HF_fine_tune_1.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aydinmyilmaz/Transformers-Tutorials/blob/master/HF_fine_tune_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "GFLHX9vTQa5p"
      },
      "outputs": [],
      "source": [
        "#!pip install transformers\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b09iYCoKQnGQ",
        "outputId": "3674f8a3-346c-4056-a1ec-7a399760ccec"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 325 kB 5.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 43.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 40.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 136 kB 45.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 46.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 144 kB 48.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 94 kB 3.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 271 kB 9.1 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## "
      ],
      "metadata": {
        "id": "1Zd0D6rEQ_zS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0. Datasets Overview"
      ],
      "metadata": {
        "id": "eozI3n0TRA7Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import list_datasets"
      ],
      "metadata": {
        "id": "2fUybW6YQs22"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_datasets = list_datasets()"
      ],
      "metadata": {
        "id": "gl_i1_mmQ2Xk"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_datasets[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENhYpNkRREhe",
        "outputId": "31799489-bbf5-4d38-f4b5-07a18c04c796"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['acronym_identification',\n",
              " 'ade_corpus_v2',\n",
              " 'adversarial_qa',\n",
              " 'aeslc',\n",
              " 'afrikaans_ner_corpus',\n",
              " 'ag_news',\n",
              " 'ai2_arc',\n",
              " 'air_dialogue',\n",
              " 'ajgt_twitter_ar',\n",
              " 'allegro_reviews']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(all_datasets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA93pYtcRGYn",
        "outputId": "4670f264-7974-4641-dea1-ec81fff58979"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3902"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "emotions = load_dataset(\"emotion\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
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        "id": "6QLedt7FRJ6v",
        "outputId": "3af38f44-47cf-4a3f-b884-bc7dbae88c53"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.66k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c210a4dfb4104a48a99bb65dffc66be5"
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        },
        {
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          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.61k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset emotion/default (download: 1.97 MiB, generated: 2.07 MiB, post-processed: Unknown size, total: 4.05 MiB) to /root/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705...\n"
          ]
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.66M [00:00<?, ?B/s]"
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        {
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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": "7eb8eccab0af4599bb74a4a561cc37b5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "29bc8e3430384f6a9040e048d8af6a36"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
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          "text": [
            "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705. Subsequent calls will reuse this data.\n"
          ]
        },
        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NhhUROERSGl",
        "outputId": "9e91190e-47f0-4541-a7d2-7d54c765b2c7"
      },
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions[\"train\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8_whCu1RVqV",
        "outputId": "444472b1-a8f4-49f3-9825-b35ce9eac9d7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df_sample = pd.DataFrame(list(zip(emotions[\"train\"][\"text\"][0:10], emotions[\"train\"][\"label\"][0:10])), columns=['text','label'])\n",
        "df_sample"
      ],
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        "colab": {
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          "height": 363
        },
        "id": "qrV8wRcuRcSU",
        "outputId": "7f390a36-6978-4f0c-87e9-01d6d349fdee"
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      "execution_count": 12,
      "outputs": [
        {
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              "                                                text  label\n",
              "0                            i didnt feel humiliated      0\n",
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              "3  i am ever feeling nostalgic about the fireplac...      2\n",
              "4                               i am feeling grouchy      3\n",
              "5  ive been feeling a little burdened lately wasn...      0\n",
              "6  ive been taking or milligrams or times recomme...      5\n",
              "7  i feel as confused about life as a teenager or...      4\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = emotions['train']\n",
        "train_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhH-JugmRhzL",
        "outputId": "7df983f1-0382-434c-b26a-220e1e15da51"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_ds)"
      ],
      "metadata": {
        "colab": {
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        "id": "W65kKenTSX6k",
        "outputId": "a2242817-fc50-4e93-ac7b-78610ecb7a84"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4qIjGFRScGz",
        "outputId": "da8afba3-1980-4b63-b182-1c94c465ffa8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'label': 0, 'text': 'i didnt feel humiliated'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds.column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wbai2ETrSd4A",
        "outputId": "807aec1e-95e2-4c70-ec88-b3dfe8284682"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_SG9mltSgm8",
        "outputId": "7722cee3-727a-4680-ce39-1a774e6a61ba"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None),\n",
              " 'text': Value(dtype='string', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. From Datasets to DataFrames"
      ],
      "metadata": {
        "id": "jhNteFadTBAq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "emotions.set_format(type=\"pandas\")\n",
        "df = emotions['train'][:]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6W1y8nNySlus",
        "outputId": "de101c41-d7df-4c4a-e897-fcd816b2c1a6"
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      "execution_count": 18,
      "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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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def label_int2str(row):\n",
        "  return emotions['train'].features['label'].int2str(row)\n",
        "\n",
        "df['label_name'] =df['label'].apply(label_int2str)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LMzyHp0eTPuU",
        "outputId": "d48d5891-8cb6-4251-8119-133f782603f2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label label_name\n",
              "0                            i didnt feel humiliated      0    sadness\n",
              "1  i can go from feeling so hopeless to so damned...      0    sadness\n",
              "2   im grabbing a minute to post i feel greedy wrong      3      anger\n",
              "3  i am ever feeling nostalgic about the fireplac...      2       love\n",
              "4                               i am feeling grouchy      3      anger"
            ],
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              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>i can go from feeling so hopeless to so damned...</td>\n",
              "      <td>0</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
              "      <td>3</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
              "      <td>2</td>\n",
              "      <td>love</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i am feeling grouchy</td>\n",
              "      <td>3</td>\n",
              "      <td>anger</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ad8f4cd-dc9f-4344-98ce-4e4cf91f1e66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ad8f4cd-dc9f-4344-98ce-4e4cf91f1e66 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-0ad8f4cd-dc9f-4344-98ce-4e4cf91f1e66');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df['label_name'].value_counts(ascending=True).plot.barh()\n",
        "plt.title(\"Frequency of Classes\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "BWHvfk01TuOj",
        "outputId": "205c52e5-88d0-4c49-b0ad-64b306198ebd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['words_per_tweet'] = df['text'].str.split().apply(len)\n",
        "df.boxplot(\"words_per_tweet\", by=\"label_name\", grid=False,\n",
        "           showfliers=False, color='black')\n",
        "plt.suptitle('')\n",
        "plt.xlabel(\"\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "wjR9c4PBUGva",
        "outputId": "eea0a4d8-0d93-4fe3-f70a-ceee28799827"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions.reset_format()"
      ],
      "metadata": {
        "id": "X7azU9JTVGc_"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Tokenization"
      ],
      "metadata": {
        "id": "ACiyEK1-ecIg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, DistilBertTokenizer\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201,
          "referenced_widgets": [
            "ca81c914c7804eaf933a8f924a1693cd",
            "33c3adc4631d4fae867958f52e937661",
            "01a11c98fc5944aca24c374de83ab1a9",
            "8811cc3121a448e095908153e2b75719",
            "aa61e0bdaebc46a9918b234f3e547934",
            "48c2edb848bd4b039a4c803298145533",
            "b3af76a46a5d4e0ea3fa7d08ba672445",
            "87ab2871ff114f78a01bd7dece8aff05",
            "307455baa12d4fae99e4789f1477c48d",
            "9f3f07e870f240f2b031f29f9c9a9a52",
            "aa822f7751c44867b3f9cc9beb7ce394",
            "dcd1787ef14d4881bd6d0f55817f1260",
            "32ff4308fb2d4069866125bfd637aea4",
            "a94e3db854dd4f43bf9a3738f891c838",
            "c1a1306883624c8d904e0ae35d1ee87f",
            "2eb7d4a203ef4c33b5888d93aabd49d3",
            "e3970facbcf84e7bbc2a954a3296d34f",
            "864ce9b4c0e9467e86fb5e547ab99e75",
            "15a7779ca1d747baa05bdb46fce05a0f",
            "b52b12b5a180411993f89491969355f7",
            "98007f6c9bae410eaf11927c1fc68095",
            "e43d8f22353149ba8c00b2b636506623",
            "4a556fe5ad284054a8f9434277c43d1f",
            "953577e14dd346aeb05c9f5fb70c5130",
            "a07ece40e2fe4b139d14729895034d1b",
            "90953ce2316348ceadb2920d5bf5c14a",
            "af64f07ecc7f41388198c9f2905896b0",
            "dd4f3c5b48344dcdb45533c1211c6c9b",
            "5775fa10f7cf43fc93827380ff98d764",
            "33be460bc6384e38a262f97a7ef35dc1",
            "7098bd1fb410412ea888b4bb0826ace4",
            "7ea1c0ed8f994903acebbb75f160ecad",
            "afe31303894a4219a118b3ec7f4642f6",
            "b03cec8b595d4a51bdcd84c52f20c34a",
            "cb797955df6b424e8e4e9f053669f518",
            "2ec4060cf7f0436fb7240dd29b82b686",
            "06dda5f9c2a04f25947c14620c298d76",
            "4f616633536c47c7b3d54db663c4da7d",
            "aafa0b320f524915986c6539bc33e185",
            "79c5a416c43a4b468d4cd85ac2179f72",
            "e6c98df8a89d473c8abf18f01d45e5d0",
            "e7f86e1df8a543b3980c953859b578b3",
            "5ada776fba3e4da2a1d76fc9cc2d5135",
            "811e4cb8358e431dad78ddb2cfbe0e0a"
          ]
        },
        "id": "vuaVL1slVPp_",
        "outputId": "06fd3c08-b78f-497d-d687-d1bce52c70de"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca81c914c7804eaf933a8f924a1693cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcd1787ef14d4881bd6d0f55817f1260"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a556fe5ad284054a8f9434277c43d1f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b03cec8b595d4a51bdcd84c52f20c34a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_text = tokenizer(\"Tokenizing a text is a core task in NLP\")\n",
        "print(encoded_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9GQxJuhe5QL",
        "outputId": "e939893b-7544-4688-aef8-e7ed670e55fb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 19204, 6026, 1037, 3793, 2003, 1037, 4563, 4708, 1999, 17953, 2361, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(encoded_text.input_ids)\n",
        "print(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Vw3vpg2fVAp",
        "outputId": "515853e0-f87c-4eac-ebec-20c5afe3af93"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'token', '##izing', 'a', 'text', 'is', 'a', 'core', 'task', 'in', 'nl', '##p', '[SEP]']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.convert_tokens_to_string(tokens))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y27VqQSufeRR",
        "outputId": "beaa8a65-bed8-499d-8c6f-731919348c09"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] tokenizing a text is a core task in nlp [SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.model_input_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n86uOGBUf7UR",
        "outputId": "21408ef0-d981-4784-be7d-b94a5d5cccce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Tokenizing whole dataset"
      ],
      "metadata": {
        "id": "nLn8qRh1h3sJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(batch):\n",
        "    return tokenizer(batch['text'], padding=True, truncation=True)"
      ],
      "metadata": {
        "id": "pXhbwtVIgBoH"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotions['train']['text'][1:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKPSa1X-i4E8",
        "outputId": "07de2fbc-b3dc-45ee-fcd4-9f4c5ac0a58b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake']"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenize(emotions['train'][1:2]).keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e4tvYXUiPt-",
        "outputId": "75e9cebe-44f2-46c2-d2ef-c21057e51023"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['input_ids', 'attention_mask'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenize(emotions['train'][1:2])['input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHHwjk-iiaJ9",
        "outputId": "f90da1f6-c8fc-410e-c2d7-89818a2be949"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[101, 1045, 2064, 2175, 2013, 3110, 2061, 20625, 2000, 2061, 9636, 17772, 2074, 2013, 2108, 2105, 2619, 2040, 14977, 1998, 2003, 8300, 102]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenize(emotions['train'][0:2])['attention_mask'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "makhWfWNig3e",
        "outputId": "751571b1-9ae6-4a87-8bd9-488e58e9573a"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenize(emotions['train'][:2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-lKRWjhii1c",
        "outputId": "d3ac474e-c8cc-4762-f9ca-acd67db53adb"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[101, 1045, 2134, 2102, 2514, 26608, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1045, 2064, 2175, 2013, 3110, 2061, 20625, 2000, 2061, 9636, 17772, 2074, 2013, 2108, 2105, 2619, 2040, 14977, 1998, 2003, 8300, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "a6d5facba8834d56973fc62897f1825e",
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            "cae6e197af734f18bc237901c2ea8da6",
            "1489aa757f524aa988cb52661f813784",
            "c906a6bcf7f34030a11181d541e38544",
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            "b3f6695b88774bd8a7d6b37d9070652d",
            "a808715e160b466fb86a79bb92de2970",
            "371bac3c378d4e5ba2490473571d7605",
            "1a70d5d1987745a5b5e1578a399f1e08",
            "d839de1d792a47a4aa36f3f3cb0440a5",
            "520e6f8b785141f0b6e2819ed2c039c3",
            "a495d57e43df4531848b06fbf3f56fde",
            "4e1a90a6ab9a4ca68dcb085846f0b7e3",
            "36a6d0ab2e994fa1bf7ca6ed54485a3d",
            "ae3cd6f5d7294169a2fda3030bd8efd3",
            "5d1435cb0b9548ecb650e1fa145666a9",
            "b34293e70be448c5bd40e37114a63c55",
            "af8b603a82d346ca8ccc3b6c8fc6ccc7"
          ]
        },
        "id": "FgU2jPOSjNGZ",
        "outputId": "e6164f1c-1ace-489a-9e39-5572b853ce0d"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6d5facba8834d56973fc62897f1825e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "338ce9636e3b45429f58f96d905b7d25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "371bac3c378d4e5ba2490473571d7605"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(emotions_encoded['train'].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1gYLLqHSjsd6",
        "outputId": "aae3bfa4-be1c-4728-b6dc-881a323a2cb1"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['text', 'label', 'input_ids', 'attention_mask']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#emotions_encoded['train'][0]"
      ],
      "metadata": {
        "id": "zSRCix5fkFub"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Training a Text Classifier"
      ],
      "metadata": {
        "id": "NqNWW_iYkRH1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.1 Using Transformers as Feature Extractors"
      ],
      "metadata": {
        "id": "_EVzYBx9kaR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModel"
      ],
      "metadata": {
        "id": "7wDV7fz2kK9a"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "AutoModel class converts the token encodings to embeddings, then feeds them through the encoder stack to return the hidden states"
      ],
      "metadata": {
        "id": "3HOEcHgDpDdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = AutoModel.from_pretrained(model_ckpt).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149,
          "referenced_widgets": [
            "91179659057446c88d914928b0eaec25",
            "f62b31d97a9a4507b5dff7bf36b6cf0a",
            "6196cf94e1ac48508c0560dd1cf0eb4d",
            "84e267abc33d43649f48f1b1c314da1d",
            "bd4533e42a824f929385aae774b065bc",
            "d86f76600b5a4377ac7e93b46b905885",
            "41c6fc09e72e4a8184648586bcd4731a",
            "88a39f251b234201b4ec51c257c449fb",
            "b6571652cd0146319dd92a254cfc0c02",
            "8f99ecb48e3c49429df3078bfa8df64b",
            "b003f6e6fbb04eb4afd619b91ed87643"
          ]
        },
        "id": "yf67ze2SkQdT",
        "outputId": "447592d9-2823-4199-bdcb-6dc14ffa65a9"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91179659057446c88d914928b0eaec25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_transform.weight', 'vocab_layer_norm.weight']\n",
            "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Extracting the last hidden states"
      ],
      "metadata": {
        "id": "6vOJy_Gqpgr9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = ['this is a test',\"this is also another test\"]\n",
        "inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)\n",
        "print(f\"Input tensor shape: {inputs['input_ids'].size()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiU9TgJIo2Y8",
        "outputId": "f3186a61-7b69-4d80-c765-2a3a622d5d3a"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input tensor shape: torch.Size([2, 7])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9NZmTymp3iQ",
        "outputId": "dda3cdde-6206-4f49-b263-d661eff4f277"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs['input_ids']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wl1SMjNp46S",
        "outputId": "f5634639-d173-4d38-9c46-3f6a8e8f0ac1"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 101, 2023, 2003, 1037, 3231,  102,    0],\n",
              "        [ 101, 2023, 2003, 2036, 2178, 3231,  102]])"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs['attention_mask']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYhCP-Bup8r-",
        "outputId": "c3b4d217-303c-42f1-9b9e-9e9076eea855"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1, 1, 1, 1, 1, 0],\n",
              "        [1, 1, 1, 1, 1, 1, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "YH7FQ6o_p-XN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next step to place tensor of encodings on to the same deevice as the model"
      ],
      "metadata": {
        "id": "Aht5XsUSt5sw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = {k:v.to(device) for k, v in inputs.items()}\n",
        "with torch.no_grad():\n",
        "  outputs = model(**inputs)\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7p1kQWPZuFC4",
        "outputId": "43b29c9d-abb1-4d07-b9ff-27a8242dd3f2"
      },
      "execution_count": 86,
      "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",
            "         ...,\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",
            "         [ 0.0352, -0.3883,  0.0161,  ...,  0.0221,  0.0190,  0.4101]],\n",
            "\n",
            "        [[-0.1180, -0.1797,  0.0433,  ..., -0.0840,  0.1692,  0.4245],\n",
            "         [-0.2321, -0.5506, -0.1626,  ..., -0.0848,  0.5411,  0.2210],\n",
            "         [-0.4013, -0.4071,  0.0957,  ..., -0.0099,  0.2094,  0.7743],\n",
            "         ...,\n",
            "         [-0.3098, -0.4207,  0.0505,  ...,  0.1818,  0.4265,  0.3154],\n",
            "         [ 0.1886, -0.4392, -0.3297,  ..., -0.2462,  0.0583, -0.3996],\n",
            "         [ 1.0088, -0.0087, -0.4521,  ...,  0.1389, -0.5730, -0.2557]]],\n",
            "       device='cuda:0'), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs[0][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wl1Gn0NPuMwc",
        "outputId": "caed74f7-d036-4e65-c53d-c421401a7dc5"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-0.1180, -0.1797,  0.0433,  ..., -0.0840,  0.1692,  0.4245],\n",
            "        [-0.2321, -0.5506, -0.1626,  ..., -0.0848,  0.5411,  0.2210],\n",
            "        [-0.4013, -0.4071,  0.0957,  ..., -0.0099,  0.2094,  0.7743],\n",
            "        ...,\n",
            "        [-0.3098, -0.4207,  0.0505,  ...,  0.1818,  0.4265,  0.3154],\n",
            "        [ 0.1886, -0.4392, -0.3297,  ..., -0.2462,  0.0583, -0.3996],\n",
            "        [ 1.0088, -0.0087, -0.4521,  ...,  0.1389, -0.5730, -0.2557]],\n",
            "       device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FF83OPp8ugjl",
        "outputId": "4c5c4810-f566-4b09-fd3f-cbbc8f7a2950"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 7, 768])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs.last_hidden_state.size())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dXGrXwGunk-",
        "outputId": "3be3bfc7-bac5-4ac8-ed04-b0e21422a8b2"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 7, 768])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "def extract_hidden_states(batch):\n",
        "  inputs = {k:v.to(device) for k, v in batch.items() if k in tokenizer.model_input_names}\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()}\n"
      ],
      "metadata": {
        "id": "sGT42-oYvhV1"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.model_input_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kzXN-9Sx26J",
        "outputId": "65a0f885-17dc-43c7-8274-cb0edee103ac"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded.set_format(\"torch\",\n",
        "                            columns = [\"input_ids\", \"attention_mask\", \"label\"])\n",
        "\n",
        "emotions_hidden = emotions_encoded.map(extract_hidden_states, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "a899135875144aef90bc32705dbafbb7",
            "216981c77f57487e9f55c28939bcadd7",
            "72c3a5ad843845eb9fa549d5adfa004d",
            "18f26c1ba8a84387b77f13de16a9c5ca",
            "963654e6b3474ad4b7da30902104f1d7",
            "f5e035d3bb1543dca715747126df31f0",
            "875608bab6f54c99a19aab6f6af432f7",
            "2228376d34e04206b1f3ea9572814834",
            "63a99cc4735d4b87a671311dcaed4b2b",
            "de139f021c4948f19ecf78d26f7ee782",
            "6800e1bc5ce3401295bb94d3d37dc5ea",
            "315136316b694283a58b0cebda0be4d8",
            "96e5206db6ae48e2a14d52d85469743f",
            "d057a47df74f4ae1acd41210bf5aa11a",
            "a452ca0e466a48959fb972645095be6d",
            "ff4be0525535430fb8d37dd1591dcf2d",
            "fc6056e6af8a47ec8fd05f20db976386",
            "e1c10fc8bedf4627a04a504b9213445e",
            "2e8325ac87c5436fb5113516448fa583",
            "e19071a5f3ab4283976b8c1f685b3629",
            "fb1d1381e95048589d84bf3bad333641",
            "81d2253e2f8940c9a12bacb14824e17b",
            "8ba8eaaec3e04c9fbc9cfa35436a7a1a",
            "f3ee118455d64ac99d9a9b3c1154523d",
            "97625031d5b84b31b88942906baa52cc",
            "b2f21a3321244bcc9705beaf05b10584",
            "c47c9221f9b842a195b3886d158302c4",
            "301627c465a04f3092c8ff2c6cd35f0d",
            "ab6d6997ac0d428f96fa26f60ca251c0",
            "9d1241034e894649bb8cf0445cb77f5e",
            "8ce89d6d4f6e47c4b96b78f1efd75a32",
            "472074dcff90423a9e21fcba42b3783e",
            "8b32083fbbc8498094322807354ef435"
          ]
        },
        "id": "UA3TMKPXx3OX",
        "outputId": "df458bfa-9853-4da0-e0ac-47d58a6e1c35"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/16 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a899135875144aef90bc32705dbafbb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "315136316b694283a58b0cebda0be4d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ba8eaaec3e04c9fbc9cfa35436a7a1a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_hidden['train'].column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jqndr49xykCS",
        "outputId": "e296d52e-324d-4b9d-a00f-75d56d98499e"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label', 'input_ids', 'attention_mask', 'hidden_state']"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Generating a feature matrix"
      ],
      "metadata": {
        "id": "3DRDdCAs1Yor"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "ycndpabKzOYn"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array(emotions_hidden['train']['hidden_state'])\n",
        "X_valid = np.array(emotions_hidden['validation']['hidden_state'])\n",
        "\n",
        "y_train = np.array(emotions_hidden['train']['label'])\n",
        "y_valid = np.array(emotions_hidden['validation']['label'])\n",
        "\n",
        "X_train.shape, X_valid.shape\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6DSZkQEI1fve",
        "outputId": "a644f714-b5ac-4717-a0cf-b2d2f74e41d2"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16000, 768), (2000, 768))"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_clf = LogisticRegression(max_iter=3000)\n",
        "lr_clf.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "k_fMsEOg5bDq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_clf.score(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6dvsetf5Zh_",
        "outputId": "5a6db015-6844-4b5e-d0c9-3f649c2a80c7"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.633"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "hn7957rJ4FH3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}