submission_57_train_and_inference_code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/purang2/KERC-Challenge/blob/main/submission_57_train_and_inference_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDV4IQOg183j"
      },
      "source": [
        "## **configurations**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THbsq5CEaa6C"
      },
      "source": [
        "### Memory cleaner"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fMeg11ijaaCt"
      },
      "outputs": [],
      "source": [
        "memory_clean = True \n",
        "#memory_clean = False \n",
        "\n",
        "import gc\n",
        "import torch\n",
        "\n",
        "if memory_clean: \n",
        "  gc.collect()\n",
        "  torch.cuda.empty_cache()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiS7SUMPsOix",
        "outputId": "b9e110b5-49c3-43b3-b485-17047f90233d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jpH-3Tk17_n"
      },
      "outputs": [],
      "source": [
        "num_kfold = 15 #\n",
        "NUM_EPOCH = 4\n",
        "TRAIN_BATCH = 32\n",
        "EVAL_BATCH = 16\n",
        "#num_kfold = 7 #\n",
        "test_private = True\n",
        "\n",
        "#submission_csv length\n",
        "if test_private:\n",
        "  test_num = 4950\n",
        "else:\n",
        "  test_num = 2566\n",
        "\n",
        "#서로 다른 BERT 앙상블 5개 할 것 -> 모델 history는 주석처리를 하여 남겨둘 것 \n",
        "\n",
        "model_name = \"tunib/electra-ko-base\" #사용할 모델 1\n",
        "#model_name = \"klue/bert-base\" #사용할 모델 1\n",
        "#model_name = \"snunlp/KR-ELECTRA-discriminator\" #사용할 모델 2\n",
        "#model_name = \"kykim/bert-kor-base\" #사용할 모델 3: colab pro 기준 2epoch: 2~3분 x 5폴드\n",
        "#model_name = \"beomi/KcELECTRA-base\" #사용할 모델 4\n",
        "#model_name = \"lighthouse/mdeberta-v3-base-kor-further\" #사용할 모델 5\n",
        "\n",
        "\n",
        "DATE = '10-21'\n",
        "\n",
        "#submission_comment = f\"{DATE} using KLUE-BERT\" #최종 저장되는 submission.csv의 이름이 됨\n",
        "submission_comment = f\"{DATE} using tunib\" #최종 저장되는 submission.csv의 이름이 됨\n",
        "#submission_comment = f\"{DATE} using KRELECTRA\" #최종 저장되는 submission.csv의 이름이 됨\n",
        "#submission_comment = f\"{DATE} using BERT-Kor-base\" #최종 저장되는 submission.csv의 이름이 됨\n",
        "#submission_comment = f\"{DATE} using KcELECTRA-base\" #최종 저장되는 submission.csv의 이름이 됨\n",
        "#submission_comment = f\"{DATE} using mDeBERTa-kor\" #최종 저장되는 submission.csv의 이름이 됨\n",
        "\n",
        "#model_nickname = \"KLUE-BERT\"  #마찬가지로 submission.csv의 이름 자동 지정에 사용됨\n",
        "model_nickname = \"tunib\"  #마찬가지로 submission.csv의 이름 자동 지정에 사용됨\n",
        "#model_nickname = \"KR-ELECTRA\"  #마찬가지로 submission.csv의 이름 자동 지정에 사용됨\n",
        "#model_nickname = \"BERT-Kor-base\"  #마찬가지로 submission.csv의 이름 자동 지정에 사용됨\n",
        "#model_nickname = \"kc-electra-base\"  #마찬가지로 submission.csv의 이름 자동 지정에 사용됨\n",
        "#model_nickname = \"mDeberta-kor\"  #마찬가지로 submission.csv의 이름 자동 지정에 사용됨\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4U00xPsN2qkp"
      },
      "source": [
        "## install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6_IWf9Krgc5",
        "outputId": "95a9ff57-08c3-48f5-f15b-c8032d6ffe42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets #from huggingface\n",
        "!pip install evaluate\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "M44NyHchrmHO",
        "outputId": "299bdbb9-fd52-4be3-cfe5-d1d53daa4727"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sentence_id person                                           sentence  \\\n",
              "0               1     어영  야! 전화 받아. 아무리 바빠도 내전화는 받아야 되는거 아냐? 약속 하나도 못지키는...   \n",
              "1               2     어영     우리 아무래도 안되겠다. 이게 최선인거 같애. 평생 잊지 않을게. 행복하길 바란다.   \n",
              "2               3    지구대                          김경사님, 아직 안가셨어요? 시간 다됐을텐데.   \n",
              "3               4     순경                                         근무중인데 어딜가?   \n",
              "4               5    지구대    다녀오세요. 이런날은 무조건 가서 축하해주셔야죠. 이순경이 대신 근무선다고 나온대요.   \n",
              "...           ...    ...                                                ...   \n",
              "7334        12285     청난                               종남이 맛있는것도 사주고 잘 챙겨줘.   \n",
              "7335        12286     건강                        걱정마라. 먹어도 같이 먹고 굶어도 같이 굶는다.   \n",
              "7336        12287     종남                                       아빠. 우리 저거 타.   \n",
              "7337        12288     건강  나 못타는데. 한번도 안타봤어. 툭하면 차멀미까지 하는데, 봐라, 너도 내몸땡이에 ...   \n",
              "7338        12289     건강                              여기 꼼짝말고 있어. 어디 가면 안돼.   \n",
              "\n",
              "      scene                                            context  \n",
              "0     S0104                                                NaN  \n",
              "1     S0108           포기한듯 탁 일어서는데, 띵동 문자. 후다닥 보는 어영. 기막혀 읽어보는  \n",
              "2     S0109    초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대  \n",
              "3     S0109    초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대  \n",
              "4     S0109    초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대  \n",
              "...     ...                                                ...  \n",
              "7334  S3576                                                NaN  \n",
              "7335  S3576                                                NaN  \n",
              "7336  S3576                                                NaN  \n",
              "7337  S3576                                                NaN  \n",
              "7338  S3576  시간경과. 놀이기구 타는 건강종남. 사람들 비명. 신난 종남 소리지르고. 건강 하얗...  \n",
              "\n",
              "[7339 rows x 5 columns]"
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentence_id</th>\n",
              "      <th>person</th>\n",
              "      <th>sentence</th>\n",
              "      <th>scene</th>\n",
              "      <th>context</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>어영</td>\n",
              "      <td>야! 전화 받아. 아무리 바빠도 내전화는 받아야 되는거 아냐? 약속 하나도 못지키는...</td>\n",
              "      <td>S0104</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>어영</td>\n",
              "      <td>우리 아무래도 안되겠다. 이게 최선인거 같애. 평생 잊지 않을게. 행복하길 바란다.</td>\n",
              "      <td>S0108</td>\n",
              "      <td>포기한듯 탁 일어서는데, 띵동 문자. 후다닥 보는 어영. 기막혀 읽어보는</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>지구대</td>\n",
              "      <td>김경사님, 아직 안가셨어요? 시간 다됐을텐데.</td>\n",
              "      <td>S0109</td>\n",
              "      <td>초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>순경</td>\n",
              "      <td>근무중인데 어딜가?</td>\n",
              "      <td>S0109</td>\n",
              "      <td>초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>지구대</td>\n",
              "      <td>다녀오세요. 이런날은 무조건 가서 축하해주셔야죠. 이순경이 대신 근무선다고 나온대요.</td>\n",
              "      <td>S0109</td>\n",
              "      <td>초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7334</th>\n",
              "      <td>12285</td>\n",
              "      <td>청난</td>\n",
              "      <td>종남이 맛있는것도 사주고 잘 챙겨줘.</td>\n",
              "      <td>S3576</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7335</th>\n",
              "      <td>12286</td>\n",
              "      <td>건강</td>\n",
              "      <td>걱정마라. 먹어도 같이 먹고 굶어도 같이 굶는다.</td>\n",
              "      <td>S3576</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7336</th>\n",
              "      <td>12287</td>\n",
              "      <td>종남</td>\n",
              "      <td>아빠. 우리 저거 타.</td>\n",
              "      <td>S3576</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7337</th>\n",
              "      <td>12288</td>\n",
              "      <td>건강</td>\n",
              "      <td>나 못타는데. 한번도 안타봤어. 툭하면 차멀미까지 하는데, 봐라, 너도 내몸땡이에 ...</td>\n",
              "      <td>S3576</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7338</th>\n",
              "      <td>12289</td>\n",
              "      <td>건강</td>\n",
              "      <td>여기 꼼짝말고 있어. 어디 가면 안돼.</td>\n",
              "      <td>S3576</td>\n",
              "      <td>시간경과. 놀이기구 타는 건강종남. 사람들 비명. 신난 종남 소리지르고. 건강 하얗...</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "import pandas as pd \n",
        "\n",
        "data_path =\"drive/MyDrive/호반우NLP_한국인감정/KERC22Dataset/KERC22Dataset_PublicTest\"\n",
        "\n",
        "\n",
        "train_data = pd.read_csv(data_path+\"/train_data.tsv\", sep='\\t')\n",
        "train_labels = pd.read_csv(data_path+\"/train_labels.csv\")\n",
        "\n",
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImycBmmusGLA",
        "outputId": "0c0a1a65-ac73-4661-cb23-fb27b71d2a69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "초조하게 시계보면서 왔다갔다 서성이는 김순경. 순찰차(경차)와서 멈추고 내리는 지구대 [SEP] 지구대가 \"김경사님, 아직 안가셨어요? 시간 다됐을텐데.\"라고 말하고, 순경가 근무중인데 어딜가?라고 말하자, 지구대가 \"다녀오세요. 이런날은 무조건 가서 축하해주셔야죠. 이순경이 대신 근무선다고 나온대요.\"라고 말하고, 순경가 \"그럴거 없어.\" 라고 말하였다.\n"
          ]
        }
      ],
      "source": [
        "train_drop = train_data[['person', 'sentence', 'context']]\n",
        "train_drop['label'] = train_labels['label']\n",
        "train_pair = train_drop\n",
        "\n",
        "import numpy as np \n",
        "\n",
        "train_pair['context2'] = train_pair['context'].fillna(' ')\n",
        "train_pair['sentence2'] = train_pair['sentence'].fillna(' ')\n",
        "\n",
        "train_pair\n",
        "\n",
        "\n",
        "train_pair = train_pair[['person', 'context2', 'sentence2', 'label']]\n",
        "train_pair['scene'] = train_data['scene']\n",
        "train_pair\n",
        "\n",
        "train = list()\n",
        "scene_df = train_pair['scene']\n",
        "scene_df = scene_df.drop_duplicates().reset_index(drop=True)\n",
        "\n",
        "def mk_scenario(i, df):\n",
        "  scenario = df.iloc[0]['context2']+\" [SEP] \" \n",
        "  if(i==0):\n",
        "    scenario = scenario + df.iloc[i]['person'] + \"가 말하길 \" +\"\\\"\"+ df.iloc[i]['sentence2'] +\"\\\"\"\n",
        "    return scenario\n",
        "  elif(i==1):\n",
        "    scenario = scenario +\"\\\"\"+ df.iloc[i-1]['sentence2']+\"\\\"\" + \"라고 \" +df.iloc[i-1]['person']+\"가 말하자, \"+df.iloc[i]['person'] + \"가 \" +\"\\\"\"+df.iloc[i]['sentence2'] +\"\\\"\"+\" 라고 말하였다.\"\n",
        "    return scenario\n",
        "  elif(i==2):\n",
        "    scenario = scenario + df.iloc[i-2]['person'] + \"가 \" + \"\\\"\"+ df.iloc[i-2]['sentence2']+\"\\\"\"+ \"라고 말하고, \"+ df.iloc[i-1]['person'] + \"가 \" + df.iloc[i-1]['sentence2']+\"라고 말하자, \"+ df.iloc[i]['person'] + \"가 \" +\"\\\"\"+df.iloc[i]['sentence2'] +\"\\\"\"+\" 라고 말하였다.\"\n",
        "    return scenario\n",
        "  elif(2<i and i <= len(df)-1):\n",
        "    scenario = scenario + df.iloc[i-3]['person'] + \"가 \" + \"\\\"\"+ df.iloc[i-3]['sentence2']+\"\\\"\"+ \"라고 말하고, \"+ df.iloc[i-2]['person'] + \"가 \" + df.iloc[i-2]['sentence2']+\"라고 말하자, \"+ df.iloc[i-1]['person'] + \"가 \" + \"\\\"\"+ df.iloc[i-1]['sentence2']+\"\\\"\"+ \"라고 말하고, \"+ df.iloc[i]['person'] + \"가 \" +\"\\\"\"+df.iloc[i]['sentence2'] +\"\\\"\"+\" 라고 말하였다.\"\n",
        "    return scenario\n",
        "\n",
        "\n",
        "def trans_df(train_pair, train):\n",
        "  for i in scene_df[:]:\n",
        "    temp = train_pair[train_pair['scene']==i].reset_index(drop=True)\n",
        "    train+=[mk_scenario(j, temp) for j in range(len(temp))]\n",
        "    \n",
        "trans_df(train_pair, train)\n",
        "train = pd.DataFrame(train, columns=('scenario',))\n",
        "print(train.iloc[5]['scenario'])\n",
        "\n",
        "train['label'] = train_pair['label']\n",
        "train\n",
        "train_pairs = train\n",
        "\n",
        "labels = {'euphoria':0,\n",
        "          'neutral':1,\n",
        "          'dysphoria':2\n",
        "          }\n",
        "\n",
        "train_pairs['label'] = [labels[label] for label in train_pairs['label']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1K_JQ1Dp-I7H"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "# https://discuss.pytorch.org/t/is-this-a-correct-implementation-for-focal-loss-in-pytorch/43327/8\n",
        "class FocalLoss(nn.Module):\n",
        "    def __init__(self, weight=None,\n",
        "                 gamma=1.0, reduction='mean'):\n",
        "        nn.Module.__init__(self)\n",
        "        self.weight = weight\n",
        "        self.gamma = gamma\n",
        "        self.reduction = reduction\n",
        "\n",
        "    def forward(self, input_tensor, target_tensor):\n",
        "        log_prob = F.log_softmax(input_tensor, dim=-1)\n",
        "        prob = torch.exp(log_prob)\n",
        "        return F.nll_loss(\n",
        "            ((1 - prob) ** self.gamma) * log_prob,\n",
        "            target_tensor,\n",
        "            weight=self.weight,\n",
        "            reduction=self.reduction\n",
        "        )"
      ]
    },
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          "text": [
            "Some weights of the model checkpoint at tunib/electra-ko-base were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.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 tunib/electra-ko-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from datasets import Dataset\n",
        "import pandas as pd \n",
        "from transformers import AutoTokenizer,AutoModelForSequenceClassification\n",
        "from transformers import DataCollatorWithPadding, TrainingArguments, Trainer\n",
        "from transformers import DataCollatorForLanguageModeling,AutoModelForMaskedLM\n",
        "import evaluate\n",
        "\n",
        "\n",
        "max_input_length = 196\n",
        "#tokenizer = AutoTokenizer.from_pretrained(\"monologg/koelectra-base-discriminator\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    metric = evaluate.load(\"f1\")\n",
        "    #metric = evaluate.load('glue', 'qnli')\n",
        "    logits, labels = eval_preds\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    #return metric.compute(predictions=predictions, references=labels)\n",
        "    return metric.compute(predictions=predictions, references=labels, average='micro')\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "#model = AutoModelForSequenceClassification.from_pretrained(\"monologg/koelectra-base-discriminator\", num_labels=3)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)\n",
        "#klue/roberta-large"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "lmj7MQMesT7H",
        "outputId": "5f7d3cec-1e6e-46bd-e11c-ac3a8f9248c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cuda_amp half precision backend\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--tunib--electra-ko-base/snapshots/edfb795c9f667b3c5cb7085ca9112997823ce4e8/config.json\n",
            "Model config ElectraConfig {\n",
            "  \"_name_or_path\": \"tunib/electra-ko-base\",\n",
            "  \"architectures\": [\n",
            "    \"ElectraForPreTraining\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"embedding_size\": 768,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"electra\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"summary_activation\": \"gelu\",\n",
            "  \"summary_last_dropout\": 0.1,\n",
            "  \"summary_type\": \"first\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--tunib--electra-ko-base/snapshots/edfb795c9f667b3c5cb7085ca9112997823ce4e8/pytorch_model.bin\n",
            "Some weights of the model checkpoint at tunib/electra-ko-base were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.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 tunib/electra-ko-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Using cuda_amp half precision backend\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "7370c2dc7c434deda56260b906e2e99f"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a6987d50cce46788b8cf309e4f57172"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 6605\n",
            "  Num Epochs = 9\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1863\n",
            "You're using a ElectraTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1505' max='1863' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1505/1863 03:27 < 00:49, 7.25 it/s, Epoch 7/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>215</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.568960</td>\n",
              "      <td>0.792916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>430</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.516907</td>\n",
              "      <td>0.817439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>645</td>\n",
              "      <td>0.583600</td>\n",
              "      <td>0.532861</td>\n",
              "      <td>0.826975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>860</td>\n",
              "      <td>0.583600</td>\n",
              "      <td>0.522611</td>\n",
              "      <td>0.828338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1075</td>\n",
              "      <td>0.265800</td>\n",
              "      <td>0.581894</td>\n",
              "      <td>0.844687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1290</td>\n",
              "      <td>0.265800</td>\n",
              "      <td>0.648716</td>\n",
              "      <td>0.825613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1505</td>\n",
              "      <td>0.137600</td>\n",
              "      <td>0.641766</td>\n",
              "      <td>0.832425</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-215\n",
            "Configuration saved in ./results/checkpoint-215/config.json\n",
            "Model weights saved in ./results/checkpoint-215/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-215/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-215/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-430\n",
            "Configuration saved in ./results/checkpoint-430/config.json\n",
            "Model weights saved in ./results/checkpoint-430/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-430/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-430/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-645\n",
            "Configuration saved in ./results/checkpoint-645/config.json\n",
            "Model weights saved in ./results/checkpoint-645/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-645/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-645/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-860\n",
            "Configuration saved in ./results/checkpoint-860/config.json\n",
            "Model weights saved in ./results/checkpoint-860/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-860/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-860/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1075\n",
            "Configuration saved in ./results/checkpoint-1075/config.json\n",
            "Model weights saved in ./results/checkpoint-1075/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1075/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1075/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1290\n",
            "Configuration saved in ./results/checkpoint-1290/config.json\n",
            "Model weights saved in ./results/checkpoint-1290/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1290/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1290/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1505\n",
            "Configuration saved in ./results/checkpoint-1505/config.json\n",
            "Model weights saved in ./results/checkpoint-1505/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1505/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1505/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./results/checkpoint-1075 (score: 0.8446866485013623).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "303a1b1d56954e699ca996e0d8b6067c"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `ElectraForSequenceClassification.forward` and have been ignored: sentence_id. If sentence_id are not expected by `ElectraForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 4950\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--tunib--electra-ko-base/snapshots/edfb795c9f667b3c5cb7085ca9112997823ce4e8/config.json\n",
            "Model config ElectraConfig {\n",
            "  \"_name_or_path\": \"tunib/electra-ko-base\",\n",
            "  \"architectures\": [\n",
            "    \"ElectraForPreTraining\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"embedding_size\": 768,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"electra\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"summary_activation\": \"gelu\",\n",
            "  \"summary_last_dropout\": 0.1,\n",
            "  \"summary_type\": \"first\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--tunib--electra-ko-base/snapshots/edfb795c9f667b3c5cb7085ca9112997823ce4e8/pytorch_model.bin\n",
            "Some weights of the model checkpoint at tunib/electra-ko-base were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.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 tunib/electra-ko-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Using cuda_amp half precision backend\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/7 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f8916905cf845629f2137c1e25dcbf7"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9aac4b938e7a4fe7b689218770081097"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 6605\n",
            "  Num Epochs = 9\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1863\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='568' max='1863' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 568/1863 01:18 < 02:58, 7.24 it/s, Epoch 2.74/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>215</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.555414</td>\n",
              "      <td>0.783379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>430</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.488079</td>\n",
              "      <td>0.816076</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-215\n",
            "Configuration saved in ./results/checkpoint-215/config.json\n",
            "Model weights saved in ./results/checkpoint-215/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-215/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-215/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 734\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-430\n",
            "Configuration saved in ./results/checkpoint-430/config.json\n",
            "Model weights saved in ./results/checkpoint-430/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-430/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-430/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-973895c77ef8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;31m#best_run = trainer.hyperparameter_search(n_trials=10, direction=\"maximize\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m     \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1502\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m             \u001b[0mtrial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1504\u001b[0;31m             \u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_keys_for_eval\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   1505\u001b[0m         )\n\u001b[1;32m   1506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1787\u001b[0m                             nn.utils.clip_grad_norm_(\n\u001b[1;32m   1788\u001b[0m                                 \u001b[0mamp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaster_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_apex\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\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-> 1789\u001b[0;31m                                 \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_grad_norm\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   1790\u001b[0m                             )\n\u001b[1;32m   1791\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36mclip_grad_norm_\u001b[0;34m(parameters, max_norm, norm_type, error_if_nonfinite)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mclip_coef_clamped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip_coef\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclip_coef_clamped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\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[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal_norm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "from transformers import EarlyStoppingCallback\n",
        "\n",
        "#Gather your training arguments~\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    learning_rate=1.777e-5,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "#    per_device_train_batch_size=32,\n",
        "#    per_device_eval_batch_size=32,\n",
        "    per_device_train_batch_size=32, #16\n",
        "    per_device_eval_batch_size=32, #16\n",
        "    num_train_epochs=9,\n",
        "    save_strategy = 'steps',\n",
        "    save_steps = 215,\n",
        "    eval_steps =215,\n",
        "    load_best_model_at_end = True,\n",
        "    metric_for_best_model = 'f1',\n",
        "    #warmup_steps=1000,\n",
        "    #gradient_accumulation_steps = 1,\n",
        "    weight_decay=0.01,\n",
        "    fp16 = True, \n",
        "    #warmup_ratio = 0.1,\n",
        ")\n",
        "#Gather your training arguments~\n",
        "'''\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "#    per_device_train_batch_size=32,\n",
        "#    per_device_eval_batch_size=32,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=2,\n",
        "    #eval_steps=50,\n",
        "    #warmup_steps=500,\n",
        "    weight_decay=0.01,\n",
        "    #logging_steps=50, \n",
        "    #load_best_model_at_end=True,\n",
        "    #gradient_accumulation_steps=64,  # total number of steps before back propagation\n",
        ")\n",
        "'''\n",
        "\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "        model=model,\n",
        "        args=training_args,\n",
        "        #train_dataset = train_dataset,\n",
        "        #eval_dataset= val_dataset,\n",
        "        data_collator=data_collator, \n",
        "        tokenizer=tokenizer,  \n",
        "        compute_metrics = compute_metrics,\n",
        "        #model_init=model_init,  \n",
        "    )\n",
        "\n",
        "#Focal Loss\n",
        "class CustomLossTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "          labels = inputs.get(\"labels\")\n",
        "          # forward pass\n",
        "          outputs = model(**inputs)\n",
        "          logits = outputs.get(\"logits\")  \n",
        "          # compute custom loss (suppose one has 3 labels with different weights)\n",
        "          loss_fct = FocalLoss().cuda()\n",
        "          loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
        "          return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [doc for doc in examples[\"scenario\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True,padding='max_length')\n",
        "\n",
        "    return model_inputs\n",
        "\n",
        "\n",
        "def mk_scenario(i, df):\n",
        "  scenario = df.iloc[0]['context2']+\" [SEP] \" \n",
        "  if(i==0):\n",
        "    scenario = scenario + df.iloc[i]['person'] + \"가 말하길 \" +\"\\\"\"+ df.iloc[i]['sentence2'] +\"\\\"\"\n",
        "    return scenario\n",
        "  elif(i==1):\n",
        "    scenario = scenario +\"\\\"\"+ df.iloc[i-1]['sentence2']+\"\\\"\" + \"라고 \" +df.iloc[i-1]['person']+\"가 말하자, \"+df.iloc[i]['person'] + \"가 \" +\"\\\"\"+df.iloc[i]['sentence2'] +\"\\\"\"+\" 라고 말하였다.\"\n",
        "    return scenario\n",
        "  elif(i==2):\n",
        "    scenario = scenario + df.iloc[i-2]['person'] + \"가 \" + \"\\\"\"+ df.iloc[i-2]['sentence2']+\"\\\"\"+ \"라고 말하고, \"+ df.iloc[i-1]['person'] + \"가 \" + df.iloc[i-1]['sentence2']+\"라고 말하자, \"+ df.iloc[i]['person'] + \"가 \" +\"\\\"\"+df.iloc[i]['sentence2'] +\"\\\"\"+\" 라고 말하였다.\"\n",
        "    return scenario\n",
        "  elif(2<i and i <= len(df)-1):\n",
        "    scenario = scenario + df.iloc[i-3]['person'] + \"가 \" + \"\\\"\"+ df.iloc[i-3]['sentence2']+\"\\\"\"+ \"라고 말하고, \"+ df.iloc[i-2]['person'] + \"가 \" + df.iloc[i-2]['sentence2']+\"라고 말하자, \"+ df.iloc[i-1]['person'] + \"가 \" + \"\\\"\"+ df.iloc[i-1]['sentence2']+\"\\\"\"+ \"라고 말하고, \"+ df.iloc[i]['person'] + \"가 \" +\"\\\"\"+df.iloc[i]['sentence2'] +\"\\\"\"+\" 라고 말하였다.\"\n",
        "    return scenario\n",
        "\n",
        "\n",
        "def trans_df2(test_pair, test):\n",
        "  for i in scene_df2[:]:\n",
        "    temp = test_pair[test_pair['scene']==i].reset_index(drop=True)\n",
        "    test+=[mk_scenario(j, temp) for j in range(len(temp))]\n",
        "\n",
        "\n",
        "k_fold_logits = []\n",
        "\n",
        "#num_kfold = 10\n",
        "kfold = StratifiedKFold(n_splits=10, random_state=7, shuffle=True)\n",
        "#kfold = StratifiedKFold(n_splits=5)\n",
        "\n",
        "for train_df_idx, val_df_idx in kfold.split(train_pairs, train_pairs['label']):\n",
        "\n",
        "    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)\n",
        "\n",
        "    trainer = Trainer(\n",
        "    #trainer = CustomLossTrainer(\n",
        "            model=model,\n",
        "            args=training_args,\n",
        "            #train_dataset = train_dataset,\n",
        "            #eval_dataset= val_dataset,\n",
        "            data_collator=data_collator, \n",
        "            tokenizer=tokenizer,  \n",
        "            compute_metrics = compute_metrics,\n",
        "            callbacks = [EarlyStoppingCallback(early_stopping_patience=2)]  \n",
        "        )\n",
        "    \n",
        "\n",
        "    train_df = train_pairs.iloc[train_df_idx]\n",
        "    val_df = train_pairs.iloc[val_df_idx]\n",
        "\n",
        "  \n",
        "    train_dataset = Dataset.from_pandas(train_df)\n",
        "    val_dataset = Dataset.from_pandas(val_df)\n",
        "\n",
        "    train_dataset =train_dataset.map(preprocess_function, batched=True).remove_columns([\"__index_level_0__\",'scenario'])\n",
        "    val_dataset = val_dataset.map(preprocess_function, batched=True).remove_columns([\"__index_level_0__\",'scenario'])\n",
        "\n",
        "    #Collect your model, training args, dataset, data collator, tokenizer in Trainer\n",
        "    trainer.train_dataset = train_dataset\n",
        "    trainer.eval_dataset = val_dataset\n",
        "    \n",
        "\n",
        "    #best_run = trainer.hyperparameter_search(n_trials=10, direction=\"maximize\")    \n",
        "    trainer.train()\n",
        "\n",
        "\n",
        "\n",
        "    data_path =\"drive/MyDrive/호반우NLP_한국인감정/KERC22Dataset/KERC22Dataset_PublicTest\"\n",
        "\n",
        "    test_data = pd.read_csv(data_path+\"/public_test_data.tsv\", sep='\\t')\n",
        "    test_pair = test_data[['sentence_id','context','sentence','person', 'scene']]\n",
        "    \n",
        "    #PRIVATE\n",
        "    if test_private:\n",
        "      test_data_private = pd.read_csv(data_path+\"/private_test_data.tsv\", sep='\\t')\n",
        "      test_pair_private = test_data_private[['sentence_id','context','sentence','person', 'scene']]\n",
        "      test_pair = pd.concat([test_pair,test_pair_private],ignore_index=True)\n",
        "    test_pair['context2'] = test_pair['context'].fillna('none')\n",
        "    test_pair['sentence2'] = test_pair['sentence'].fillna('none')\n",
        "    #모든 글자 합치기\n",
        "    test = list()\n",
        "    scene_df2 = test_pair['scene']\n",
        "    scene_df2 = scene_df2.drop_duplicates().reset_index(drop=True)\n",
        "        \n",
        "    trans_df2(test_pair, test)\n",
        "    test = pd.DataFrame(test, columns=('scenario',))\n",
        "    #print(\"--------------------\\n\", test)\n",
        "\n",
        "    #test_pairs = test_pair[['label','new']]\n",
        "    test['sentence_id'] = test_pair['sentence_id']\n",
        "\n",
        "    test_dataset = Dataset.from_pandas(test)\n",
        "    test_dataset =test_dataset.map(preprocess_function, batched=True).remove_columns(['scenario'])\n",
        "\n",
        "\n",
        "    predictions = trainer.predict(test_dataset)\n",
        "\n",
        "    k_fold_logits.append(predictions.predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "mtp_C2S9salS",
        "outputId": "38bb8fbe-4eef-4ed4-924a-154dc0b2e0f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Id  Predicted\n",
              "0        10   euphoria\n",
              "1        11    neutral\n",
              "2        12    neutral\n",
              "3        13    neutral\n",
              "4        14    neutral\n",
              "...     ...        ...\n",
              "4945  12235   euphoria\n",
              "4946  12236  dysphoria\n",
              "4947  12237  dysphoria\n",
              "4948  12238  dysphoria\n",
              "4949  12239  dysphoria\n",
              "\n",
              "[4950 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-beabcfe1-0c45-4132-ba24-7d428f872788\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>10</td>\n",
              "      <td>euphoria</td>\n",
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              "      <th>1</th>\n",
              "      <td>11</td>\n",
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              "      <td>12</td>\n",
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              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>neutral</td>\n",
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              "      <th>4945</th>\n",
              "      <td>12235</td>\n",
              "      <td>euphoria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4946</th>\n",
              "      <td>12236</td>\n",
              "      <td>dysphoria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4947</th>\n",
              "      <td>12237</td>\n",
              "      <td>dysphoria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4948</th>\n",
              "      <td>12238</td>\n",
              "      <td>dysphoria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4949</th>\n",
              "      <td>12239</td>\n",
              "      <td>dysphoria</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4950 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-beabcfe1-0c45-4132-ba24-7d428f872788');\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "\n",
        "#predict= k_fold_logits[0] + k_fold_logits[1] + k_fold_logits[2] +k_fold_logits[3] +k_fold_logits[4]\n",
        "#predict= k_fold_logits[0] + k_fold_logits[1] + k_fold_logits[2] +k_fold_logits[3] +k_fold_logits[4]\n",
        "\n",
        "num_kfold = 1\n",
        "for i in range(num_kfold):\n",
        "  if i==0:\n",
        "    predict = k_fold_logits[i]\n",
        "  else:\n",
        "    predict += k_fold_logits[i]\n",
        "\n",
        "\n",
        "#predict = k_fold_logits[0]\n",
        "\n",
        "list1 = []\n",
        "list2 = []\n",
        "\n",
        "\n",
        "for i in range(test_num):\n",
        "  list1.append(i)\n",
        "  list2.append(-1)\n",
        "\n",
        "\n",
        "\n",
        "raw_data = {'Id': list1,\n",
        "            'Predicted': list2\n",
        "            }\n",
        "df = pd.DataFrame(raw_data)\n",
        "\n",
        "\n",
        "\n",
        "labels = {'euphoria':0,\n",
        "          'neutral':1,\n",
        "          'dysphoria':2\n",
        "          }\n",
        "\n",
        "df['Predicted'] = np.argmax(predict, axis=-1)\n",
        "\n",
        "d = ['euphoria','neutral','dysphoria']\n",
        "\n",
        "df['Predicted'] = [d[label] for label in df['Predicted']]\n",
        "\n",
        "\n",
        "df['Id'] = test_dataset['sentence_id']\n",
        "\n",
        "#x = [d[labels[label]] for label in ['euphoria','euphoria','dysphoria','neutral']]\n",
        "\n",
        "df\n",
        "\n",
        "\n",
        "\n",
        "df.to_csv(f'drive/MyDrive/호반우NLP_한국인감정/submission_{model_nickname}_{num_kfold}FOLD_{submission_comment}_batch_{TRAIN_BATCH}_{EVAL_BATCH}_epoch_{NUM_EPOCH}.csv', index=False)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vy9WdAEkJjoH",
        "outputId": "c1a77414-f6fa-401b-d75f-dd2a6c2e2ce7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.27    , -0.795   , -1.371   ],\n",
              "       [-0.005184,  2.043   , -1.709   ],\n",
              "       [-0.13    ,  2.176   , -1.758   ],\n",
              "       ...,\n",
              "       [-1.205   , -2.207   ,  3.215   ],\n",
              "       [-1.736   , -2.158   ,  3.676   ],\n",
              "       [ 0.941   , -2.773   ,  1.689   ]], dtype=float16)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "k_fold_logits[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDTRfoHMskfR",
        "outputId": "9310965e-d3fb-4c30-fa34-01ea06087d4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dysphoria    3205\n",
              "euphoria     1351\n",
              "neutral       394\n",
              "Name: Predicted, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['Predicted'].value_counts()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2hAb3QqJMTD",
        "outputId": "26deeafe-b62c-4774-b3fe-b7598ce1738c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dysphoria    1654\n",
              "euphoria      713\n",
              "neutral       199\n",
              "Name: Predicted, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df['Predicted'][:2566].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cnN-kdhDKQxt"
      },
      "outputs": [],
      "source": [
        "df['Predicted'].to_csv('drive/MyDrive/호반우NLP_한국인감정/'+'df_predicted_kluerobertaL_try5.txt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dOWUxf-71Cj"
      },
      "source": [
        "### Lastly, save predict(np) file for the Ensemble!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6lhLMxd_7-UF"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "\n",
        "ensemble_save =True\n",
        "day = '1023'\n",
        "nick = 'KLUE-RBTA-15FOLD_오후6TRY'\n",
        "if ensemble_save == True:\n",
        "  np.save(f'drive/MyDrive/호반우NLP_한국인감정/{day}_{nick}_predict_{model_nickname}_kfold_0', k_fold_logits[0]) #predict_{model_nickname}.npy\n",
        "  np.save(f'drive/MyDrive/호반우NLP_한국인감정/{day}_{nick}_predict_{model_nickname}', predict) #predict_{model_nickname}.npy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QsNX2I6i8fed"
      },
      "outputs": [],
      "source": [
        "from scipy.special import softmax\n",
        "\n",
        "\n",
        "predict_ensemble = softmax(np.load('drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-5FOLD_오후1TRY_predict_KLUE-ROBERTA-L_kfold_1.npy'))\n",
        "predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-5FOLD_오후1TRY_predict_KLUE-ROBERTA-L_kfold_4.npy')) \n",
        "#predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-5FOLD_오후22TRY_predict_KLUE-ROBERTA-L_kfold_0.npy')) \n",
        "predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-10FOLD_오후3TRY_predict_KLUE-ROBERTA-L_kfold_1.npy')) \n",
        "#/content/drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-10FOLD_오후3TRY_predict_KLUE-ROBERTA-L_kfold_0.npy\n",
        "predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-10FOLD_오후3TRY_predict_KLUE-ROBERTA-L_kfold_2.npy')) \n",
        "predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-10FOLD_오후3TRY_predict_KLUE-ROBERTA-L_kfold_3.npy')) \n",
        "predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-10FOLD_오후3TRY_predict_KLUE-ROBERTA-L_kfold_4.npy')) \n",
        "predict_ensemble += softmax(np.load(f'drive/MyDrive/호반우NLP_한국인감정/1023_KLUE-RBTA-15FOLD_오후4TRY_predict_KLUE-ROBERTA-L_kfold_1.npy')) \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_ensemble"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e8wvtEJUgo1",
        "outputId": "66d960ed-796a-4486-dc26-39c7fa030be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.845e-04, 8.810e-05, 6.974e-06],\n",
              "       [4.857e-04, 4.488e-05, 3.815e-06],\n",
              "       [3.529e-04, 7.153e-05, 3.636e-06],\n",
              "       ...,\n",
              "       [1.276e-05, 2.503e-06, 1.812e-03],\n",
              "       [7.987e-06, 2.146e-06, 2.346e-03],\n",
              "       [2.098e-05, 2.921e-06, 8.745e-04]], dtype=float16)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WbvGwgi_HXM"
      },
      "source": [
        "## If you wanna get a ensemble results csv, make run_ensemble =  'True'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oVN4Hajx8wcC"
      },
      "outputs": [],
      "source": [
        "run_ensemble = True\n",
        "#run_ensemble = False\n",
        "\n",
        "submission_comment_ensemble = '10-21 ensemble'\n",
        "#run_ensemble = False\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yRj2SgZ0_U8p"
      },
      "outputs": [],
      "source": [
        "if run_ensemble:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  raw_data = {'Id': list1,\n",
        "              'Predicted': list2\n",
        "              }\n",
        "  df = pd.DataFrame(raw_data)\n",
        "\n",
        "  labels = {'euphoria':0,\n",
        "            'neutral':1,\n",
        "            'dysphoria':2\n",
        "            }\n",
        "\n",
        "  df['Predicted'] = np.argmax(predict_ensemble, axis=-1)\n",
        "\n",
        "  d = ['euphoria','neutral','dysphoria']\n",
        "\n",
        "  df['Predicted'] = [d[label] for label in df['Predicted']]\n",
        "\n",
        "\n",
        "  df['Id'] = test_dataset['sentence_id']\n",
        "\n",
        "  #x = [d[labels[label]] for label in ['euphoria','euphoria','dysphoria','neutral']]\n",
        "\n",
        "  df.to_csv(f'drive/MyDrive/호반우NLP_한국인감정/submission_ensemble_{submission_comment_ensemble}.csv', index=False)\n",
        "  df['Predicted'].value_counts()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b2Ew0ULxAiOo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tAGa1ZWI4bcT"
      },
      "outputs": [],
      "source": []
    }
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