KERC_1022_args수정1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/purang2/BERT/blob/main/KERC_1022_args%EC%88%98%EC%A0%951.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": 1,
      "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": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiS7SUMPsOix",
        "outputId": "d25bda28-963a-40c3-afba-039bc1c83e01"
      },
      "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": 3,
      "metadata": {
        "id": "1jpH-3Tk17_n"
      },
      "outputs": [],
      "source": [
        "num_kfold = 5 #\n",
        "NUM_EPOCH = 2\n",
        "TRAIN_BATCH = 8\n",
        "EVAL_BATCH = 32\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 = \"klue/roberta-large\" #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 KLUE-ROBERTA-L\" #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 = \"KLUE-ROBERTA-L\"  #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": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6_IWf9Krgc5",
        "outputId": "4e7547bc-62e7-4cb5-e6b6-5063a0bb4b94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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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": 5,
      "metadata": {
        "id": "M44NyHchrmHO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "b2b97ba8-6c97-491e-9c63-1698589de458"
      },
      "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",
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              "      <td>어영</td>\n",
              "      <td>야! 전화 받아. 아무리 바빠도 내전화는 받아야 되는거 아냐? 약속 하나도 못지키는...</td>\n",
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              "      <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",
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              "      <th>2</th>\n",
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              "      <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",
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              "      <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",
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              "      <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",
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              "      <td>나 못타는데. 한번도 안타봤어. 툭하면 차멀미까지 하는데, 봐라, 너도 내몸땡이에 ...</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": 6,
      "metadata": {
        "id": "ImycBmmusGLA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0036a3ff-ce18-4872-87fd-43068951330a"
      },
      "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",
        "\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": 7,
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          "text": [
            "Some weights of the model checkpoint at klue/roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.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",
        "import evaluate\n",
        "\n",
        "\n",
        "max_input_length = 200\n",
        "#tokenizer = AutoTokenizer.from_pretrained(\"monologg/koelectra-base-discriminator\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\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"
      ]
    },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "Using cuda_amp half precision backend\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"klue/roberta-large\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"tokenizer_class\": \"BertTokenizer\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 1,\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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/pytorch_model.bin\n",
            "Some weights of the model checkpoint at klue/roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.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"
          ]
        },
        {
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              "version_minor": 0,
              "model_id": "aaf77309306b46bab51645428a6469cb"
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        },
        {
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              "model_id": "a25e0d317f89487c8ca9b1668fd230de"
            }
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          "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 = 5871\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2202\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2202' max='2202' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2202/2202 06:41, Epoch 3/3]\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>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.510489</td>\n",
              "      <td>0.785422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.740300</td>\n",
              "      <td>0.470999</td>\n",
              "      <td>0.845368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.488200</td>\n",
              "      <td>0.496553</td>\n",
              "      <td>0.839918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.392600</td>\n",
              "      <td>0.559173</td>\n",
              "      <td>0.852861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.250600</td>\n",
              "      <td>0.515902</td>\n",
              "      <td>0.865804</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-400\n",
            "Configuration saved in ./results/checkpoint-400/config.json\n",
            "Model weights saved in ./results/checkpoint-400/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-400/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-400/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-800\n",
            "Configuration saved in ./results/checkpoint-800/config.json\n",
            "Model weights saved in ./results/checkpoint-800/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-800/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-800/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1200\n",
            "Configuration saved in ./results/checkpoint-1200/config.json\n",
            "Model weights saved in ./results/checkpoint-1200/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1200/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1200/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1600\n",
            "Configuration saved in ./results/checkpoint-1600/config.json\n",
            "Model weights saved in ./results/checkpoint-1600/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1600/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1600/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-2000\n",
            "Configuration saved in ./results/checkpoint-2000/config.json\n",
            "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-2000/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-2000 (score: 0.8658038147138964).\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": "4adcd8c063c74a1b8bcd7bec7d6bbf2e"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: sentence_id. If sentence_id are not expected by `RobertaForSequenceClassification.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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"klue/roberta-large\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"tokenizer_class\": \"BertTokenizer\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 1,\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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/pytorch_model.bin\n",
            "Some weights of the model checkpoint at klue/roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.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",
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            ],
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        },
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        },
        {
          "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 = 5871\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2202\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2202' max='2202' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2202/2202 06:53, Epoch 3/3]\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>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.599655</td>\n",
              "      <td>0.762262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.732900</td>\n",
              "      <td>0.651425</td>\n",
              "      <td>0.805177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.470100</td>\n",
              "      <td>0.559569</td>\n",
              "      <td>0.824251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.374300</td>\n",
              "      <td>0.692369</td>\n",
              "      <td>0.835150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.242300</td>\n",
              "      <td>0.747431</td>\n",
              "      <td>0.841962</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-400\n",
            "Configuration saved in ./results/checkpoint-400/config.json\n",
            "Model weights saved in ./results/checkpoint-400/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-400/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-400/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-800\n",
            "Configuration saved in ./results/checkpoint-800/config.json\n",
            "Model weights saved in ./results/checkpoint-800/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-800/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-800/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1200\n",
            "Configuration saved in ./results/checkpoint-1200/config.json\n",
            "Model weights saved in ./results/checkpoint-1200/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1200/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1200/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1600\n",
            "Configuration saved in ./results/checkpoint-1600/config.json\n",
            "Model weights saved in ./results/checkpoint-1600/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1600/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1600/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-2000\n",
            "Configuration saved in ./results/checkpoint-2000/config.json\n",
            "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-2000/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-2000 (score: 0.8419618528610355).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "538adf507b504243a9ba376c69d9769b"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: sentence_id. If sentence_id are not expected by `RobertaForSequenceClassification.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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"klue/roberta-large\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"tokenizer_class\": \"BertTokenizer\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 1,\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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/pytorch_model.bin\n",
            "Some weights of the model checkpoint at klue/roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.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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            ],
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        },
        {
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          "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 = 5871\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2202\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2202' max='2202' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2202/2202 06:53, Epoch 3/3]\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>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.620007</td>\n",
              "      <td>0.765668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.728900</td>\n",
              "      <td>0.599312</td>\n",
              "      <td>0.803134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.463300</td>\n",
              "      <td>0.627553</td>\n",
              "      <td>0.817439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.380600</td>\n",
              "      <td>0.793047</td>\n",
              "      <td>0.818120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.224700</td>\n",
              "      <td>0.841472</td>\n",
              "      <td>0.822888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-400\n",
            "Configuration saved in ./results/checkpoint-400/config.json\n",
            "Model weights saved in ./results/checkpoint-400/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-400/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-400/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-800\n",
            "Configuration saved in ./results/checkpoint-800/config.json\n",
            "Model weights saved in ./results/checkpoint-800/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-800/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-800/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1200\n",
            "Configuration saved in ./results/checkpoint-1200/config.json\n",
            "Model weights saved in ./results/checkpoint-1200/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1200/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1200/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1600\n",
            "Configuration saved in ./results/checkpoint-1600/config.json\n",
            "Model weights saved in ./results/checkpoint-1600/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1600/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1600/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-2000\n",
            "Configuration saved in ./results/checkpoint-2000/config.json\n",
            "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-2000/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-2000 (score: 0.8228882833787466).\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: sentence_id. If sentence_id are not expected by `RobertaForSequenceClassification.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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"klue/roberta-large\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"tokenizer_class\": \"BertTokenizer\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 1,\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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/pytorch_model.bin\n",
            "Some weights of the model checkpoint at klue/roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.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"
          ]
        },
        {
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        {
          "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 = 5871\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2202\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2202' max='2202' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2202/2202 06:52, Epoch 3/3]\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>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.582482</td>\n",
              "      <td>0.783379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.737300</td>\n",
              "      <td>0.629649</td>\n",
              "      <td>0.801090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.484800</td>\n",
              "      <td>0.584870</td>\n",
              "      <td>0.829019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.378600</td>\n",
              "      <td>0.718138</td>\n",
              "      <td>0.830381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.246800</td>\n",
              "      <td>0.733720</td>\n",
              "      <td>0.830381</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-400\n",
            "Configuration saved in ./results/checkpoint-400/config.json\n",
            "Model weights saved in ./results/checkpoint-400/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-400/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-400/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-800\n",
            "Configuration saved in ./results/checkpoint-800/config.json\n",
            "Model weights saved in ./results/checkpoint-800/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-800/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-800/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1200\n",
            "Configuration saved in ./results/checkpoint-1200/config.json\n",
            "Model weights saved in ./results/checkpoint-1200/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1200/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1200/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1600\n",
            "Configuration saved in ./results/checkpoint-1600/config.json\n",
            "Model weights saved in ./results/checkpoint-1600/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1600/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1600/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1468\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-2000\n",
            "Configuration saved in ./results/checkpoint-2000/config.json\n",
            "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-2000/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-1600 (score: 0.8303814713896458).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: sentence_id. If sentence_id are not expected by `RobertaForSequenceClassification.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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/config.json\n",
            "Model config RobertaConfig {\n",
            "  \"_name_or_path\": \"klue/roberta-large\",\n",
            "  \"architectures\": [\n",
            "    \"RobertaForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"roberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"tokenizer_class\": \"BertTokenizer\",\n",
            "  \"transformers_version\": \"4.23.1\",\n",
            "  \"type_vocab_size\": 1,\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--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/pytorch_model.bin\n",
            "Some weights of the model checkpoint at klue/roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.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",
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "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 = 5872\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2202\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2202' max='2202' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2202/2202 06:58, Epoch 3/3]\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>400</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.568723</td>\n",
              "      <td>0.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.752500</td>\n",
              "      <td>0.498251</td>\n",
              "      <td>0.819359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.492100</td>\n",
              "      <td>0.587890</td>\n",
              "      <td>0.781868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.398400</td>\n",
              "      <td>0.602604</td>\n",
              "      <td>0.852761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.244900</td>\n",
              "      <td>0.676979</td>\n",
              "      <td>0.850716</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1467\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-400\n",
            "Configuration saved in ./results/checkpoint-400/config.json\n",
            "Model weights saved in ./results/checkpoint-400/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-400/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-400/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1467\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-800\n",
            "Configuration saved in ./results/checkpoint-800/config.json\n",
            "Model weights saved in ./results/checkpoint-800/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-800/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-800/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1467\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1200\n",
            "Configuration saved in ./results/checkpoint-1200/config.json\n",
            "Model weights saved in ./results/checkpoint-1200/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1200/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1200/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1467\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-1600\n",
            "Configuration saved in ./results/checkpoint-1600/config.json\n",
            "Model weights saved in ./results/checkpoint-1600/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1600/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1600/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1467\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to ./results/checkpoint-2000\n",
            "Configuration saved in ./results/checkpoint-2000/config.json\n",
            "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-2000/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-1600 (score: 0.852760736196319).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?ba/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: sentence_id. If sentence_id are not expected by `RobertaForSequenceClassification.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>"
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      ],
      "source": [
        "\n",
        "#Gather your training arguments~\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    learning_rate=1e-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=TRAIN_BATCH, #16\n",
        "    per_device_eval_batch_size=EVAL_BATCH, #16\n",
        "    #num_train_epochs=NUM_EPOCH,\n",
        "    save_strategy = 'steps',\n",
        "    save_steps = 400,\n",
        "    eval_steps =400,\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=0.001,\n",
        "    fp16 = True, \n",
        "    warmup_ratio = 0.1,\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",
        "    )\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",
        "\n",
        "k_fold_logits = []\n",
        "\n",
        "#num_kfold = 10\n",
        "kfold = StratifiedKFold(n_splits=num_kfold, random_state=1104, shuffle=True)\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",
        "            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",
        "        )\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",
        "    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(' ')\n",
        "    test_pair['sentence2'] = test_pair['sentence'].fillna(' ')\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": {
        "id": "mtp_C2S9salS"
      },
      "outputs": [],
      "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",
        "\n",
        "\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}.csv', index=False)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rDTRfoHMskfR"
      },
      "outputs": [],
      "source": [
        "df['Predicted'].value_counts()\n"
      ]
    },
    {
      "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 =False\n",
        "\n",
        "if ensemble_save == True:\n",
        "  np.save(f'drive/MyDrive/호반우NLP_한국인감정/predict_{model_nickname}', predict) #predict_{model_nickname}.npy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QsNX2I6i8fed"
      },
      "outputs": [],
      "source": []
    },
    {
      "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",
        "  predict_ensemble = np.load(f'drive/MyDrive/호반우NLP_한국인감정/predict_mDeberta-kor.npy') *0.83\n",
        "  predict_ensemble += np.load(f'drive/MyDrive/호반우NLP_한국인감정/predict_kc-electra-base.npy') *0.83\n",
        "  predict_ensemble += np.load(f'drive/MyDrive/호반우NLP_한국인감정/predict_BERT-Kor-base.npy') *0.80\n",
        "  #predict_ensemble += np.load(f'drive/MyDrive/호반우NLP_한국인감정/predict_KR-ELECTRA.npy') *0.80\n",
        "  predict_ensemble += np.load(f'drive/MyDrive/호반우NLP_한국인감정/predict_KLUE-ROBERTA-L.npy') *0.85\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"
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      "source": []
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