KoBERT_ver1.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xUxEvWPo7zsl"
      },
      "outputs": [],
      "source": [
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install mxnet\n",
        "!pip install sentencepiece\n",
        "!pip install transformers\n",
        "!pip install torch\n",
        "!pip install mxnet-mkl==1.6.0 numpy==1.23.1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install 'git+https://github.com/SKTBrain/KoBERT.git#egg=kobert_tokenizer&subdirectory=kobert_hf'"
      ],
      "metadata": {
        "id": "ZHdQO4q8OZbZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import gluonnlp as nlp\n",
        "import numpy as np\n",
        "from tqdm import tqdm, tqdm_notebook"
      ],
      "metadata": {
        "id": "h4TxMuTVRa35"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from kobert_tokenizer import KoBERTTokenizer\n",
        "from transformers import BertModel\n",
        "\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup\n",
        "\n",
        "#GPU 사용 시\n",
        "device = torch.device(\"cuda:0\")"
      ],
      "metadata": {
        "id": "C1Nc6WlURfwB"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = KoBERTTokenizer.from_pretrained('skt/kobert-base-v1')\n",
        "bertmodel = BertModel.from_pretrained('skt/kobert-base-v1', return_dict=False)\n",
        "vocab = nlp.vocab.BERTVocab.from_sentencepiece(tokenizer.vocab_file, padding_token='[PAD]')"
      ],
      "metadata": {
        "id": "YMhWVV0OTYos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/BERT/dataset.csv')"
      ],
      "metadata": {
        "id": "L4fbtLZnTdrI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc[(data['Emotion'] == \"공포\"), 'Emotion'] = 0  #공포 => 0\n",
        "data.loc[(data['Emotion'] == \"놀람\"), 'Emotion'] = 1  #놀람 => 1\n",
        "data.loc[(data['Emotion'] == \"분노\"), 'Emotion'] = 2  #분노 => 2\n",
        "data.loc[(data['Emotion'] == \"슬픔\"), 'Emotion'] = 3  #슬픔 => 3\n",
        "data.loc[(data['Emotion'] == \"중립\"), 'Emotion'] = 4  #중립 => 4\n",
        "data.loc[(data['Emotion'] == \"행복\"), 'Emotion'] = 5  #행복 => 5\n",
        "data.loc[(data['Emotion'] == \"혐오\"), 'Emotion'] = 6  #혐오 => 6\n",
        "\n",
        "\n",
        "data_list = []\n",
        "for ques, label in zip(data['Sentence'], data['Emotion'])  :\n",
        "    data = []\n",
        "    data.append(ques)\n",
        "    data.append(str(label))\n",
        "\n",
        "    data_list.append(data)"
      ],
      "metadata": {
        "id": "bZV-s_8_c6Kb"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer,vocab, max_len,\n",
        "                 pad, pair):\n",
        "\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len,vocab=vocab, pad=pad, pair=pair)\n",
        "\n",
        "        self.sentences = [transform([i[sent_idx]]) for i in dataset]\n",
        "        self.labels = [np.int32(i[label_idx]) for i in dataset]\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return (self.sentences[i] + (self.labels[i], ))\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        return (len(self.labels))"
      ],
      "metadata": {
        "id": "AEc-AEeWeZuh"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 5\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "h0T_mtz1ec8J"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train & test 데이터로 나누기\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "dataset_train, dataset_test = train_test_split(data_list, test_size=0.2, shuffle=True, random_state=34)"
      ],
      "metadata": {
        "id": "hq35VY_2eloh"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tok=tokenizer.tokenize\n",
        "data_train = BERTDataset(dataset_train, 0, 1, tok, vocab, max_len, True, False)\n",
        "data_test = BERTDataset(dataset_test,0, 1, tok, vocab,  max_len, True, False)"
      ],
      "metadata": {
        "id": "ej7GisVnenjr"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=5)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=5)"
      ],
      "metadata": {
        "id": "kxlM28TrermD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=7,   ##클래스 수 조정##\n",
        "                 dr_rate=None,\n",
        "                 params=None):\n",
        "        super(BERTClassifier, self).__init__()\n",
        "        self.bert = bert\n",
        "        self.dr_rate = dr_rate\n",
        "\n",
        "        self.classifier = nn.Linear(hidden_size , num_classes)\n",
        "        if dr_rate:\n",
        "            self.dropout = nn.Dropout(p=dr_rate)\n",
        "\n",
        "    def gen_attention_mask(self, token_ids, valid_length):\n",
        "        attention_mask = torch.zeros_like(token_ids)\n",
        "        for i, v in enumerate(valid_length):\n",
        "            attention_mask[i][:v] = 1\n",
        "        return attention_mask.float()\n",
        "\n",
        "    def forward(self, token_ids, valid_length, segment_ids):\n",
        "        attention_mask = self.gen_attention_mask(token_ids, valid_length)\n",
        "\n",
        "        _, pooler = self.bert(input_ids = token_ids, token_type_ids = segment_ids.long(), attention_mask = attention_mask.float().to(token_ids.device),return_dict=False)\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        return self.classifier(out)"
      ],
      "metadata": {
        "id": "K77doepBevQy"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#BERT 모델 불러오기\n",
        "model = BERTClassifier(bertmodel,  dr_rate=0.5).to(device)\n",
        "\n",
        "#optimizer와 schedule 설정\n",
        "no_decay = ['bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss() # 다중분류를 위한 대표적인 loss func\n",
        "\n",
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)\n",
        "\n",
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)\n",
        "\n",
        "#정확도 측정을 위한 함수 정의\n",
        "def calc_accuracy(X,Y):\n",
        "    max_vals, max_indices = torch.max(X, 1)\n",
        "    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]\n",
        "    return train_acc\n",
        "\n",
        "train_dataloader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAV7OpubexlX",
        "outputId": "77fdf44b-7d81-4298-fa61-c350f6f555d9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:429: 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",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x7a7613a325f0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_history=[]\n",
        "test_history=[]\n",
        "loss_history=[]\n",
        "for e in range(num_epochs):\n",
        "    train_acc = 0.0\n",
        "    test_acc = 0.0\n",
        "    model.train()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n",
        "        optimizer.zero_grad()\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "\n",
        "        #print(label.shape,out.shape)\n",
        "        loss = loss_fn(out, label)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)\n",
        "        optimizer.step()\n",
        "        scheduler.step()  # Update learning rate schedule\n",
        "        train_acc += calc_accuracy(out, label)\n",
        "        if batch_id % log_interval == 0:\n",
        "            print(\"epoch {} batch id {} loss {} train acc {}\".format(e+1, batch_id+1, loss.data.cpu().numpy(), train_acc / (batch_id+1)))\n",
        "            train_history.append(train_acc / (batch_id+1))\n",
        "            loss_history.append(loss.data.cpu().numpy())\n",
        "    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n",
        "    #train_history.append(train_acc / (batch_id+1))\n",
        "\n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))\n",
        "    test_history.append(test_acc / (batch_id+1))"
      ],
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            "8a86adaea58a40d7b956f3304d799dac",
            "a62b5756633448d8964abc9787d3c3d0",
            "f93e458988c947bfab1ec037b0dfd0ac",
            "05b3f89a4ae5442eb223fa98a3d04743",
            "c59d64a9ca2a4284bbe1429089187efb",
            "aa2b955a17d8402c8e16316220c8a349",
            "f0c70c7297ba41b99357430824cc952a"
          ]
        },
        "id": "1YGcWncue0VC",
        "outputId": "8cd3e45a-c5ce-427c-d50f-2b54b53ef183"
      },
      "execution_count": 15,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-15-0cd8b201d8af>:8: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4b7cceff7d654c60a36707721d0b65bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/483 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 batch id 1 loss 1.9386061429977417 train acc 0.140625\n",
            "epoch 1 batch id 201 loss 1.5141950845718384 train acc 0.28910136815920395\n",
            "epoch 1 batch id 401 loss 1.2556464672088623 train acc 0.39023534912718205\n",
            "epoch 1 train acc 0.4097425906755617\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-0cd8b201d8af>:31: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3fe361983fb94c8ea43438ce854ac1e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.5304255403687222\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/483 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "098171a8ef084ff3beaf470a5b6f0648"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 1.308307409286499 train acc 0.484375\n",
            "epoch 2 batch id 201 loss 1.1286014318466187 train acc 0.535214552238806\n",
            "epoch 2 batch id 401 loss 0.9771908521652222 train acc 0.5681109725685786\n",
            "epoch 2 train acc 0.5758017981749866\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/121 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ed86eca7cd84af9a6855569e6716a27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.541871953803772\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bac4fe3458f24ed8b6b50073fc2bc2e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 1.0418237447738647 train acc 0.640625\n",
            "epoch 3 batch id 201 loss 1.0312364101409912 train acc 0.634639303482587\n",
            "epoch 3 batch id 401 loss 0.7468793392181396 train acc 0.6643547381546134\n",
            "epoch 3 train acc 0.6726286327735603\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/121 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9767f04ade045ff86bf107d24447163"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 test acc 0.5550798633184997\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8f430eda1784437b8d1a1ad1cc6b4c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 batch id 1 loss 0.6754167675971985 train acc 0.765625\n",
            "epoch 4 batch id 201 loss 0.7550702095031738 train acc 0.7259017412935324\n",
            "epoch 4 batch id 401 loss 0.5620211958885193 train acc 0.7507013715710723\n",
            "epoch 4 train acc 0.7575423184571736\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/121 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f07fdae3ef34a12bbc36fc13c1f8459"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 test acc 0.5467326234371689\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/483 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04ac99c716cb4a1092ee37cb600737fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 batch id 1 loss 0.5787398815155029 train acc 0.796875\n",
            "epoch 5 batch id 201 loss 0.6507788896560669 train acc 0.7921330845771144\n",
            "epoch 5 batch id 401 loss 0.46304717659950256 train acc 0.805447319201995\n",
            "epoch 5 train acc 0.8090110037573807\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/121 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95ecc4a17b0149718385d2d14ec9a1d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 test acc 0.5546924666242848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(predict_sentence):\n",
        "\n",
        "    data = [predict_sentence, '0']\n",
        "    dataset_another = [data]\n",
        "\n",
        "    another_test = BERTDataset(dataset_another, 0, 1, tok, vocab, max_len, True, False)\n",
        "    test_dataloader = torch.utils.data.DataLoader(another_test, batch_size=batch_size, num_workers=5)\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(test_dataloader):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "\n",
        "\n",
        "        test_eval=[]\n",
        "        for i in out:\n",
        "            logits=i\n",
        "            logits = logits.detach().cpu().numpy()\n",
        "\n",
        "            if np.argmax(logits) == 0:\n",
        "                test_eval.append(\"공포가\")\n",
        "            elif np.argmax(logits) == 1:\n",
        "                test_eval.append(\"놀람이\")\n",
        "            elif np.argmax(logits) == 2:\n",
        "                test_eval.append(\"분노가\")\n",
        "            elif np.argmax(logits) == 3:\n",
        "                test_eval.append(\"슬픔이\")\n",
        "            elif np.argmax(logits) == 4:\n",
        "                test_eval.append(\"중립이\")\n",
        "            elif np.argmax(logits) == 5:\n",
        "                test_eval.append(\"행복이\")\n",
        "            elif np.argmax(logits) == 6:\n",
        "                test_eval.append(\"혐오가\")\n",
        "\n",
        "        print(\">> 입력하신 내용에서 \" + test_eval[0] + \" 느껴집니다.\")"
      ],
      "metadata": {
        "id": "q6nU-22GjfI8"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#질문 무한반복하기! 0 입력시 종료\n",
        "end = 1\n",
        "while end == 1 :\n",
        "    sentence = input(\"하고싶은 말을 입력해주세요 : \")\n",
        "    if sentence == \"0\" :\n",
        "        break\n",
        "    predict(sentence)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "id": "pcbfRQpojlVl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8049f7bf-b4df-4bc5-b4a5-de0718e16c32"
      },
      "execution_count": 17,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "하고싶은 말을 입력해주세요 : 집에 가고싶어\n",
            ">> 입력하신 내용에서 슬픔이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 하... 지겨워\n",
            ">> 입력하신 내용에서 슬픔이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 아 개빡치네\n",
            ">> 입력하신 내용에서 분노가 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 우우우우 진짜 개실망\n",
            ">> 입력하신 내용에서 혐오가 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 오늘 데이트한다!\n",
            ">> 입력하신 내용에서 행복이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 엥!! 오늘도 할 일이 가득이네\n",
            ">> 입력하신 내용에서 행복이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 0\n"
          ]
        }
      ]
    }
  ]
}