{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o39pc2uKWp5z",
        "outputId": "1dfd1f07-18e6-4e89-cefc-f7d9e35f5965"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.16.1-py3-none-any.whl (507 kB)\n",
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            "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "Installing collected packages: dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15\n",
            "Collecting umap-learn\n",
            "  Downloading umap-learn-0.5.5.tar.gz (90 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting pynndescent>=0.5 (from umap-learn)\n",
            "  Downloading pynndescent-0.5.11-py3-none-any.whl (55 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.8/55.8 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn) (4.66.1)\n",
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            "Building wheels for collected packages: umap-learn\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.5-py3-none-any.whl size=86832 sha256=fd4cd695f2f76bfb1f30325e43d8805bd26465e6b57fb41f39b723eb4f33bd33\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/70/07/428d2b58660a1a3b431db59b806a10da736612ebbc66c1bcc5\n",
            "Successfully built umap-learn\n",
            "Installing collected packages: pynndescent, umap-learn\n",
            "Successfully installed pynndescent-0.5.11 umap-learn-0.5.5\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.26.1-py3-none-any.whl (270 kB)\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.26.1\n",
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[torch]) (2.1.5)\n",
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            "Collecting git+https://github.com/huggingface/accelerate\n",
            "  Cloning https://github.com/huggingface/accelerate to /tmp/pip-req-build-jek0qu8j\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate /tmp/pip-req-build-jek0qu8j\n",
            "  Resolved https://github.com/huggingface/accelerate to commit 06b138d84537ffb2d1d404f2f198a0446e8d7ec3\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.27.0.dev0) (1.23.5)\n",
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            "Building wheels for collected packages: accelerate\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for accelerate: filename=accelerate-0.27.0.dev0-py3-none-any.whl size=278693 sha256=81a7c5cd370339f5154dd428f47199f4e86950d395e5767378cf41fd50e172e1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-q3zlw7zc/wheels/f6/c7/9d/1b8a5ca8353d9307733bc719107acb67acdc95063bba749f26\n",
            "Successfully built accelerate\n",
            "Installing collected packages: accelerate\n",
            "  Attempting uninstall: accelerate\n",
            "    Found existing installation: accelerate 0.26.1\n",
            "    Uninstalling accelerate-0.26.1:\n",
            "      Successfully uninstalled accelerate-0.26.1\n",
            "Successfully installed accelerate-0.27.0.dev0\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.37.2-py3-none-any.whl (8.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.9.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "Successfully installed transformers-4.37.2\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install umap-learn\n",
        "!pip install accelerate -U\n",
        "!pip install transformers[torch]\n",
        "!pip install transformers\n",
        "!pip install git+https://github.com/huggingface/accelerate\n",
        "!pip install --upgrade transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQbGrQ2hPjUB"
      },
      "source": [
        "1. distilbert variant of bert\n",
        "2. change checkpoint of the pretrained model to train a large model\n",
        "3. checkpoint corresponds set of weights that are loaded into a given transformer architecture\n",
        "4. dataset -> tokenizers -> transformers -> metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6-Upmko0i4aa"
      },
      "outputs": [],
      "source": [
        "import accelerate\n",
        "import transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-9uxUTf-WocT"
      },
      "outputs": [],
      "source": [
        "# from datasets import list_datasets\n",
        "# all_datasets = list_datasets()\n",
        "# print(f\"The first 10 are : {all_datasets[:10]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751,
          "referenced_widgets": [
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            "50b32c7037de42789911a760a9c2bce5",
            "b8d79413a8fc4c06b620180d8752c4fd",
            "25393e00b5b044bf96651fa96feec740",
            "ed628e4cb304497baba5c85a94364b9d",
            "2a202b0b544d480cac2c1da6c0f55859",
            "4c4a54c5457b425490a1b1a3cd5db286",
            "142469f2afad4aa9852459a7da2037d9",
            "054175d0529d4844a24c5b0d2d134203",
            "8bdba9c585da4a45ba9658b67f3603ac",
            "55e40eba44f0448e91b219005226ab7a",
            "7697e41a22cb446bbe24bf563fcf6b41",
            "69795c8c46b5446e959dcd57e65b290a",
            "6e13d52a17424c36b9396e1250250533",
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            "9cc088c1318b4801bd096139c910711d",
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            "458b076041ab47588cd5ea754f25d6ec",
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            "6cfc011613af4238a71228c8d8da40f5",
            "a02c8518d6e048679643c099253d9414",
            "5224625a2e294c37978d172f6b124e2e",
            "23aca3ae7830431b8389372f89d6e6ad",
            "8c39d02c863043d3866ba53f0359d1e7"
          ]
        },
        "id": "-monJaFJXf8d",
        "outputId": "cd9efb1d-f66a-40c1-b63a-eb35ee3cb2f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1429: FutureWarning: The repository for emotion contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/emotion\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.97k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8db1557beea7498b97542503ed2e500b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bdba9c585da4a45ba9658b67f3603ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.78k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b4671f3cd4347d1b91d86f85b1f138c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2e2313ce9bf4ba48a403fa2260915e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc129f63ef9b40fca327548176d0bc59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "302ced3e93804cc7bd16f286c87cdb16"
            }
          },
          "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,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03ce90d0dd3d4996b1a057b22be5406f"
            }
          },
          "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": "a73f09107b4349b489c3d21b2e75c596"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# loading the dataset\n",
        "from datasets import load_dataset\n",
        "emotions = load_dataset(\"emotion\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC1yJ083XwZW",
        "outputId": "2c3ec7e5-906d-46bc-9147-acad9b10584c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(emotions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJx2UnS0X1q_",
        "outputId": "70d09602-baf5-4e08-8f1b-35a485427ee8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 16000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_ds = emotions[\"train\"]\n",
        "train_ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLjWJ_QKYCMt",
        "outputId": "32248495-77f2-4376-e402-7b8b916fbec5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'i didnt feel humiliated', 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train_ds[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSRGLR2kYTct",
        "outputId": "41086103-a025-468f-984d-202f99fc2d03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "train_ds.column_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_9ZWbFRYc-r",
        "outputId": "9495a7a1-ad0c-4b9f-933a-d99c6996e931"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train_ds.features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61-304U-Ys4z",
        "outputId": "d1fc8950-d95e-483f-d995-69f1079bd97e"
      },
      "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": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7ottL-mY9Vl",
        "outputId": "93683d16-3997-4d29-8b76-1c5777e53a61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i didnt feel humiliated',\n",
              " 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
              " 'im grabbing a minute to post i feel greedy wrong',\n",
              " 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n",
              " 'i am feeling grouchy']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "train_ds[\"text\"][:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nBK_345zZPGb",
        "outputId": "b48fbc88-f1c5-4cd3-8200-b57e5e25ec74"
      },
      "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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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "import pandas as pd\n",
        "emotions.set_format(type = \"pandas\")\n",
        "df = emotions[\"train\"][:]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "PcXyItN6aSNG"
      },
      "outputs": [],
      "source": [
        "labels = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "hn0c0u9Bal40"
      },
      "outputs": [],
      "source": [
        "df['label_name'] = df['label'].apply(lambda x: labels[x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "BC-t-hv0bCpg",
        "outputId": "4c91cfcf-16dd-43af-df56-8e234bbe9eac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# class distribution\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize = (10, 7))\n",
        "plt.title(\"Frequency of classes\")\n",
        "plt.bar(df[\"label_name\"].value_counts().index, df[\"label_name\"].value_counts().values)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlIyrX-4QaIB"
      },
      "source": [
        "1. the joy and sadness classes appear frequently where love and surprise are about 5 to 10 times rarer\n",
        "2. dealing with imbalanced data\n",
        "  1. randomly oversample the minority class\n",
        "  2. randomly undersample the majority class\n",
        "  3. gather mode labeled data from the underrepresented classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "W-OKKsPwcfTm"
      },
      "outputs": [],
      "source": [
        "df[\"words per tweet\"] = df[\"text\"].str.split().apply(len)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EtbI-djQ-p7"
      },
      "source": [
        "1. transformer model have max input seq length - max context size\n",
        "2. for distilbert the max context size is 512 tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Xi14m8Z9eZS_"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "1839yCbKeHSP",
        "outputId": "16b2bd22-5fd0-4237-9c3d-8e5cb33c1eef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x = df[\"label_name\"], y = df[\"words per tweet\"])\n",
        "plt.title(\"words per tweet\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZBx_GVVRKNa"
      },
      "source": [
        "1. most tweets are around 15 words long and the longest tweets are well below distilbert's max context size\n",
        "2. text that are longer than a model's context size need to be truncated which leads to a loss in perf if truncated data contains crucial information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "mYbogFlveSAj"
      },
      "outputs": [],
      "source": [
        "# resetting the format of the dataset\n",
        "emotions.reset_format()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_n37_4xxfgPE"
      },
      "outputs": [],
      "source": [
        "# character tokenization\n",
        "text = \"Tokenizing text is a core task of NLP.\"\n",
        "tokenized_text = list(text)\n",
        "# tokenized_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clzKknE7gP8W",
        "outputId": "00dc74c5-646d-491a-f98d-78942d17f874"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{' ': 0,\n",
              " '.': 1,\n",
              " 'L': 2,\n",
              " 'N': 3,\n",
              " 'P': 4,\n",
              " 'T': 5,\n",
              " 'a': 6,\n",
              " 'c': 7,\n",
              " 'e': 8,\n",
              " 'f': 9,\n",
              " 'g': 10,\n",
              " 'i': 11,\n",
              " 'k': 12,\n",
              " 'n': 13,\n",
              " 'o': 14,\n",
              " 'r': 15,\n",
              " 's': 16,\n",
              " 't': 17,\n",
              " 'x': 18,\n",
              " 'z': 19}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "token2idx = {ch : idx for idx, ch in enumerate (sorted(set(tokenized_text)))}\n",
        "token2idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBoY3uSHgx-3",
        "outputId": "2ccba703-28cf-4bf5-de64-50de73cdb619"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5,\n",
              " 14,\n",
              " 12,\n",
              " 8,\n",
              " 13,\n",
              " 11,\n",
              " 19,\n",
              " 11,\n",
              " 13,\n",
              " 10,\n",
              " 0,\n",
              " 17,\n",
              " 8,\n",
              " 18,\n",
              " 17,\n",
              " 0,\n",
              " 11,\n",
              " 16,\n",
              " 0,\n",
              " 6,\n",
              " 0,\n",
              " 7,\n",
              " 14,\n",
              " 15,\n",
              " 8,\n",
              " 0,\n",
              " 17,\n",
              " 6,\n",
              " 16,\n",
              " 12,\n",
              " 0,\n",
              " 14,\n",
              " 9,\n",
              " 0,\n",
              " 3,\n",
              " 2,\n",
              " 4,\n",
              " 1]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "input_ids = [token2idx[token] for token in tokenized_text]\n",
        "input_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrSIq8HXhJ2q",
        "outputId": "9096564b-7cd1-43f3-b912-87dadaf4b75c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([38, 20])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "input_ids = torch.tensor(input_ids)\n",
        "one_hot_encodings = F.one_hot(input_ids, num_classes = len(token2idx))\n",
        "one_hot_encodings.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4CK2alBh5so",
        "outputId": "6295cded-da59-49fb-bce6-c828f6fa909a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "len(input_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCsdEa9WiC41",
        "outputId": "eebccb4a-9474-4b8d-c8aa-d79731f328df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "len(token2idx)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7Pw4_tqRpK-"
      },
      "source": [
        "1. treats the whole string as a stream of characters\n",
        "2. helps in misspellings and rare words\n",
        "3. lacks structure\n",
        "4. lingustic structure such as words need to be learned from the data that requires compute, memory and data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifyXReyriFif",
        "outputId": "c9e771fb-c7ad-436b-b120-f424244b8409"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['T', 'o', 'k', 'e', 'n', 'i', 'z', 'i', 'n', 'g', ' ', 't', 'e', 'x', 't', ' ', 'i', 's', ' ', 'a', ' ', 'c', 'o', 'r', 'e', ' ', 't', 'a', 's', 'k', ' ', 'o', 'f', ' ', 'N', 'L', 'P', '.']\n"
          ]
        }
      ],
      "source": [
        "# word tokenization\n",
        "\n",
        "tokeized_text = text.split()\n",
        "print(tokenized_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aqxjq1PBSEIT"
      },
      "source": [
        "1. some stemming or lemmatization can be used to normalize the words to their steam(great, greatest, greater becomes great)\n",
        "2. increases the vocab size\n",
        "3. limiting the vocab size and not common words are classificed as unk token this may lead to loss in info if it contains useful information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "hE9rNnQD-gJD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201,
          "referenced_widgets": [
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            "57866040c23941b7875e074ce6204852"
          ]
        },
        "outputId": "395ce065-dcad-47d4-a4c5-1af8474fe6fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "575eacb9aba04f9bb31aaf29b2b46506"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bd4735760084178b4ad948b65d3126a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80b1d89c3d1a4a7f96caa2c294c15920"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "87f4b88e77c343c392cabf43f942898c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# subword tokenization\n",
        "from transformers import AutoTokenizer\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTDUeBknS_m5"
      },
      "source": [
        "1. combines the aspect of both character and word tokenization\n",
        "2. we want to split rare words into smaller units to deal with complex and misspellings\n",
        "3. we want to keep the vocab size to a manageable size\n",
        "4. ##prefix in some words indicate the preceding text is not a white space and any token with that prefix is merged with the previous token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "EuJchusk__VE"
      },
      "outputs": [],
      "source": [
        "# subword tokenization(manual tokenizer)\n",
        "from transformers import DistilBertTokenizer\n",
        "distilbert_tokenizer = DistilBertTokenizer.from_pretrained(model_ckpt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rw3_90FTA-Qv",
        "outputId": "5c8e49ec-d3e5-424f-f590-007cc4fca867"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [101, 19204, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ],
      "source": [
        "encoded_text = tokenizer(text)\n",
        "print(encoded_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4I0_verEBeA1",
        "outputId": "459dac3b-6d3e-43da-9330-6406f08d7bd3"
      },
      "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": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6T1nLkepBp8D",
        "outputId": "7f0bc969-9bff-44df-c721-31a15b416a8d"
      },
      "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": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsLB1wDJCVH_",
        "outputId": "5516fbd0-6f83-4b0a-98d6-6b4e7b5f1def"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30522"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "tokenizer.vocab_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8XsH4mUj1rM",
        "outputId": "5d82f35b-6c25-4a50-dac5-7b993417d0a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "tokenizer.model_max_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sD0H8P-0j40f",
        "outputId": "4013d358-e05d-4906-e414-386ee96622be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# names of the fields that the model expects in forward pass\n",
        "tokenizer.model_input_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbA3OZrWkCQv",
        "outputId": "c4b9b8c5-cd66-4037-9aa5-0ce444089d3f"
      },
      "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": 35
        }
      ],
      "source": [
        "emotions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyqjexWWkkKT"
      },
      "source": [
        "## Tokenizing the entire dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "_McFj1_-kj2k"
      },
      "outputs": [],
      "source": [
        "# mapping function\n",
        "def tokenize(batch):\n",
        "  return tokenizer(batch[\"text\"], padding = True, truncation = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPSc3ETskYyx",
        "outputId": "84f090ee-4af9-4adc-c7aa-83eb462a59bd"
      },
      "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": "9QXyV7y2_1P0"
      },
      "source": [
        "* padding = True means it adds zero to match the longest length of words\n",
        "* truncation = True means it truncates the tokens if it exceeds the models max token size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "8FOTg1pjk_Ik",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197,
          "referenced_widgets": [
            "ebf50e454dba4559ba393d8ac9997276",
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            "76b3e16006d647a0aef7c80f3bfa045e",
            "987bae8ae02d40baad84816ca2c7492b",
            "fd2725f8ed514fbb9803d756a5be92e1",
            "f8e331f08edf42d8ba4fca47db0950ee",
            "3f860c38a5ca4a5792895431247b50c6"
          ]
        },
        "outputId": "82f6ea73-b653-4910-b3d5-11208a7f8786"
      },
      "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": "ebf50e454dba4559ba393d8ac9997276"
            }
          },
          "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": "c534e454ca754973bd1660fd3f559485"
            }
          },
          "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": "592848c3aa174426a927e52dac331447"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# applying the tokenize function to the dataset\n",
        "emotions_encoded = emotions.map(tokenize, batched = True, batch_size = None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hVp6V0uBL6R",
        "outputId": "59e825a1-a31d-4346-879e-364c9df8b126"
      },
      "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": "dXglUbXNB2z1"
      },
      "source": [
        "## Training a text classificer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "0a0tliEzBbcW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77,
          "referenced_widgets": [
            "870b170e0a9f48b29233961ee3c455a9",
            "3a6e14cfe4024253bd2d73eca2fb7b9c",
            "a11a957456314fbebea17ce6d26b9ffa",
            "68738b70b01a490f92f9f84f4a2bc4a4",
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            "54d0c6c527be40cbb33e71e3242a6c3b",
            "1d2bcae843d24f83ab9c9f6a9f17921f",
            "6fc0e143b091492a8c5dee129858c947"
          ]
        },
        "outputId": "9516a519-d8f5-49ae-aca1-73d884c68ace"
      },
      "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": "870b170e0a9f48b29233961ee3c455a9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "model_ckpt = \"distilbert-base-uncased\"\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = AutoModel.from_pretrained(model_ckpt).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6KY2dlQFhZj",
        "outputId": "b83f6cad-0093-4dd1-c019-0d817e8fef13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "text = \"this is a text\"\n",
        "inputs = tokenizer(text, return_tensors = \"pt\")\n",
        "inputs[\"input_ids\"].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cO5BsRPGPoT",
        "outputId": "2a5ab500-27b2-4fb5-ee65-c86231a3dfcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BaseModelOutput(last_hidden_state=tensor([[[-0.2535, -0.1091,  0.0759,  ..., -0.1054,  0.2257,  0.4615],\n",
            "         [-0.7154, -0.2742, -0.0954,  ..., -0.3597,  0.5174,  0.3450],\n",
            "         [-0.4100, -0.2195,  0.1850,  ..., -0.0136,  0.1607,  1.0422],\n",
            "         [-0.2523, -0.0254,  0.2472,  ..., -0.1637,  0.0865,  1.0516],\n",
            "         [-0.0570, -0.1057,  0.0662,  ...,  0.0584,  0.1034,  0.2971],\n",
            "         [ 0.9184,  0.1577, -0.4105,  ...,  0.1509, -0.7327, -0.1572]]],\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.inference_mode():\n",
        "  outputs = model(**inputs)\n",
        "print(outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzb9iwNYHEt8",
        "outputId": "73986a17-6ca8-4bf6-ab94-5eb8d44d7568"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 6, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "outputs[\"last_hidden_state\"].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rdOiiCz8HUAi"
      },
      "source": [
        "1. for classification task we can take the hidden state associated with the [CLS] token as input feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "uhb57jpfH6bB"
      },
      "outputs": [],
      "source": [
        "# func for extracting the hidden states for every batch\n",
        "def extract_hidden_states(batch):\n",
        "\n",
        "  # place model inputs on the GPU\n",
        "  inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names}\n",
        "\n",
        "  # Extract last hidden states\n",
        "  with torch.inference_mode():\n",
        "    last_hidden_state = model(**inputs).last_hidden_state\n",
        "\n",
        "  # return vector for [CLS] token\n",
        "  return {\"hidden_state\" : last_hidden_state[:, 0].cpu().numpy()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "X3vv5EzTI6wk"
      },
      "outputs": [],
      "source": [
        "# converting the dataset to torch.tensors\n",
        "emotions_encoded.set_format(\"torch\", columns = [\"input_ids\", \"attention_mask\", \"label\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197,
          "referenced_widgets": [
            "46bb77e00d9f41f9985942cead8f924f",
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            "20a3efa0977b4755afab3b8e966a99af",
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            "d2d5306042234ef0992c73991b1b7dcd"
          ]
        },
        "id": "pSxDi7HGKRcM",
        "outputId": "33cd59d8-623a-4a25-e395-116be79ad5fc"
      },
      "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": "46bb77e00d9f41f9985942cead8f924f"
            }
          },
          "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": "6fb7fc6f125b42a59451e3b02c48a9c8"
            }
          },
          "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": "79a52db8569e4ed0850e5352a665fb57"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# extracting hidden states\n",
        "emotions_hidden = emotions_encoded.map(extract_hidden_states, batched = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "DaWVdq8_LHtx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bd071b9-80ef-4c61-933e-276a92595c61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['text', 'label', 'input_ids', 'attention_mask', 'hidden_state']"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "emotions_hidden[\"train\"].column_names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJcjFdOYL6YB"
      },
      "source": [
        "## creating a feature matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "ChSQbES3Oc2A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00db487f-2e1c-46c1-f81d-9ba452350069"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((16000, 768), (2000, 768))"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "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": "XDgmcRW5Oy7k"
      },
      "source": [
        "## visualizing the training set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahLoiG93UTHW"
      },
      "source": [
        "1. visualizng the hidden states in 768 dimensions is tricky using umap to project vectors down to 2-d"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "SXHnUFs0Uov6"
      },
      "outputs": [],
      "source": [
        "# import umap\n",
        "# from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# # scale features to [0, 1] range\n",
        "# X_scaled = MinMaxScaler().fit_transform(X_train)\n",
        "\n",
        "# # initilize and fit umap\n",
        "# mapper = umap.UMAP(n_components = 2, metric = \"cosine\").fit(X_scaled)\n",
        "\n",
        "# # create a data frame 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": "code",
      "execution_count": 50,
      "metadata": {
        "id": "Yo2iZfMP4Nzv"
      },
      "outputs": [],
      "source": [
        "# fig, axes = plt.subplots(2, 3, figsize = (7, 5))\n",
        "# axes = axes.flatten()\n",
        "# cmaps = [\"Greys\", \"Blues\", \"Oranges\", \"Reds\", \"Purples\", \"Greens\"]\n",
        "# labels = emotions[\"train\"].features[\"label\"].names\n",
        "\n",
        "# for i, (label, cmap) in enumerate(zip(labels, cmaps)):\n",
        "#   df_emb_sub = df_emb.query(f\"label == {i}\")\n",
        "#   axes[i].hexbin(df_emb_sub[\"X\"], df_emb_sub[\"Y\"], cmap = cmap, gridsize = 20, linewidths = (0,))\n",
        "#   axes[i].set_title(label)\n",
        "#   axes[i].set_xticks([])\n",
        "#   axes[i].set_yticks([])\n",
        "# plt.tight_layout()\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_DEgGg6Mzz0"
      },
      "source": [
        "1. There are the projections onto a lower dimensional space\n",
        "2. if some categories overlap here it does not mean that they are not seperable in the original space\n",
        "3. if they are seperable in the prjected space they will be seperable in the orginal space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "voHpj_XEPFGi"
      },
      "source": [
        "1. sadness, anger, fear all occupy similar regions with slightly varying distributions\n",
        "2. joy and love are well seprated from negative emotions and share similar space\n",
        "3. surprise is scatter all over the place"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMpbrO0W5HGb"
      },
      "source": [
        "In the context of Natural Language Processing (NLP), masked words refer to a technique commonly used in pre-trained language models, such as BERT (Bidirectional Encoder Representations from Transformers). The idea behind masked words is to randomly replace some of the words in a given sentence with a special token, often \"[MASK],\" and then train the model to predict the original words based on the context provided by the surrounding words.\n",
        "\n",
        "During pre-training, a language model like BERT is exposed to vast amounts of text data and learns to understand the relationships between words by predicting missing or masked words. This process helps the model capture contextual information and semantic meaning, allowing it to perform well on a variety of downstream NLP tasks such as text classification, named entity recognition, and question answering.\n",
        "\n",
        "After pre-training, the fine-tuning process adapts the model to specific tasks using labeled datasets. This enables the model to generalize its understanding to various natural language tasks by leveraging the knowledge gained during pre-training. The masked word prediction task is just one of the techniques used in the training of modern language models to improve their ability to understand and generate human-like language."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGdh0j-KOjR6"
      },
      "source": [
        "## Training a simple classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "zcmT_-O4PdN8"
      },
      "outputs": [],
      "source": [
        "# from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# lr_clf = LogisticRegression(max_iter = 3000)\n",
        "# lr_clf.fit(X_train, y_train)\n",
        "# lr_clf.score(X_valid, y_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUVJ2EnlRZzp"
      },
      "source": [
        "1. above model is better than random since we are dealing with unbalanced data it's actually better\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "fDB-cr_3RkYm"
      },
      "outputs": [],
      "source": [
        "# # comparing above model against simple baseline\n",
        "# # chooses the most frequent class\n",
        "# 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": "qWD3P_SjSTLM"
      },
      "source": [
        "1. simple classifier with distilbert embeddings is better than the baseline model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "I4vPKgwmSRXO"
      },
      "outputs": [],
      "source": [
        "# plotting confusion matrix\n",
        "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "2EyY4MpwTIYs"
      },
      "outputs": [],
      "source": [
        "# y_preds = lr_clf.predict(X_valid)\n",
        "# plot_confusion_matrix(y_preds, y_valid, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74gbSoFiTzQQ"
      },
      "source": [
        "# Fine-Tuning Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZKvJbwBacU2"
      },
      "source": [
        "using trainer API from transformers to simplify the training look"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BkI9Hcgsak62"
      },
      "source": [
        "## Loading a pretrained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoDfOYk0asdY"
      },
      "source": [
        "using AutoModelForSequenceClassification model instead of AutoModel. The difference is that AutoModelSequenceClassification model has classification head on top of 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "DqPHDLURbMh4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "654b0bc0-6f35-4b4b-98bc-14536921cfaa"
      },
      "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": [
        "from transformers import AutoModelForSequenceClassification\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": "4TX5yvekbeEK"
      },
      "source": [
        "the classification head has not yet been trained"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knCQp7e9cK9-"
      },
      "source": [
        "## Defining the performance metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "pugWdvwUcNGe"
      },
      "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": "2z-K6otvcm-x"
      },
      "source": [
        "## training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "NHHOT-Lct5Xf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "d107b886-a339-43bf-ba29-15563dc56455"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "SecretNotFoundError",
          "evalue": "Secret textclassification does not exist.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mSecretNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-ad1ec9bf31c8>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0muserdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muserdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"textclassification\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/userdata.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(key)\u001b[0m\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'exists'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mSecretNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'access'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mNotebookAccessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mSecretNotFoundError\u001b[0m: Secret textclassification does not exist."
          ]
        }
      ],
      "source": [
        "from google.colab import userdata\n",
        "token = userdata.get(\"textclassification\")\n",
        "print(token)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "yiK-uXL1c4d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "545c638590eb45059153e786f88e8dd3",
            "52e3daebda8a44b88c9db55b88bc1064",
            "6d2a59f420ae4e9f982554336e57735e",
            "7653cd149ef546398f0fd4606639a86d",
            "7cdd82a488c441698c720be367094a41",
            "4eecf3e66e6947b9b1cd3df2ad351d21",
            "4c16f052c6c84a3dbdfb61605a357607",
            "2639eafe1c074097b5a380477e05ccf2",
            "6deaa60b9014458a94db2107d2ea8883",
            "65b6707b8da4456f8bb85556b09b28f4",
            "f14d39fd5a94426eb26ebf4f63efac32",
            "c9961a4391b749969aebdd45cc51fe6e",
            "cde68eba6dda4d03959449752afed9c0",
            "eae0362eb6234a5f8219e7270703f28a",
            "e54755f7974e49458e65a9197016351f",
            "a4589d92aa1a41cf93dc622d15e61b52",
            "89e75fe7be76453e98efffd1fd455240",
            "36e4a2c1bc094c46b278125b2e97684d",
            "8b0ebb59fabb44d8baee145fe6476605",
            "84cd7af910814a509ab9bb7683542e69",
            "9820912ca1b54a249c32f4e11f0dc7fa",
            "c1b6a406d0064854b758717f8c3f3ba5",
            "ade78783ee214612be7275b5c5ef67b1",
            "62de0208630b47df86f289bdad129c93",
            "9e7c861cdc2547a3aef3e0d5c132d62a",
            "af130730fa2844c6b52f7a83e40a630f",
            "f13e7c53087846e5982a098b565200ba",
            "bf0d9b6daf80464bb5e0cf2076487699",
            "2ce1db8f815147749ea0fc7594b88b7c",
            "b06f4acc2b47494986179da20a30291c",
            "457bf5f1764a429caacb645bdff07646",
            "e6cf1817ffe74198a294d13cd7929ced"
          ]
        },
        "outputId": "1c9a05a2-779e-4164-95d7-3b93b443d90d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "545c638590eb45059153e786f88e8dd3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "IsvmNvvUc8gt"
      },
      "outputs": [],
      "source": [
        "# from transformers import  TrainingArguments\n",
        "import accelerate\n",
        "import transformers\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 = transformers.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",
        "                                  disable_tqdm = False,\n",
        "                                  evaluation_strategy = \"epoch\",\n",
        "                                  logging_steps = logging_steps,\n",
        "                                  push_to_hub = True,\n",
        "                                  log_level = \"error\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "O7Wi-PoNj1Sy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "d0c0fb18-30b0-4851-9c10-06d9a720e092"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 03:50, 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>0.853700</td>\n",
              "      <td>0.323516</td>\n",
              "      <td>0.897000</td>\n",
              "      <td>0.895785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.250600</td>\n",
              "      <td>0.219451</td>\n",
              "      <td>0.923500</td>\n",
              "      <td>0.923296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import Trainer\n",
        "trainer = Trainer(model = model,\n",
        "                  args = training_args,\n",
        "                  compute_metrics = compute_metrics,\n",
        "                  train_dataset = emotions_encoded[\"train\"],\n",
        "                  eval_dataset = emotions_encoded[\"validation\"],\n",
        "                  tokenizer = tokenizer)\n",
        "trainer.train();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "wwIAXzxRtbdr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f522d2cb-09fd-4764-a1b0-b3f562b7ffbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ],
      "source": [
        "# predicting on the validation data\n",
        "preds_pt = trainer.predict(emotions_encoded[\"validation\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "vlAi8DWkB6yT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97c8ab2a-fb09-4853-acdf-046f70991678"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.2194512039422989,\n",
              " 'test_accuracy': 0.9235,\n",
              " 'test_f1': 0.923296474937779,\n",
              " 'test_runtime': 4.4213,\n",
              " 'test_samples_per_second': 452.361,\n",
              " 'test_steps_per_second': 7.238}"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "preds_pt.metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTH6p8apDwEE"
      },
      "source": [
        "1. it also contains the raw predictions for each class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "mrLMP_bWCVSE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "4bc1b26c-a317-4064-dad6-1ccd2f180da1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_preds_pt = np.argmax(preds_pt.predictions, axis = 1)\n",
        "\n",
        "# plotting confusion matrix\n",
        "plot_confusion_matrix(y_preds_pt, y_valid, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmonaWOOGzLC"
      },
      "source": [
        "1. the model got confused with love and joy. It predicted joy when some of them is love which seems natural\n",
        "2. also the model got confused with surprise. It predicted some of them as joy and fear instead of surprise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paasM8SyEFFY"
      },
      "source": [
        "## Fine-Tunning with Keras\n",
        "while using tensorflow we can fine-tune models using the KERAS API. It has built in fit() method and no trainer class like the one in PyTorch API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "rE3rlKyXFW2O"
      },
      "outputs": [],
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "tf_model = (TFAutoModelForSequenceClassification\n",
        "            .from_pretrained(model_ckpt, num_labels = num_labels))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2hy9--QTIjXw"
      },
      "source": [
        "converting the dataset to tf dataset using to_tf_dataset() to meet the requirements for using keras api"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "HPu_NwmFFk1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42a1328b-f1f5-4e6c-b3cc-52683cb9a7d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:400: 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": [
        "# converting the tokenized dataset to tf dataset\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\"],\n",
        "    shuffle = True,\n",
        "    batch_size = batch_size\n",
        ")\n",
        "\n",
        "tf_eval_dataset = emotions_encoded[\"validation\"].to_tf_dataset(\n",
        "    columns = tokenizer_columns,\n",
        "    label_cols = [\"label\"],\n",
        "    shuffle = False,\n",
        "    batch_size = batch_size\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "FihUeuNyHTo0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e64d55d0-b0d9-441b-dc79-2d1f257103c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "250/250 [==============================] - 187s 548ms/step - loss: 0.5285 - sparse_categorical_accuracy: 0.8153 - val_loss: 0.2016 - val_sparse_categorical_accuracy: 0.9240\n",
            "Epoch 2/2\n",
            "250/250 [==============================] - 130s 520ms/step - loss: 0.1430 - sparse_categorical_accuracy: 0.9387 - val_loss: 0.1268 - val_sparse_categorical_accuracy: 0.9425\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7978982d4550>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# training the model using keras api\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",
        "\n",
        "tf_model.fit(tf_train_dataset,\n",
        "             validation_data = tf_eval_dataset,\n",
        "             epochs = 2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sparse categorical accuracy is a metric commonly used in machine learning, especially in the context of neural networks for classification tasks. It is calculated as the number of correctly predicted instances for the target class divided by the total number of instances.\n",
        "\n",
        "To be more specific:\n",
        "\n",
        "Sparse Categorical Accuracy = Number of Correctly Predicted Instances / Total Number of Instances\n",
        "\n",
        "In the case of sparse categorical accuracy, it is often used when the labels are integers (e.g., class indices) rather than one-hot encoded vectors. The \"sparse\" in its name refers to the fact that the true labels are provided as integers (sparse representation) rather than one-hot encoded vectors (dense representation).\n",
        "\n",
        "So, in summary, it is indeed the count of correctly predicted instances divided by the total number of predictions, but it's specifically applied when dealing with integer-encoded class labels."
      ],
      "metadata": {
        "id": "aZhNe9LjTKgF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "tbVpYC9BLbo9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "ef8a1a47-86cd-46f7-aca3-e911450fd84d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 6s 139ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_preds_tf = np.argmax(tf_model.predict(tf_eval_dataset).logits, axis = 1)\n",
        "\n",
        "# plotting confusion matrix for tf model\n",
        "plot_confusion_matrix(y_preds_tf, y_valid, labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "wbk-R7c8VsJx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "577f1988-0cb2-4246-c802-5c2a4abde69d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plotting confusion matrix for both pytorch and tensorflow model and both are trained for 2 epochs\n",
        "def plot_confusion_matrix(y_preds_tf, y_preds_pt, y_true, labels):\n",
        "  fig, axes = plt.subplots(1, 2, figsize = (12, 5))\n",
        "  cm_tf = confusion_matrix(y_true, y_preds_tf, normalize = 'true')\n",
        "  cm_pt = confusion_matrix(y_true, y_preds_pt, normalize = 'true')\n",
        "  disp_tf = ConfusionMatrixDisplay(confusion_matrix = cm_tf, display_labels = labels)\n",
        "  disp_pt = ConfusionMatrixDisplay(confusion_matrix = cm_pt, display_labels = labels)\n",
        "  disp_tf.plot(cmap = \"Blues\", values_format = \".2f\", ax = axes[0], colorbar = False)\n",
        "  disp_pt.plot(cmap = \"Blues\", values_format = \".2f\", ax = axes[1], colorbar = False)\n",
        "  axes[0].set_title(\"Tensorflow\")\n",
        "  axes[1].set_title(\"PyTorch\")\n",
        "  plt.show()\n",
        "\n",
        "plot_confusion_matrix(y_preds_tf, y_preds_pt, y_valid, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Error analysis"
      ],
      "metadata": {
        "id": "PdKPJV2YQ15a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "unFtcZHDURTE"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "WxPolEXYWxed"
      },
      "outputs": [],
      "source": [
        "from torch.nn.functional import cross_entropy\n",
        "\n",
        "def forward_pass_with_label(batch):\n",
        "  # place all input tensorss on the same device as the model\n",
        "  inputs = {k : v.to(device) for k, v in batch.items()\n",
        "            if k in tokenizer.model_input_names}\n",
        "\n",
        "  with torch.inference_mode():\n",
        "    output = model(**inputs)\n",
        "    pred_label = torch.argmax(output.logits, axis = -1),\n",
        "    loss = cross_entropy(output.logits,\n",
        "                         batch[\"label\"].to(device),\n",
        "                         reduction = \"none\")\n",
        "\n",
        "  # place outputs on cpu for compatibility with other dataset columns\n",
        "  return {\"loss\" : loss,\n",
        "            \"output_logits\" : output.logits}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "n93cWj4SX1j-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77,
          "referenced_widgets": [
            "5ed007695d9f48c2980d47b6190a477d",
            "473785d41f4044289c5ed46eddc0351e",
            "9bf8f7fb95fc421eb413d83aee8bd60f",
            "57ef3ac27ffa41c581376434822d6d7d",
            "80a26df4496a4809b201d0269c32c3bd",
            "22ff0cfc94ef4cfea0ccc189e8039e1f",
            "f0ce428a653e46678175ab3e0ebcf063",
            "f5609260b73d4aa3b58629eea02e6e47",
            "21418dc66c3c465f950666e72b6fba7f",
            "7383ca96858a48528e1e2a085339c07e",
            "274f528f440547668c0b09d780522f98"
          ]
        },
        "outputId": "337f6596-8c05-482d-ce1d-fa22965654e5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ed007695d9f48c2980d47b6190a477d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# using map method to compute the losses for all the samples\n",
        "\n",
        "# convert our dataset back to pytorch tensors\n",
        "emotions_encoded.set_format(\"torch\",\n",
        "                            columns = [\"input_ids\", \"attention_mask\", \"label\"])\n",
        "\n",
        "# compute loss values\n",
        "emotions_encoded[\"validation\"] = emotions_encoded[\"validation\"].map(forward_pass_with_label,\n",
        "                                                                    batched = True,\n",
        "                                                                    batch_size = 16)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_encoded.set_format(\"pandas\")\n",
        "cols = [\"text\", \"label\", \"output_logits\", \"loss\"]\n",
        "df_test = emotions_encoded[\"validation\"][:][cols]"
      ],
      "metadata": {
        "id": "ALw1RBBPZHnF"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test[\"predicted_label\"] = df_test[\"output_logits\"].apply(lambda x : np.argmax(x))"
      ],
      "metadata": {
        "id": "gaAhmcUqeJjM"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_dict = {i : labels[i] for i in range(len(labels))}\n",
        "label_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChICVMW0e4rM",
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      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'sadness', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'surprise'}"
            ]
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          "metadata": {},
          "execution_count": 74
        }
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        "df_test[[\"predicted_label\", \"label\"]] = df_test[[\"predicted_label\", \"label\"]].replace(label_dict)"
      ],
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        "id": "LYvAlU5ZfMIO"
      },
      "execution_count": 75,
      "outputs": []
    },
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        "df_test.sort_values(\"loss\", ascending = False).head(10)"
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      "execution_count": 90,
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              "                                                   text     label  \\\n",
              "1950  i as representative of everything thats wrong ...  surprise   \n",
              "1963  i called myself pro life and voted for perry w...       joy   \n",
              "882   i feel badly about reneging on my commitment t...      love   \n",
              "1801  i feel that he was being overshadowed by the s...      love   \n",
              "1509  i guess this is a memoir so it feels like that...       joy   \n",
              "1870  i guess i feel betrayed because i admired him ...       joy   \n",
              "1500  i guess we would naturally feel a sense of lon...     anger   \n",
              "1111  im lazy my characters fall into categories of ...       joy   \n",
              "405   i have been feeling extraordinarily indecisive...      fear   \n",
              "1683  i had applied for a job and they had assured m...     anger   \n",
              "\n",
              "                                          output_logits      loss  \\\n",
              "1950  [3.4680815, -1.1708108, -1.8415996, 1.1100867,...  5.556944   \n",
              "1963  [3.8983426, -1.3576488, -1.1388916, -0.1126643...  5.305911   \n",
              "882   [3.4326234, -0.59110284, -1.6783965, 0.7716352...  5.220197   \n",
              "1801  [4.071471, -0.75424206, -0.87233037, -0.929799...  4.979969   \n",
              "1509  [0.13551404, -1.288383, -1.3879347, -0.9505784...  4.970778   \n",
              "1870  [3.5769184, -1.1778387, -0.46349433, -0.818754...  4.823200   \n",
              "1500  [4.2843747, -0.8608792, -1.1037171, -0.4985274...  4.809717   \n",
              "1111  [0.23640306, -1.6873248, -2.0475214, 1.9557683...  4.565818   \n",
              "405   [-2.1552153, 3.273236, 0.4525951, -2.016532, -...  4.296227   \n",
              "1683  [-0.907729, 3.4793534, -1.1827734, -0.7358772,...  4.273014   \n",
              "\n",
              "     predicted_label  \n",
              "1950         sadness  \n",
              "1963         sadness  \n",
              "882          sadness  \n",
              "1801         sadness  \n",
              "1509            fear  \n",
              "1870         sadness  \n",
              "1500         sadness  \n",
              "1111            fear  \n",
              "405              joy  \n",
              "1683             joy  "
            ],
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          "metadata": {},
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      "execution_count": 92,
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        {
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          "data": {
            "text/plain": [
              "                                                   text label  \\\n",
              "1873  i feel practically virtuous this month i have ...   joy   \n",
              "1505  im feeling hopeful about a great deal of thing...   joy   \n",
              "1205  i log on feeling vaguely sociable and after a ...   joy   \n",
              "578   i got to christmas feeling positive about the ...   joy   \n",
              "941   i expected but it did feel hopeful and it defi...   joy   \n",
              "1265  im feeling more hopeful today than i did yeste...   joy   \n",
              "1263                 i feel this way about blake lively   joy   \n",
              "530   i feel pretty safe but i do realize that we do...   joy   \n",
              "1421  i feel undeservingly lucky to be surrounded by...   joy   \n",
              "1449  im also feeling brave enough to publish my tho...   joy   \n",
              "\n",
              "                                          output_logits      loss  \\\n",
              "1873  [-1.4237986, 4.5504737, -0.7130102, -1.2840729...  0.016220   \n",
              "1505  [-1.3845941, 4.5399165, -0.666625, -1.4946276,...  0.016611   \n",
              "1205  [-1.3749591, 4.5414786, -0.58795214, -1.436796...  0.016634   \n",
              "578   [-1.3239833, 4.533265, -0.67640454, -1.3442787...  0.016641   \n",
              "941   [-1.3034065, 4.5182724, -0.6394909, -1.4151322...  0.016916   \n",
              "1265  [-1.2545577, 4.517784, -0.73379093, -1.4223977...  0.016923   \n",
              "1263  [-1.5053567, 4.519288, -0.5932072, -1.4356557,...  0.017054   \n",
              "530   [-1.103302, 4.513775, -0.7744781, -1.2526917, ...  0.017084   \n",
              "1421  [-1.333859, 4.5334177, -0.5009244, -1.4169365,...  0.017085   \n",
              "1449  [-1.4296385, 4.545243, -0.4256305, -1.3768531,...  0.017189   \n",
              "\n",
              "     predicted_label  \n",
              "1873             joy  \n",
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              "941              joy  \n",
              "1265             joy  \n",
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              "      <td>i feel practically virtuous this month i have ...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.4237986, 4.5504737, -0.7130102, -1.2840729...</td>\n",
              "      <td>0.016220</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1505</th>\n",
              "      <td>im feeling hopeful about a great deal of thing...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.3845941, 4.5399165, -0.666625, -1.4946276,...</td>\n",
              "      <td>0.016611</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1205</th>\n",
              "      <td>i log on feeling vaguely sociable and after a ...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.3749591, 4.5414786, -0.58795214, -1.436796...</td>\n",
              "      <td>0.016634</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>i got to christmas feeling positive about the ...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.3239833, 4.533265, -0.67640454, -1.3442787...</td>\n",
              "      <td>0.016641</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>i expected but it did feel hopeful and it defi...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.3034065, 4.5182724, -0.6394909, -1.4151322...</td>\n",
              "      <td>0.016916</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>im feeling more hopeful today than i did yeste...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.2545577, 4.517784, -0.73379093, -1.4223977...</td>\n",
              "      <td>0.016923</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1263</th>\n",
              "      <td>i feel this way about blake lively</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.5053567, 4.519288, -0.5932072, -1.4356557,...</td>\n",
              "      <td>0.017054</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>i feel pretty safe but i do realize that we do...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.103302, 4.513775, -0.7744781, -1.2526917, ...</td>\n",
              "      <td>0.017084</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1421</th>\n",
              "      <td>i feel undeservingly lucky to be surrounded by...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.333859, 4.5334177, -0.5009244, -1.4169365,...</td>\n",
              "      <td>0.017085</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1449</th>\n",
              "      <td>im also feeling brave enough to publish my tho...</td>\n",
              "      <td>joy</td>\n",
              "      <td>[-1.4296385, 4.545243, -0.4256305, -1.3768531,...</td>\n",
              "      <td>0.017189</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9ed4f77-1cd6-49fa-bfd6-075c7fe917d3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a9ed4f77-1cd6-49fa-bfd6-075c7fe917d3 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-a9ed4f77-1cd6-49fa-bfd6-075c7fe917d3');\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-31b30284-02d9-42ee-bbe2-d110c67b6675\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31b30284-02d9-42ee-bbe2-d110c67b6675')\"\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-31b30284-02d9-42ee-bbe2-d110c67b6675 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving and Sharing the model"
      ],
      "metadata": {
        "id": "G4DhsxH-f0bt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(commit_message = \"Training Completed!...\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192,
          "referenced_widgets": [
            "d373d88496bb48a085ef3765b094bf00",
            "488c2ecded7742198a26b1c617237efd",
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            "d6b357becd7b428c9ca182e57cb18b76",
            "7c495ab6cabf4413854290c2f9b74738",
            "262afffe437341a7b16d3bd35bc810de",
            "3c8347f460904610873b08bd2502beed",
            "92b42a2fc09a46b69a433bf8bec894ea",
            "1c45126d45fe43d8929876cd3a75ac41",
            "984a2f7bcd274d43a54a2a1c9098ca05",
            "6c7b586bd05341139d269cd8314eb663"
          ]
        },
        "id": "eX1lSlVAju1A",
        "outputId": "7d3420bc-8b21-43ac-e93a-b3cf69e8a310"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "events.out.tfevents.1707460041.41fe190b7f8c.2023.0:   0%|          | 0.00/5.98k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "d373d88496bb48a085ef3765b094bf00"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/pt-sk/distilbert-base-uncased-finetuned-emotion/commit/cdb3b0d0225fcfac326862d338a22769baf620fa', commit_message='Training Completed!...', commit_description='', oid='cdb3b0d0225fcfac326862d338a22769baf620fa', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# making predictions on the custom tweets\n",
        "from transformers import pipeline\n",
        "\n",
        "model_id = f\"pt-sk/{model_name}\"\n",
        "classifier = pipeline(\"text-classification\", model = model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321,
          "referenced_widgets": [
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        },
        "id": "XouABTivjzNH",
        "outputId": "0d96b742-14e6-4b6a-ccba-9984be844b62"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/883 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1522f5e5e5d4006b86884fb7d0c9adb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "480b996c191c40258434cea3b8467e6f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "858bd07f9fc64fe7b113f27399870a44"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc504daf47f1485f81fe2165725f07ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47b5585f296f457498e4aa64df307cf4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "custom_tweet = \"I saw a movie today and it was really good.\"\n",
        "preds = classifier(custom_tweet, return_all_scores = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHAycju4kIpk",
        "outputId": "42b0c876-a168-470d-f924-2de8f34f7114"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoaLiKe8mSQF",
        "outputId": "98b19097-9c22-4d0b-bae9-374965029fb3"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'LABEL_0', 'score': 0.01733517087996006},\n",
              " {'label': 'LABEL_1', 'score': 0.9460930824279785},\n",
              " {'label': 'LABEL_2', 'score': 0.0109243830665946},\n",
              " {'label': 'LABEL_3', 'score': 0.010435751639306545},\n",
              " {'label': 'LABEL_4', 'score': 0.0063101500272750854},\n",
              " {'label': 'LABEL_5', 'score': 0.008901547640562057}]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_df = pd.DataFrame(preds[0])\n",
        "plt.bar(labels, 100 * preds_df[\"score\"], color = \"C0\")\n",
        "plt.title(f\"{custom_tweet}\")\n",
        "plt.xlabel(\"Classes\")\n",
        "plt.ylabel(\"Class Probability (%)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Wh635YIdkUqt",
        "outputId": "4aeecfd1-1ecf-41e5-b223-5a429a96789b"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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