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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jason9693/APEACH/blob/master/apeach.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MeVhUwvb5MAf"
      },
      "outputs": [],
      "source": [
        "# !pip install transformers\n",
        "# !pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"monologg/koelectra-small-v3-discriminator\"\n",
        "dataset_name = \"jason9693/APEACH\""
      ],
      "metadata": {
        "id": "VPR24ysr5Q7k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)\n",
        "\n",
        "# set device\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIGegxPe5xN0",
        "outputId": "dc5602f2-216b-4a05-fdf9-afcc402dba8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at monologg/koelectra-small-v3-discriminator were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight']\n",
            "- This IS expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at monologg/koelectra-small-v3-discriminator and are newly initialized: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElectraForSequenceClassification(\n",
              "  (electra): ElectraModel(\n",
              "    (embeddings): ElectraEmbeddings(\n",
              "      (word_embeddings): Embedding(35000, 128, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 128)\n",
              "      (token_type_embeddings): Embedding(2, 128)\n",
              "      (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (embeddings_project): Linear(in_features=128, out_features=256, bias=True)\n",
              "    (encoder): ElectraEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): ElectraClassificationHead(\n",
              "    (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "    (out_proj): Linear(in_features=256, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261,
          "referenced_widgets": [
            "bec9bb5fd5b74f31b9b33854e68b88ed",
            "b83ee9931fd3405a9975ef4004cbe99a",
            "4600415e4ea3473cb37e86bf2b4f4155",
            "e3fdf97c3f334d969f51600feb8a4d48",
            "7cb591c5bd0a425c9b808642bc0bfd5b",
            "a1bc90d617fd43b2bd8c663877221926",
            "05ebd6a32996499ab6483f375941b803",
            "5bd83cfe988b480e824de5f91a0c6b1d",
            "7674b71c83944df7af89b42f1ed80207",
            "5d3316935e2f480dab4fcd17106f99c8",
            "b503935ec7e248839cd5bdc5c51cc166",
            "fbfeb59e3ddf4b73beec6033baca0bd2",
            "d245936bce744e93851eec9312800518",
            "b19a479af84e418a81232692d36929a9",
            "e0e4585f4d8846cea7a0bd303bdc7f29",
            "894225e5a10d411cb0eaef9b1ef0421c",
            "b59b1fdc7ef94fa9ae66e9673fc1e3c5",
            "ceb09568c9044cb7858f7d2e2ef38317",
            "1bf90aebc0da47169b102e1f3d51e14d",
            "ca4263fb1b5943269ce3c1dd346917a5",
            "be1d468c2f48461f9e81d07d1b98beb1",
            "b5a1ee51be574cc18e20e817deb67069",
            "de3343f8b59a4a8ea789c225e0eedd86",
            "a9ad8d69e950472a93a88b45c664dd65",
            "557f6037d1c248daa487d11bb4fdc033",
            "28d1342e2a914242a3013d79cead67d5",
            "1d363fc689f04f13bd3363871ef20030",
            "788f387328d14ac39b5ef6730ccdd7fd",
            "0d561f82455e41ddb4045fc4ade9a889",
            "2f20c171ccef4a3c9fd688123b81762a",
            "439a2c586a3a410caeb45f07028d144c",
            "9bc0acb2f5c7460582725e41aebf9289",
            "7bbf492465dd41a8a457ccb2468eaa5a",
            "b9f2a7044ca84366a22200c4d44d5394",
            "daa8733a4ad64c648a482d1b6b938705",
            "46c6bb400ed444d99882d342ed4f6415",
            "5c7e61514cad47dfa61579b0c8137df6",
            "0688a88c0ffd45e3aee57fad54f4cb84",
            "09be0739fe7e4c0cb117475cd1553c46",
            "d2ddeab22fe545dfb0f84d3fc6541c57",
            "0a322f0a393240a9a66c63ffc320bbe6",
            "5124c08cd0aa4b0595ed41aeed258cd5",
            "dc62e80c46b94dac89182d2b08a4dcac",
            "96118b60298541378497070eb5f45643",
            "a4c5373b552947748ebc23532b67aa94",
            "11e5cbe4562a47cc9afd51664170f624",
            "eb5868eccc6c41adb59a8cdab1c6cabb",
            "f47522bdb40b4cddb794bfb0d004ed5b",
            "6880bd7ba1354c6481b1be8339f82ce5",
            "713762e550d9408db74e21dfc8d11984",
            "a814088a3e0d40ec95821645943f394f",
            "9f18c0ee164c41ed877c9a975dd3e9e5",
            "1046260610774885ab877d81757701d1",
            "042fdd3130374e479912ba683d638e86",
            "44f4ae75c579477ab29df130916425bd",
            "c100c8e6fba6493cb3bc262c0249c80a",
            "57156d1c6ab84d0a9fbfea6b08dc25e9",
            "6a37201c81a1411981f926b560638b92",
            "48b51b5263d341a698e1ae0348c7ca99",
            "1b1c725c43bb4a73b7afa9b5793b8abe",
            "afb8f1de68194d488d20ae5bbe2fa60d",
            "e7ead8a8fea14280b752264756af76e3",
            "640a38126e1f4db68d69fe503abccfce",
            "3833a218c50f4d7988fa8f835d1684f1",
            "29c36e11ecbb47af8846f8e8030bdfea",
            "af1ff1e60e52455e894f3c1eed1e095e"
          ]
        },
        "id": "4rtEDilm6AyX",
        "outputId": "43a3c37e-52b0-423f-82b4-83c6e3a10ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bec9bb5fd5b74f31b9b33854e68b88ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-1448d8c4b3f7cc27\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/APEACH (download: 1.15 MiB, generated: 1.21 MiB, post-processed: Unknown size, total: 2.36 MiB) to /root/.cache/huggingface/datasets/csv/default-1448d8c4b3f7cc27/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbfeb59e3ddf4b73beec6033baca0bd2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/787k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de3343f8b59a4a8ea789c225e0eedd86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/344k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9f2a7044ca84366a22200c4d44d5394"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4c5373b552947748ebc23532b67aa94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-1448d8c4b3f7cc27/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c100c8e6fba6493cb3bc262c0249c80a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size =  32#@param {type:\"integer\"}\n",
        "learning_rate =  5e-5#@param {type:\"number\"}\n",
        "num_epochs = 3 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "num_training_steps = 5000 #@param {type:\"slider\", min:0, max:50000, step:1}"
      ],
      "metadata": {
        "cellView": "form",
        "id": "VyQXLESX9GeA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(dataset[\"train\"], shuffle=True, batch_size=batch_size)\n",
        "eval_dataloader = DataLoader(dataset[\"test\"], batch_size=batch_size)"
      ],
      "metadata": {
        "id": "u_vtix1Y6aQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.optim import AdamW\n",
        "from transformers import get_scheduler\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
        "\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    name=\"linear\", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps\n",
        ")"
      ],
      "metadata": {
        "id": "oBfo4XFW6vue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train"
      ],
      "metadata": {
        "id": "8xUqUB_R-Ugf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dataloader:\n",
        "        # make inputs\n",
        "        inputs = tokenizer(batch['text'], padding=True, return_tensors='pt')\n",
        "        inputs['labels'] = torch.tensor(batch['class'])\n",
        "        # to GPU\n",
        "        inputs = {k: v.to(device) for k,v in inputs.items()}\n",
        "        # forward\n",
        "        outputs = model(**inputs)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "d52430755ded42fb9d1049cea5b2f8cc",
            "4a9438e5a3874fd38f453d024ebe10dd",
            "f8d50d756dd94adcaf22707c00fd8f8a",
            "b6aed53bea604f4a90e713495973d372",
            "8d4145ba18cf43b5b1d9ccc10c5465d6",
            "8ff00c785bb441158969cd40d0b0836d",
            "6d94438ffa0943498038057ed91d7ee8",
            "5a8968619e534d21affa75a05655d501",
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        "id": "gDU_oFi19_eD",
        "outputId": "7da2f085-0e09-467a-c3ba-d13d58d621ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/741 [00:00<?, ?it/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  if __name__ == '__main__':\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metrics = [\n",
        "    load_metric(\"accuracy\"), \n",
        "    load_metric(\"f1\"), \n",
        "    load_metric(\"precision\"),\n",
        "    load_metric(\"recall\")]\n",
        "\n",
        "model.eval()\n",
        "for batch in eval_dataloader:\n",
        "    # make inputs\n",
        "    inputs = tokenizer(batch['text'], padding=True, return_tensors='pt')\n",
        "    inputs['labels'] = torch.tensor(batch['class'])\n",
        "    # to GPU\n",
        "    inputs = {k: v.to(device) for k,v in inputs.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "\n",
        "    for metric in metrics:\n",
        "      metric.add_batch(predictions=predictions, references=inputs[\"labels\"])\n",
        "\n",
        "for metric in metrics:\n",
        "  print(metric.compute())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185,
          "referenced_widgets": [
            "d7ee542557ea4dfd9b357f17b9d2283d",
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          ]
        },
        "id": "MEG6THyf_eHM",
        "outputId": "67037077-4076-431e-a0c9-de31c97046c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47130ca6e9454b2cbf126aa7303b6799"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy': 0.7323607427055703}\n",
            "{'f1': 0.7829640782964077}\n",
            "{'precision': 0.6674000733406674}\n",
            "{'recall': 0.9469302809573361}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "88RRaayvDGuP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}