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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mouaWEUA3dek",
        "outputId": "c8e0b1e6-b1c0-4c2a-a1d4-3daaafab8f35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "print(os.getcwd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWeRBcrh3ktd",
        "outputId": "b9878e76-6159-4216-c92e-230a4cd1ad95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install sentencepiece\n",
        "!pip install transformers # >= 4.x.x\n",
        "!pip install torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bd_I0sE83xl7",
        "outputId": "1691ba3a-fe85-44a1-ada2-0a984d313c8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mxnet\n",
            "  Downloading mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 MB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting graphviz<0.9.0,>=0.8.1\n",
            "  Downloading graphviz-0.8.4-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.9/dist-packages (from mxnet) (1.22.4)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.9/dist-packages (from mxnet) (2.25.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.20.0->mxnet) (1.26.14)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.20.0->mxnet) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.20.0->mxnet) (2022.12.7)\n",
            "Installing collected packages: graphviz, mxnet\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.10.1\n",
            "    Uninstalling graphviz-0.10.1:\n",
            "      Successfully uninstalled graphviz-0.10.1\n",
            "Successfully installed graphviz-0.8.4 mxnet-1.9.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gluonnlp\n",
            "  Downloading gluonnlp-0.10.0.tar.gz (344 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m344.5/344.5 KB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (1.3.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (4.65.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.9/dist-packages (from gluonnlp) (1.22.4)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.9/dist-packages (from gluonnlp) (0.29.33)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from gluonnlp) (23.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.9/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.9/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Building wheels for collected packages: gluonnlp\n",
            "  Building wheel for gluonnlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gluonnlp: filename=gluonnlp-0.10.0-cp39-cp39-linux_x86_64.whl size=680533 sha256=87521190b0f413abb4999b2c3ae41cd395f1aea4d04c398cd6c839d06d697958\n",
            "  Stored in directory: /root/.cache/pip/wheels/47/17/70/b257bc53879a458c4bfcc900e89271aa8b4f19366a54bd2455\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.97\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m55.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m59.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.25.1)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.1-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.9.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.14)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (4.0.0)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.1 tokenizers-0.13.2 transformers-4.26.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (1.13.1+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch) (4.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#깃허브에서 KoBERT 파일 로드\n",
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soy6mFYK4Oa2",
        "outputId": "23d71390-8da1-4d05-fc01-a893f0c91317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://****@github.com/SKTBrain/KoBERT.git@master\n",
            "  Cloning https://****@github.com/SKTBrain/KoBERT.git (to revision master) to /tmp/pip-req-build-jvulgg_5\n",
            "  Running command git clone --filter=blob:none --quiet 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-jvulgg_5\n",
            "  Resolved https://****@github.com/SKTBrain/KoBERT.git to commit 47a69af87928fc24e20f571fe10c3cc9dd9af9a3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting boto3<=1.15.18\n",
            "  Downloading boto3-1.15.18-py2.py3-none-any.whl (129 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.1/129.1 KB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: gluonnlp<=0.10.0,>=0.6.0 in /usr/local/lib/python3.9/dist-packages (from kobert==0.2.3) (0.10.0)\n",
            "Collecting mxnet<=1.7.0.post2,>=1.4.0\n",
            "  Downloading mxnet-1.7.0.post2-py2.py3-none-manylinux2014_x86_64.whl (54.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.7/54.7 MB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting onnxruntime<=1.8.0,==1.8.0\n",
            "  Downloading onnxruntime-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m67.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting sentencepiece<=0.1.96,>=0.1.6\n",
            "  Downloading sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch<=1.10.1,>=1.7.0\n",
            "  Downloading torch-1.10.1-cp39-cp39-manylinux1_x86_64.whl (881.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m881.9/881.9 MB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting transformers<=4.8.1,>=4.8.1\n",
            "  Downloading transformers-4.8.1-py3-none-any.whl (2.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m64.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: flatbuffers in /usr/local/lib/python3.9/dist-packages (from onnxruntime<=1.8.0,==1.8.0->kobert==0.2.3) (23.3.3)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.9/dist-packages (from onnxruntime<=1.8.0,==1.8.0->kobert==0.2.3) (3.19.6)\n",
            "Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.9/dist-packages (from onnxruntime<=1.8.0,==1.8.0->kobert==0.2.3) (1.22.4)\n",
            "Collecting botocore<1.19.0,>=1.18.18\n",
            "  Downloading botocore-1.18.18-py2.py3-none-any.whl (6.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m94.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting s3transfer<0.4.0,>=0.3.0\n",
            "  Downloading s3transfer-0.3.7-py2.py3-none-any.whl (73 kB)\n",
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            "  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n",
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            "  Downloading tokenizers-0.10.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from sacremoses->transformers<=4.8.1,>=4.8.1->kobert==0.2.3) (1.2.0)\n",
            "Building wheels for collected packages: kobert, sacremoses\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert: filename=kobert-0.2.3-py3-none-any.whl size=15708 sha256=6eadb3db73c5689e4bc06c55df61852afca59750ecd29666740d891dca9454bf\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-leage4dh/wheels/0b/20/d8/031374f3d29b5150c59c814bed091fca7d6d4c8218148bf286\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=b4c5100fda4e5b2e2ef6ecfab2899ac055f9a5ce309b7d274cc1aba36662ed78\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/1c/3d/46cf06718d63a32ff798a89594b61e7f345ab6b36d909ce033\n",
            "Successfully built kobert sacremoses\n",
            "Installing collected packages: tokenizers, sentencepiece, urllib3, torch, sacremoses, onnxruntime, jmespath, botocore, s3transfer, mxnet, huggingface-hub, transformers, boto3, kobert\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.13.2\n",
            "    Uninstalling tokenizers-0.13.2:\n",
            "      Successfully uninstalled tokenizers-0.13.2\n",
            "  Attempting uninstall: sentencepiece\n",
            "    Found existing installation: sentencepiece 0.1.97\n",
            "    Uninstalling sentencepiece-0.1.97:\n",
            "      Successfully uninstalled sentencepiece-0.1.97\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.26.14\n",
            "    Uninstalling urllib3-1.26.14:\n",
            "      Successfully uninstalled urllib3-1.26.14\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.13.1+cu116\n",
            "    Uninstalling torch-1.13.1+cu116:\n",
            "      Successfully uninstalled torch-1.13.1+cu116\n",
            "  Attempting uninstall: mxnet\n",
            "    Found existing installation: mxnet 1.9.1\n",
            "    Uninstalling mxnet-1.9.1:\n",
            "      Successfully uninstalled mxnet-1.9.1\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.13.1\n",
            "    Uninstalling huggingface-hub-0.13.1:\n",
            "      Successfully uninstalled huggingface-hub-0.13.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.26.1\n",
            "    Uninstalling transformers-4.26.1:\n",
            "      Successfully uninstalled transformers-4.26.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchvision 0.14.1+cu116 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchtext 0.14.1 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\n",
            "torchaudio 0.13.1+cu116 requires torch==1.13.1, but you have torch 1.10.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed boto3-1.15.18 botocore-1.18.18 huggingface-hub-0.0.12 jmespath-0.10.0 kobert-0.2.3 mxnet-1.7.0.post2 onnxruntime-1.8.0 s3transfer-0.3.7 sacremoses-0.0.53 sentencepiece-0.1.96 tokenizers-0.10.3 torch-1.10.1 transformers-4.8.1 urllib3-1.25.11\n"
          ]
        }
      ]
    },
    {
      "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": "859uNRmq5jJ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#kobert\n",
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model\n",
        "\n",
        "#transformers\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ],
      "metadata": {
        "id": "ixJZauck5oX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#GPU 사용\n",
        "device = torch.device(\"cuda:0\")"
      ],
      "metadata": {
        "id": "0ZR9f6z35r0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#BERT 모델, Vocabulary 불러오기\n",
        "bertmodel, vocab = get_pytorch_kobert_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5Wbhq4J5vR1",
        "outputId": "665d40d8-973d-41b6-c616-1b02d5840f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
            "/content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece[██████████████████████████████████████████████████]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "chatbot_data = pd.read_csv('/content/sentiment_dialogues.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1gZmeXmB5ywf",
        "outputId": "35cd8b2c-2584-4348-af9f-f873ccab5de2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot_data.sample(n=10)"
      ],
      "metadata": {
        "id": "tCgrO7bu59iL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = chatbot_data.drop(['Unnamed: 0', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', '공포', '5468'], axis=1)"
      ],
      "metadata": {
        "id": "ymBBf3zzn6kZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "eSkZEMHaoDm9",
        "outputId": "a8d60c39-0d52-4b83-8380-bdd010c933ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                       발화  감정\n",
              "146557                   빨갱이 종북 박사모 알바들이 아주 여어엄벼엉을 하고 있네.  혐오\n",
              "146558                               트럼프는 박그네한테 고마워할듯? ㅋㅋ  혐오\n",
              "146559                                   정신병자새끼 밥되게 놔두지!!  혐오\n",
              "146560                          결국에는 아베가 짜놓은 판에 놀아나고 있는거네  혐오\n",
              "146561                                     비박, 너희들도 자격없다.  혐오\n",
              "146562               솔직히 예보 제대로 못하는 데 세금이라도 아끼게 그냥 폐지해라..  혐오\n",
              "146563                                        재미가 없으니 망하지  혐오\n",
              "146564  공장 도시락 비우생적임 아르바이트했는데 화장실가성 손도 않씯고 재료 담고 바닥 떨어...  혐오\n",
              "146565               코딱지 만한 나라에서 지들끼리 피터지게 싸우는 센징 클래스 ㅉㅉㅉ  혐오\n",
              "146566               와이프도 그렇고 댓글 다 볼텐데 이휘재 좀 하차 하라고 전해주세요  혐오"
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              "      <th>발화</th>\n",
              "      <th>감정</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>146557</th>\n",
              "      <td>빨갱이 종북 박사모 알바들이 아주 여어엄벼엉을 하고 있네.</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146558</th>\n",
              "      <td>트럼프는 박그네한테 고마워할듯? ㅋㅋ</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146559</th>\n",
              "      <td>정신병자새끼 밥되게 놔두지!!</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146560</th>\n",
              "      <td>결국에는 아베가 짜놓은 판에 놀아나고 있는거네</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146561</th>\n",
              "      <td>비박, 너희들도 자격없다.</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146562</th>\n",
              "      <td>솔직히 예보 제대로 못하는 데 세금이라도 아끼게 그냥 폐지해라..</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146563</th>\n",
              "      <td>재미가 없으니 망하지</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146564</th>\n",
              "      <td>공장 도시락 비우생적임 아르바이트했는데 화장실가성 손도 않씯고 재료 담고 바닥 떨어...</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146565</th>\n",
              "      <td>코딱지 만한 나라에서 지들끼리 피터지게 싸우는 센징 클래스 ㅉㅉㅉ</td>\n",
              "      <td>혐오</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146566</th>\n",
              "      <td>와이프도 그렇고 댓글 다 볼텐데 이휘재 좀 하차 하라고 전해주세요</td>\n",
              "      <td>혐오</td>\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5be5e44c-48f2-4946-8584-b4640b557bc0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5be5e44c-48f2-4946-8584-b4640b557bc0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "add_data1 = pd.read_csv('/content/추가 데이터셋 1.csv')\n",
        "add_data2 = pd.read_csv('/content/추가 데이터셋 2.csv')\n",
        "\n",
        "add_data1.head(10)"
      ],
      "metadata": {
        "id": "EXe8QulPojxV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "add_data1 = add_data1.rename(columns={'Text': '발화', 'Sentiment': '감정'})\n",
        "add_data2 = add_data2.rename(columns={'Text': '발화', 'Sentiment': '감정'})"
      ],
      "metadata": {
        "id": "FGDzqXU8pzPZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_concat = pd.concat([df, add_data1], axis=0)\n",
        "df_concat = pd.concat([df_concat, add_data2], axis=0)\n",
        "df_concat.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Sy80RuuipY6l",
        "outputId": "71943751-eb34-46f8-bc60-159ba0f40156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                       발화  감정\n",
              "111     그녀가 나에게 사귀자고 고백했어. 나도 원하고 있었는데 지금 기분이 너무 좋아.일단...  행복\n",
              "89955                          아저씨. 이거. 30만원씩이나 주구 산거에요!!  분노\n",
              "32729   학교 은사님이 돌아가셨어. 너무 슬퍼.내가 세상에서 제일 존경하던 분이야. 마음이 ...  슬픔\n",
              "8183    남자친구가 있는데 몰래 소개팅을 했어.남자친구는 결혼 상대는 아닌 것 같아. 하지만...  당황\n",
              "124680                         무기징역에 전 재산 환수하는 꼴을 보고야 말겠다  분노\n",
              "11562   국가검진 대장암 검사를 받았는데 대장암 의심이 된다고 했어.응. 그런데 정밀검사를 ...  불안\n",
              "99058                                      팀장 빨리 들어오라 그래.  중립\n",
              "20073   자율적으로 일하는 좋은 팀원들을 만나서 감사해.내 팀에는 스스로 열심히 하는 팀원들...  행복\n",
              "21620      수학을 너무 못 봐서 선생님께 혼이 나서 속상해.점수를 올리고 싶은데 방법이 뭘까?  슬픔\n",
              "7359    EX) 일봉이 5일이상 횡보한 후 전날 대비 2 상승한 종목들을 선정 및 매수 그 ...  중립"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cc167c5-f531-4581-a1cd-c7d5186577bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>발화</th>\n",
              "      <th>감정</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>그녀가 나에게 사귀자고 고백했어. 나도 원하고 있었는데 지금 기분이 너무 좋아.일단...</td>\n",
              "      <td>행복</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89955</th>\n",
              "      <td>아저씨. 이거. 30만원씩이나 주구 산거에요!!</td>\n",
              "      <td>분노</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32729</th>\n",
              "      <td>학교 은사님이 돌아가셨어. 너무 슬퍼.내가 세상에서 제일 존경하던 분이야. 마음이 ...</td>\n",
              "      <td>슬픔</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8183</th>\n",
              "      <td>남자친구가 있는데 몰래 소개팅을 했어.남자친구는 결혼 상대는 아닌 것 같아. 하지만...</td>\n",
              "      <td>당황</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124680</th>\n",
              "      <td>무기징역에 전 재산 환수하는 꼴을 보고야 말겠다</td>\n",
              "      <td>분노</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11562</th>\n",
              "      <td>국가검진 대장암 검사를 받았는데 대장암 의심이 된다고 했어.응. 그런데 정밀검사를 ...</td>\n",
              "      <td>불안</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99058</th>\n",
              "      <td>팀장 빨리 들어오라 그래.</td>\n",
              "      <td>중립</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20073</th>\n",
              "      <td>자율적으로 일하는 좋은 팀원들을 만나서 감사해.내 팀에는 스스로 열심히 하는 팀원들...</td>\n",
              "      <td>행복</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21620</th>\n",
              "      <td>수학을 너무 못 봐서 선생님께 혼이 나서 속상해.점수를 올리고 싶은데 방법이 뭘까?</td>\n",
              "      <td>슬픔</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7359</th>\n",
              "      <td>EX) 일봉이 5일이상 횡보한 후 전날 대비 2 상승한 종목들을 선정 및 매수 그 ...</td>\n",
              "      <td>중립</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cc167c5-f531-4581-a1cd-c7d5186577bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cc167c5-f531-4581-a1cd-c7d5186577bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cc167c5-f531-4581-a1cd-c7d5186577bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbout_data = df_concat\n",
        "len(chatbout_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8NhKkkuqbJK",
        "outputId": "1ea84b80-54f2-4e2e-fa4d-64454ea1495b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "156837"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot_data.loc[(chatbot_data['감정'] == \"불안\"), '감정'] = 0  #불안 => 0\n",
        "chatbot_data.loc[(chatbot_data['감정'] == \"당황\"), '감정'] = 1  #당황 => 1\n",
        "chatbot_data.loc[(chatbot_data['감정'] == \"분노\"), '감정'] = 2  #분노 => 2\n",
        "chatbot_data.loc[(chatbot_data['감정'] == \"슬픔\"), '감정'] = 3  #슬픔 => 3\n",
        "chatbot_data.loc[(chatbot_data['감정'] == \"중립\"), '감정'] = 4  #중립 => 4\n",
        "chatbot_data.loc[(chatbot_data['감정'] == \"행복\"), '감정'] = 5  #행복 => 5\n",
        "chatbot_data.loc[(chatbot_data['감정'] == \"혐오\"), '감정'] = 6  #혐오 => 6\n",
        "\n",
        "data_list = []\n",
        "for q, label in zip(chatbot_data['발화'], chatbot_data['감정'])  :\n",
        "    data = []\n",
        "    data.append(q)\n",
        "    data.append(str(label))\n",
        "\n",
        "    data_list.append(data)"
      ],
      "metadata": {
        "id": "eaAhIzYk5_xI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data_list[0])\n",
        "print(data_list[6000])\n",
        "print(data_list[12000])\n",
        "print(data_list[18000])\n",
        "print(data_list[24000])\n",
        "print(data_list[30000])\n",
        "print(data_list[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9VeLZrsG6ynE",
        "outputId": "dc132095-42a5-4c01-baf2-0bb29035a8e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['일은 왜 해도 해도 끝이 없을까? 화가 난다.그냥 내가 해결하는 게 나아. 남들한테 부담 주고 싶지도 않고.', '2']\n",
            "['최근에 업무가 너무 많이 늘어난 것 같아 힘들어.안 그래도 할 일 많은데 왜 자꾸 나한테 추가 업무를 주는지 모르겠다니깐.', '2']\n",
            "['친구들은 다 취업에 성공했는데 나만 못한 것 같아.나도 졸업하고 바로 일하고 싶었는데.', '0']\n",
            "['친구에게 내가 간암에 걸려서 술을 마실 수 없다고 하자 거짓말하지 말라고 했어.간암에 걸렸다는 말을 왜 안 믿을까?', '3']\n",
            "['의사가 분명히 수술이 잘 되었다고 했거든. 그런데 삼 주가 되도록 몸을 움직일 수 없어.그러니 불안해서 견딜 수가 있어야지. 아예 움직일 수 없을까 봐.', '3']\n",
            "['이번에 주식을 샀는데 주가가 너무 떨어져 손해가 이만저만이 아냐.지금은 없어. 난 왜 이렇게 되는일이 없나 몰라.', '3']\n",
            "['와이프도 그렇고 댓글 다 볼텐데 이휘재 좀 하차 하라고 전해주세요', '6']\n"
          ]
        }
      ]
    },
    {
      "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=0.13, random_state=0)"
      ],
      "metadata": {
        "id": "Uueg0TrQ635G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(dataset_train))\n",
        "print(len(dataset_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvglevof6_mA",
        "outputId": "1404db35-c203-41f3-beb9-25e095549117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127513\n",
            "19054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, 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",
        "    def __len__(self):\n",
        "        return (len(self.labels))"
      ],
      "metadata": {
        "id": "aga0aKAk7L78"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 15\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "metadata": {
        "id": "1gyYaw6a7B6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)\n",
        "\n",
        "data_train = BERTDataset(dataset_train, 0, 1, tok, max_len, True, False)\n",
        "data_test = BERTDataset(dataset_test, 0, 1, tok, max_len, True, False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gFCt6h87GIQ",
        "outputId": "1581838e-9d7f-4ba7-d7cc-b7fa02e7a086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=4)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Kin5eAQ7PL_",
        "outputId": "16bede7b-92b6-4bfc-9703-55ab6518d121"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 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",
            "  assert self._num_workers == 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for data in train_dataloader:\n",
        "#   print(data)\n",
        "#   break\n",
        "#kobert 학습모델 만들기\n",
        "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))[1]\n",
        "        #bert model returns 'last_hidden_state' and 'pooler_output'\n",
        "\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        return self.classifier(out)"
      ],
      "metadata": {
        "id": "AzYW4nO-7VU_"
      },
      "execution_count": null,
      "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()\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": {
        "id": "W_9SebJ27ch9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#kobert 모델 학습시키기\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",
        "        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",
        "    print(\"epoch {} train acc {}\".format(e+1, 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",
        "        with torch.no_grad():\n",
        "            out = model(token_ids, valid_length, segment_ids)\n",
        "        test_loss=loss_fn(out,label)\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {} test loss {}\".format(e+1, test_acc / (batch_id+1),test_loss.data.cpu().numpy()))\n",
        "\n",
        "    PATH = '/content/drive/MyDrive/Colab Notebooks/KHUDA 3기/감성분석/epoch{}_model.pt'.format(e+1)\n",
        "    torch.save(model.state_dict(), PATH)"
      ],
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        },
        "id": "6YXojQFl7hFy",
        "outputId": "c8ed4052-e50d-4e89-c4ff-9f6ed22f3370"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-60-412a5ece418c>:6: 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": "9e38be98a0db4a06bd08e5fc9fe66300",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1993 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 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",
            "  assert self._num_workers == 0\n"
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            "epoch 1 batch id 1 loss 0.9896848797798157 train acc 0.609375\n",
            "epoch 1 batch id 201 loss 0.8160349130630493 train acc 0.6692319651741293\n",
            "epoch 1 batch id 401 loss 0.8216909766197205 train acc 0.6738622194513716\n",
            "epoch 1 batch id 601 loss 0.9253036975860596 train acc 0.6749428036605657\n",
            "epoch 1 batch id 801 loss 0.7981774806976318 train acc 0.6786243757802747\n",
            "epoch 1 batch id 1001 loss 1.0179569721221924 train acc 0.6814279470529471\n",
            "epoch 1 batch id 1201 loss 0.7285289168357849 train acc 0.6826602830974188\n",
            "epoch 1 batch id 1401 loss 1.0165231227874756 train acc 0.6829385260528195\n",
            "epoch 1 batch id 1601 loss 0.8665224313735962 train acc 0.68183947532792\n",
            "epoch 1 batch id 1801 loss 0.8185220956802368 train acc 0.6838388395335925\n",
            "epoch 1 train acc 0.6843220020070245\n"
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          "text": [
            "<ipython-input-60-412a5ece418c>:24: 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"
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          "text": [
            "epoch 1 test acc 0.698086883571637 test loss 0.8028789758682251\n"
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            "epoch 2 batch id 1 loss 0.9025831818580627 train acc 0.703125\n",
            "epoch 2 batch id 201 loss 0.48664945363998413 train acc 0.7037468905472637\n",
            "epoch 2 batch id 401 loss 0.7751131653785706 train acc 0.7079956359102244\n",
            "epoch 2 batch id 601 loss 0.8071013689041138 train acc 0.7083506655574043\n",
            "epoch 2 batch id 801 loss 0.6946686506271362 train acc 0.7108887328339576\n",
            "epoch 2 batch id 1001 loss 0.8171172142028809 train acc 0.7135052447552448\n",
            "epoch 2 batch id 1201 loss 0.6745534539222717 train acc 0.7155105120732723\n",
            "epoch 2 batch id 1401 loss 0.9820184111595154 train acc 0.7169544075660242\n",
            "epoch 2 batch id 1601 loss 0.5827211737632751 train acc 0.7176471736414741\n",
            "epoch 2 batch id 1801 loss 0.6757507920265198 train acc 0.7197216823986674\n",
            "epoch 2 train acc 0.721241219267436\n"
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            "epoch 2 test acc 0.7049761088415524 test loss 0.7214878797531128\n"
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          "text": [
            "epoch 3 batch id 1 loss 0.9208101630210876 train acc 0.734375\n",
            "epoch 3 batch id 201 loss 0.40946850180625916 train acc 0.7527207711442786\n",
            "epoch 3 batch id 401 loss 0.6090109348297119 train acc 0.7537796134663342\n",
            "epoch 3 batch id 601 loss 0.6851930618286133 train acc 0.7536657653910149\n",
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            "epoch 3 batch id 1001 loss 0.8049905896186829 train acc 0.7583666333666333\n",
            "epoch 3 batch id 1201 loss 0.5085482001304626 train acc 0.7612926727726894\n",
            "epoch 3 batch id 1401 loss 0.8024582266807556 train acc 0.7623795503211992\n",
            "epoch 3 batch id 1601 loss 0.419740229845047 train acc 0.7630094472204872\n",
            "epoch 3 batch id 1801 loss 0.49059832096099854 train acc 0.764818156579678\n",
            "epoch 3 train acc 0.7664855117912694\n"
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            "epoch 3 test acc 0.7081334804493726 test loss 0.7153271436691284\n"
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          "text": [
            "epoch 4 batch id 1 loss 0.7781026363372803 train acc 0.71875\n",
            "epoch 4 batch id 201 loss 0.3944350481033325 train acc 0.7951648009950248\n",
            "epoch 4 batch id 401 loss 0.5984541773796082 train acc 0.7968360349127181\n",
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            "epoch 4 batch id 1401 loss 0.7660026550292969 train acc 0.8061540863668808\n",
            "epoch 4 batch id 1601 loss 0.41235044598579407 train acc 0.806556449094316\n",
            "epoch 4 batch id 1801 loss 0.3627162277698517 train acc 0.8081361049416991\n",
            "epoch 4 train acc 0.8095233316608129\n"
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            "epoch 4 test acc 0.7008658265246571 test loss 0.7625819444656372\n"
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            "epoch 5 batch id 1 loss 0.7185840010643005 train acc 0.765625\n",
            "epoch 5 batch id 201 loss 0.22704832255840302 train acc 0.8345771144278606\n",
            "epoch 5 batch id 401 loss 0.5336748957633972 train acc 0.8365804239401496\n",
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            "epoch 5 batch id 1601 loss 0.2156115472316742 train acc 0.8439549500312304\n",
            "epoch 5 batch id 1801 loss 0.259046733379364 train acc 0.8452335508051083\n",
            "epoch 5 train acc 0.8469007150025089\n"
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            "epoch 5 test acc 0.6995960388094544 test loss 0.8259850144386292\n"
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            "epoch 6 batch id 1 loss 0.44264835119247437 train acc 0.796875\n",
            "epoch 6 batch id 201 loss 0.13566887378692627 train acc 0.8724347014925373\n",
            "epoch 6 batch id 401 loss 0.4707795977592468 train acc 0.8718438279301746\n",
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            "epoch 6 test acc 0.6921300517945724 test loss 0.8892400860786438\n"
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            "epoch 7 batch id 1 loss 0.3550863564014435 train acc 0.859375\n",
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            "epoch 7 test acc 0.6916285198424278 test loss 1.2044382095336914\n"
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            "epoch 8 batch id 1 loss 0.32826969027519226 train acc 0.84375\n",
            "epoch 8 batch id 201 loss 0.0796877071261406 train acc 0.9178327114427861\n",
            "epoch 8 batch id 401 loss 0.31901809573173523 train acc 0.9212125935162094\n",
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            "epoch 8 test acc 0.6947950102130143 test loss 1.1823824644088745\n"
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            "epoch 9 batch id 1 loss 0.14844514429569244 train acc 0.9375\n",
            "epoch 9 batch id 201 loss 0.04009724035859108 train acc 0.9428638059701493\n",
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            "epoch 9 train acc 0.946790328650276\n"
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            "epoch 9 test acc 0.6938717354829297 test loss 1.2285844087600708\n"
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            "epoch 10 batch id 1 loss 0.11984764784574509 train acc 0.953125\n",
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            "epoch 10 batch id 401 loss 0.28170737624168396 train acc 0.9554629052369077\n",
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          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-412a5ece418c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msegment_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_grad_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[1;32m    487\u001b[0m         return torch.istft(self, n_fft, hop_length, win_length, window, center,\n\u001b[0;32m--> 488\u001b[0;31m                            normalized, onesided, length, return_complex=return_complex)\n\u001b[0m\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    195\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mall\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthen\u001b[0m \u001b[0mthis\u001b[0m \u001b[0margument\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefault\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0mused\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcompute\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m             \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mfreed\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mNote\u001b[0m \u001b[0mthat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnearly\u001b[0m \u001b[0mall\u001b[0m \u001b[0mcases\u001b[0m \u001b[0msetting\u001b[0m \u001b[0mthis\u001b[0m \u001b[0moption\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m             \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mneeded\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0moften\u001b[0m \u001b[0mcan\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mworked\u001b[0m \u001b[0maround\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mmuch\u001b[0m \u001b[0mmore\u001b[0m \u001b[0mefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0mway\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mof\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbout_data.to_csv(\"total_data.csv\", mode='w')"
      ],
      "metadata": {
        "id": "vG-w0ja9eXL0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}