Bert_ver2.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hACMSuqwUoub",
        "outputId": "55d295b9-e791-4889-fda4-6528c25cf335"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gluonnlp in /usr/local/lib/python3.10/dist-packages (0.10.0)\n",
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            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n",
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            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.99)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: mxnet-mkl==1.6.0 in /usr/local/lib/python3.10/dist-packages (1.6.0)\n",
            "Requirement already satisfied: numpy==1.23.1 in /usr/local/lib/python3.10/dist-packages (1.23.1)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from mxnet-mkl==1.6.0) (2.31.0)\n",
            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from mxnet-mkl==1.6.0) (0.8.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet-mkl==1.6.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet-mkl==1.6.0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet-mkl==1.6.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet-mkl==1.6.0) (2024.2.2)\n",
            "Collecting kobert_tokenizer\n",
            "  Cloning https://github.com/SKTBrain/KoBERT.git to /tmp/pip-install-_g_t753l/kobert-tokenizer_5f3a5df785a846a189d0aba0d125f10f\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/SKTBrain/KoBERT.git /tmp/pip-install-_g_t753l/kobert-tokenizer_5f3a5df785a846a189d0aba0d125f10f\n",
            "  Resolved https://github.com/SKTBrain/KoBERT.git to commit 47a69af87928fc24e20f571fe10c3cc9dd9af9a3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: kobert_tokenizer\n",
            "  Building wheel for kobert_tokenizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert_tokenizer: filename=kobert_tokenizer-0.1-py3-none-any.whl size=4633 sha256=342c6b7161380173af2a30f195a167318016a38c41b0b06132c7e65ff5fc9d74\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-mi6v7nyx/wheels/e9/1a/3f/a864970e8a169c176befa3c4a1e07aa612f69195907a4045fe\n",
            "Successfully built kobert_tokenizer\n",
            "Installing collected packages: kobert_tokenizer\n",
            "Successfully installed kobert_tokenizer-0.1\n"
          ]
        }
      ],
      "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\n",
        "!pip install 'git+https://github.com/SKTBrain/KoBERT.git#egg=kobert_tokenizer&subdirectory=kobert_hf'"
      ]
    },
    {
      "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",
        "import pandas as pd\n",
        "from tqdm import tqdm, tqdm_notebook"
      ],
      "metadata": {
        "id": "plTgFppOU5bW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from kobert_tokenizer import KoBERTTokenizer\n",
        "from transformers import BertModel\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "_L9ACpdAU6_v"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\")"
      ],
      "metadata": {
        "id": "fFVwuxu-U9MK"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZxfCglSU-dP",
        "outputId": "700cc4b0-99b8-4da1-eca5-5dfde1aa1734"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'XLNetTokenizer'. \n",
            "The class this function is called from is 'KoBERTTokenizer'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/train_data.csv')\n",
        "test = pd.read_csv('/content/drive/MyDrive/test_data.csv')\n",
        "\n",
        "train = train.astype(str)\n",
        "test = test.astype(str)"
      ],
      "metadata": {
        "id": "Wh0zNTcNVAM3"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emotion_mapping = {\n",
        "    \"불안\": 0,\n",
        "    \"슬픔\": 1,\n",
        "    \"당황\": 2,\n",
        "    \"기쁨\": 3,\n",
        "    \"분노\": 4,\n",
        "    \"상처\": 5\n",
        "}\n",
        "\n",
        "data_train_list = []\n",
        "data_test_list = []\n",
        "\n",
        "for index, row in train.iterrows():\n",
        "    for i in range(1, 4):\n",
        "        sentence_column = f'SENTENCE{i}'\n",
        "        data_point = []\n",
        "        data_point.append(row[sentence_column])\n",
        "        data_point.append(str(emotion_mapping[row['EMOTION']]))\n",
        "        data_train_list.append(data_point)\n",
        "\n",
        "for index, row in test.iterrows():\n",
        "    for i in range(1, 4):\n",
        "        sentence_column = f'SENTENCE{i}'\n",
        "        data_point = []\n",
        "        data_point.append(row[sentence_column])\n",
        "        data_point.append(str(emotion_mapping[row['EMOTION']]))\n",
        "        data_test_list.append(data_point)"
      ],
      "metadata": {
        "id": "0gk4RY14VHIF"
      },
      "execution_count": 18,
      "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))\n",
        ""
      ],
      "metadata": {
        "id": "8O9FjUEFVHsO"
      },
      "execution_count": 19,
      "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": "6wYaeGeqVJ1y"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tok = tokenizer.tokenize\n",
        "data_train = BERTDataset(data_train_list, 0, 1, tok, vocab, max_len, True, False)\n",
        "data_test = BERTDataset(data_test_list, 0, 1, tok, vocab,  max_len, True, False)"
      ],
      "metadata": {
        "id": "whGINsu1VLeT"
      },
      "execution_count": 21,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5yBzJkdVOyT",
        "outputId": "b133026a-ee7a-4742-ceb8-131fcd88c898"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes = 6,   ##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": "N0pIG7CLVQBH"
      },
      "execution_count": 23,
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iztEmti3VRlb",
        "outputId": "cdc112a3-ec6f-444a-c20f-d89510c98c8e"
      },
      "execution_count": 24,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_history = []\n",
        "test_history = []\n",
        "loss_history = []\n",
        "\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))"
      ],
      "metadata": {
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          "height": 1000,
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        },
        "id": "xUYPsUoSVTzU",
        "outputId": "0721b0ed-5024-4b3e-cf41-6be4c670c5fc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-399d4d6d3ff5>:9: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2421 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d8f4de5345246e6bd9ea632f63382d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: 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 1.8033721446990967 train acc 0.1875\n",
            "epoch 1 batch id 201 loss 1.778183937072754 train acc 0.1832245024875622\n",
            "epoch 1 batch id 401 loss 1.4183591604232788 train acc 0.23636221945137156\n",
            "epoch 1 batch id 601 loss 1.4826570749282837 train acc 0.290817387687188\n",
            "epoch 1 batch id 801 loss 1.4024393558502197 train acc 0.32379447565543074\n",
            "epoch 1 batch id 1001 loss 1.3409618139266968 train acc 0.3407373876123876\n",
            "epoch 1 batch id 1201 loss 0.8876580595970154 train acc 0.36042880932556204\n",
            "epoch 1 batch id 1401 loss 1.4101577997207642 train acc 0.36380264097073517\n",
            "epoch 1 batch id 1601 loss 1.9646973609924316 train acc 0.37484384759525297\n",
            "epoch 1 batch id 1801 loss 1.6549607515335083 train acc 0.3777849111604664\n",
            "epoch 1 batch id 2001 loss 1.6270402669906616 train acc 0.3773113443278361\n",
            "epoch 1 batch id 2201 loss 1.273194432258606 train acc 0.38574085642889594\n",
            "epoch 1 batch id 2401 loss 0.7981763482093811 train acc 0.39441899208663056\n",
            "epoch 1 train acc 0.39569780049566294\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-399d4d6d3ff5>:32: 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": "01c1ead2c7754c3396f4173848365a0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.21334661774628882\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2421 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31717cda83c747d4b3d2b53570b9abc9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 2.1932501792907715 train acc 0.296875\n",
            "epoch 2 batch id 201 loss 1.2267755270004272 train acc 0.42257462686567165\n",
            "epoch 2 batch id 401 loss 1.1786028146743774 train acc 0.438824812967581\n",
            "epoch 2 batch id 601 loss 1.2558658123016357 train acc 0.4504211730449251\n",
            "epoch 2 batch id 801 loss 1.4250551462173462 train acc 0.45456850811485644\n",
            "epoch 2 batch id 1001 loss 1.222942590713501 train acc 0.4569336913086913\n",
            "epoch 2 batch id 1201 loss 0.5214970707893372 train acc 0.46588780183180684\n",
            "epoch 2 batch id 1401 loss 1.411962866783142 train acc 0.4619022127052106\n",
            "epoch 2 batch id 1601 loss 1.6199805736541748 train acc 0.46597829481574016\n",
            "epoch 2 batch id 1801 loss 1.5520050525665283 train acc 0.46525367851193783\n",
            "epoch 2 batch id 2001 loss 1.5496073961257935 train acc 0.46117566216891553\n",
            "epoch 2 batch id 2201 loss 1.1457571983337402 train acc 0.46598137210358925\n",
            "epoch 2 batch id 2401 loss 0.7578147053718567 train acc 0.4709951582673886\n",
            "epoch 2 train acc 0.47194728418009085\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/312 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55455b2f1bdf4edba655ea9dca78c91d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.2774860829959514\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2421 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3c309a82ba344b6aab237722670b459"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 1.9389736652374268 train acc 0.28125\n",
            "epoch 3 batch id 201 loss 1.1611016988754272 train acc 0.47699004975124376\n",
            "epoch 3 batch id 401 loss 0.993028998374939 train acc 0.48885598503740646\n",
            "epoch 3 batch id 601 loss 1.1166234016418457 train acc 0.5007539517470881\n",
            "epoch 3 batch id 801 loss 1.396532416343689 train acc 0.5039794007490637\n",
            "epoch 3 batch id 1001 loss 1.1503820419311523 train acc 0.5063061938061938\n",
            "epoch 3 batch id 1201 loss 0.36639174818992615 train acc 0.5132311615320566\n",
            "epoch 3 batch id 1401 loss 1.3037829399108887 train acc 0.5085876159885796\n",
            "epoch 3 batch id 1601 loss 1.471816062927246 train acc 0.5122677232979388\n",
            "epoch 3 batch id 1801 loss 1.342246174812317 train acc 0.5132912270960578\n",
            "epoch 3 batch id 2001 loss 1.397107720375061 train acc 0.5102058345827086\n",
            "epoch 3 batch id 2201 loss 1.0300010442733765 train acc 0.5152416515220355\n",
            "epoch 3 batch id 2401 loss 0.67333984375 train acc 0.5205448250728864\n",
            "epoch 3 train acc 0.5214838909541512\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": "1f6a4fffa06b43b3ae1bf4d8a881a50c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 test acc 0.29631093960863697\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": "68832c29c6a14e72944f1408d0de8503"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 batch id 1 loss 1.796980619430542 train acc 0.390625\n",
            "epoch 4 batch id 201 loss 1.0148851871490479 train acc 0.523476368159204\n",
            "epoch 4 batch id 401 loss 0.8003101348876953 train acc 0.5341334164588528\n",
            "epoch 4 batch id 601 loss 0.8716274499893188 train acc 0.5465370216306157\n",
            "epoch 4 batch id 801 loss 1.305177927017212 train acc 0.5473041510611736\n",
            "epoch 4 batch id 1001 loss 1.2046211957931519 train acc 0.550949050949051\n",
            "epoch 4 batch id 1201 loss 0.24522733688354492 train acc 0.5572309533721899\n",
            "epoch 4 batch id 1401 loss 1.236859917640686 train acc 0.5525740542469665\n",
            "epoch 4 batch id 1601 loss 1.2422398328781128 train acc 0.5570639444097439\n",
            "epoch 4 batch id 1801 loss 1.3264189958572388 train acc 0.5588561910049972\n",
            "epoch 4 batch id 2001 loss 1.2285081148147583 train acc 0.5549725137431284\n",
            "epoch 4 batch id 2201 loss 0.944153904914856 train acc 0.5605619604725125\n",
            "epoch 4 batch id 2401 loss 0.6064562201499939 train acc 0.5660727301124532\n",
            "epoch 4 train acc 0.5669325691862867\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "115756eeba514306910faf3613e2bee3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 test acc 0.3094319331983806\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1979b212fd8142f3b926a710b9c8c156"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 batch id 1 loss 1.6334104537963867 train acc 0.4375\n",
            "epoch 5 batch id 201 loss 1.0014060735702515 train acc 0.533193407960199\n",
            "epoch 5 batch id 401 loss 0.718906581401825 train acc 0.5553693890274314\n",
            "epoch 5 batch id 601 loss 0.6888638734817505 train acc 0.5722493760399334\n",
            "epoch 5 batch id 801 loss 1.1933661699295044 train acc 0.574438202247191\n",
            "epoch 5 batch id 1001 loss 1.2040009498596191 train acc 0.5765484515484516\n",
            "epoch 5 batch id 1201 loss 0.1983967274427414 train acc 0.5839144462947544\n",
            "epoch 5 batch id 1401 loss 1.2522974014282227 train acc 0.5804559243397573\n",
            "epoch 5 batch id 1601 loss 1.1566877365112305 train acc 0.5843125390381012\n",
            "epoch 5 batch id 1801 loss 1.4348952770233154 train acc 0.5844496113270405\n",
            "epoch 5 batch id 2001 loss 1.7557414770126343 train acc 0.5608523863068465\n",
            "epoch 5 batch id 2201 loss 1.2133095264434814 train acc 0.5493951612903226\n",
            "epoch 5 batch id 2401 loss 0.9773550629615784 train acc 0.5419942211578509\n",
            "epoch 5 train acc 0.5412277984304007\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d952179e6c4468a9de661b86079d03f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 test acc 0.5204748650472335\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": "cDNUtiwaVVvF"
      },
      "execution_count": 26,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQTzMhfDVXnb",
        "outputId": "623ccd2b-aeb2-45e9-a55e-f1992a6c1419"
      },
      "execution_count": 27,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "하고싶은 말을 입력해주세요 : 오늘은 정말 우울했어\n",
            ">> 입력하신 내용에서 놀람이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 슬프다\n",
            ">> 입력하신 내용에서 놀람이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 미친 바보 아니야?\n",
            ">> 입력하신 내용에서 중립이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : ㅋㅋ 아 얘 진짜 하나도 못맞추네\n",
            ">> 입력하신 내용에서 분노가 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 행복해~\n",
            ">> 입력하신 내용에서 슬픔이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 와 진짜 너 진짜 최악이야........\n",
            ">> 입력하신 내용에서 중립이 느껴집니다.\n",
            "\n",
            "\n",
            "하고싶은 말을 입력해주세요 : 0\n"
          ]
        }
      ]
    }
  ]
}