ascending=False).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "dwk7J6mpjKb1",
        "outputId": "9e25ac21-8b33-43cc-9d2c-297a07a89690"
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      "execution_count": 66,
      "outputs": [
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              "                                                   text     label  \\\n",
              "1950  i as representative of everything thats wrong ...  surprise   \n",
              "882   i feel badly about reneging on my commitment t...      love   \n",
              "1801  i feel that he was being overshadowed by the s...      love   \n",
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              "1274  i am going to several holiday parties and i ca...       joy   \n",
              "765     i feel super awkward and out of place right now       joy   \n",
              "1509  i guess this is a memoir so it feels like that...       joy   \n",
              "1963  i called myself pro life and voted for perry w...       joy   \n",
              "1870  i guess i feel betrayed because i admired him ...       joy   \n",
              "318   i felt ashamed of these feelings and was scare...      fear   \n",
              "\n",
              "     predicted_label      loss  \n",
              "1950         sadness  5.954078  \n",
              "882          sadness  5.528685  \n",
              "1801         sadness  5.462533  \n",
              "465             fear  5.455673  \n",
              "1274         sadness  5.437659  \n",
              "765          sadness  5.259309  \n",
              "1509            fear  5.189106  \n",
              "1963         sadness  4.882952  \n",
              "1870         sadness  4.870529  \n",
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        "We can clearly see that the model predicted some of the labels incorrectly. On the\n",
        "other hand, it seems that there are quite a few examples with no clear class, which\n",
        "might be either mislabeled or require a new class altogether. In particular, joy seems\n",
        "to be mislabeled several times. With this information we can refine the dataset, which\n",
        "often can lead to as big a performance gain (or more) as having more data or larger\n",
        "models!"
      ],
      "metadata": {
        "id": "8C_9tLXsjhoA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When looking at the samples with the lowest losses, we observe that the model seems\n",
        "to be most confident when predicting the sadness class. Deep learning models are\n",
        "exceptionally good at finding and exploiting shortcuts to get to a prediction. For this\n",
        "reason, it is also worth investing time into looking at the examples that the model is\n",
        "most confident about, so that we can be confident that the model does not improp‐\n",
        "erly exploit certain features of the text."
      ],
      "metadata": {
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      "execution_count": 67,
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              "                                                   text    label  \\\n",
              "1873  i feel practically virtuous this month i have ...      joy   \n",
              "1638  i feel like if you shop smart you can still ad...      joy   \n",
              "19    i had lunch with an old friend and it was nice...      joy   \n",
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              "     predicted_label      loss  \n",
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              "131              joy  0.016498  \n",
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              "    }\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-d2c38843-b586-4679-88a3-f063752d32f6 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-d2c38843-b586-4679-88a3-f063752d32f6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving and sharing the model\n",
        "trainer.push_to_hub(commit_message=\"Training completed!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "u-mvRkF5mLQU",
        "outputId": "83afd86d-fbd6-4b88-96bf-aa4b17908598"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion\n",
            "   8fdae86..3216b5d  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion\n",
            "   8fdae86..3216b5d  main -> main\n",
            "\n",
            "To https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion\n",
            "   3216b5d..79fb4f1  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion\n",
            "   3216b5d..79fb4f1  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/alaeddinehamroun/distilbert-base-uncased-finetunde-emotion/commit/3216b5da0b6e509eda8509c72d13350e0fa08435'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the fine-tuned model to make predictions on new tweets\n",
        "\n",
        "from transformers import pipeline\n",
        "\n",
        "# Change `transformersbook` to your Hub username\n",
        "model_id = \"transformersbook/distilbert-base-uncased-finetuned-emotion\"\n",
        "classifier = pipeline(\"text-classification\", model=model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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        "id": "5PbK6oyxmjQN",
        "outputId": "bb2e617a-8276-4465-9b6e-8f112f79199c"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/872 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f4530bcd53f43a2887a2d61656d9db0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "918d2dd2911845a8bb3889a522aa3c5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/333 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b51972a31b34f86aecde912334babfe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26d4bd64ef63426e8056b196e1c45e8b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab12f17c70ca4f8e90bff550f5bbc773"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "custom_tweet = \"I saw a movie today and it was really good.\"\n",
        "preds = classifier(custom_tweet, return_all_scores=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-wadFI1m3mD",
        "outputId": "a99ecf84-9302-41e4-fbb9-360e34c46b4c"
      },
      "execution_count": 70,
      "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 funcionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_df = pd.DataFrame(preds[0])\n",
        "plt.bar(labels, 100 * preds_df[\"score\"], color='C0')\n",
        "plt.title(f'\"{custom_tweet}\"')\n",
        "plt.ylabel(\"Class probability (%)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "mzhlNB3Tm7Jf",
        "outputId": "a7dc01ce-1389-4c7d-892a-0944bdc0c76e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}