KLUE_NLI.ipynb

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    {
      "cell_type": "markdown",
      "id": "_kbUSCb_Egcu",
      "metadata": {
        "id": "_kbUSCb_Egcu"
      },
      "source": [
        "# HuggingFace Transformers를 활용한 문장 분류 모델 학습"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "of2izij8Eqe0",
      "metadata": {
        "id": "of2izij8Eqe0"
      },
      "source": [
        "본 노트북에서는 `klue/roberta-base` 모델을 **KLUE** 내 **NLI** 데이터셋을 활용하여 모델을 훈련하는 예제를 다루게 됩니다.\n",
        "\n",
        "학습을 통해 얻어질 `klue-roberta-base-nli` 모델은 입력된 두 문장의 추론 관계를 예측하는데 사용할 수 있게 됩니다.\n",
        "\n",
        "학습 과정 이후에는 간단한 예제 코드를 통해 모델이 어떻게 활용되는지도 함께 알아보도록 할 것입니다.\n",
        "\n",
        "모든 소스 코드는 [`huggingface-notebooks`](https://github.com/huggingface/notebooks)를 참고하였습니다.\n",
        "\n",
        "먼저, 노트북을 실행하는데 필요한 라이브러리를 설치합니다. 모델 훈련을 위해서는 `transformers`가, 학습 데이터셋 로드를 위해서는 `datasets` 라이브러리의 설치가 필요합니다. 그 외 모델 성능 검증을 위해 `scipy`, `scikit-learn`을 추가로 설치해주도록 합니다."
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2d-omseoCdqL"
      },
      "id": "2d-omseoCdqL",
      "execution_count": null,
      "outputs": []
    },
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        "outputId": "b0798375-2382-4f1b-d337-e1ee4f89c56c"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install -U transformers datasets scipy scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8wSLfX-jaIqV",
      "metadata": {
        "id": "8wSLfX-jaIqV"
      },
      "source": [
        "## 문장 분류 모델 학습"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "92e868b7-3cf7-4977-a6e7-ebbb99ba298e",
      "metadata": {
        "id": "92e868b7-3cf7-4977-a6e7-ebbb99ba298e"
      },
      "source": [
        "노트북을 실행하는데 필요한 라이브러리들을 모두 임포트합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "243b461f",
      "metadata": {
        "id": "243b461f"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import logging\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datasets\n",
        "from datasets import load_dataset, load_metric, ClassLabel, Sequence\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59b1e5ae-471f-461f-850f-4241a6037471",
      "metadata": {
        "id": "59b1e5ae-471f-461f-850f-4241a6037471"
      },
      "source": [
        "학습에 필요한 정보를 변수로 기록합니다.\n",
        "\n",
        "본 노트북에서는 `klue-roberta-base` 모델을 활용하지만, https://huggingface.co/klue 페이지에서 더 다양한 사전학습 언어 모델을 확인하실 수 있습니다.\n",
        "\n",
        "학습 태스크로는 `nli`를, 배치 사이즈로는 32를 지정하겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ecaacbe",
      "metadata": {
        "id": "6ecaacbe"
      },
      "outputs": [],
      "source": [
        "model_checkpoint = \"klue/roberta-base\"\n",
        "batch_size = 32\n",
        "task = \"nli\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "816b5893-d03e-4c6e-8536-e246b1def987",
      "metadata": {
        "id": "816b5893-d03e-4c6e-8536-e246b1def987"
      },
      "source": [
        "이제 HuggingFace `datasets` 라이브러리에 등록된 KLUE 데이터셋 중, NLI 데이터를 내려받습니다."
      ]
    },
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            "b6b541b6f0584c93b24096323b23e13c",
            "b5293935b98b47bda03d059bea71cde6",
            "eef13404785e489fbf76f23e7d8df8c4"
          ]
        },
        "id": "f8feeba0",
        "outputId": "d47b8358-3156-48f2-ca6a-f73751a534d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/23.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "404d69e9687949749e30ff96dbd132e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/22.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "093c763c14cf4df0a178894bb12d5e6c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/21.5k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94463dfef78f458ca68f20159f5de2cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset klue/nli to /root/.cache/huggingface/datasets/klue/nli/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1fcd3b435e74bf380749f7509e123c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/24998 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0c721f246a3407c8742fb1d37c54211"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f4cee0cf9a6493fa7b3450d55428a83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset klue downloaded and prepared to /root/.cache/huggingface/datasets/klue/nli/1.0.0/e0fc3bc3de3eb03be2c92d72fd04a60ecc71903f821619cb28ca0e1e29e4233e. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f444c4f52a14ffb8c006f242f97c96f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "datasets = load_dataset(\"klue\", task)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f90b06e4-3795-435a-aec8-701da9bd5f04",
      "metadata": {
        "id": "f90b06e4-3795-435a-aec8-701da9bd5f04"
      },
      "source": [
        "다운로드 혹은 로드 후 얻어진 `datasets` 객체를 살펴보면, 훈련 데이터와 검증 데이터가 포함되어 있는 것을 확인할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "QzlmWQ2DrZ7P",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzlmWQ2DrZ7P",
        "outputId": "726a9c8c-5b78-4899-aea6-34067cefbcc7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['guid', 'source', 'premise', 'hypothesis', 'label'],\n",
              "        num_rows: 24998\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['guid', 'source', 'premise', 'hypothesis', 'label'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f39_pKKnarD3",
      "metadata": {
        "id": "f39_pKKnarD3"
      },
      "source": [
        "각 예시 데이터는 아래와 같이 두 개의 문장과 두 문장의 추론 관계를 라벨로 지니고 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e46bf44d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e46bf44d",
        "outputId": "c4fb83a9-b849-4959-9b0a-82ebfff0c9ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'guid': 'klue-nli-v1_train_00000',\n",
              " 'source': 'NSMC',\n",
              " 'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다',\n",
              " 'hypothesis': '힛걸 진심 최고로 멋지다.',\n",
              " 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "datasets[\"train\"][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "pc1BXYLYaupD",
      "metadata": {
        "id": "pc1BXYLYaupD"
      },
      "source": [
        "데이터셋을 전반적으로 살펴보기 위한 시각화 함수를 다음과 같이 정의합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8110a3ee",
      "metadata": {
        "id": "8110a3ee"
      },
      "outputs": [],
      "source": [
        "def show_random_elements(dataset, num_examples=10):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "\n",
        "    picks = []\n",
        "    \n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "\n",
        "        # 이미 등록된 예제가 뽑힌 경우, 다시 추출\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "\n",
        "        picks.append(pick)\n",
        "\n",
        "    # 임의로 추출된 인덱스들로 구성된 데이터 프레임 선언\n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "\n",
        "    for column, typ in dataset.features.items():\n",
        "        # 라벨 클래스를 스트링으로 변환\n",
        "        if isinstance(typ, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "\n",
        "    display(HTML(df.to_html()))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "u1Ia2wGh1xaF",
      "metadata": {
        "id": "u1Ia2wGh1xaF"
      },
      "source": [
        "앞서 정의한 함수를 활용해 훈련 데이터를 살펴보도록 합시다.\n",
        "\n",
        "이처럼 데이터를 살펴보는 것의 장점으로는 각 라벨에 어떠한 문장들이 해당하는지에 대한 감을 익힐 수 있다는데에 있습니다.\n",
        "\n",
        "**KLUE NLI**는 `entailment`, `neutral` 그리고 `contradiction` 세 개의 라벨을 지니는 데이터셋임을 확인할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c7888d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "8c7888d1",
        "outputId": "3d285919-776d-4b92-ae83-ce718248ecac",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>guid</th>\n",
              "      <th>source</th>\n",
              "      <th>premise</th>\n",
              "      <th>hypothesis</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>klue-nli-v1_train_04040</td>\n",
              "      <td>airbnb</td>\n",
              "      <td>근처에 피티 궁전과 괜찮은 식당들이 있습니다.</td>\n",
              "      <td>피티 궁전이 가까이에 있습니다.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>klue-nli-v1_train_08273</td>\n",
              "      <td>wikinews</td>\n",
              "      <td>무죄 판결을 받은 서울대 교수에게 학교가 내린 해임처분은 정당하다는 법원 판결이 나왔다.</td>\n",
              "      <td>서울대 교수는 무죄 판결을 받는 것을 당연시했다.</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>klue-nli-v1_train_17032</td>\n",
              "      <td>wikitree</td>\n",
              "      <td>이에 고리본부는 주변 지역 내 구직자를 위한 양질의 일자리 창출을 최우선으로 삼고 지역과 상생하기 위한 정책을 추진하고 있다.</td>\n",
              "      <td>주변 지역은 고리본부의 상생 정책을 반기고 있다.</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>klue-nli-v1_train_08450</td>\n",
              "      <td>NSMC</td>\n",
              "      <td>미국 영화로서 최초로 7광구급 영화를 만들었다</td>\n",
              "      <td>최초로 7광구같은 영화를 제작했다.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>klue-nli-v1_train_07480</td>\n",
              "      <td>airbnb</td>\n",
              "      <td>마리나는 친절하고 유쾌한 호스트입니다.</td>\n",
              "      <td>유쾌한 호스트는 마리나뿐입니다.</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>klue-nli-v1_train_23616</td>\n",
              "      <td>wikinews</td>\n",
              "      <td>한편 남경필 경기도지사의 아들이 행한 가혹행위는 6사단의 상급부대인 5군단에서 보강수사를 할 것이라고 밝혔습니다.</td>\n",
              "      <td>남경필 경기도지사의 아들이 행한 가혹행위에 대해 어떠한 수사도 없을 것이라고 밝혔습니다.</td>\n",
              "      <td>contradiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>klue-nli-v1_train_15507</td>\n",
              "      <td>NSMC</td>\n",
              "      <td>이 다큐는 방법과 마인드 자체가 삐뚤어졌다</td>\n",
              "      <td>다큐의 방법과 마인드가 올바르지 않다.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>klue-nli-v1_train_08851</td>\n",
              "      <td>NSMC</td>\n",
              "      <td>반지의 제왕의 세계를 다시 만날 수 있다</td>\n",
              "      <td>반지의 제왕의 세계를 다시 볼 수 있다.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>klue-nli-v1_train_08640</td>\n",
              "      <td>NSMC</td>\n",
              "      <td>밀양송전탑공사시위를 막았던 의경으로써 날조가 심하네요.</td>\n",
              "      <td>날조가 정말 심하네요.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>klue-nli-v1_train_05479</td>\n",
              "      <td>wikipedia</td>\n",
              "      <td>다음 날, 병원 의사들은 에드워드 박사가 병원에 오기 전 함께 일했던 여비서를 호출하고 그녀는 에드워드 박사의 사진을 의사들에게 보여준다.</td>\n",
              "      <td>에드워드 박사와 함께 일했던 여비서는 박사의 사진을 갖고 있었다.</td>\n",
              "      <td>entailment</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_random_elements(datasets[\"train\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "zakMS4Y61sLm",
      "metadata": {
        "id": "zakMS4Y61sLm"
      },
      "source": [
        "훈련 과정 중 모델의 성능을 파악하기 위한 메트릭을 설정합니다.\n",
        "\n",
        "`datasets` 라이브러리에는 이미 구현된 메트릭을 사용할 수 있는 `load_metric` 함수가 있습니다.\n",
        "\n",
        "그 중 **GLUE** 데이터셋에 이미 다양한 메트릭이 구현되어 있으므로, **GLUE** 그 중에서도 **KLUE NLI**와 동일한 `accuracy` 메트릭을 사용하는 `qnli` 태스크의 메트릭을 사용합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c214cfb-efa2-4e31-9268-9d1038626095",
      "metadata": {
        "id": "5c214cfb-efa2-4e31-9268-9d1038626095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "43123ec14530456a9b81861489a65e54",
            "fc57b85bbe224f1193cf9e5b9df88719",
            "755e9f5e8333409c83e9511190c849a7",
            "ff24bdfc71e1405b9886f8979ad7c1b3",
            "18b570453a2f4e2c9d11ad11e5cf366c",
            "a3c540bb3f1041599ba1f00fd797966a",
            "a728849f891c4944865dfe4472427cbe",
            "80634bbe52104c34bb9e82df719fbc63",
            "221b52cfa12e45aa9639e59b9f6fd254",
            "c5816059411345dd9918ff98a53199b1",
            "fbb8e6bc749545f28b8a8fbbd7a1a373"
          ]
        },
        "outputId": "d1e1e167-7034-4c2b-d922-2f737dae48f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-4b8a033d74b0>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"glue\", \"qnli\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43123ec14530456a9b81861489a65e54"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "metric = load_metric(\"glue\", \"qnli\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "I07wSysL2fRs",
      "metadata": {
        "id": "I07wSysL2fRs"
      },
      "source": [
        "`accuracy` 메트릭이 정상적으로 작동하는지 확인하기 위해, 랜덤한 예측 값과 라벨 값을 생성합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e988c49f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e988c49f",
        "outputId": "f1f3598b-affe-479f-e7eb-5362c8ab96a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
              "        0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
              "        0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0]),\n",
              " array([1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
              "        1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n",
              "        1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "fake_preds = np.random.randint(0, 2, size=(64,))\n",
        "fake_labels = np.random.randint(0, 2, size=(64,))\n",
        "fake_preds, fake_labels"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ryxIEDZR2oR-",
      "metadata": {
        "id": "ryxIEDZR2oR-"
      },
      "source": [
        "앞서 생성한 랜덤 예측, 랜덤 라벨 값을 `compute()` 함수에 입력해 잘 동작하는지 확인해봅시다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2dcf392-5c0a-4bbb-b9f6-cc42be6a382a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2dcf392-5c0a-4bbb-b9f6-cc42be6a382a",
        "outputId": "f4caf7f1-5f5e-451c-d454-73e07e4f262b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.5}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "metric.compute(predictions=fake_preds, references=fake_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "rPvvy_wC24i_",
      "metadata": {
        "id": "rPvvy_wC24i_"
      },
      "source": [
        "이제 학습에 활용할 토크나이저를 로드해오도록 합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b64e70af",
      "metadata": {
        "id": "b64e70af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "bef6d23e41134c6a9cab1ffe054cb0ce",
            "5401beae3e894882841c9d46a9e2aa1b",
            "ab9fd9a67bc7462ca3bb215ac49cc2c7",
            "681a75b15b29435f9b1e05a02294a288",
            "ea4242b98bfe4108843645bda4d29a93",
            "eba2ec83f66a4ed591c8d996bdc59c36",
            "1a5ddb106689463e8bb00fe1fcbd29c0",
            "3559c6af442846b1a3061f27d19b6936",
            "bcefc827444846a9b7db4c0cabe536d2",
            "23c0cefb12284562938f48ba048b1b10",
            "ca4d4cee2ee44110a9c44c8de5902cf5",
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            "508be9b24af249759e46addcb76b3291",
            "94a26f6c69994919b2f0df2eb9fd29d3",
            "f8edecb90a9847af9bf5e495f4772574",
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            "3e52d4647dd34c5483966ba2e84e3988",
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            "3942da6e80344f0fb1ca2c9cd07fe670",
            "7dbc11bbc1da4de383ac4fe535a302da",
            "5fb914d18fdd4dd6a8ec2531a301d504",
            "5bea13bab0bd4181a2959c534e7239cd"
          ]
        },
        "outputId": "f6533099-a22d-40e8-f8d1-5f5d6393f655"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/375 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bef6d23e41134c6a9cab1ffe054cb0ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/248k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d011173ef184fc998b16418604d9f56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/752k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8edecb90a9847af9bf5e495f4772574"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/173 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3b6d6ae2a43494e995abc26c25cb1a1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YIVjwvm428Q_",
      "metadata": {
        "id": "YIVjwvm428Q_"
      },
      "source": [
        "로드된 토크나이저가 두 개 문장을 토큰화하는 방식을 파악하기 위해 두 문장을 입력 값으로 넣어줘보도록 합시다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0520aba3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0520aba3",
        "outputId": "73d0d27b-26fd-4d64-f1f2-e16f94f30360"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 3, 7254, 3841, 2200, 11980, 2062, 18, 2, 3, 7254, 3841, 2062, 636, 3711, 12717, 2178, 2062, 11980, 2062, 2], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tokenizer(\"힛걸 진심 최고로 멋지다.\", \"힛걸 진심 최고다 그 어떤 히어로보다 멋지다\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "FDAaxujL7l1X",
      "metadata": {
        "id": "FDAaxujL7l1X"
      },
      "source": [
        "`input_ids`를 보시면 `cls_token`에 해당하는 2번 토큰이 가장 좌측에 붙게 되며, `sep_token`의 3번 토큰이 각각 중간과 가장 우측에 더해진 것을 확인할 수 있습니다.\n",
        "\n",
        "이제 앞서 로드한 데이터셋에서 각 문장에 해당하는 *value* 를 뽑아주기 위한 *key* 를 정의합니다.\n",
        "\n",
        "앞서 **KLUE NLI** 데이터셋의 두 문장은 각각 `premise`와 `hypothesis`라는 이름으로 정의된 것을 확인하였으니, 두 문장의 *key* 는 마찬가지로 각각 `premise`, `hypothesis`가 되게 됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95127c8c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95127c8c",
        "outputId": "499455be-09d7-4637-8e52-32b651bc92e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence 1: 힛걸 진심 최고다 그 어떤 히어로보다 멋지다\n",
            "Sentence 2: 힛걸 진심 최고로 멋지다.\n"
          ]
        }
      ],
      "source": [
        "sentence1_key, sentence2_key = (\"premise\", \"hypothesis\")\n",
        "print(f\"Sentence 1: {datasets['train'][0][sentence1_key]}\")\n",
        "print(f\"Sentence 2: {datasets['train'][0][sentence2_key]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fMDIRs1M4-5a",
      "metadata": {
        "id": "fMDIRs1M4-5a"
      },
      "source": [
        "이제 *key* 도 확인이 되었으니, 데이터셋에서 각 예제들을 뽑아와 토큰화 할 수 있는 함수를 아래와 같이 정의해줍니다.\n",
        "\n",
        "해당 함수는 모델을 훈련하기 앞서 데이터셋을 미리 토큰화 시켜놓는 작업을 위한 콜백 함수로 사용되게 됩니다.\n",
        "\n",
        "인자로 넣어주는 `truncation`는 모델이 입력 받을 수 있는 최대 길이 이상의 토큰 시퀀스가 들어오게 될 경우, 최대 길이 기준으로 시퀀스를 자르라는 의미를 지닙니다.\n",
        "\n",
        "( \\* `return_token_type_ids`는 토크나이저가 `token_type_ids`를 반환하도록 할 것인지를 결정하는 인자입니다. `transformers==4.7.0` 기준으로 `token_type_ids`가 기본적으로 반환되므로 `token_type_ids` 자체를 사용하지 않는 `RoBERTa` 모델을 활용하기 위해 해당 인자를 `False`로 설정해주도록 합니다.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2f8f8cc0",
      "metadata": {
        "id": "2f8f8cc0"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(\n",
        "        examples[sentence1_key],\n",
        "        examples[sentence2_key],\n",
        "        truncation=True,\n",
        "        return_token_type_ids=False,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "F-qK5fgl4_6t",
      "metadata": {
        "id": "F-qK5fgl4_6t"
      },
      "source": [
        "앞서 정의한 `process_function`은 여러 개의 예제 데이터를 받을 수도 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "x8KrAGLpyxNO",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8KrAGLpyxNO",
        "outputId": "805cb21a-096f-43e3-c252-d150e9d6325c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[0, 3, 7254, 3841, 2062, 636, 3711, 12717, 2178, 2062, 11980, 2062, 2, 3, 7254, 3841, 2200, 11980, 2062, 18, 2], [0, 3911, 2377, 2366, 1521, 3061, 4785, 1282, 2955, 3308, 3515, 2170, 22, 2532, 5675, 2, 3911, 2377, 2366, 1525, 2062, 18, 2], [0, 3911, 2377, 2366, 1521, 3061, 4785, 1282, 2955, 3308, 3515, 2170, 22, 2532, 5675, 2, 1282, 2955, 3308, 2052, 3944, 11580, 2359, 2062, 18, 2], [0, 3911, 2377, 2366, 1521, 3061, 4785, 1282, 2955, 3308, 3515, 2170, 22, 2532, 5675, 2, 3911, 2377, 2366, 5105, 2318, 831, 717, 2886, 2069, 575, 555, 2062, 18, 2], [0, 10522, 2548, 2500, 6328, 2170, 6189, 5916, 4015, 2116, 1039, 2219, 3606, 18, 2, 10522, 2548, 2500, 6328, 27135, 5916, 4015, 1642, 2015, 2259, 4258, 2219, 3606, 18, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "preprocess_function(datasets[\"train\"][:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a_usMC7U5AyW",
      "metadata": {
        "id": "a_usMC7U5AyW"
      },
      "source": [
        "이제 정의된 전처리 함수를 활용해 데이터셋을 미리 토큰화시키는 작업을 수행합니다.\n",
        "\n",
        "`datasets` 라이브러리를 통해 얻어진 `DatasetDict` 객체는 `map()` 함수를 지원하므로, 정의된 전처리 함수를 데이터셋 토큰화를 위한 콜백 함수로 `map()` 함수 인자로 넘겨주면 됩니다.\n",
        "\n",
        "보다 자세한 내용은 [문서](https://huggingface.co/docs/datasets/processing.html#processing-data-with-map)를 참조해주시면 됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7c72286",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "f60fa352e14349bf9607763659289f71",
            "9ccc5dc5803042d7b71303cba471b786",
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            "f34f13b67e214434a882f3f4b0cd75d2",
            "fee8620664e845e5a6a3b945b95fc99a",
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            "c829a682f91b4448b889cf02c98c9c94"
          ]
        },
        "id": "f7c72286",
        "outputId": "f6c1f186-9f1b-4713-81d5-a3ee337dec2e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/24998 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f60fa352e14349bf9607763659289f71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cf457e45ff94903a8a8309326b9707a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "encoded_datasets = datasets.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "n6p7zIL75Bif",
      "metadata": {
        "id": "n6p7zIL75Bif"
      },
      "source": [
        "학습을 위한 모델을 로드할 차례입니다.\n",
        "\n",
        "앞서 살펴본 바와 같이 **KLUE NLI**에는 총 3개의 클래스가 존재하므로, 3개의 클래스를 예측할 수 있는 *SequenceClassification* 구조로 모델을 로드하도록 합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ae93dd8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
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        },
        "id": "7ae93dd8",
        "outputId": "1ff29c8e-9365-4861-91a2-a50b2e3a60fb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/546 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d1f4e1f6b7a44ab583d366ca23472afe"
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        {
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          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/443M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07648bb1512546838df042761f757c19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at klue/roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.bias']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "num_labels = 3\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ZSc4c6K5oR_R",
      "metadata": {
        "id": "ZSc4c6K5oR_R"
      },
      "source": [
        "모델을 로드할 때 발생하는 경고 문구는 두 가지 의미를 지닙니다.\n",
        "\n",
        "1. *Masked Language Modeling* 을 위해 존재했던 `lm_head`가 현재는 사용되지 않고 있음을 의미합니다.\n",
        "2. 문장 분류를 위한 `classifier` 레이어를 백본 모델 뒤에 이어 붙였으나 아직 훈련이 되지 않았으므로, 학습을 수행해야 함을 의미합니다."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "x6bvKcsv5CPo",
      "metadata": {
        "id": "x6bvKcsv5CPo"
      },
      "source": [
        "마지막으로 앞서 정의한 메트릭을 모델 예측 결과에 적용하기 위한 함수를 정의합니다.\n",
        "\n",
        "입력으로 들어오는 `eval_pred`는 [*EvalPrediction*](https://huggingface.co/transformers/internal/trainer_utils.html#transformers.EvalPrediction) 객체이며, 모델의 클래스 별 예측 값과 정답 값을 지닙니다.\n",
        "\n",
        "클래스 별 예측 중 가장 높은 라벨을 `argmax()`를 통해 뽑아낸 후, 정답 라벨과 비교를 하게 됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cbfb67d3",
      "metadata": {
        "id": "cbfb67d3"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aU_dmMAVE_FA",
      "metadata": {
        "id": "aU_dmMAVE_FA"
      },
      "source": [
        "이제 앞서 정의한 정보들을 바탕으로 `transformers`에서 제공하는 *Trainer* 객체를 활용하기 위한 인자 관리 클래스를 초기화합니다.\n",
        "\n",
        "`metric_name`은 앞서 얻어진 메트릭 함수를 활용했을 때, 아래와 같이 `dict` 형식으로 결과 값이 반환되는데 여기서 우리가 사용할 *key* 를 정의해준다고 생각하시면 됩니다.\n",
        "\n",
        "```python\n",
        ">>> metric.compute(predictions=fake_preds, references=fake_labels)\n",
        "{'accuracy': 0.515625}\n",
        "```\n",
        "\n",
        "각 인자에 대한 자세한 설명은 [문서](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments)에서 참조해주시면 됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "864b6408",
      "metadata": {
        "id": "864b6408"
      },
      "outputs": [],
      "source": [
        "metric_name = \"accuracy\"\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"test-nli\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=5,\n",
        "    weight_decay=0.01,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=metric_name,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "PcvI6ZHe3Ty6",
      "metadata": {
        "id": "PcvI6ZHe3Ty6"
      },
      "source": [
        "이제 로드한 모델, 인자 관리 클래스, 데이터셋 등을 *Trainer* 클래스를 초기화에 넘겨주도록 합니다.\n",
        "\n",
        "(TIP: Q: 이미 `encoded_datasets`을 만드는 과정에 토큰화가 이루어졌는데 토크나이저를 굳이 넘겨주는 이유가 무엇인가요?,<br>A: 토큰화는 이루어졌지만 학습 과정 시, 데이터를 배치 단위로 넘겨주는 과정에서 배치에 포함된 가장 긴 시퀀스 기준으로 `truncation`을 수행하고 최대 길이 시퀀스 보다 짧은 시퀀스들은 그 길이만큼 `padding`을 수행해주기 위함입니다.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c91650f2",
      "metadata": {
        "id": "c91650f2"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=encoded_datasets[\"train\"],\n",
        "    eval_dataset=encoded_datasets[\"validation\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "KImxQi1ZFAO4",
      "metadata": {
        "id": "KImxQi1ZFAO4"
      },
      "source": [
        "이제 정의된 *Trainer* 객체를 다음과 같이 훈련시킬 수 있습니다.\n",
        "\n",
        "에폭이 지남에 따라 *Loss* 는 떨어지고, 앞서 선정한 메트릭인 *Accuracy* 는 증가하는 것을 확인할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73261f8e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "73261f8e",
        "outputId": "e31824f7-4cfc-44f6-ce31-069d4951213c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='415' max='3910' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 415/3910 02:33 < 21:36, 2.70 it/s, Epoch 0.53/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
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            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1631\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1632\u001b[0m         )\n\u001b[0;32m-> 1633\u001b[0;31m         return inner_training_loop(\n\u001b[0m\u001b[1;32m   1634\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1635\u001b[0m             \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\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/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1902\u001b[0m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1904\u001b[0;31m                 if (\n\u001b[0m\u001b[1;32m   1905\u001b[0m                     \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogging_nan_inf_filter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1906\u001b[0m                     \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_torch_tpu_available\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "avS6mBu8779g",
      "metadata": {
        "id": "avS6mBu8779g"
      },
      "source": [
        "*Trainer* 는 학습을 마치게 되면, `load_best_model_at_end=True` 인자에 따라 메트릭 기준 가장 좋은 성능을 보였던 체크포인트를 로드하게 됩니다.\n",
        "\n",
        "본 노트북에서는 마지막 에폭 때 가장 좋은 성능을 얻었기에 `evaluate`를 수행해도 같은 결과가 나오겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58239e17",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "58239e17",
        "outputId": "c6bb569e-9a90-4134-f555-c0af0cb72d29"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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          "metadata": {
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        {
          "data": {
            "text/plain": [
              "{'epoch': 5.0,\n",
              " 'eval_accuracy': 0.8663333333333333,\n",
              " 'eval_loss': 0.6880185604095459,\n",
              " 'eval_runtime': 12.7427,\n",
              " 'eval_samples_per_second': 235.429,\n",
              " 'eval_steps_per_second': 7.377}"
            ]
          },
          "execution_count": 27,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "stk6rOUyFJuM",
      "metadata": {
        "id": "stk6rOUyFJuM"
      },
      "source": [
        "지금까지 `transformers`를 라이브러리 내 문장 분류 모델을 학습하는 과정을 KLUE NLI 데이터셋을 통해 알아보았습니다.\n",
        "\n",
        "본 노트북을 통해 습득한 지식이 여러분의 업무와 학습에 도움이 되었으면 좋겠습니다.\n",
        "\n",
        "```\n",
        "허 훈 (huffonism@gmail.com)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SkNpAqSJ78oM",
      "metadata": {
        "id": "SkNpAqSJ78oM"
      },
      "source": [
        "APPENDIX: 앞서 학습된 모델을 HuggingFace 모델 허브에 업로드하였으니, 아래 예제와 같이 `pipeline` 함수를 통해 사용이 가능합니다.\n",
        "\n",
        "먼저 `text-classification` 태스크로 파이프라인 객체를 초기화합니다.\n",
        "\n",
        "( \\* `return_all_scores`는 모델이 입력 문장에 대해 측정한 각 라벨에 대한 확률 값을 모두 보여줄 것인지를 결정하는 인자입니다.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "htUZ0ws19Ksk",
      "metadata": {
        "id": "htUZ0ws19Ksk"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\n",
        "    \"text-classification\",\n",
        "    model=\"Huffon/klue-roberta-base-nli\",\n",
        "    return_all_scores=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2uW7UQgBFBPw",
      "metadata": {
        "id": "2uW7UQgBFBPw"
      },
      "source": [
        "NLI는 두 문장의 페어를 입력 값으로 주어야 하기 때문에 구분자 스페셜 토큰을 두 문장 사이에 넣어주기 위해 토크나이저 객체를 로드합니다.\n",
        "\n",
        "`[SEP]` 문자열을 하드코딩하여 넣어줄 수도 있겠지만, 스페셜 토큰은 토크나이저 마다 다르게 정의되므로 다른 모델을 활용할 때 보다 코드를 재사용할 수 있도록 토크나이저의 `sep_token` 프로퍼티에 접근하는 방식으로 코드를 작성합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "UoXTmep0Dcwg",
      "metadata": {
        "id": "UoXTmep0Dcwg"
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"Huffon/klue-roberta-base-nli\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "MuOQhiVPDiMq",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MuOQhiVPDiMq",
        "outputId": "6c7f4d51-ceb9-4650-e5d6-19d4f6b604ac"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[SEP]'"
            ]
          },
          "execution_count": 87,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.sep_token"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "iCP9OgWHFDLG",
      "metadata": {
        "id": "iCP9OgWHFDLG"
      },
      "source": [
        "`sep_token`으로 두 문장을 이어 하나의 입력 값으로 파이프라인에 넘겨줍니다.\n",
        "\n",
        "입력된 문장에 대해 모델이 각 라벨에 대해 어떤 확률을 가지고 예측했는지를 확인할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "tVCGIVQDChcQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVCGIVQDChcQ",
        "outputId": "f2703b6f-90c0-4107-cd6b-9f2e265713cc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[{'label': 'ENTAILMENT', 'score': 0.9865273833274841},\n",
              "  {'label': 'NEUTRAL', 'score': 0.013215206563472748},\n",
              "  {'label': 'CONTRADICTION', 'score': 0.0002573762903921306}]]"
            ]
          },
          "execution_count": 105,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classifier(f\"흡연하려면 발코니 있는 방을 선택하면 됩니다. {tokenizer.sep_token} 흡연자분들은 발코니가 있는 방이면 발코니에서 흡연이 가능합니다.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Ud2kuxlNCmGI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ud2kuxlNCmGI",
        "outputId": "7d53c878-9fb1-4e05-da40-ab06df811a1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[{'label': 'ENTAILMENT', 'score': 0.0002685467479750514},\n",
              "  {'label': 'NEUTRAL', 'score': 0.0006742384284734726},\n",
              "  {'label': 'CONTRADICTION', 'score': 0.9990572333335876}]]"
            ]
          },
          "execution_count": 106,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classifier(f\"호스트분은 영어밖에 못하십니다. {tokenizer.sep_token} 호스트분도 엄청 친절하시고 영어도 잘하십니다.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Uvx8PPTsrPyC",
      "metadata": {
        "id": "Uvx8PPTsrPyC"
      },
      "source": [
        "검증 데이터에 존재하는 임의의 문장 페어를 입력해보니, 우리가 원하던 결과를 얻을 수 있었습니다.\n",
        "\n",
        "*(cf. NLI 데이터에 대해 학습된 모델을 활용해 Zero-shot Classification을 수행하는 예제가 궁금하신 분들은 해당 [노트북](https://colab.research.google.com/github/Huffon/klue-transformers-tutorial/blob/master/zero_shot_classification.ipynb)을 참조해주세요.)*"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#출처 : https://github.com/Huffon/klue-transformers-tutorial"
      ],
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        "id": "1tgYaH9ACg8d"
      },
      "id": "1tgYaH9ACg8d",
      "execution_count": null,
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    }
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