kobert_sentiment_classification.ipynb

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        "!pip install mxnet\n",
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install sentencepiece\n",
        "!pip install transformers==3\n",
        "!pip install torch"
      ],
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          "text": [
            "Collecting mxnet\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/07/66174e78c12a3048db9039aaa09553e35035ef3a008ba3e0ed8d2aa3c47b/mxnet-1.8.0.post0-py2.py3-none-manylinux2014_x86_64.whl (46.9MB)\n",
            "\u001b[K     |████████████████████████████████| 46.9MB 111kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from mxnet) (2.23.0)\n",
            "Collecting graphviz<0.9.0,>=0.8.1\n",
            "  Downloading https://files.pythonhosted.org/packages/53/39/4ab213673844e0c004bed8a0781a0721a3f6bb23eb8854ee75c236428892/graphviz-0.8.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.7/dist-packages (from mxnet) (1.19.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (3.0.4)\n",
            "Installing collected packages: graphviz, mxnet\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-1.8.0.post0\n",
            "Collecting gluonnlp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9c/81/a238e47ccba0d7a61dcef4e0b4a7fd4473cb86bed3d84dd4fe28d45a0905/gluonnlp-0.10.0.tar.gz (344kB)\n",
            "\u001b[K     |████████████████████████████████| 348kB 16.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.1.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.41.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from gluonnlp) (1.19.5)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from gluonnlp) (0.29.23)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from gluonnlp) (20.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->gluonnlp) (2.4.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/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-cp37-cp37m-linux_x86_64.whl size=595702 sha256=9a44682be80edb293fc128de1f4f2ee447db5f2c0f6a51aa0cb6507a6fc6ef7c\n",
            "  Stored in directory: /root/.cache/pip/wheels/37/65/52/63032864a0f31a08b9a88569f803b5bafac8abd207fd7f7534\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.10.0\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ac/aa/1437691b0c7c83086ebb79ce2da16e00bef024f24fec2a5161c35476f499/sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 14.7MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.96\n",
            "Collecting transformers==3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9c/35/1c3f6e62d81f5f0daff1384e6d5e6c5758682a8357ebc765ece2b9def62b/transformers-3.0.0-py3-none-any.whl (754kB)\n",
            "\u001b[K     |████████████████████████████████| 757kB 13.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from transformers==3) (1.19.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==3) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==3) (4.41.1)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from transformers==3) (0.1.96)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==3) (3.0.12)\n",
            "Collecting tokenizers==0.8.0-rc4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/82/0e82a95bd9db2b32569500cc1bb47aa7c4e0f57aa5e35cceba414096917b/tokenizers-0.8.0rc4-cp37-cp37m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 37.6MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n",
            "\u001b[K     |████████████████████████████████| 901kB 49.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==3) (2019.12.20)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==3) (20.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (2021.5.30)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3) (1.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3) (1.15.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==3) (2.4.7)\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.45 tokenizers-0.8.0rc4 transformers-3.0.0\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.9.0+cu102)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (3.7.4.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2RqBwai4csP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36848730-249f-433a-b7f9-043d447afeb3"
      },
      "source": [
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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-x2k1mkg4\n",
            "  Running command git clone -q 'https://****@github.com/SKTBrain/KoBERT.git' /tmp/pip-req-build-x2k1mkg4\n",
            "Building wheels for collected packages: kobert\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert: filename=kobert-0.1.2-cp37-none-any.whl size=12718 sha256=7c0291ceb5c32cf04bb075fde39f4492d5000d8d1cc227eaf34dd8e73b8e5fdb\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-uje6p_t0/wheels/a2/b0/41/435ee4e918f91918be41529283c5ff86cd010f02e7525aecf3\n",
            "Successfully built kobert\n",
            "Installing collected packages: kobert\n",
            "Successfully installed kobert-0.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZ9FLIKa4csQ"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EpioRud4csR"
      },
      "source": [
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUvVvyH34csR"
      },
      "source": [
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2D3yaaUW4csR"
      },
      "source": [
        "##GPU 사용 시\n",
        "device = torch.device(\"cuda:0\")\n",
        "#device = torch.device(\"cpu\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnpzB4QJ4csS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7d80ce2-ec9f-4edd-c0b2-260f7a05e024"
      },
      "source": [
        "bertmodel, vocab = get_pytorch_kobert_model()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[██████████████████████████████████████████████████]\n",
            "[██████████████████████████████████████████████████]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJJgElIb4csS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "299fdd8e-4b44-442f-9a71-f5643b0bb811"
      },
      "source": [
        "!wget \"https://drive.google.com/uc?export=download&id=1fhEBkjvl0g7EHYPjN-r8wefEUi_pZ7RZ\" -O datatest.tsv\n",
        "!wget \"https://drive.google.com/uc?export=download&id=1rmjklQfdEPaDgpQu1UKi64iagytxrC92\" -O datatrain.tsv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-28 21:57:40--  https://drive.google.com/uc?export=download&id=1fhEBkjvl0g7EHYPjN-r8wefEUi_pZ7RZ\n",
            "Resolving drive.google.com (drive.google.com)... 108.177.119.139, 108.177.119.113, 108.177.119.100, ...\n",
            "Connecting to drive.google.com (drive.google.com)|108.177.119.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-0k-8g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/qo4sc7q35ap6k4734vptpojgm269ti8f/1624917450000/16667090296722432029/*/1fhEBkjvl0g7EHYPjN-r8wefEUi_pZ7RZ?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2021-06-28 21:57:41--  https://doc-0k-8g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/qo4sc7q35ap6k4734vptpojgm269ti8f/1624917450000/16667090296722432029/*/1fhEBkjvl0g7EHYPjN-r8wefEUi_pZ7RZ?e=download\n",
            "Resolving doc-0k-8g-docs.googleusercontent.com (doc-0k-8g-docs.googleusercontent.com)... 172.217.218.132, 2a00:1450:4013:c08::84\n",
            "Connecting to doc-0k-8g-docs.googleusercontent.com (doc-0k-8g-docs.googleusercontent.com)|172.217.218.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 950455 (928K) [text/tab-separated-values]\n",
            "Saving to: ‘datatest.tsv’\n",
            "\n",
            "datatest.tsv        100%[===================>] 928.18K  --.-KB/s    in 0.007s  \n",
            "\n",
            "2021-06-28 21:57:41 (122 MB/s) - ‘datatest.tsv’ saved [950455/950455]\n",
            "\n",
            "--2021-06-28 21:57:41--  https://drive.google.com/uc?export=download&id=1rmjklQfdEPaDgpQu1UKi64iagytxrC92\n",
            "Resolving drive.google.com (drive.google.com)... 108.177.119.100, 108.177.119.101, 108.177.119.138, ...\n",
            "Connecting to drive.google.com (drive.google.com)|108.177.119.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-0c-8g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/n61jidmj5mubderpnggnhl1adadmpldj/1624917450000/16667090296722432029/*/1rmjklQfdEPaDgpQu1UKi64iagytxrC92?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2021-06-28 21:57:42--  https://doc-0c-8g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/n61jidmj5mubderpnggnhl1adadmpldj/1624917450000/16667090296722432029/*/1rmjklQfdEPaDgpQu1UKi64iagytxrC92?e=download\n",
            "Resolving doc-0c-8g-docs.googleusercontent.com (doc-0c-8g-docs.googleusercontent.com)... 172.217.218.132, 2a00:1450:4013:c08::84\n",
            "Connecting to doc-0c-8g-docs.googleusercontent.com (doc-0c-8g-docs.googleusercontent.com)|172.217.218.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/tab-separated-values]\n",
            "Saving to: ‘datatrain.tsv’\n",
            "\n",
            "datatrain.tsv           [ <=>                ]   3.21M  --.-KB/s    in 0.02s   \n",
            "\n",
            "2021-06-28 21:57:43 (206 MB/s) - ‘datatrain.tsv’ saved [3365957]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vzbwqQRBUxP"
      },
      "source": [
        "dataset_train = nlp.data.TSVDataset(\"datatrain.tsv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fT6U9aBq4csS"
      },
      "source": [
        "\n",
        "dataset_test = nlp.data.TSVDataset(\"datatest.tsv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWfZ-tet4csS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3971ab62-eccc-4618-9b1c-7f198722a244"
      },
      "source": [
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "using cached model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_LwlT7T4csT"
      },
      "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(int(float(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))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6MtDR0u4csT"
      },
      "source": [
        "## Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 8\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3Rf1X_XDt1g",
        "outputId": "4a58494f-611f-4b1b-96e1-d34e7354cf04"
      },
      "source": [
        "print(dataset_train[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['나이 들어서 걸리면 치사 율도 높을껀데 전부 자기 들 자초 일이 의료 진 죽어나는거지', '0'], ['축하 합니다', '1'], ['저 것 들 한 곳 몰아 넣고 지 들 끼리 감염 되서 물 뜯 고함 다 디지길', '0'], ['몇 일 전 부터 우한 중국 입국 시키더니 우한 폐렴 확 진자 급증 이 거 아무래도 연관 성 의심 안 할수가 없다', '0'], ['왜 또 교회 몰려서 이런 난리 피우는 건지 집단 장소 가지말라고 그렇게 당부 메세지 받았을텐데 정말 이번 말 되면 참 많은 사망자 발생 할것 같다 ㅠㅠ', '0']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vznvxhdV4csT"
      },
      "source": [
        "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvg58sCInU5Z"
      },
      "source": [
        "#print(data_train[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAfAcX-Q4csT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2441f91-8f88-417d-b14f-e9d6a947800c"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 4, 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",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRrNAmFo4csU"
      },
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=2,\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))\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        return self.classifier(out)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hm1Ftm7Z4csV"
      },
      "source": [
        "model = BERTClassifier(bertmodel,  dr_rate=0.5).to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZ2Nkk1m4csV"
      },
      "source": [
        "# Prepare optimizer and schedule (linear warmup and decay)\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",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpLwRuVn4csV"
      },
      "source": [
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFNhG4NV4csV"
      },
      "source": [
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VulDvI7c4csW"
      },
      "source": [
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cL8vZhUK4csW"
      },
      "source": [
        "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"
      ],
      "execution_count": null,
      "outputs": []
    },
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        },
        "outputId": "da4e5edd-bfc4-4b71-a5b1-0dc7fb151f4b"
      },
      "source": [
        "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",
        "    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",
        "        loss = loss_fn(out, label)\n",
        "        loss.backward()\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))\n",
        "    print(\"epoch {} test loss {}\".format(e+1,loss.data.cpu().numpy() ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            ]
          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 4, 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",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "epoch 1 batch id 1 loss 0.7749324440956116 train acc 0.359375\n",
            "epoch 1 batch id 201 loss 0.5158419013023376 train acc 0.8232276119402985\n",
            "\n",
            "epoch 1 train acc 0.8477496483825597\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "epoch 1 test acc 0.9021120124549328\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "text": [
            "epoch 2 batch id 1 loss 0.2425202578306198 train acc 0.859375\n",
            "epoch 2 batch id 201 loss 0.17843963205814362 train acc 0.8767101990049752\n",
            "\n",
            "epoch 2 train acc 0.8870780590717299\n"
          ],
          "name": "stdout"
        },
        {
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "epoch 2 test acc 0.9480703048180924\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "epoch 3 batch id 1 loss 0.2956775426864624 train acc 0.921875\n",
            "epoch 3 batch id 201 loss 0.1916491985321045 train acc 0.8884483830845771\n",
            "\n",
            "epoch 3 train acc 0.8919831223628691\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "epoch 3 test acc 0.9469385037692559\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "epoch 4 batch id 1 loss 0.31551361083984375 train acc 0.90625\n",
            "epoch 4 batch id 201 loss 0.21505817770957947 train acc 0.8888370646766169\n",
            "\n",
            "epoch 4 train acc 0.8997758438818565\n"
          ],
          "name": "stdout"
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        {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "epoch 4 test acc 0.9374231809242871\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "epoch 5 batch id 1 loss 0.24647943675518036 train acc 0.921875\n",
            "epoch 5 batch id 201 loss 0.2070228010416031 train acc 0.8951337064676617\n",
            "\n",
            "epoch 5 train acc 0.9077663502109704\n"
          ],
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          "text": [
            "\n",
            "epoch 5 test acc 0.9434816453621763\n"
          ],
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          "text": [
            "epoch 6 batch id 1 loss 0.15012405812740326 train acc 0.953125\n",
            "epoch 6 batch id 201 loss 0.17255307734012604 train acc 0.9008861940298507\n",
            "\n",
            "epoch 6 train acc 0.9159150843881856\n"
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            "\n",
            "epoch 6 test acc 0.9437581940347427\n"
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            "epoch 7 batch id 1 loss 0.11171405762434006 train acc 0.96875\n",
            "epoch 7 batch id 201 loss 0.18566884100437164 train acc 0.908193407960199\n",
            "\n",
            "epoch 7 train acc 0.9203059071729958\n"
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            "epoch 7 test acc 0.9401630612913798\n"
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        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9374e01e61584d95ba73e14b15b6f208",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=395.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "epoch 8 batch id 1 loss 0.10573995858430862 train acc 0.96875\n",
            "epoch 8 batch id 201 loss 0.17050890624523163 train acc 0.9125466417910447\n",
            "\n",
            "epoch 8 train acc 0.9223057313642757\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d74321a9b774d049618cfe6028f918b",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=113.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "epoch 8 test acc 0.9443112913798755\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0nGs6VXqF1c"
      },
      "source": [
        "\n",
        "import pandas as pd\n",
        "\n",
        "def getSentimentValue(comment, tok, max_len, batch_size, device):\n",
        "  commnetslist = []\n",
        "  emo_list = []\n",
        "  for c in comment:\n",
        "    commnetslist.append( [c, 5] )\n",
        "  pdData = pd.DataFrame( commnetslist, columns = [['댓글', '감성']] )\n",
        "  pdData = pdData.values\n",
        "  test_set = BERTDataset(pdData, 0, 1, tok, max_len, True, False) \n",
        "  test_input = torch.utils.data.DataLoader(test_set, batch_size=batch_size, num_workers=5)\n",
        "  for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(test_input):#enumerate(tqdm_notebook(test_input)):\n",
        "    token_ids = token_ids.long().to(device)\n",
        "    segment_ids = segment_ids.long().to(device)\n",
        "    valid_length= valid_length \n",
        "    out = model(token_ids, valid_length, segment_ids)\n",
        "    #print(\"out one:\",out)\n",
        "  \n",
        "    for e in out:\n",
        "      #print(e)\n",
        "      if e[0]>e[1]: # 부정\n",
        "        value = 0\n",
        "      else: #긍정\n",
        "        value = 1\n",
        "      emo_list.append(value)\n",
        "\n",
        "  return emo_list\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2W3J90bLxsTi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3638cfed-d1ca-4b96-b086-57451140c75d"
      },
      "source": [
        "from google.colab import drive\n",
        "import json\n",
        "\n",
        "tlist = [] # database table name list\n",
        "for i in range(1,6):\n",
        "    t = \"daum\"\n",
        "    tlist.append(t+\"after\"+str(i))\n",
        "    tlist.append(t+\"before\"+str(i))\n",
        "for i in range(1,6):\n",
        "    t = \"naver\"\n",
        "    tlist.append(t+\"after\"+str(i))\n",
        "    tlist.append(t+\"before\"+str(i))\n",
        "\n",
        "drive.mount(\"/content/drive\")\n",
        "path = \"/content/drive/My Drive/ColabNotebooks/jsonComment/\" # comment  json file directory path\n",
        "for tablename in tlist:\n",
        "    \n",
        "    with open(path+tablename+'-dict.json', encoding=\"utf-8\") as json_file:\n",
        "        data = json.load(json_file) # load json file\n",
        "    \n",
        "    for date in data.keys(): \n",
        "        #print(\">>\",date,\"/\",tablename)\n",
        "        \n",
        "        day_article = data[date]\n",
        "        for article in day_article.keys():\n",
        "            #if len(day_article[article][0])>100: print(\"카운팅:\",len(day_article[article][0]))\n",
        "            comments = day_article[article][0]\n",
        "            if len(comments)==1: continue\n",
        "            \n",
        "            comments.remove(\"\")\n",
        "            \n",
        "            emotion = getSentimentValue(comments, tok, max_len, batch_size, device)\n",
        "            \n",
        "            data[date][article][-1].extend(emotion)\n",
        "            #data[date][article][-1].append(emotion) # dictionary value append sentiment\n",
        "    print(tablename, \"--comment save\")\n",
        "    result_path = \"/content/drive/My Drive/ColabNotebooks/predictComment/\" #  result save json path\n",
        "    #drive.mount(\"/Colab Notebooks/predictComment\")\n",
        "    with open(result_path+\"finish-\"+tablename+\"-dict.json\", \"w\", encoding=\"utf-8\") as json_file:\n",
        "        json.dump(data, json_file, indent=\"\\t\",ensure_ascii = False)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 5 worker processes in total. Our suggested max number of worker in current system is 4, 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",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "daumafter1 --comment save\n",
            "daumbefore1 --comment save\n",
            "daumafter2 --comment save\n",
            "daumbefore2 --comment save\n",
            "daumafter3 --comment save\n",
            "daumbefore3 --comment save\n",
            "daumafter4 --comment save\n",
            "daumbefore4 --comment save\n",
            "daumafter5 --comment save\n",
            "daumbefore5 --comment save\n",
            "naverafter1 --comment save\n",
            "naverbefore1 --comment save\n",
            "naverafter2 --comment save\n",
            "naverbefore2 --comment save\n",
            "naverafter3 --comment save\n",
            "naverbefore3 --comment save\n",
            "naverafter4 --comment save\n",
            "naverbefore4 --comment save\n",
            "naverafter5 --comment save\n",
            "naverbefore5 --comment save\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}