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          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
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            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
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        "ef89479c700e4235ad3b74f27672984f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
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            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# load library"
      ],
      "metadata": {
        "id": "pKEnZIWt01_f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master"
      ],
      "metadata": {
        "id": "tnfh93PT5acJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "fDt0KC8axEla"
      },
      "outputs": [],
      "source": [
        "#util\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "#torch\n",
        "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",
        "\n",
        "#numpy\n",
        "import numpy as np\n",
        "\n",
        "#model\n",
        "from kobert import get_tokenizer\n",
        "from kobert import get_pytorch_kobert_model\n",
        "\n",
        "#transformer\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# hyperparameter"
      ],
      "metadata": {
        "id": "8uaUSjcC3ZMU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "is_cuda = torch.cuda.is_available()\n",
        "\n",
        "if is_cuda:\n",
        "    device = torch.device(\"cuda:0\")\n",
        "    print(\"GPU is available\")\n",
        "else:\n",
        "    device = torch.device(\"cpu\")\n",
        "    print(\"GPU not available, CPU used\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTc89DOU04GX",
        "outputId": "fa58bac9-7ea2-41b8-80ca-a4f21beee6f5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU is available\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#setting\n",
        "\n",
        "max_len = 128\n",
        "batch_size = 32\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 5\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate = 5e-5"
      ],
      "metadata": {
        "id": "1Oitz_6nBCCi"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# define matrix"
      ],
      "metadata": {
        "id": "3UjOOI7EKIi9"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "1CA8nZ1eKIN5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# load pretrained model"
      ],
      "metadata": {
        "id": "U7tV_r0l6kpN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bertmodel, vocab = get_pytorch_kobert_model(cachedir = \".cache\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EC05sv_Y6kRW",
        "outputId": "f5ee1d4b-15d4-4e57-e07d-2fa435bb360b"
      },
      "execution_count": 7,
      "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": [
        "#tokenizer\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer,vocab,lower=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHA6qMjN61yG",
        "outputId": "384c5647-c047-4981-f2b8-a2d88091bbf4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# define BERTDataset"
      ],
      "metadata": {
        "id": "Q_ki8kJi7Pcm"
      }
    },
    {
      "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])-1 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": "6gI-HXl67SWC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# load dataset"
      ],
      "metadata": {
        "id": "C-wFQGJE05wq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"/content/drive/MyDrive/text_dataset/review_data.pickle\", 'rb') as f:\n",
        "    review_data = pickle.load(f)\n",
        "\n",
        "with open(\"/content/drive/MyDrive/text_dataset/star_data.pickle\", 'rb') as f:\n",
        "    star_data = pickle.load(f)"
      ],
      "metadata": {
        "id": "F7x8Ww2308DJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for review in review_data:\n",
        "    \n",
        "    print(review)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRNSsWb_1O3z",
        "outputId": "67ea4bde-456f-46ff-b842-7b5d6dc570b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "선생님 항상 존경합니다. 저도 한 때는 16만원 들고 가출한 적이 있으나, 선생님의 뼈아픈 글들을 읽고 노력하며, 16만원부터 시작하여 누구의 도움없이, 현재는 작은 사업체를 3개 운영하고 있습니다. 저도 훗날, 선생님처럼 우여곡절을 겪고 있는 젊은이들에게 도움을 주는 일을 하고 싶습니다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for star in star_data:\n",
        "    \n",
        "    print(star)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmE4Xl1d1e0G",
        "outputId": "85363928-ed78-47e1-e3b3-61ac6f99816e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#unbalanced\n",
        "Counter(star_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmMIhIFe1lCL",
        "outputId": "539133c4-9edd-4cdb-e583-4f70153f4eda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({5: 4528, 1: 213, 2: 207, 3: 577, 4: 1483})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "\n",
        "for review,star in zip(review_data,star_data):\n",
        "    \n",
        "    data.append([review,str(star)])\n",
        "\n",
        "print(len(data))\n",
        "print(len(review_data))\n",
        "print(len(star_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "694dZP6OGoLJ",
        "outputId": "0664b3cf-1725-4d53-8a2e-be990cf579e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7008\n",
            "7008\n",
            "7008\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/text_dataset/data.pickle', 'wb') as f:\n",
        "    pickle.dump(data, f)"
      ],
      "metadata": {
        "id": "bP_K08nBHBQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ind = set(np.random.choice(range(len(data)),6300,replace=False))\n",
        "test_ind = set(range(len(data))) - train_ind\n",
        "\n",
        "print(len(train_ind))\n",
        "print(len(test_ind))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooKowykEIMLq",
        "outputId": "27ec2e68-8219-4ee7-bcef-9ddf8340212f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6300\n",
            "708\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train = []\n",
        "dataset_test = []\n",
        "\n",
        "for t in train_ind:\n",
        "    \n",
        "    dataset_train.append(data[t])\n",
        "\n",
        "for t in test_ind:\n",
        "    \n",
        "    dataset_test.append(data[t])\n",
        "\n",
        "print(len(dataset_train))\n",
        "print(len(dataset_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXrGwgo8JGOR",
        "outputId": "6333fa8d-e518-43ad-b72b-abdee8d1893f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6300\n",
            "708\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/text_dataset/train_data.pickle', 'wb') as f:\n",
        "    pickle.dump(dataset_train, f)\n",
        "\n",
        "with open('/content/drive/MyDrive/text_dataset/test_data.pickle', 'wb') as f:\n",
        "    pickle.dump(dataset_test, f)"
      ],
      "metadata": {
        "id": "TZDUT7o7JSkL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"/content/drive/MyDrive/text_dataset/train_data.pickle\", 'rb') as f:\n",
        "    dataset_train = pickle.load(f)\n",
        "\n",
        "with open(\"/content/drive/MyDrive/text_dataset/test_data.pickle\", 'rb') as f:\n",
        "    dataset_test = pickle.load(f)"
      ],
      "metadata": {
        "id": "OFEloYDFLBaz"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "AWOTmOHjJpJU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=0)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=0)"
      ],
      "metadata": {
        "id": "au4lAbwXJvRQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for d in data_train:\n",
        "    \n",
        "    print(d)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JC6Nq2E6YuGL",
        "outputId": "36188f0d-ad53-4da4-82e2-c2c1cfcde3d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([   2, 2752, 4996, 4192, 5424, 7843,  517,   54, 3990, 5859, 4955,\n",
            "       1846,  545, 6153, 1802,  517, 5330, 7468, 7828, 4007, 3876,  517,\n",
            "         46, 2752, 7095,  517, 6477, 6797, 7760, 1231, 5938, 3824, 5439,\n",
            "       1481, 7810,  517,   46,  545, 6153, 6410, 2986, 7815, 1528, 7095,\n",
            "       1717, 6883,  517,   46, 5064, 5760, 3939, 2609, 7436, 6116,  589,\n",
            "       5357, 3517, 7788, 3867,  517,   54, 3990, 5859,    3], dtype=int32), array(64, dtype=int32), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
            "      dtype=int32), 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "kOT09OMLbCzu",
        "outputId": "86b4c35c-c609-4067-a1be-dd0dabe69fe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-1f8a688cae5d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# define model"
      ],
      "metadata": {
        "id": "ebjbIwDGBeS7"
      }
    },
    {
      "cell_type": "code",
      "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.to(\"cpu\"))\n",
        "        for i, v in enumerate(valid_length):\n",
        "            attention_mask[i][:v] = 1\n",
        "        return attention_mask\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, attention_mask = attention_mask.to(token_ids.device))\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        else:\n",
        "            out = pooler\n",
        "        return self.classifier(out)"
      ],
      "metadata": {
        "id": "B1-OSfRxBdtJ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BERTClassifier(bertmodel, num_classes=5, dr_rate = 0.5).to(device)"
      ],
      "metadata": {
        "id": "sJG4p-vAC0Ka"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bLSAmT12C8Ki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# define finetune layer"
      ],
      "metadata": {
        "id": "4MfckLQZJ3tu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "]"
      ],
      "metadata": {
        "id": "Fw8MPDAlJ2-_"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# optimizer, loss, scheduler"
      ],
      "metadata": {
        "id": "wpoPwhqBJ6M4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "nHOWzwLNJ7-1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)"
      ],
      "metadata": {
        "id": "Ztk-dhVAJ-Oj"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)"
      ],
      "metadata": {
        "id": "SyCkVuTOKErW"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# training"
      ],
      "metadata": {
        "id": "aYnHfO08KCcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "save_path = \"/content/drive/MyDrive/text_dataset/model.pth\""
      ],
      "metadata": {
        "id": "DU0QeB5wLp0B"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for token_ids,valid_length,segment_ids,label in train_dataloader:\n",
        "    \n",
        "    print(token_ids)\n",
        "    print(valid_length)\n",
        "    print(segment_ids)\n",
        "    print(label)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ui09SpdaOXcP",
        "outputId": "488b0a8a-e2a4-46ff-e0ae-2abe942c8219"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[   2, 2752, 4996,  ...,    1,    1,    1],\n",
            "        [   2,  517, 6983,  ...,    1,    1,    1],\n",
            "        [   2, 4299, 5064,  ...,    1,    1,    1],\n",
            "        ...,\n",
            "        [   2, 3489, 3647,  ...,    1,    1,    1],\n",
            "        [   2, 3647, 5384,  ...,    1,    1,    1],\n",
            "        [   2, 2811, 6896,  ...,    1,    1,    1]], dtype=torch.int32)\n",
            "tensor([ 90,  37,  89,  87,  86,  23,  21, 110,  29,  57,  16,  57,  41,   6,\n",
            "         20,  37,  91,  92,  52,  14,  22,  49,  20,  64,  29,  51,  17,  21,\n",
            "         76,  37,   8, 109], dtype=torch.int32)\n",
            "tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0],\n",
            "        [0, 0, 0,  ..., 0, 0, 0]], dtype=torch.int32)\n",
            "tensor([4, 0, 4, 1, 4, 0, 4, 0, 4, 4, 4, 4, 2, 4, 0, 4, 3, 4, 1, 4, 4, 2, 4, 4,\n",
            "        4, 4, 4, 4, 4, 4, 4, 4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(\"cpu\")"
      ],
      "metadata": {
        "id": "gggjALAz1tg5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out = model(token_ids,valid_length,segment_ids)"
      ],
      "metadata": {
        "id": "0f8DG4NJ1wv4"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2yPX6s7e5V_Z",
        "outputId": "e35abc88-d1d3-4355-871e-9635639438bc"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_88R49m5Xcu",
        "outputId": "cfbe7cef-9d2d-410d-b506-bc292e09b4ab"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn(out,label.long())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrY6R9Ao5IoB",
        "outputId": "3bc821cf-4f8e-40c7-e296-6a1b4b599221"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.8314, grad_fn=<NllLossBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(device)\n",
        "\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)"
      ],
      "metadata": {
        "id": "jGdZ-dbr2P5_"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out = model(token_ids,valid_length,segment_ids)"
      ],
      "metadata": {
        "id": "3jaBxcv82oHu"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IT8cBMlC3_iZ",
        "outputId": "acc624fc-602e-4ffe-e0c2-78e168e007ad"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0.5946, -0.3587,  0.1753,  0.1961, -0.1310],\n",
              "        [ 0.3773,  0.2950,  0.1893,  0.6181,  0.3538],\n",
              "        [ 0.6343,  0.1857, -0.4185, -0.0601, -0.3275],\n",
              "        [ 0.2640, -0.2442,  0.2683,  0.4642,  0.2723],\n",
              "        [ 0.2513, -0.5970, -0.5154,  0.6713,  0.3567],\n",
              "        [ 0.3911, -0.2206,  0.3732,  0.2277, -0.2413],\n",
              "        [-0.3429, -0.0183, -0.2726, -0.2314,  0.7278],\n",
              "        [ 0.3198,  0.2199, -0.2292,  0.6113, -0.3891]], device='cuda:0',\n",
              "       grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhF90_RE4BQG",
        "outputId": "5b80caf0-ff70-4edf-d24c-57fa0c075fad"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([4, 0, 4, 1, 4, 0, 4, 0], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn(out,label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9B6YIpD3igN",
        "outputId": "e518b6f1-4d32-4e08-8d56-43925343f99e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.5961, device='cuda:0', grad_fn=<NllLossBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bfor batch_id, (token_ids, valid_length, segment_ids, label) in tqdm(enumerate(train_dataloader), total=len(train_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",
        "    print(out)\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDnLGjlbQWra",
        "outputId": "b0f55ec3-73cd-47fa-bbae-6d163ff29735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[   2, 2752, 4996,  ..., 3990, 5859,    3],\n",
              "        [   2,  517, 6983,  ...,    1,    1,    1],\n",
              "        [   2, 4299, 5064,  ...,  517,   54,    3],\n",
              "        ...,\n",
              "        [   2, 3097,  517,  ...,    1,    1,    1],\n",
              "        [   2, 1169, 7850,  ...,    1,    1,    1],\n",
              "        [   2,  517, 6983,  ...,    1,    1,    1]], dtype=torch.int32)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "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 tqdm(enumerate(train_dataloader), total=len(train_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",
        "        optimizer.zero_grad()\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",
        "            torch.save(model.state_dict(), save_path)\n",
        "    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for batch_id, (token_ids, valid_length, segment_ids, label) in tqdm(enumerate(test_dataloader), total=len(test_dataloader)):\n",
        "            token_ids = token_ids.long().to(device)\n",
        "            segment_ids = segment_ids.long().to(device)\n",
        "            #valid_length= valid_length.long()\n",
        "            label = label.long().to(device)\n",
        "            out = model(token_ids, valid_length, segment_ids)\n",
        "            test_acc += calc_accuracy(out, label)\n",
        "        print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609,
          "referenced_widgets": [
            "897e667918bb4e0a94c7bd3831944f7c",
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            "feb6fd604e63412bbd698d75c5b3a631",
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            "ef150f2e576c4e609e50ff8f607cfc43",
            "8ceb4d12f27e46ee879e03adf86e2452",
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            "ef89479c700e4235ad3b74f27672984f"
          ]
        },
        "id": "oihGXeg7KOIF",
        "outputId": "e9edfb7c-b92f-4243-d152-2f43505b346c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/197 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "897e667918bb4e0a94c7bd3831944f7c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 batch id 1 loss 1.5748028755187988 train acc 0.28125\n",
            "epoch 1 train acc 0.5956988759970994\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c92dc57a63c044faa34c4488c76006f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 test acc 0.6331521739130435\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/197 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e7125aac9cb40c9a2800ede2b281c56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 batch id 1 loss 1.0894697904586792 train acc 0.71875\n",
            "epoch 2 train acc 0.6461430384336475\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d8b4bf67ecb4087968023c55619e507"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 test acc 0.6331521739130435\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/197 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2817718c5a884815b1c1e7720b3e871b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 batch id 1 loss 1.08392333984375 train acc 0.71875\n",
            "epoch 3 train acc 0.6464602973168962\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91bc4ec6ddad45ea9b38c045397e1c10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 test acc 0.6331521739130435\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/197 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdf609b3b84e41dfb448e9f17d7402f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 batch id 1 loss 1.122100830078125 train acc 0.71875\n",
            "epoch 4 train acc 0.6458031182015954\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf17ee7f06584601827e0755e776357b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 test acc 0.6019021739130435\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/197 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a94516ca09c414daac0d778833c4f3b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 batch id 1 loss 1.0216034650802612 train acc 0.65625\n",
            "epoch 5 train acc 0.6463016678752719\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f487da87e0e4dfbaf406bb7fbba815a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 test acc 0.6331521739130435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "siehceIgRn9w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# load pretrained model"
      ],
      "metadata": {
        "id": "MTZFAk30-yIk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resume = \"/content/drive/MyDrive/text_dataset/model.pth\"\n",
        "\n",
        "checkpoint = torch.load(resume, map_location=torch.device('cpu'))\n",
        "\n",
        "model.load_state_dict(checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oF4XSLuW-zQw",
        "outputId": "bfb58da4-faef-4b17-e4f7-36aee7adca09"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# inference"
      ],
      "metadata": {
        "id": "0WmIMXrk9Bqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "죗값을 편견이라는 연좌제로 치르는 가해자의 가족 시점에서 서술되는 작품으로 사람들이 왜 범죄자의 가족들에게까지 차가워지는지 적나라하게 묘사되며 추리 소설이라기보다는 드라마 소설에 가까운 씁쓸한 현실을 다룬 이야기\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "RM6Pr3OV9BXN"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text2 = \"\"\"\n",
        "1월의 첫 책인데 감동과 재미 그리고 사유(차별과 속죄의 한계와 범위에 대한)를 충분히 제공하는 책입니다~대만족~\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "ypmAQVaIBeiC"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = text.strip(\"\\n\")"
      ],
      "metadata": {
        "id": "iVgDqxrV-HwO"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text2 = text2.strip(\"\\n\").strip(\" \")"
      ],
      "metadata": {
        "id": "YqWRqw5CBhKr"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "nHrU7rXuBmfa",
        "outputId": "b76d6dde-6f1c-4dd3-8187-f55b483fcda9"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1월의 첫 책인데 감동과 재미 그리고 사유(차별과 속죄의 한계와 범위에 대한)를 충분히 제공하는 책입니다~대만족~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transform = nlp.data.BERTSentenceTransform(\n",
        "            tok, max_seq_length=max_len, pad=True, pair=False)"
      ],
      "metadata": {
        "id": "Tt_7Uzp0-JVt"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = transform(text2)"
      ],
      "metadata": {
        "id": "zFaO0X-R-bqK"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9Ew_O2Q-juk",
        "outputId": "63899d91-1615-4143-b73c-8ae90221b12b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  2, 517,   0,   3,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
              "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1], dtype=int32),\n",
              " array(4, dtype=int32),\n",
              " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32))"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token_ids = torch.tensor(test_data[0]).unsqueeze(0).long()\n",
        "valid_length = torch.tensor(test_data[1]).unsqueeze(0)\n",
        "segment_ids = torch.tensor(test_data[2]).unsqueeze(0).long()"
      ],
      "metadata": {
        "id": "U4UAGCff_Hte"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "id": "cAKZJC6xAp9a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "segment_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WRvh2euAtcX",
        "outputId": "36bbceb8-6bb5-43bd-c742-b37211b65bf3"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(\"cpu\")\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "maxPblwPAyHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = model(token_ids, valid_length, segment_ids)"
      ],
      "metadata": {
        "id": "TJ0Gj2y1-krY"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2jNTWze_f2U",
        "outputId": "34fee9fc-0ec3-422b-a0a2-47095087d478"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.9738, -0.8896, -0.0021,  0.7148,  1.3730]],\n",
              "       grad_fn=<AddmmBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percentage_output = F.softmax(output, dim = 1)\n",
        "\n",
        "print(percentage_output)\n",
        "pred = output.cpu().detach().numpy()\n",
        "\n",
        "print(pred)\n",
        "\n",
        "sorted_pred = np.argsort(pred,axis = 1)\n",
        "\n",
        "print(sorted_pred[0][-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCQsZ8TP_eUc",
        "outputId": "94445fa3-7481-4975-ad35-bbba23326ece"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.0486, 0.0528, 0.1283, 0.2628, 0.5075]], grad_fn=<SoftmaxBackward0>)\n",
            "[[-0.9737689  -0.8895578  -0.00208349  0.71481854  1.3729838 ]]\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GS0zJ27LAdZt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}