11. BERT/NLP10_3_Classification with Transformers.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8UkALHYsCoov"
      },
      "source": [
        "# Text Classification with Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJV6dGDhCoow"
      },
      "source": [
        " we'll tackle this a sentiment analysis use case using a variant of BERT called DistilBERT.footnote:[V. Sanh et al., [\"DistilBERT, a Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter\"](https://arxiv.org/abs/1910.01108), (2019).]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NaQDF0zCoow"
      },
      "source": [
        "## The Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-u6QxyHCoow"
      },
      "source": [
        "To build our emotion detector we'll use a great dataset from the paper\n",
        "* E. Saravia et al., \"CARER: Contextualized Affect Representations for Emotion Recognition,\" _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ (Oct–Nov 2018): 3687–3697, http://dx.doi.org/10.18653/v1/D18-1404.\n",
        "\n",
        "this dataset contains six basic emotions: anger, disgust, fear, joy, sadness, and surprise. Given a tweet, our task will be to train a model that can classify it into one of these emotions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBc6kk-BCoox"
      },
      "source": [
        "### A First Look at Hugging Face Datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VS9pgsfGCoox"
      },
      "source": [
        "First we download the dataset from the [Hugging Face Hub](https://huggingface.co/datasets) so let's load the `emotion` dataset with the `load_dataset()` function:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvSpQsdvC5uJ",
        "outputId": "7ac4bdae-06ee-4d52-805c-1d94072dee9d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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        }
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        "id": "wEOndkVKCoox",
        "outputId": "79b0eb8e-555e-49af-f30b-73dddca5dbb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1c01f3a59964faf9426ce3bff9c502f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64beebbe88e64934b6bd9cfe80d4ed0c"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_minor": 0,
              "model_id": "7d26307d6b9c463ca9f8509b5a25fb11"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4be183e8f2ab4d87bd5fd7149dd64ceb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72716f634ac24e68993e1cfbe6ada124"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c263ad818a642d3bdb1012f3ead63ab"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "emotions = load_dataset(\"emotion\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pVXtRDfCoox"
      },
      "source": [
        "If we look inside our `emotions` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69VOlbGnCoox",
        "outputId": "00170561-cd89-4197-99e0-5a0e0bc73a5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "emotions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwKmMd0nCoox"
      },
      "source": [
        "we see it is similar to a Python dictionary, with each key corresponding to a different split. And we can use the usual dictionary syntax to access an individual split:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKJkXuONCooy",
        "outputId": "38e6568d-70fe-4321-da10-fa1ce59e96a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train_ds = emotions[\"train\"]\n",
        "train_ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0nGLe71Cooy",
        "outputId": "fa0a9450-55ae-41b9-d151-46b1380bfbb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "len(train_ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t22Ken1xCooy",
        "outputId": "c233b822-438f-4f56-b845-aa2866fef80b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'i didnt feel humiliated', 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_ds[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOudB1WWCooy",
        "outputId": "9cc8960f-fd8d-4d63-f275-25141de824e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train_ds.column_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLW7UvNwCooy"
      },
      "source": [
        "We can see what data types are being used under the hood by accessing the `features` attribute of a `Dataset` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_CVGtwPCooy",
        "outputId": "9292febe-20c1-4797-e628-b4823c35856d"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(train_ds.features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pc9lTPBvCooy"
      },
      "source": [
        "In this case, the data type of the `text` column is `string`, while the `label` column is a special `ClassLabel` object that contains information about the class names and their mapping to integers.\n",
        "\n",
        "We can also access several rows with a slice:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zI5conluCooy",
        "outputId": "10853b56-a0c7-4885-81f7-7bddb8a898be"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(train_ds[:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XyZ0OKMCooy"
      },
      "source": [
        "Note that in this case, the dictionary values are now lists instead of individual elements. We can also get the full column by name:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egH-iVzaCooy",
        "outputId": "e22a9e0e-86fd-4dbc-dd39-ebf3070d41ed"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(train_ds[\"text\"][:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6CE8QZ3Cooz"
      },
      "source": [
        "### From Datasets to DataFrames"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bq41bEiCooz"
      },
      "source": [
        "Let's convert a `Dataset` object to a Pandas `DataFrame` with `set_format()` method that allows us to change the _output format_ of the `Dataset`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "szOD4lm0Cooz",
        "outputId": "77463c92-c42f-40a0-c8fb-2fab6c0dea83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "3  i am ever feeling nostalgic about the fireplac...      2\n",
              "4                               i am feeling grouchy      3"
            ],
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              "\n",
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              "        document.querySelector('#df-2ca1b2ff-4028-43c9-8cb0-558978bad940 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-2ca1b2ff-4028-43c9-8cb0-558978bad940');\n",
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              "          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",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 16000,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15969,\n        \"samples\": [\n          \"i feel rather imbicilic or at least complacent\",\n          \"i was in the bathroom i had sat down to pee it was to make me feel submissive again per instructions\",\n          \"i am thrilled with the way my skin and hair feel if you are like me you are skeptical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "emotions.set_format(type=\"pandas\")\n",
        "df = emotions[\"train\"][:]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GCTLL_9Cooz"
      },
      "source": [
        " so let's use the `int2str()` method of the `label` feature to create a new column in our `DataFrame` with the corresponding label names:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AL-EI5SFCooz",
        "outputId": "8598663b-6f00-4853-869c-ff144285e7b6"
      },
      "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"
            ],
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              "      <td>anger</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-987487b9-8eb3-4e75-ad8c-dc8849fb0ae3 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-987487b9-8eb3-4e75-ad8c-dc8849fb0ae3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dbc009d9-ab94-4387-b358-9b900fde8c5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dbc009d9-ab94-4387-b358-9b900fde8c5f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dbc009d9-ab94-4387-b358-9b900fde8c5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 16000,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15969,\n        \"samples\": [\n          \"i feel rather imbicilic or at least complacent\",\n          \"i was in the bathroom i had sat down to pee it was to make me feel submissive again per instructions\",\n          \"i am thrilled with the way my skin and hair feel if you are like me you are skeptical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"sadness\",\n          \"anger\",\n          \"joy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bA9nRIXLCooz"
      },
      "source": [
        "### Explore Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "YBpQs0wmCooz",
        "outputId": "c65c1e27-ee11-4b6c-a7ae-f76a4d677ecd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df[\"label_name\"].value_counts(ascending=True).plot.barh()\n",
        "plt.title(\"Frequency of Classes\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ggowcv_kCooz"
      },
      "source": [
        "In this case, we can see that the dataset is heavily imbalanced; the `joy` and `sadness` classes appear frequently, whereas `love` and `surprise` are about 5–10 times rarer. There are several ways to deal with imbalanced data, including:\n",
        "\n",
        "* Randomly oversample the minority class.\n",
        "* Randomly undersample the majority class.\n",
        "* Gather more labeled data from the underrepresented classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjM8lteUCooz"
      },
      "source": [
        "### How Long Are Our Tweets?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKue2HPfCooz"
      },
      "source": [
        "Transformer models have a maximum input sequence length that is referred to as the _maximum context size_.\n",
        "\n",
        "* DistilBERT, the maximum context size is 512 tokens https://huggingface.co/transformers/v2.10.0/model_doc/distilbert.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "0BSkelUBCooz",
        "outputId": "48fdbc53-caa5-4122-aef4-393ac11d6bfa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df[\"Words Per Tweet\"] = df[\"text\"].str.split().apply(len)\n",
        "df.boxplot(\"Words Per Tweet\", by=\"label_name\", grid=False, showfliers=False,\n",
        "           color=\"black\")\n",
        "plt.suptitle(\"\")\n",
        "plt.xlabel(\"\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvEGeubqCoo0"
      },
      "source": [
        "most tweets are around 15 words long and the longest tweets are well below DistilBERT's maximum context size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "BWUwED4WCoo0"
      },
      "outputs": [],
      "source": [
        "emotions.reset_format()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AplpoiZECoo0"
      },
      "source": [
        "## From Text to Tokens"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGBoQatlCoo0"
      },
      "source": [
        "we need to tokenized the text and vectorize them... what else!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2pZ6sHeCoo0"
      },
      "source": [
        "### Subword Tokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrI4717yCoo0"
      },
      "source": [
        "*  On the one hand, we want to split rare words into smaller units to allow the model to deal with complex words and misspellings.\n",
        "\n",
        "* On the other hand, we want to keep frequent words as unique entities so that we can keep the length of our inputs to a manageable size.\n",
        "\n",
        "BERT and DistilBert use a Wordpiece algorithm for subword tokenization - let see how it works (https://arxiv.org/pdf/1609.08144v2.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251,
          "referenced_widgets": [
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        },
        "id": "bDgX-V8TCoo0",
        "outputId": "407f9365-11f5-42b9-f764-50f8b7583b0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
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              "0it [00:00, ?it/s]"
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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          "metadata": {}
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            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f999c084c0544fb9a646ea6ba26282d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_RbE3hFCoo0"
      },
      "source": [
        "Let's examine how this tokenizer works by feeding it our simple \"Tokenizing text is a core task of NLP.\" example text:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = 'Tokenizing text is a core task of NLP'"
      ],
      "metadata": {
        "id": "KqNLRQ0VGxNI"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUONr-xnCoo0",
        "outputId": "1c84353f-1a5c-45cd-ff27-603009b2e307"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 19204, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ],
      "source": [
        "encoded_text = tokenizer(text)\n",
        "print(encoded_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGu9lXoJCoo0",
        "outputId": "f8598f63-6cc9-496f-ef5e-9e4d541c0847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'token', '##izing', 'text', 'is', 'a', 'core', 'task', 'of', 'nl', '##p', '[SEP]']\n"
          ]
        }
      ],
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(encoded_text.input_ids)\n",
        "print(tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYpgybK1Coo0"
      },
      "source": [
        "1. some special `[CLS]` and `[SEP]` tokens have been added to the start and end of the sequence.\n",
        "1. the tokens have each been lowercased, which is a feature of this particular checkpoint.\n",
        "1. \"tokenizing\" and \"NLP\" have been split into two tokens, which makes sense since they are not common words.\n",
        "\n",
        "The `##` prefix in `##izing` and `##p` means that the preceding string is not whitespace; any token with this prefix should be merged with the previous token when you convert the tokens back to a string. The `AutoTokenizer` class has a `convert_tokens_to_string()` method for doing just that, so let's apply it to our tokens:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BtGedpa-Coo1",
        "outputId": "c7191846-3cec-4a96-c9f4-385b375fecf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] tokenizing text is a core task of nlp [SEP]\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.convert_tokens_to_string(tokens))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E66ncAbkCoo1",
        "outputId": "c2340d6b-3487-439a-aacb-483ca7ce4911"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30522"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "tokenizer.vocab_size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lZTfm37Coo1"
      },
      "source": [
        "the model's maximum context size (DistilBERT):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_ES9MeICoo1",
        "outputId": "c45257cc-91e5-49c0-c416-3670e86aa6a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tokenizer.model_max_length"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJ1bqtzeCoo1"
      },
      "source": [
        "Another interesting attribute to know about is the names of the fields that the model expects in its forward pass:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30o3XcXMCoo1",
        "outputId": "b380dd61-b272-4d00-c1d0-81e14903f1ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "tokenizer.model_input_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOF3r8XuCoo1"
      },
      "source": [
        "Now that we have a basic understanding of the tokenization process for a single string, let's see how we can tokenize the whole dataset!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2uciY45DCoo1"
      },
      "source": [
        "### Tokenizing the Whole Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "1mXEf4wZCoo1"
      },
      "outputs": [],
      "source": [
        "def tokenize(batch):\n",
        "    return tokenizer(batch[\"text\"], padding=True, truncation=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T38yzUD6Coo1"
      },
      "source": [
        " `padding=True` will pad the examples with zeros to the size of the longest one in a batch, and `truncation=True` will truncate the examples to the model's maximum context size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAh32OqHCoo1",
        "outputId": "fa4dc391-62bf-4d9f-a85f-b6f64e02e7e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': ['i didnt feel humiliated',\n",
              "  'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake'],\n",
              " 'label': [0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "emotions[\"train\"][:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdIdVoWDCoo1",
        "outputId": "09303543-8d29-463f-9af1-804e7b24f4d8"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(tokenize(emotions[\"train\"][:2]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXO-xHGSCoo1"
      },
      "source": [
        "Here we can see the result of padding: the first element of `input_ids` is shorter than the second, so zeros have been added to that element to make them the same length. These zeros have a corresponding `[PAD]` token in the vocabulary, and the set of special tokens also includes the `[CLS]` and `[SEP]` tokens that we encountered earlier:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "67IOEH-GCoo1",
        "outputId": "0cd7f330-3c03-4c1c-d17c-626002ab0f7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      0      1      2      3       4\n",
              "Special Token     [PAD]  [UNK]  [CLS]  [SEP]  [MASK]\n",
              "Special Token ID      0    100    101    102     103"
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              "<style scoped>\n",
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              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-19f6995d-b634-42b8-a9dd-d61beabcd5f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"[PAD]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100,\n          \"[UNK]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          101,\n          \"[CLS]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          102,\n          \"[SEP]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          103,\n          \"[MASK]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "\n",
        "tokens2ids = list(zip(tokenizer.all_special_tokens, tokenizer.all_special_ids))\n",
        "data = sorted(tokens2ids, key=lambda x : x[-1])\n",
        "df = pd.DataFrame(data, columns=[\"Special Token\", \"Special Token ID\"])\n",
        "df.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PC7YT_ECoo2"
      },
      "source": [
        "Also note that in addition to returning the encoded tweets as `input_ids`, the tokenizer returns a list of `attention_mask` arrays. This is because we do not want the model to get confused by the additional padding tokens: the attention mask allows the model to ignore the padded parts of the input. <<attention-mask>> provides a visual explanation of how the input IDs and attention masks are padded.\n",
        "\n",
        "<img alt=\"attention-mask\" src=\"https://github.com/nluninja/text-mining-dataviz/blob/main/10.%20Transformers/images/attention-mask.png?raw=1\" id=\"attention-mask\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ti41sT_vCoo2"
      },
      "source": [
        "Once we've defined a processing function, we can apply it across all the splits in the corpus in a single line of code:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "60518a380c27464c8d6b0ed5fde4aaed",
            "58b338da9c364e91a77eab0d7e8ccbb2",
            "bfd14d633d4344958b292750249cdcea",
            "6a55990cbcce44c985b642e02f7151a2",
            "de76e1751c3245f9ac691857aa0ebd48",
            "da96c46e544a4a18a20ef686bec3fd65",
            "2c1d87d7c0a44947be5fdad5f99c5cff",
            "5d682c45161c4b4286f5e85e354fde47",
            "44e4503e033e4f399ed7697a4428ab1d",
            "8ae9ab5e94044c23906919ffb31e6a75",
            "097a407cd4c74104b6c91da3b31ab8c3",
            "6fc884f214724e51bd30489a9ae15e35",
            "31fa016ae5934ca294490ed0ae19cab1",
            "63186feea44b467cabcd3377bc90dc5e",
            "dcf5c67acd0749f58b704069cd074413",
            "bfab661dfbc148f283bc2f472c079768",
            "78dd30c816b54916a8b40221071d5b31",
            "f34f016a23c54b1ea3bcb07d6e00c00b",
            "0ab6dc94a1694db7a5485ecb0eb0416d",
            "ccec357e699a42f8ad6aa8a8230847cf",
            "f7191eae26d24cfb9626fecadfe31804",
            "8ace3af1e5f24f769ad7e49b324213e4",
            "976e74f44dd144648c9a177f520ea3cd",
            "bceee60b689645168e99fdb539a6c9c5",
            "b942abb636ef47cca5ce09ba54f5c8d4",
            "7eb7c211277e4915bd430772b7014fa3",
            "5c1115058eb848299ef5a9ff84b5f38e",
            "6cd06e3eac8f43cf82f297e45b616fa1",
            "9e8b557cabfe42d8bfc857648f59aa06",
            "827fe5dbd1224a67ba8b7fc0a87692e6",
            "8fea37b754ec4777b39a2533c3e3a9c4",
            "3a46f81e2c1243a7a737a78291863775",
            "17d0cf270bf246ffa7fc91acb6c72d41"
          ]
        },
        "id": "9QcUrr3JCoo2",
        "outputId": "c579d2ee-f24b-42d0-b872-09ed1c52f0a9"
      },
      "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": "60518a380c27464c8d6b0ed5fde4aaed"
            }
          },
          "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": "6fc884f214724e51bd30489a9ae15e35"
            }
          },
          "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": "976e74f44dd144648c9a177f520ea3cd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# hide_output\n",
        "emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WclUT1bLCoo2",
        "outputId": "e04f2968-420c-4c97-85ea-e64723bf246d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['text', 'label', 'input_ids', 'attention_mask']\n"
          ]
        }
      ],
      "source": [
        "print(emotions_encoded[\"train\"].column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHpeHuyNCoo2"
      },
      "source": [
        "## Training a Text Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjQ-_pb_Coo2"
      },
      "source": [
        "\n",
        "we freeze the weights during training and use the hidden states as features for the classifier.\n",
        "\n",
        "<img alt=\"encoder-features\" src=\"https://github.com/nluninja/text-mining-dataviz/blob/main/10.%20Transformers/images/encoder-feature-based.png?raw=1\" id=\"encoder-features\"/>\n",
        "\n",
        "We will use another convenient auto class `AutoModel` that has a `from_pretrained()` method to load the weights of a pretrained model."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTxuULtGG-Ky",
        "outputId": "e07b98fa-7346-4ad0-e5cc-3fb3bf51b5c0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5e4c9186758d4c6cbb91e228f04cae2a",
            "47cc6f24737d40a0bac54a1e86c694d6",
            "d41077cf5e3945f680cdeabdaf619050",
            "3ad06970e28c460096e484d77319e23c",
            "13ac039abea34b2aa93ad39d27cce24f",
            "327940119f6c4edb8943b22e2c20c123",
            "eac63785a5f94dbea355d09feb41ec5e",
            "ab3a29451e9f4020a19b66101752d4cb",
            "818bfaa8540c4d9bbb17f0702560f1aa",
            "8a75200eeeed4843a4b6f36ee84329d8",
            "c5299621acc644b1988a6369f4966a55"
          ]
        },
        "id": "wjxDcWekCoo2",
        "outputId": "6f01e387-bced-4423-b13f-5f06ae83dc9d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e4c9186758d4c6cbb91e228f04cae2a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoModel\n",
        "import torch\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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzdBzzWxCoo2"
      },
      "source": [
        "Now we extract the last hidden states for single string.\n",
        "We encode the string and convert the tokens to PyTorch tensors using the argument  `return_tensors=\"pt\"` (torch.Tensor; if tf:tf.constant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0rix4jOCoo2",
        "outputId": "f3b43984-5893-48ff-8847-8f1e8cb32e4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input tensor shape: torch.Size([1, 6])\n"
          ]
        }
      ],
      "source": [
        "text = \"this is a test\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\")\n",
        "print(f\"Input tensor shape: {inputs['input_ids'].size()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mt7rkHgTCoo2"
      },
      "source": [
        "As we can see, the resulting tensor has the shape `[batch_size, n_tokens]`. Now that we have the encodings as a tensor, the final step is to place them on the same device as the model and pass the inputs as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-U75-ejUCoo2",
        "outputId": "a9a31a5f-8314-425d-9892-6bf266f736d1"
      },
      "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"
          ]
        }
      ],
      "source": [
        "inputs = {k:v.to(device) for k,v in inputs.items()}\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "print(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTI8k0uFCoo2"
      },
      "source": [
        "Here we've used the `torch.no_grad()` context manager to disable the automatic calculation of the gradient.\n",
        "The current model returns only one attribute, which is the last hidden state, so let's examine its shape:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6WRg2-aCoo2",
        "outputId": "35dfd45d-5083-4f2d-aca7-05c59a93a8fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 6, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "outputs.last_hidden_state.size()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg90yVsaCoo3"
      },
      "source": [
        "Looking at the hidden state tensor, we see that it has the shape `[batch_size, n_tokens, hidden_dim]`. In other words, a 768-dimensional vector is returned for each of the 6 input tokens.\n",
        "\n",
        "It is common practice to just use the hidden state associated with the `[CLS]` token as the input feature for classification.\n",
        "\n",
        "Since this token appears at the start of each sequence, we can extract it by simply indexing into `outputs.last_hidden_state` as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yolfKfj4Coo3",
        "outputId": "405dabff-d7ba-4796-eadb-f0bc9770dc84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "outputs.last_hidden_state[:,0].size()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGF0bHBaCoo3"
      },
      "source": [
        "Now that we know how to get the last hidden state for a single string, let's do the same thing for the whole dataset by creating a new hidden_state column that stores all these vectors. As we did with the tokenizer, we'll use the map() method of DatasetDict to extract all the hidden states in one go. The first thing we need to do is wrap the previous steps in a processing function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "Qs92j2TlCoo3"
      },
      "outputs": [],
      "source": [
        "def extract_hidden_states(batch):\n",
        "    # Place model inputs on the GPU\n",
        "    inputs = {k:v.to(device) for k,v in batch.items()\n",
        "              if k in tokenizer.model_input_names}\n",
        "    # Extract last hidden states\n",
        "    with torch.no_grad():\n",
        "        last_hidden_state = model(**inputs).last_hidden_state\n",
        "    # Return vector for [CLS] token\n",
        "    return {\"hidden_state\": last_hidden_state[:,0].cpu().numpy()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-BSDFwRCoo3"
      },
      "source": [
        "The only difference between this function and our previous logic is the final step where we place the final hidden state back on the CPU as a NumPy array. The `map()` method requires the processing function to return Python or NumPy objects when we're using batched inputs.\n",
        "\n",
        "Since our model expects tensors as inputs, the next thing to do is convert the `input_ids` and `attention_mask` columns to the `\"torch\"` format, as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "UsukS-ElCoo3"
      },
      "outputs": [],
      "source": [
        "emotions_encoded.set_format(\"torch\",\n",
        "                            columns=[\"input_ids\", \"attention_mask\", \"label\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tuCtTdxQCoo3"
      },
      "source": [
        "We can then go ahead and extract the hidden states across all splits in one go:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "37678a7785584cd8853d6d41b39cd5a3",
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            "5c5cb2c4d40447899121a243c6cc47d3",
            "d89195aaab5b4e59952b8bb32d144636",
            "2d19b0ce40ee4787b44b1accb666e0ed",
            "cbee5c451b3f49a4820aa5bec7d18615",
            "7d20a37b66254b58a585257d122581d2",
            "8c19584ddbd2422a903e3828b8e4e7f2",
            "a36f774a43094ba9b13b0c7dfc79f98c",
            "ddcaf42bb6734bc0a73d37f02ad226b9",
            "11273394f4ac4000881d2eed37253f87",
            "43fc4b7304904d7e93ae162f797dd888",
            "d8ed2009ce0842a98f386854dd8d6dda",
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        "id": "tQraATNbCoo3",
        "outputId": "b545e975-32d9-4dd7-ced5-cf336b59d0e9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "model_id": "13b99f1c06a7419bb080fe8ece6e9a70"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "emotions_hidden = emotions_encoded.map(extract_hidden_states, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_HxOgiSCoo3"
      },
      "source": [
        "Note that we did not set `batch_size=None` in this case, so the default `batch_size=1000` is used instead. As expected, applying the +extract_&#x200b;hid&#x2060;den_&#x200b;states()+ function has added a new `hidden_state` column to our dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPgKO5uCCoo3",
        "outputId": "820b9d82-16ac-4e37-dcb3-ab3b1f9a84f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label', 'input_ids', 'attention_mask', 'hidden_state']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "emotions_hidden[\"train\"].column_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-pbJazSCoo3"
      },
      "source": [
        "Now that we have the hidden states associated with each tweet, the next step is to train a classifier on them. To do that, we'll need a feature matrix - let's take a look."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRHnTd6bCoo3"
      },
      "source": [
        "#### Creating a feature matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlCGPbUMCoo3"
      },
      "source": [
        "The preprocessed dataset now contains all the information we need to train a classifier on it. We will use the hidden states as input features and the labels as targets. We can easily create the corresponding arrays in the well-known Scikit-Learn format as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpHB8nrkCoo3",
        "outputId": "dfa16275-7900-4e71-8308-61ef19c8f995"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16000, 768), (2000, 768))"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "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",
        "X_train.shape, X_valid.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DYduWzdmCoo3"
      },
      "source": [
        "Before we train a model on the hidden states, it's good practice to perform a sanity check to ensure that they provide a useful representation of the emotions we want to classify. In the next section, we'll see how visualizing the features provides a fast way to achieve this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-JwwsdYCoo3"
      },
      "source": [
        "#### Visualizing the training set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqkKUdDOCoo4"
      },
      "source": [
        "Since visualizing the hidden states in 768 dimensions is tricky to say the least, we'll use the powerful UMAPfootnote:[L. McInnes, J. Healy, and J. Melville, [\"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction\"](https://arxiv.org/abs/1802.03426), (2018).] algorithm to project the vectors down to 2D.  Since UMAP works best when the features are scaled to lie in the [0,1] interval, we'll first apply a `MinMaxScaler` and then use the UMAP implementation from the `umap-learn` library to reduce the hidden states:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install umap-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqE5fklPHza6",
        "outputId": "aad8d85c-d104-48de-fb3d-d92b281e0833"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting umap-learn\n",
            "  Downloading umap_learn-0.5.7-py3-none-any.whl.metadata (21 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.26.4)\n",
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            "Collecting pynndescent>=0.5 (from umap-learn)\n",
            "  Downloading pynndescent-0.5.13-py3-none-any.whl.metadata (6.8 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn) (4.66.5)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.2->umap-learn) (0.43.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pynndescent>=0.5->umap-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->umap-learn) (3.5.0)\n",
            "Downloading umap_learn-0.5.7-py3-none-any.whl (88 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/88.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.8/88.8 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pynndescent-0.5.13-py3-none-any.whl (56 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/56.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.9/56.9 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pynndescent, umap-learn\n",
            "Successfully installed pynndescent-0.5.13 umap-learn-0.5.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
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          "height": 206
        },
        "id": "n2WOFO7RCoo4",
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              "type": "dataframe",
              "variable_name": "df_emb",
              "summary": "{\n  \"name\": \"df_emb\",\n  \"rows\": 16000,\n  \"fields\": [\n    {\n      \"column\": \"X\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 15997,\n        \"samples\": [\n          4.8579182624816895,\n          -0.48441168665885925,\n          -1.1536959409713745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Y\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 15991,\n        \"samples\": [\n          4.233888149261475,\n          4.311880588531494,\n          5.000857830047607\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "import umap.umap_ as 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.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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJVBv0SRCoo4"
      },
      "source": [
        "The result is an array with the same number of training samples, but with only 2 features instead of the 768 we started with! Let's investigate the compressed data a little bit further and plot the density of points for each category separately:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "V_XlrQGrCoo4",
        "outputId": "05c7a240-9e89-4f7a-c3da-db012dac1e74"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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,\n",
        "                   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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3Sik5S3Coo4"
      },
      "source": [
        "\n",
        ">note: These are only projections onto a lower-dimensional space. Just because some categories overlap does not mean that they are not separable in the original space. Conversely, if they are separable in the projected space they will be separable in the original space.\n",
        "\n",
        "From this plot we can see some clear patterns: the negative feelings such as `sadness`, `anger`, and `fear` all occupy similar regions with slightly varying distributions. On the other hand, `joy` and `love` are well separated from the negative emotions and also share a similar space. Finally, `surprise` is scattered all over the place. Although we may have hoped for some separation, this is in no way guaranteed since the model was not trained to know the difference between these emotions. It only learned them implicitly by guessing the masked words in texts.\n",
        "\n",
        "Now that we've gained some insight into the features of our dataset, let's finally train a model on it!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgIRK-JnCoo4"
      },
      "source": [
        "#### Training a simple classifier\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2L20xAhTCoo4"
      },
      "source": [
        "We've seen that the hidden states are somewhat different between the emotions, although for several of them there is no obvious boundary. Let's use these hidden states to train a logistic regression model with Scikit-Learn. Training such a simple model is fast and does not require a GPU:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Q8qM6w0YCoo4",
        "outputId": "62018f75-faec-4e6f-bacb-8d9a19d0e61c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=3000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=3000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=3000)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# We increase `max_iter` to guarantee convergence\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_clf = LogisticRegression(max_iter=3000)\n",
        "lr_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzX0S9NcCoo4",
        "outputId": "7c37ae5e-b81a-467c-cfdd-4718942f7b48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.635"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "lr_clf.score(X_valid, y_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USzXzQ5ECoo4"
      },
      "source": [
        "Looking at the accuracy, it might appear that our model is just a bit better than random—but since we are dealing with an unbalanced multiclass dataset, it's actually significantly better. We can examine whether our model is any good by comparing it against a simple baseline. In Scikit-Learn there is a `DummyClassifier`\n",
        "*  It is a classifier model that makes predictions without trying to find patterns in the data. The default model essentially looks at what label is most frequent in the training dataset and makes predictions based on that label.\n",
        "\n",
        "In this case the best-performing heuristic is to always choose the most frequent class, which yields an accuracy of about 35%:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88D-D7CyCoo4",
        "outputId": "84ec19dd-8421-49c4-9571-0c4a91d5bd0a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.352"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgGOdmFkCoo4"
      },
      "source": [
        "So, our simple classifier with DistilBERT embeddings is significantly better than our baseline. We can further investigate the performance of the model by looking at the confusion matrix of the classifier, which tells us the relationship between the true and predicted labels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "xcF9V9v2Coo6",
        "outputId": "d3927855-37fa-43dd-e869-02fa743f70a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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",
        "    fig, 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDoJdfIkCoo6"
      },
      "source": [
        "We can see that `anger` and `fear` are most often confused with `sadness`, which agrees with the observation we made when visualizing the embeddings. Also, `love` and `surprise` are frequently mistaken for `joy`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxXb0Py9Coo6"
      },
      "source": [
        "In the next section we will explore the fine-tuning approach, which leads to superior classification performance. It is, however, important to note that doing this requires more computational resources, such as GPUs, that might not be available in your organization. In cases like these, a feature-based approach can be a good compromise between doing traditional machine learning and deep learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2nAyWu6Coo6"
      },
      "source": [
        "### Fine-Tuning Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcU8zCMaCoo6"
      },
      "source": [
        "\n",
        "Let's now explore what it takes to fine-tune a transformer end-to-end. With the fine-tuning approach we do not use the hidden states as fixed features, but instead train them . This requires the classification head to be differentiable, which is why this method usually uses a neural network for classification.\n",
        "\n",
        "<img alt=\"encoder-tuning\" src=\"https://github.com/nluninja/text-mining-dataviz/blob/main/10.%20Transformers/images/encoder-fine-tuning.png?raw=1\" id=\"encoder-tuning\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Inx_1Z9NCoo6"
      },
      "source": [
        "Training the hidden states that serve as inputs to the classification model will help us avoid the problem of working with data that may not be well suited for the classification task. Instead, the initial hidden states adapt during training to decrease the model loss and thus increase its performance.\n",
        "\n",
        "We'll be using the `Trainer` API from image:images/logo.png[hf,13,13] Transformers to simplify the training loop. Let's look at the ingredients we need to set one up!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pec9mfgKCoo6"
      },
      "source": [
        "#### Loading a pretrained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "masjAI65Coo6"
      },
      "source": [
        "The first thing we need is a pretrained DistilBERT.  we use the `AutoModelForSequenceClassification`  has a classification head on top of the pretrained model outputs, which can be easily trained with the base model. We just need to specify how many labels the model has to predict (six in our case), since this dictates the number of outputs the classification head has:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfQMP4UkCoo6",
        "outputId": "e757a657-5a35-4bc2-8741-c2c5f69b4ffb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "num_labels = 6\n",
        "model = (AutoModelForSequenceClassification\n",
        "         .from_pretrained(model_ckpt, num_labels=num_labels)\n",
        "         .to(device))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDIwQipSCoo7"
      },
      "source": [
        "#### Defining the performance metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "na8ymiTPCoo7"
      },
      "source": [
        "\n",
        "To monitor metrics during training, we need to define a `compute_metrics()` function for the `Trainer`.  \n",
        "\n",
        "For our application, we'll compute the $F_1$-score and the accuracy of the model as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "HQ2ui7pbCoo7"
      },
      "outputs": [],
      "source": [
        "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",
        "    return {\"accuracy\": acc, \"f1\": f1}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKL-V6paCoo7"
      },
      "source": [
        "#### Training the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mY26hfxiCoo7"
      },
      "source": [
        "To define the training parameters, we use the `TrainingArguments` class. This class stores a lot of information and gives you fine-grained control over the training and evaluation. The most important argument to specify is `output_dir`, which is where all the artifacts from training are stored. Here is an example of `TrainingArguments` in all its glory:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjXddBZhCoo7",
        "outputId": "11b89907-48e7-4ef2-a15f-5e758157fd49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "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}-finetuned-emotion\"\n",
        "training_args = TrainingArguments(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=False,\n",
        "                                  log_level=\"error\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EqOpniUCoo7"
      },
      "source": [
        "Here we also set the batch size, learning rate, and number of epochs, and specify to load the best model at the end of the training run. With this final ingredient, we can instantiate and fine-tune our model with the `Trainer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "d67qTmAiCoo7",
        "outputId": "99b42364-ff39-41fd-e056-ad720a9696ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
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          "data": {
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              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
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              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
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              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.18.5"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20241030_155655-pb3eg8gj</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/andrea-belli/huggingface/runs/pb3eg8gj' target=\"_blank\">distilbert-base-uncased-finetuned-emotion</a></strong> to <a href='https://wandb.ai/andrea-belli/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/andrea-belli/huggingface' target=\"_blank\">https://wandb.ai/andrea-belli/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/andrea-belli/huggingface/runs/pb3eg8gj' target=\"_blank\">https://wandb.ai/andrea-belli/huggingface/runs/pb3eg8gj</a>"
            ]
          },
          "metadata": {}
        },
        {
          "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 00:59, 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>0.821400</td>\n",
              "      <td>0.301045</td>\n",
              "      <td>0.915000</td>\n",
              "      <td>0.914655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.248600</td>\n",
              "      <td>0.209655</td>\n",
              "      <td>0.926000</td>\n",
              "      <td>0.925714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "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();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "V46Gr0ynCoo7",
        "outputId": "78c24acd-fc1f-41b4-cf07-785ba391a675"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "preds_output = trainer.predict(emotions_encoded[\"validation\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PgRXjYALCoo7"
      },
      "source": [
        "The output of the `predict()` method is a `PredictionOutput` object that contains arrays of `predictions` and `label_ids`, along with the metrics we passed to the trainer. For example, the metrics on the validation set can be accessed as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-ZN24Z5Coo7",
        "outputId": "379b3d65-2633-4a80-af05-8c65e2dde78e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.20965524017810822,\n",
              " 'test_accuracy': 0.926,\n",
              " 'test_f1': 0.9257138852566903,\n",
              " 'test_runtime': 1.0454,\n",
              " 'test_samples_per_second': 1913.11,\n",
              " 'test_steps_per_second': 30.61}"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "preds_output.metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Emn2Ot4CCoo7"
      },
      "source": [
        "It also contains the raw predictions for each class. We can decode the predictions greedily using `np.argmax()`. This yields the predicted labels and has the same format as the labels returned by the Scikit-Learn models in the feature-based approach:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "lc9M4LtACoo7"
      },
      "outputs": [],
      "source": [
        "y_preds = np.argmax(preds_output.predictions, axis=1)\n",
        "y_valid = np.array(emotions_hidden[\"validation\"][\"label\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwaoUKhCCoo8"
      },
      "source": [
        "With the predictions, we can plot the confusion matrix again:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "gor4mO2cCoo8",
        "outputId": "83214492-b253-4f8e-eb3e-97dd9dbfc258"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCaw_l4BCoo8"
      },
      "source": [
        "<hr />\n",
        "\n",
        "## Fine Tuning with Keras\n",
        "in case you wantto use Tensorflow, you can see below how you can do the same with Keras API - in this case you don't have the Trainer class but everything is managed by the fit() method."
      ]
    },
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      "execution_count": 62,
      "metadata": {
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      "source": [
        "\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "tf_model = (TFAutoModelForSequenceClassification\n",
        "            .from_pretrained(model_ckpt, num_labels=num_labels))"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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        "Next, we'll convert our datasets into the `tf.data.Dataset` format. Since we have already padded our tokenized inputs, we can do this easily by applying the `to_tf_dataset()` method to `emotions_encoded`:"
      ]
    },
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      "execution_count": 63,
      "metadata": {
        "colab": {
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        },
        "id": "ArQe0X71Coo8",
        "outputId": "414563a4-ffbf-4879-bda5-1b4a49018ded"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:403: FutureWarning: The output of `to_tf_dataset` will change when a passing single element list for `labels` or `columns` in the next datasets version. To return a tuple structure rather than dict, pass a single string.\n",
            "Old behaviour: columns=['a'], labels=['labels'] -> (tf.Tensor, tf.Tensor)  \n",
            "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor)  \n",
            "New behaviour: columns=['a'],labels=['labels'] -> ({'a': tf.Tensor}, {'labels': tf.Tensor})  \n",
            "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# The column names to convert to TensorFlow tensors\n",
        "tokenizer_columns = tokenizer.model_input_names\n",
        "\n",
        "tf_train_dataset = emotions_encoded[\"train\"].to_tf_dataset(\n",
        "    columns=tokenizer_columns, label_cols=[\"label\"], shuffle=True,\n",
        "    batch_size=batch_size)\n",
        "tf_eval_dataset = emotions_encoded[\"validation\"].to_tf_dataset(\n",
        "    columns=tokenizer_columns, label_cols=[\"label\"], shuffle=False,\n",
        "    batch_size=batch_size)"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "jgcESSdcCoo8"
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      "source": [
        "Here we've also shuffled the training set, and defined the batch size for it and the validation set. The last thing to do is compile and train the model:"
      ]
    },
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      "execution_count": 64,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "9XUBiWHICoo8",
        "outputId": "fdcb584c-1301-46e2-e0d3-a43e6675ba5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "250/250 [==============================] - 47s 97ms/step - loss: 0.5334 - sparse_categorical_accuracy: 0.8109 - val_loss: 0.1713 - val_sparse_categorical_accuracy: 0.9360\n",
            "Epoch 2/2\n",
            "250/250 [==============================] - 17s 68ms/step - loss: 0.1480 - sparse_categorical_accuracy: 0.9362 - val_loss: 0.1444 - val_sparse_categorical_accuracy: 0.9385\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf_keras.src.callbacks.History at 0x7cdd3cdfd570>"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "#hide_output\n",
        "import tensorflow as tf\n",
        "\n",
        "tf_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),\n",
        "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "    metrics=tf.metrics.SparseCategoricalAccuracy())\n",
        "\n",
        "tf_model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=2)"
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