chapter07/7-summarization-generation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7nLjrAlztmq"
      },
      "source": [
        "# 第7章 要約生成"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2Aq0PSXScVd"
      },
      "source": [
        "## 7.2 データセット"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPI9xTu4xxfx"
      },
      "source": [
        "#### 準備"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "SN7OKkT9ce1g",
        "outputId": "6a851eb8-0a2b-4e19-b0b1-4112803f35ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "    Uninstalling wasabi-1.1.2:\n",
            "      Successfully uninstalled wasabi-1.1.2\n",
            "Successfully installed accelerate-0.21.0 datasets-2.13.1 dill-0.3.6 fugashi-1.2.1 huggingface-hub-0.16.4 ipadic-1.0.0 japanize-matplotlib-1.1.3 multiprocess-0.70.14 plac-1.3.5 rhoknp-1.3.0 safetensors-0.3.1 sentencepiece-0.1.99 sudachidict-core-20230110 sudachipy-0.6.7 tokenizers-0.13.3 transformers-4.30.2 unidic-1.1.0 unidic-lite-1.0.8 wasabi-0.10.1 xxhash-3.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets transformers[ja,torch] sentencepiece japanize-matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJSQURXBxzTr"
      },
      "source": [
        "#### データセットのダウンロード"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
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            "ef6c5bb8a63b41fa860653572b72b580"
          ]
        },
        "id": "APvI7igIce1i",
        "outputId": "f8a79d0d-3832-4997-a7bd-36f67022efa3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df2a7967c3c146e183020daf39774650",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.85k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aee7b200b0ca48b7b6d7720704028184",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/142 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset livedoor-news-corpus/livedoor-news-corpus to /root/.cache/huggingface/datasets/llm-book___livedoor-news-corpus/livedoor-news-corpus/1.1.0/f661aab8c45a5e487548a27a5d70a7bba801fb4510b639fa4fe040d5ceb472f1...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a345b941a11432baed41815c375c09d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/8.86M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4125c26964c349ee8eed1693819b3ce8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce9c2dec18fb4d6a8ee1ea2ecd50581c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "423df68493d34d0a9662500fd0270884",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset livedoor-news-corpus downloaded and prepared to /root/.cache/huggingface/datasets/llm-book___livedoor-news-corpus/livedoor-news-corpus/1.1.0/f661aab8c45a5e487548a27a5d70a7bba801fb4510b639fa4fe040d5ceb472f1. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "164791db0197436ea31c4159f87736e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# データセットを読み込む\n",
        "dataset = load_dataset(\"llm-book/livedoor-news-corpus\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M18k7triy_oF",
        "outputId": "d4d28f73-e9ff-476d-e65e-a0a1e4e4a0ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['url', 'date', 'title', 'content', 'category'],\n",
            "        num_rows: 5893\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['url', 'date', 'title', 'content', 'category'],\n",
            "        num_rows: 736\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['url', 'date', 'title', 'content', 'category'],\n",
            "        num_rows: 738\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# データセットの形式と事例数を確認する\n",
        "print(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tdi1OtiCsH2X",
        "outputId": "9325f9eb-a177-433a-eefd-68a588c8f31d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'category': 'livedoor-homme',\n",
            "  'content': '日常の何気ない気持ちをTwitterにつぶやいたり、実名登録のFacebookで懐かしい友人と再会したり、SNSはもはや我々の生活において欠かせない存在となりつつある。先日、国内の月間利用者数が1,000万人を突破し、mixi（1,520万人、2011年12月現在）を追い抜くのも時間の問題と思われるFacebookでは、診断やゲームなど様々なアプリが生まれ、ユーザーのタイムラインを今日も賑わしている。しかし、その一方で、Facebookを悪用するケースもまた徐々に増え始めている。  '\n",
            "             'Facebookでは、2008年1月にAPIが公開されて以来、様々なアプリが誕生しているが、同年8月にはボット型の不正プログラム「KOOBFACE」が確認され、感染を広げた。その手口とは、「あなたがビデオに出ていますよ！」というメッセージが届き、YouTubeに偽装したURLにアクセスすると、動画再生のためにプログラムのインストールを求められ、不正プログラムをダウンロードさせるというもの。  '\n",
            "             '不正プログラムには、画面に偽の感染警告を表示し、駆除のための偽セキュリティソフトを購入させ、クレジットカード情報などの個人情報を盗むものや、ブラウザでログイン時のアカウント情報を盗むものなどが存在。更に、そのユーザーのFacebookフレンド宛にも不正なメッセージを自動送信することによって、感染規模を拡大していくのだ。  '\n",
            "             '2010年12月にはFacebookの公式アカウントを騙り、不正プログラムをインストールさせようとするスパムメールや、2011年1月にはアカウントの更新に必要として、個人情報を盗むフィッシングサイトを確認。その他にも、可愛らしい女性のプロフィール画像を載せた人物から好意的なメッセージが届き、携帯アドレスへの連絡を求めるスパムメッセージなど、その手口は年々多様化している。  '\n",
            "             'Facebookのみならず、今年1月にはTwitter公式アカウントを騙るフィッシング詐欺サイトも確認され、昨年12月の警察庁による発表では、SNS以外にもフィッシング詐欺で約2,000万円、不正プログラムで約2億8,000万円もの被害が発生している実態が明らかに。そして、3月30日には“なりすまし”等の不正な手段によるID／パスワードなどの取得を禁止し、フィッシング詐欺を取り締まるための不正アクセス禁止法の改正案が成立したばかりだ。  '\n",
            "             'フィッシング詐欺の他にも、アダルトサイト等で「入場」や「年齢確認」などをクリックしただけで“登録”したと一方的に通知し、IPアドレスなどを表示して個人を特定したかのように脅迫、費用を請求するワンクリック詐欺も急増。1月18日には、京都府警サイバー犯罪対策課により、ワンクリック詐欺サイトに関連する不正指令電磁的記録供用事件、通称“ウイルス作成罪”の供用容疑で被疑者6人が逮捕された。  '\n",
            "             '怪しいリンクはクリックしないのが鉄則だが、万が一、ウイルスに感染してしまった場合、立場は被害者から加害者へと一変。ウイルスは、友人や会社にまで感染してしまう可能性があり、“転ばぬ先”のセキュリティソフトは、もはやインターネットを利用する上で最低限のマナーといえるだろう。  '\n",
            "             '各社から発売されているセキュリティソフトの中でも、4年連続で販売本数1位(※1)を記録しているのが、トレンドマイクロの「ウイルスバスター」だ。最新版「ウイルスバスター2012 '\n",
            "             'クラウド」は、商品名にもあるように、パソコン使用中に体感する“重さ”の主な原因(※2)の約80%をクラウドに移行し、圧倒的な軽さを実現。驚異的なスピードで作成される不正プログラムにも、クラウド上で常に更新される最新の脅威情報をリアルタイムで参照して、すばやく対応するから安心だ。  '\n",
            "             '左：ウイルスバスター2012 クラウド ダウンロード3年版（中〜上級者向け） 中：ウイルスバスター2012 クラウド '\n",
            "             '+保険＆PCサポート ダウンロード3年版（初心者向け） 右：ウイルスバスター モバイル for Android '\n",
            "             'ダウンロード1年版（Android端末向け）  '\n",
            "             '新機能として、Facebook、Twitter、mixi利用時にWebサイトを評価し、URLの安全性を色別に表示してくれるので、危険なサイトからユーザーを守ってくれる。パソコン初心者の方には、1日あたりわずか約3.8円(※3)をプラスするだけで、クレジットカード不正使用の保険と、365日深夜24時まで対応のPCサポートが付いてくる。また、ワンクリック詐欺などによる不正請求画面が何度も表示されてお困りの方には、パソコン1台につき7,980円（税込）で不正請求画面を削除するクリーンナップツールを提供してくれるので、被害を未然に防ぐだけでなく、被害に遭ってしまった人にも心強い味方になってくれる。  '\n",
            "             'まずは、30日無料体験版を今すぐインストールして、その“軽快さ”と“安心”を体験して欲しい。4月30日まで、ダウンロード3年版・パッケージ版3年版を購入すると2ヵ月無料延長されるキャンペーンも実施中なので、購入はお早めに。  '\n",
            "             '・livedoor HOMME「ウイルスバスター2012 クラウド」特集ページ  ・おまかせ！不正請求クリーンナップサービス '\n",
            "             '・「ウイルスバスター2012 クラウド」30日無料体験版  '\n",
            "             '※1：全国主要家電量販店等のPOS実売統計で年間（1月〜12月）販売本数第1位のベンダーを表彰するBCN '\n",
            "             'AWARDのセキュリティソフトウェア部門において、トレンドマイクロが2008〜2011年の4年連続で最優秀賞を受賞 '\n",
            "             '※2：ウイルス対策機能の重さの原因＝シグネチャのこと ※3：トレンドマイクロ・オンラインショップのウイルスバスター2012 '\n",
            "             'クラウド＋保険＆PCサポート ダウンロード3年版の金額とウイルスバスター2012 クラウド '\n",
            "             'スタンダード3年版の通常価格を日割換算した場合',\n",
            "  'date': '2012-04-18T09:45:00+0900',\n",
            "  'title': '急成長を遂げるFacebookに忍び寄る影',\n",
            "  'url': 'http://news.livedoor.com/article/detail/6475684/'},\n",
            " {'category': 'it-life-hack',\n",
            "  'content': 'Linuxの人気ディストリビューションであるUbuntu（ウブントゥ）は、使えば使うほど、便利さを実感するものだ。今回は、Ubuntuを起動するUSBメモリーの作成方法について解説しよう。これを持ち歩いていれば、いつでもUSB起動ができるPCから自分のオリジナル環境でUbuntuが起動できる。Windows/Macユーザー問わず、知っておいて損はないだろう。   '\n",
            "             '■CDからの起動ではデータ保存に難点 前回、Ubuntuはどこが便利なのか '\n",
            "             'Windowsトラブル時にデータを救出で、CD/DVDメディアから起動することで、Windowsパーティションのデータを救出する方法について述べた。  '\n",
            "             'Ubuntuは、CD/DVDから起動した状態でも相当のアプリケーションを使うことができる（これについては別の機会に譲りたい）。だが、起動が遅いことと、起動用メディアにはデータを書き込めないため、データの保存には別途、USBメモリなどを利用しなければならないことが難点だ。  '\n",
            "             '■Ubuntuが起動可能なUSBメモリーを作成する '\n",
            "             '実は、Ubuntuには起動用のUSBメモリーを作成する機能がある。起動用のUSBメモリーを作成するには、「スタートアップ・ディスクの作成」というアプリケーションを使う。このアプリを使うには、Ubuntuのデスクトップで左側に並んだランチャーのいちばん上にある［Dashホーム］をクリックする。  '\n",
            "             '「スタートアップ・ディスクの作成」は、CD/DVDから起動した状態でも使えるのだ。  ■保存領域はFAT32などでフォーマット '\n",
            "             'USBメモリーは16GB程度あれば十分。「スタートアップ・ディスクの作成」で、起動用の領域とデータ保存用の領域を分けることができるので、データ保存用に4GB程度を確保しよう。Windows環境からでも読み込めるように、データ保存用の領域はFAT32などでフォーマットしておくこと。これで、起動用のUSBメモリーを作成できる。  '\n",
            "             'このメモリーを用意しておくことで、イザというときにデータを救出できるのはもちろん、外出先で他人のパソコンを借りなければならなくなった場合でも、USBメモリーから起動することで、自分用の環境で使い、データも持ち運びできるわけだ。   '\n",
            "             '■Ubuntu Japanese Team  大島克彦＠katsuosh［digi2（デジ通）］  '\n",
            "             'digi2は「デジタル通」の略です。現在のデジタル機器は使いこなしが難しくなっています。 '\n",
            "             '皆さんがデジタル機器の「通」に近づくための情報を、皆さんよりすこし通な執筆陣が提供します。   '\n",
            "             '■ITライフハックTwitter  ■デジ通の記事をもっと見る ・「Dropbox」のUbuntu版を試す '\n",
            "             'Linuxでも同じ使いこなし！ ・「Ubuntu One」はLinux対応 楽曲ストリーミング対応のクラウド '\n",
            "             '・なぜこんなに面倒なの？\\u3000ネット時代のサービス解約手続き ・デジタルガジェット新製品は？\\u3000'\n",
            "             '6月の大規模展示会複数開催で発表あるか？ ・メモ型クラウドサービス「Droplr」 備忘録やブックマークに使える',\n",
            "  'date': '2012-06-12T13:00:00+0900',\n",
            "  'title': 'いつでもどこでも自分専用環境！\\u3000Ubuntu起動ができるUSBメモリーを作成！【デジ通】',\n",
            "  'url': 'http://news.livedoor.com/article/detail/6649789/'}]\n"
          ]
        }
      ],
      "source": [
        "from pprint import pprint\n",
        "\n",
        "# 訓練セットの最初の二つの事例を表示する\n",
        "pprint(list(dataset[\"train\"])[:2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iS6rSvSx4Uy"
      },
      "source": [
        "#### データセットの分析"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0as7pfi1XFT",
        "outputId": "a3e277ce-0adb-43dc-d930-3718719c0862"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[('sports-watch', 731),\n",
            " ('it-life-hack', 718),\n",
            " ('dokujo-tsushin', 695),\n",
            " ('smax', 690),\n",
            " ('movie-enter', 689),\n",
            " ('peachy', 677),\n",
            " ('kaden-channel', 656),\n",
            " ('topic-news', 616),\n",
            " ('livedoor-homme', 421)]\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "\n",
        "# 各カテゴリの事例数を確認する\n",
        "pprint(Counter(dataset[\"train\"][\"category\"]).most_common())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtlqX1tvxv_a",
        "outputId": "e184e4cb-def5-4a94-8023-3072d12db204"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "livedoor-homme: 急成長を遂げるFacebookに忍び寄る影\n",
            "it-life-hack: いつでもどこでも自分専用環境！　Ubuntu起動ができるUSBメモリーを作成！【デジ通】\n",
            "kaden-channel: 「PS Vita」がついに発売　—　初日は待ちわびたファンが行列を作る大盛況【話題】\n",
            "smax: ソニーモバイル、Xperia ionのLTE非対応版「Xperia ion HSPA」を発表\n",
            "peachy: 【終了しました】リムジンでお買い物の後はスイートルームで“うっとろりん”、お姫さまのような1日をプレゼント\n",
            "movie-enter: 有言実行の男、ジュード・ロウが自信作を引っ提げ来日決定\n",
            "dokujo-tsushin: 言いにくい「芸能人の○○みたいにして」の一言\n",
            "sports-watch: 日本代表敗戦、セルジオ越後氏は「ベストメンバーでなければこの程度」\n",
            "topic-news: 「柏木はブタ鼻」嫉妬ややっかみからAKB48で流行るイジメごっこ\n"
          ]
        }
      ],
      "source": [
        "categories = set() # カテゴリの集合\n",
        "for data in dataset[\"train\"]: # 訓練セットの各事例を処理する\n",
        "    category, title = data[\"category\"], data[\"title\"]\n",
        "    # すでに出現したカテゴリはスキップする\n",
        "    if category not in categories:\n",
        "        categories.add(category)\n",
        "        print(f\"{category}: {title}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "10e2d60626154c8382ff2f2b3a348275",
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            "2721da66009349f6bc51affc9d1373fc",
            "c0e9a590cee6482ba2ddaefd916a67b2",
            "4876da97d02241f7a79edbd233f88ec4",
            "c8283d88997a4ce3b1c793ab3245c2e0",
            "6ccc38d6895e4f08b149b84e2ef18b70",
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            "72ed8369f6854de7bdb7da255576d6ca",
            "9b439fd8c40947268aff1e62f79136a9",
            "8f92b62e57584698a576103342061939",
            "154e9f0faebd4c9397c9560d18e7c056",
            "cd687eb4dad44dd589d42a0c2f965dfd",
            "1fdbadf4627d42c8a0b27664dda76782",
            "fee686d5ae4e4a7d8455a0e05038b520",
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            "1b5028d69f4142dfb70b3dcd0cadb12b",
            "25f047db5c1c4ea6973bef53bfce513b",
            "77c53b9f54474afb9c601ec2007db415",
            "1c44dcd40bf046f99daacee2c3199a64",
            "519501f20bad4ac8a54dc0b91614588e"
          ]
        },
        "id": "0fo6loPjoGrA",
        "outputId": "945efbfe-247a-409c-fc66-670a57273725"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "10e2d60626154c8382ff2f2b3a348275",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.28k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4876da97d02241f7a79edbd233f88ec4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f92b62e57584698a576103342061939",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5893/5893 [00:23<00:00, 249.69it/s]\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5893/5893 [00:02<00:00, 2190.68it/s]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from collections import Counter\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import Dataset\n",
        "from tqdm import tqdm\n",
        "from transformers import AutoTokenizer, PreTrainedTokenizer\n",
        "import japanize_matplotlib\n",
        "\n",
        "# フォントサイズを18にする\n",
        "plt.rcParams[\"font.size\"] = 18\n",
        "\n",
        "def visualize_num_tokens_distribution(\n",
        "    dataset: Dataset, tokenizer: PreTrainedTokenizer, column: str\n",
        ") -> None:\n",
        "    \"\"\"トークン数の分布を可視化\"\"\"\n",
        "    # 各事例でトークン数をカウントし、トークン数ごとに結果を集約する\n",
        "    counter = Counter()\n",
        "    for data in tqdm(dataset):\n",
        "        num_tokens = len(tokenizer.tokenize(data[column]))\n",
        "        counter[num_tokens] += 1\n",
        "\n",
        "    # トークン数の分布を可視化する\n",
        "    plt.bar(counter.keys(), counter.values(), width=1.0)\n",
        "    plt.xlabel(\"トークン数\")\n",
        "    plt.ylabel(\"出現頻度\")\n",
        "    plt.gca().yaxis.set_major_locator(plt.MaxNLocator(integer=True))\n",
        "    plt.gca().yaxis.set_major_formatter(plt.FormatStrFormatter(\"%d\"))\n",
        "    plt.show()\n",
        "\n",
        "# トークナイザを読み込む\n",
        "model_name = \"retrieva-jp/t5-base-long\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "# 記事のトークン数の分布を可視化する\n",
        "visualize_num_tokens_distribution(\n",
        "    dataset[\"train\"], tokenizer, \"content\"\n",
        ")\n",
        "# 見出しのトークン数の分布を可視化する\n",
        "visualize_num_tokens_distribution(\n",
        "    dataset[\"train\"], tokenizer, \"title\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtjrEHSczyRn"
      },
      "source": [
        "## 7.3 評価指標"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ecYqShSx9gK"
      },
      "source": [
        "#### 準備"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbUgbwgx46Av",
        "outputId": "089f75f1-8ed1-4b79-e24f-d3df6ab13686"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting mecab-python3\n",
            "  Downloading mecab_python3-1.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (581 kB)\n",
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            "\u001b[?25hCollecting rouge-score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting sacrebleu\n",
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            "\u001b[?25hCollecting bert_score\n",
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            "\u001b[?25hRequirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge-score) (1.4.0)\n",
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            "  Downloading portalocker-2.7.0-py2.py3-none-any.whl (15 kB)\n",
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            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.0->bert_score) (1.3.0)\n",
            "Building wheels for collected packages: rouge-score\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24934 sha256=6bac5c141fe44cce3b5dc2be184b17aaad4da7cadced467b5bdc5db8b81d4b9f\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge-score\n",
            "Installing collected packages: mecab-python3, portalocker, colorama, sacrebleu, rouge-score, bert_score\n",
            "Successfully installed bert_score-0.3.13 colorama-0.4.6 mecab-python3-1.0.6 portalocker-2.7.0 rouge-score-0.1.2 sacrebleu-2.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip install mecab-python3 rouge-score sacrebleu bert_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_LKiGqiTBr1"
      },
      "source": [
        "### 7.3.1 ROUGE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VPZroFQyBxx"
      },
      "source": [
        "#### ROUGEを算出するための実装"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BS1w79Am-Tue"
      },
      "outputs": [],
      "source": [
        "reference = \"日本語T5モデルの公開\"\n",
        "prediction1 = \"T5モデルの日本語版を公開\"\n",
        "prediction2 = \"日本語T5をリリース\"\n",
        "prediction3 = \"Japanese T5を発表\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRSBGDfDce1l",
        "outputId": "22dc1ac2-6dfb-4a0d-ef03-96f5976f2208"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "参照文: 日本語 T 5 モデル の 公開\n",
            "生成文1: T 5 モデル の 日本語 版 を 公開\n",
            "生成文2: 日本語 T 5 を リリース\n",
            "生成文3: Japanese T 5 を 発表\n"
          ]
        }
      ],
      "source": [
        "import ipadic\n",
        "import MeCab\n",
        "\n",
        "# IPAdicを用いたMeCabを使用して、単語分割を行う\n",
        "tagger = MeCab.Tagger(f\"-O wakati {ipadic.MECAB_ARGS}\")\n",
        "ref_wakati = tagger.parse(reference).strip()\n",
        "pred_wakati1 = tagger.parse(prediction1).strip()\n",
        "pred_wakati2 = tagger.parse(prediction2).strip()\n",
        "pred_wakati3 = tagger.parse(prediction3).strip()\n",
        "print(f\"参照文: {ref_wakati}\")\n",
        "print(f\"生成文1: {pred_wakati1}\")\n",
        "print(f\"生成文2: {pred_wakati2}\")\n",
        "print(f\"生成文3: {pred_wakati3}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418,
          "referenced_widgets": [
            "604e567b82ca44248a1e6f149d5d460e",
            "af492bf229a1416abaa00f9c907278f4",
            "75b48c25836e4add81f765634c8ca831",
            "d50998fbda414a89a646a78cc0f9a7ce",
            "ccd50751037a4bee89c26264bebe2c93",
            "5ad60a3ad78a4bc7b42627b2ca0c8663",
            "e49a67548fce4643ba398ef23bd6940d",
            "c2fa86cfa3234f689ee991402c7e34b1",
            "c5498be776734352977741eee9a2d918",
            "d6bd826927d64a1f8427cb7fbd204afc",
            "a2b4e3c37eb4481e9e8ba4a504a08d66"
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        },
        "id": "5se8RJpGce1m",
        "outputId": "72f9d688-b151-47b9-bdbc-be1b1f931326"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-11-3785bd692961>:31: 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",
            "  rouge = load_metric(\"rouge\")\n"
          ]
        },
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            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
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              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f9548ff7-b9c9-45b7-bb7c-b878c2e38e98 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc06cace-3823-4fd5-9ed1-aca21fb6a134 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc06cace-3823-4fd5-9ed1-aca21fb6a134');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                rouge1  rouge2  rougeL\n",
              "生成文1 precision   0.750   0.429   0.625\n",
              "     recall      1.000   0.600   0.833\n",
              "     fmeasure    0.857   0.500   0.714\n",
              "生成文2 precision   0.600   0.500   0.600\n",
              "     recall      0.500   0.400   0.500\n",
              "     fmeasure    0.545   0.444   0.545\n",
              "生成文3 precision   0.400   0.250   0.400\n",
              "     recall      0.333   0.200   0.333\n",
              "     fmeasure    0.364   0.222   0.364"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "import pandas as pd\n",
        "from rouge_score import rouge_scorer, scoring\n",
        "\n",
        "# pandasの小数点以下の桁数を3に設定する\n",
        "pd.options.display.precision = 3\n",
        "\n",
        "def convert_words_to_ids(\n",
        "    predictions: list[str], references: list[str]\n",
        ") -> tuple[list[str], list[str]]:\n",
        "    \"\"\"単語列をID列に変換\"\"\"\n",
        "    # 単語にユニークなIDを割り当てるためのdefaultdictを作成する\n",
        "    word2id = defaultdict(lambda: len(word2id))\n",
        "\n",
        "    # 単語区切りの文字列をID文字列に変換する\n",
        "    pred_ids = [\n",
        "        \" \".join([str(word2id[w]) for w in p.split()])\n",
        "        for p in predictions\n",
        "    ]\n",
        "    ref_ids = [\n",
        "        \" \".join([str(word2id[w]) for w in r.split()])\n",
        "        for r in references\n",
        "    ]\n",
        "    return pred_ids, ref_ids\n",
        "\n",
        "def compute_rouge(\n",
        "    predictions: list[str], references: list[str]\n",
        ") -> dict[str, dict[str, float]]:\n",
        "    \"\"\"ROUGEを算出\"\"\"\n",
        "    # RougeScorerを設定する\n",
        "    rouge = rouge_scorer.RougeScorer(\n",
        "        rouge_types=[\"rouge1\", \"rouge2\", \"rougeL\"], use_stemmer=False\n",
        "    )\n",
        "    aggregator = scoring.BootstrapAggregator()\n",
        "    # 単語列をID列に変換する\n",
        "    pred_ids, ref_ids = convert_words_to_ids(predictions, references)\n",
        "    # ROUGEスコアを計算して結果を集約する\n",
        "    for pred, ref in zip(pred_ids, ref_ids):\n",
        "        aggregator.add_scores(rouge.score(ref, pred))\n",
        "    scores = aggregator.aggregate()\n",
        "    return {k: v.mid for k, v in scores.items()}\n",
        "\n",
        "# ROUGEを算出した結果を表示する\n",
        "rouge_results = {\n",
        "    \"生成文1\": compute_rouge([pred_wakati1], [ref_wakati]),\n",
        "    \"生成文2\": compute_rouge([pred_wakati2], [ref_wakati]),\n",
        "    \"生成文3\": compute_rouge([pred_wakati3], [ref_wakati]),\n",
        "}\n",
        "df_list = [\n",
        "    pd.DataFrame.from_dict(rouge_results[k], orient=\"index\")\n",
        "    for k in rouge_results.keys()\n",
        "]\n",
        "display(pd.concat(df_list, keys=rouge_results.keys(), axis=1).T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zIr-MocTGXi"
      },
      "source": [
        "### 7.3.2 BLEU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBCAHh6pyGTr"
      },
      "source": [
        "#### BLEUを算出するための実装"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175,
          "referenced_widgets": [
            "2b59aa47418043b196e1bb7a8cb99bf0",
            "379c11f93e094f4cba26a69d73487f07",
            "50c44cfec99d4d95a0ac1bbd6cfc0083",
            "e1181e692b27453b95b9079000507d9a",
            "decfef99dec74063baab5aae8f5f7765",
            "6b2eb097f867480188738571c7620e38",
            "3c8a8eec9fea4e569e288d2434ac8bf7",
            "01575a94036149308609e75454b52824",
            "542170c1a7d541839c0704d84c134cbe",
            "de6e4506bc1e4546a3d33ce8e5473fce",
            "eb25165e88c7448c9580899b156ee5c1"
          ]
        },
        "id": "4xs3FOp8ce1m",
        "outputId": "be605669-4d90-4ce2-eaa4-8027fea8a6c0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b59aa47418043b196e1bb7a8cb99bf0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.85k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-2a53fb4c-88a4-4891-a618-7d8b1dcdb66c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "      <th>counts</th>\n",
              "      <th>totals</th>\n",
              "      <th>precisions</th>\n",
              "      <th>bp</th>\n",
              "      <th>sys_len</th>\n",
              "      <th>ref_len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>生成文1</th>\n",
              "      <td>38.26</td>\n",
              "      <td>[6, 3, 2, 1]</td>\n",
              "      <td>[8, 7, 6, 5]</td>\n",
              "      <td>[75.0, 42.86, 33.33, 20.0]</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>生成文2</th>\n",
              "      <td>32.556</td>\n",
              "      <td>[3, 2, 1, 0]</td>\n",
              "      <td>[5, 4, 3, 2]</td>\n",
              "      <td>[60.0, 50.0, 33.33, 25.0]</td>\n",
              "      <td>0.819</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>生成文3</th>\n",
              "      <td>17.492</td>\n",
              "      <td>[2, 1, 0, 0]</td>\n",
              "      <td>[5, 4, 3, 2]</td>\n",
              "      <td>[40.0, 25.0, 16.67, 12.5]</td>\n",
              "      <td>0.819</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a53fb4c-88a4-4891-a618-7d8b1dcdb66c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b49f154f-5a07-4389-ad03-4741d4acd219\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b49f154f-5a07-4389-ad03-4741d4acd219')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b49f154f-5a07-4389-ad03-4741d4acd219 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2a53fb4c-88a4-4891-a618-7d8b1dcdb66c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a53fb4c-88a4-4891-a618-7d8b1dcdb66c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       score        counts        totals                  precisions     bp  \\\n",
              "生成文1   38.26  [6, 3, 2, 1]  [8, 7, 6, 5]  [75.0, 42.86, 33.33, 20.0]    1.0   \n",
              "生成文2  32.556  [3, 2, 1, 0]  [5, 4, 3, 2]   [60.0, 50.0, 33.33, 25.0]  0.819   \n",
              "生成文3  17.492  [2, 1, 0, 0]  [5, 4, 3, 2]   [40.0, 25.0, 16.67, 12.5]  0.819   \n",
              "\n",
              "     sys_len ref_len  \n",
              "生成文1       8       6  \n",
              "生成文2       5       6  \n",
              "生成文3       5       6  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sacrebleu import corpus_bleu\n",
        "\n",
        "def compute_bleu(\n",
        "    predictions: list[str], references: list[list[str]]\n",
        ") -> dict[str, int | float | list[float]]:\n",
        "    \"\"\"BLUEを算出\"\"\"\n",
        "    # BLUEを算出する\n",
        "    result = corpus_bleu(predictions, references)\n",
        "    return {\n",
        "            \"score\": result.score,\n",
        "            \"counts\": result.counts,\n",
        "            \"totals\": result.totals,\n",
        "            \"precisions\": [round(p, 2) for p in result.precisions],\n",
        "            \"bp\": result.bp,\n",
        "            \"sys_len\": result.sys_len,\n",
        "            \"ref_len\": result.ref_len,\n",
        "    }\n",
        "\n",
        "# BLEUを算出した結果を表示する\n",
        "bleu_results = {\n",
        "    \"生成文1\": compute_bleu([pred_wakati1], [[ref_wakati]]),\n",
        "    \"生成文2\": compute_bleu([pred_wakati2], [[ref_wakati]]),\n",
        "    \"生成文3\": compute_bleu([pred_wakati3], [[ref_wakati]]),\n",
        "}\n",
        "df_list = [\n",
        "    pd.DataFrame.from_dict(bleu_results[k], orient=\"index\")[0]\n",
        "    for k in bleu_results.keys()\n",
        "]\n",
        "display(pd.concat(df_list, keys=bleu_results.keys(), axis=1).T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-HqYJf1TMeM"
      },
      "source": [
        "### 7.3.3 BERTScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795,
          "referenced_widgets": [
            "18bce861b31c42e881845f5c8587cdb4",
            "4ed5789b2ce742deacabe0a2aa2a8f3f",
            "80f237b06451449ab1046ef6d65c9546",
            "cde3308c8a7a471389db331f6aeaebb7",
            "a9dbbc9385914bf8aa5d5fc8e571ed4b",
            "04bee36857674af6a97ed20baa99a388",
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            "870e0f7a55f44c75b2daa1c417381cf2"
          ]
        },
        "id": "wbeLAk7P1vep",
        "outputId": "0455faed-7889-4690-a742-081ad4448905"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "18bce861b31c42e881845f5c8587cdb4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54b206e022f14de19ab69b1288c2ed18",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8ac293b6e6d4327ba6dadf3beac2787",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cf5f89442a044105a3757fca800ae67c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/714M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from bert_score import plot_example\n",
        "\n",
        "# 生成文3と参照文の類似度行列を作成する\n",
        "plot_example(prediction3, reference, lang=\"ja\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v0JRAPuzyOVM"
      },
      "source": [
        "#### BERTScoreを算出するための実装"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175,
          "referenced_widgets": [
            "237dd066424b496896e6c683464f84a5",
            "933d4585efba42d3a262e7ac0ff6de39",
            "6690a24b09974acda4323a942ad562d5",
            "54b7289aa9e04496ac9ae8b433618679",
            "f935492b8af542949c09e4cc409f7677",
            "9607b77061984fe088ec5fe34801ec17",
            "6dd3a2013be04fd6a24570b2113e0655",
            "aea0f933b10742b48a165106c156b07b",
            "75c8d4bed2014f07a7d4a8f9848d7c3e",
            "e3833b7b209444c09151f5b1db8e2f60",
            "16ca41597ac94d0bad36967b0739097e"
          ]
        },
        "id": "bONqueTpqx-E",
        "outputId": "a5593f7a-6a07-4ea9-de30-420836375a97"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "237dd066424b496896e6c683464f84a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.92k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "\n",
              "\n",
              "  <div id=\"df-85f6f15d-1e1a-4420-8f7e-296806abf994\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>生成文1</th>\n",
              "      <td>0.877</td>\n",
              "      <td>0.903</td>\n",
              "      <td>0.890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>生成文2</th>\n",
              "      <td>0.879</td>\n",
              "      <td>0.824</td>\n",
              "      <td>0.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>生成文3</th>\n",
              "      <td>0.834</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.809</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85f6f15d-1e1a-4420-8f7e-296806abf994')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "\n",
              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a5a273db-9d49-4f4f-86fe-3044ad0ebf73')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a5a273db-9d49-4f4f-86fe-3044ad0ebf73 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85f6f15d-1e1a-4420-8f7e-296806abf994 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85f6f15d-1e1a-4420-8f7e-296806abf994');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      precision  recall     f1\n",
              "生成文1      0.877   0.903  0.890\n",
              "生成文2      0.879   0.824  0.850\n",
              "生成文3      0.834   0.785  0.809"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import bert_score\n",
        "\n",
        "def compute_bertscore(\n",
        "    predictions: list[str], references: list[str]\n",
        ") -> dict[str, float]:\n",
        "    \"\"\"BERTScoreを算出\"\"\"\n",
        "    # BERTScoreを計算する\n",
        "    scorer = bert_score.BERTScorer(\n",
        "        model_type=bert_score.utils.lang2model[\"ja\"],\n",
        "    )\n",
        "    p, r, f = scorer.score(\n",
        "        cands=predictions, refs=references\n",
        "    )\n",
        "    return {\n",
        "        \"precision\": sum(p.tolist()) / len(p),\n",
        "        \"recall\": sum(r.tolist()) / len(r),\n",
        "        \"f1\": sum(f.tolist()) / len(f)\n",
        "    }\n",
        "\n",
        "# BERTScoreを算出した結果を表示する\n",
        "bertscore_results = {\n",
        "    \"生成文1\": compute_bertscore([prediction1], [reference]),\n",
        "    \"生成文2\": compute_bertscore([prediction2], [reference]),\n",
        "    \"生成文3\": compute_bertscore([prediction3], [reference]),\n",
        "}\n",
        "df_list = [\n",
        "    pd.DataFrame.from_dict(bertscore_results[k], orient=\"index\")[0]\n",
        "    for k in bertscore_results.keys()\n",
        "]\n",
        "display(pd.concat(df_list, keys=bertscore_results.keys(), axis=1).T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fVJs-7bKTR5m"
      },
      "source": [
        "## 7.4 見出し生成モデルの実装"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tx7P1O1CTU-N"
      },
      "source": [
        "### 7.4.1 T5のファインチューニング"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wq9s0Q57yTpv"
      },
      "source": [
        "#### データセットの前処理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "6f82129cdb514b2894b1456d3cf9e0f3",
            "b6e36f57960d43fdaf6db91947544352",
            "e4578aa51c7d400d9a4ac493a01087e7",
            "37fb96bec96c4e0eaa86a127902d49a6",
            "015c9ce2c72e4d4a8020493739a247bb",
            "49fcaf3f3f5d462f8032e8969fd39f5d",
            "3e3905bac8c342648a1210a495afcb53",
            "8c66abf05e2f4fb98b2b7d0eaa243807",
            "630dd743911e44c2a151548a1004663f",
            "d193f9e3526943cbbbd0c755351e3956",
            "cfa70d82418848e8a65c60c5697fe51a",
            "ca31c43acb8543209f1a8b0ffe6f7b0a",
            "3d5dc191cd3b4afcb895a26fcfe65663",
            "d16e38e031ce4367935f7e35de742030",
            "dc78e17cd2a44117a894e9e163e89993",
            "f56067e5e7e6452bb45aef176363e600",
            "006829b5131444ccb86a17ecfdb78368",
            "88f8145b44fb415da30126ed7c61bf54",
            "4878d87169164dfda5c1e4bf46f27e4d",
            "0fdec19e839c4385b794726de39bc3c4",
            "190b35274d6f4a9aa86f07bd892c2107",
            "6ded464ca3bb43db931d64eb38b17589"
          ]
        },
        "id": "2B9twlMLce1n",
        "outputId": "c683f043-d52f-448a-f4dd-cdb9d6208753"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f82129cdb514b2894b1456d3cf9e0f3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/5893 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca31c43acb8543209f1a8b0ffe6f7b0a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/736 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from typing import Any\n",
        "from transformers import BatchEncoding, PreTrainedTokenizer\n",
        "\n",
        "def preprocess_data(\n",
        "    data: dict[str, Any], tokenizer: PreTrainedTokenizer\n",
        ") -> BatchEncoding:\n",
        "    \"\"\"データの前処理\"\"\"\n",
        "    # 記事のトークナイゼーションを行う\n",
        "    inputs = tokenizer(\n",
        "        data[\"content\"], max_length=512, truncation=True\n",
        "    )\n",
        "    # 見出しのトークナイゼーションを行う\n",
        "    # 見出しはトークンIDのみ使用する\n",
        "    inputs[\"labels\"] = tokenizer(\n",
        "        data[\"title\"], max_length=128, truncation=True\n",
        "    )[\"input_ids\"]\n",
        "    return inputs\n",
        "\n",
        "# 訓練セットに対して前処理を行う\n",
        "train_dataset = dataset[\"train\"].map(\n",
        "    preprocess_data,\n",
        "    fn_kwargs={\"tokenizer\": tokenizer},\n",
        "    remove_columns=dataset[\"train\"].column_names,\n",
        ")\n",
        "# 検証セットに対して前処理を行う\n",
        "validation_dataset = dataset[\"validation\"].map(\n",
        "    preprocess_data,\n",
        "    fn_kwargs={\"tokenizer\": tokenizer},\n",
        "    remove_columns=dataset[\"validation\"].column_names,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrZpq42GyWa5"
      },
      "source": [
        "#### モデルのファインチューニング"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "df9e9114fbb648f192494d2c3b88f3dc",
            "a1d676a6e407483db0fc11a06f535f3a",
            "f2f1d46bd867488390bc860ade5ccb92",
            "a91810aa54b8428f87aa5a43e31b10be",
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            "0bfde1c27583407384c899d0101c0dd3",
            "58c1eaa233764c4387ec4ee3bb431a64",
            "d2ae3b9448fd44249f0d35e1ef39ad89",
            "9e0a8690b6054a8e91099ebcd190cb27",
            "331540197b844ffa80352582489d4e26",
            "3fbd526e28d04c2ba0bfd4407c739084",
            "29c623266fce49f7869d0c397e176d56",
            "0a7dafd1848a423da5f5615d80365c96",
            "de1d011082974aee824d587dfcfbe0ac",
            "c670c7df26da46e1b6de5e7ac3676603",
            "f36c802aaace489f9c1ee451e38ad04e",
            "21f99a8d4c2b477b8457bfca93ec0a99",
            "a0a4a19e80ad49569c0456490e878084",
            "8508e0f21ce04593a249b72211b2bc61",
            "04c24eb501344172912a4fca4deeac79",
            "29f4c47879f040cdab34239317180df0",
            "de1729dab0e54b2ba8e3b87f41c11e82",
            "b580dd99aafa4cf7922f485af1ff6765",
            "11135b69013c469d9d504f2f8343d6ae",
            "0383ed7527ea49288b12bc3aa07e4715",
            "dc4809228ea84e48b635d39d0f462377",
            "1523946bf1554c1f8c8e9b4f247cf1cf",
            "5f5e51185ec54218ab1ac99f6b49419b",
            "2bd0fc4f4bc24a85b2ef4e0d644fc0e0",
            "85773ac639ca4a30a69f63badf3f0642",
            "3d85fe3fc58f4052bc6e62bad0a2c355",
            "8882a976904f46c5a8a93a46847c9600"
          ]
        },
        "id": "vrnvFjd-ce1n",
        "outputId": "ac79da98-581d-4336-fade-18534b5d645a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df9e9114fbb648f192494d2c3b88f3dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/791 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fbd526e28d04c2ba0bfd4407c739084",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "de1729dab0e54b2ba8e3b87f41c11e82",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
        "\n",
        "# モデルを読み込む\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
        "# collate関数にDataCollatorForSeq2Seqを用いる\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "CgfX3Pohce1n",
        "outputId": "2b4cccb4-779b-4146-84a0-12ec4f17adca"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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 T5TokenizerFast 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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3685' max='3685' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3685/3685 1:13:09, Epoch 5/5]\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",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.874000</td>\n",
              "      <td>2.002162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.287300</td>\n",
              "      <td>1.892111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.036900</td>\n",
              "      <td>1.866196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.900900</td>\n",
              "      <td>1.858175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.819400</td>\n",
              "      <td>1.858388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3685, training_loss=2.3836804083276797, metrics={'train_runtime': 4390.7493, 'train_samples_per_second': 6.711, 'train_steps_per_second': 0.839, 'total_flos': 2.017042656038093e+16, 'train_loss': 2.3836804083276797, 'epoch': 5.0})"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "from transformers.trainer_utils import set_seed\n",
        "\n",
        "# 乱数シードを42に固定する\n",
        "set_seed(42)\n",
        "\n",
        "# Trainerに渡す引数を初期化する\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"output_t5_summarization\", # 結果の保存フォルダ\n",
        "    per_device_train_batch_size=8, # 訓練時のバッチサイズ\n",
        "    per_device_eval_batch_size=8, # 評価時のバッチサイズ\n",
        "    learning_rate=1e-4, # 学習率\n",
        "    lr_scheduler_type=\"linear\", # 学習率スケジューラ\n",
        "    warmup_ratio=0.1, # 学習率のウォームアップ\n",
        "    num_train_epochs=5, # 訓練エポック数\n",
        "    evaluation_strategy=\"epoch\", # 評価タイミング\n",
        "    save_strategy=\"epoch\", # チェックポイントの保存タイミング\n",
        "    logging_strategy=\"epoch\", # ロギングのタイミング\n",
        "    load_best_model_at_end=True, # 訓練後に検証セットで最良のモデルをロード\n",
        "    report_to=\"none\",  # 外部ツールへのログを無効化\n",
        ")\n",
        "\n",
        "# Trainerを初期化する\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=validation_dataset,\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "\n",
        "# 訓練する\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7QW8nUC5ZMS",
        "outputId": "6b2771d3-6a1f-4b37-d7c6-d81b15b9e8c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Google ドライブをマウントする\n",
        "drive.mount(\"drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gFrDSRit5aST"
      },
      "outputs": [],
      "source": [
        "# 保存されたモデルをGoogleドライブのフォルダにコピーする\n",
        "!mkdir -p drive/MyDrive/llm-book\n",
        "!cp -r output_t5_summarization drive/MyDrive/llm-book"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glCrd45uTcaQ"
      },
      "source": [
        "### 7.4.2 見出しの生成とモデルの評価"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "a93872a8bf844615b75b5cd39628bd5d",
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            "480de1fd62f94b6684afc6d754e50db3",
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            "da5f1dc3ecbb4edc8dc8e69c179a03ff",
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            "aeb04f04984b490f94652503d74c6184",
            "b280761f662f4d919662844e95e0b5e7"
          ]
        },
        "id": "rtgI7UBuce1n",
        "outputId": "d035c803-3f87-43b2-b43e-bae2f97a12c9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a84b07119580453f8fb90f3bf998c979",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/768 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e39768af6c444f9f873f41b24ce056a1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53b76cb3fc144eae96601d2ff80899fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/142 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# モデルを読み込む\n",
        "model_name = \"llm-book/t5-base-long-livedoor-news-corpus\"\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(\"cuda:0\")\n",
        "# パラメータをメモリ上に隣接した形で配置\n",
        "# これを実行しない場合、モデルの保存でエラーになることがある\n",
        "for param in model.parameters():\n",
        "    param.data = param.data.contiguous()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pm9wawyDybe7"
      },
      "source": [
        "#### 見出しの生成"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89,
          "referenced_widgets": [
            "6a1f687147d843b89f2b6a944890f6e3",
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            "c080135f59ff40eabc692c505a860f2c",
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            "915200a8c233473198963eae662ab26d",
            "5eeb721035c94d0c9d1e9e0fbb7e261b"
          ]
        },
        "id": "O_JAUcQ5ce1n",
        "outputId": "78bce845-454a-4c1c-c86e-12596b12e1da"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a1f687147d843b89f2b6a944890f6e3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/738 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/93 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1353: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "100%|██████████| 93/93 [01:23<00:00,  1.12it/s]\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from transformers import PreTrainedModel\n",
        "\n",
        "def convert_list_dict_to_dict_list(\n",
        "    list_dict: dict[str, list]\n",
        ") -> list[dict[str, list]]:\n",
        "    \"\"\"ミニバッチのデータを事例単位のlistに変換\"\"\"\n",
        "    dict_list = []\n",
        "    # dictのキーのlistを作成する\n",
        "    keys = list(list_dict.keys())\n",
        "    for idx in range(len(list_dict[keys[0]])):  # 各事例で処理する\n",
        "        # dictの各キーからデータを取り出してlistに追加する\n",
        "        dict_list.append({key: list_dict[key][idx] for key in keys})\n",
        "    return dict_list\n",
        "\n",
        "def run_generation(\n",
        "    dataloader: DataLoader, model: PreTrainedModel\n",
        ") -> list[dict[str, Any]]:\n",
        "    \"\"\"見出しを生成\"\"\"\n",
        "    generations = []\n",
        "    for batch in tqdm(dataloader):  # 各ミニバッチを処理する\n",
        "        batch = {k: v.to(model.device) for k, v in batch.items() if k != \"labels\"}\n",
        "        # 見出しのトークンのIDを生成する\n",
        "        batch[\"generated_title_ids\"] = model.generate(**batch)\n",
        "        batch = {k: v.cpu().tolist() for k, v in batch.items()}\n",
        "        # ミニバッチのデータを事例単位のlistに変換する\n",
        "        generations += convert_list_dict_to_dict_list(batch)\n",
        "    return generations\n",
        "\n",
        "# テストセットに対して前処理を行う\n",
        "test_dataset = dataset[\"test\"].map(\n",
        "    preprocess_data,\n",
        "    fn_kwargs={\"tokenizer\": tokenizer},\n",
        "    remove_columns=dataset[\"test\"].column_names,\n",
        ")\n",
        "test_dataset = test_dataset.remove_columns([\"labels\"])\n",
        "# ミニバッチの作成にDataLoaderを用いる\n",
        "test_dataloader = DataLoader(\n",
        "    test_dataset,\n",
        "    batch_size=8,\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "# 見出しを生成する\n",
        "generations = run_generation(test_dataloader, model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHdd_jc9LonX",
        "outputId": "f76de1d6-01ea-4b73-8436-b498d2482433"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['<pad>', '▁', '今日は', 'そういう', '日', 'だった', 'のか', '!', 'Google', 'ロゴ', 'が', '変わって', 'いる', '理由', '</s>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']\n"
          ]
        }
      ],
      "source": [
        "# 生成した見出しのトークンのIDのlistをトークンのlistに変換する\n",
        "tokens = tokenizer.convert_ids_to_tokens(\n",
        "    generations[0][\"generated_title_ids\"]\n",
        ")\n",
        "print(tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeBXFODB33Ed",
        "outputId": "ed383d12-e992-4ea3-876f-33adfaf3a3c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "今日はそういう日だったのか!Googleロゴが変わっている理由\n"
          ]
        }
      ],
      "source": [
        "def postprocess_title(\n",
        "    generations: list[dict[str, Any]],\n",
        "    dataset: list[dict[str, Any]],\n",
        "    tokenizer: PreTrainedTokenizer,\n",
        "):\n",
        "    \"\"\"見出しの後処理\"\"\"\n",
        "    results = []\n",
        "    # 各事例を処理する\n",
        "    for generation, data in zip(generations, dataset):\n",
        "        # IDのlistをテキストに変換する\n",
        "        data[\"generated_title\"] = tokenizer.decode(\n",
        "            generation[\"generated_title_ids\"],\n",
        "            skip_special_tokens=True,\n",
        "        )\n",
        "        results.append(data)\n",
        "    return results\n",
        "\n",
        "# 見出しテキストを生成する\n",
        "results = postprocess_title(generations, dataset[\"test\"], tokenizer)\n",
        "print(results[0][\"generated_title\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywPj9pRyyg8Q"
      },
      "source": [
        "#### モデルの評価"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "KEkv7wjDce1o",
        "outputId": "48d2b803-fa3e-4f82-eaf0-ae94e54a29c0"
      },
      "outputs": [
        {
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-cb35f755-4223-4e60-ab1b-435cc912d934 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cb5f941-e890-4cc9-86ae-5e34413bcc5b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cb5f941-e890-4cc9-86ae-5e34413bcc5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        precision  recall  fmeasure\n",
              "rouge1      0.417   0.319     0.352\n",
              "rouge2      0.214   0.156     0.175\n",
              "rougeL      0.364   0.278     0.306"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ROUGEを算出して表示する\n",
        "generated_titles = [\n",
        "    tagger.parse(r[\"generated_title\"]).strip() for r in results\n",
        "]\n",
        "ref_titles = [tagger.parse(r[\"title\"]).strip() for r in results]\n",
        "rouge_results = compute_rouge(generated_titles, ref_titles)\n",
        "display(pd.DataFrame.from_dict(rouge_results, orient=\"index\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Xmm-6PJ6vnIW",
        "outputId": "c6e0c1f9-0ddc-4c91-ad78-8f7641be8a61"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "\n",
              "  <div id=\"df-9480724e-c5d0-41f1-b100-8a1bf2561fe6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BLEU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>score</th>\n",
              "      <td>13.523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>counts</th>\n",
              "      <td>[4439, 2151, 1292, 804]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>totals</th>\n",
              "      <td>[10611, 9873, 9135, 8397]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precisions</th>\n",
              "      <td>[41.83, 21.79, 14.14, 9.57]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bp</th>\n",
              "      <td>0.721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sys_len</th>\n",
              "      <td>10611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ref_len</th>\n",
              "      <td>14075</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </script>\n",
              "\n",
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              "\n",
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              "    domScope.querySelector('#df-035771db-6d4b-4406-a23e-c278a595c109 button.colab-df-quickchart');\n",
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              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9480724e-c5d0-41f1-b100-8a1bf2561fe6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9480724e-c5d0-41f1-b100-8a1bf2561fe6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                   BLEU\n",
              "score                            13.523\n",
              "counts          [4439, 2151, 1292, 804]\n",
              "totals        [10611, 9873, 9135, 8397]\n",
              "precisions  [41.83, 21.79, 14.14, 9.57]\n",
              "bp                                0.721\n",
              "sys_len                           10611\n",
              "ref_len                           14075"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# BLEUを算出して表示する\n",
        "generated_titles = [\n",
        "    tagger.parse(r[\"generated_title\"]).strip() for r in results\n",
        "]\n",
        "ref_titles = [[tagger.parse(r[\"title\"]).strip() for r in results]]\n",
        "bleu_results = compute_bleu(generated_titles, ref_titles)\n",
        "display(pd.DataFrame([bleu_results]).rename(index={0: \"BLEU\"}).T)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "4-Um1orkvnjb",
        "outputId": "7dd6b1d1-2d03-45f9-d875-b42309023859"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b190ee6a-8baf-45e7-bc15-0efc5362cce8\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BERTScore</th>\n",
              "      <td>0.757</td>\n",
              "      <td>0.726</td>\n",
              "      <td>0.741</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
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              "\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
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              "    background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b190ee6a-8baf-45e7-bc15-0efc5362cce8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b190ee6a-8baf-45e7-bc15-0efc5362cce8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           precision  recall     f1\n",
              "BERTScore      0.757   0.726  0.741"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# BERTScoreを算出して表示する\n",
        "generated_titles = [r[\"generated_title\"].strip() for r in results]\n",
        "ref_titles = [r[\"title\"].strip() for r in results]\n",
        "bertscore_results = compute_bertscore(generated_titles, ref_titles)\n",
        "display(\n",
        "    pd.DataFrame([bertscore_results]).rename(index={0: \"BERTScore\"})\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "RytwHT2tnFzi",
        "outputId": "3396073c-c432-4ba0-a51b-99eb69c47662"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "  <div id=\"df-46f825b8-bc57-4d1c-b4e0-c6c4fa597b17\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>content</th>\n",
              "      <th>title</th>\n",
              "      <th>generated_title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>「今日はそういう日だったのか！Googleロゴが変わっている理由」で紹介したように、Googleはたまにトップページのロゴを変える。今、Googleにアクセスすると、トップページのGoogleロゴが変わっているのに気づくだろう。  Googleロゴは、折り紙に見える。クリックすると、「吉澤章」という言葉が検索される。今日（3月14日）は、折り紙作家 吉澤章の誕生日だからだ。  吉澤章は、栃木県出身の折り紙作家だ。日本の創作折り紙の第一人者であるとともに、折り紙の世界的な普及に尽力したことで知られる。  興味を持った人は、インターネットで検索してみては如何だろうか。  ■Google     ■Googleに関連した記事をもっと見る ・今日はそういう日だったのか！Googleロゴが変わっている理由 ・女子のすこやかな成長を祈る日！Googleロゴがひな祭りのひな壇に ・今日はそういう日だったのか！Googleロゴが歌うカエルである理由 周波数の単位に関係あり！Googleロゴが波のアニメの理由  できるポケット Google スマートに使いこなす基本＆活用ワザ 180 ［グーグル...</td>\n",
              "      <td>なるほど、そういうことか！Googleロゴが折り紙である理由</td>\n",
              "      <td>今日はそういう日だったのか!Googleロゴが変わっている理由</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ビデオサロン12月号の記事連動の動画です。今回のテーマは「音量の合わせ方」です。詳しくは2011年12月号本誌をご覧ください。 関連記事 【ビデオマイスター】養成講座の内田氏がビデオ編集講座   ■ビデオSALON　イベント・製品レポート 最新記事 ・11月号付録 キヤノン iVIS HF G10 Perfect Book 丸かじりダダウンロード ・【Special Report】ミドルレンジのビデオ制作者の有志が集まって結成したHD Usersが選んだATEM 1M/E Switcher ・第9回上映 中平悠里 監督作品『猫と少女の物語』 ・連載●After Effects 天国への階段第9回    ◆ビデオSALON2011年12月号 http://www.genkosha.co.jp/vs/backnumber/866.html  ■関連記事 ・ソニーNXCAMのファームアップ情報【ビデオSALONI】 ・今度の貞子はネットを使って呪いを拡散？　「リング」シリーズ最新作の構想が明らかに【話題】 ・土屋アンナが「今日はかわいく」紹介！　パナソニックが少数世帯向けのプチ家電を...</td>\n",
              "      <td>【記事連動】音の編集講座「音量の合わせ方」【ビデオSALON】</td>\n",
              "      <td>音量の合わせ方【ビデオSALON】</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>みずみずしい理想の肌に欠かせないのが”しっとり感”。しかし、冬が近づいてくると、乾燥によるカサカサ肌に悩む方も多いのではないでしょうか。乾燥に負けない理想のうるおい肌になるためには、自分にあった化粧品を惜しみなく使うことが大切です。  今、ちふれでは”自分にあった化粧品を、たっぷり使ってもらいたい”という想いのもと「なりたいきれい、好きなだけ。」キャンペーンを行なっています。女優の臼田あさみさんとモデルの雅姫さんをダブルで起用し、CMやキャンペーンサイトを展開。キャンペーンサイトでは、ちふれの人気アイテムが詰まった「なりたいきれい実感セット」が合計200名様に当たるプレゼントも12月27日まで実施中です。  この臼田あさみさんが出演するCMでもおなじみの「モイスチャー パウダー ファンデーション」は、3種の「しっとりやわらかパウダー」や、水分をつつみ込むダブルのうるおいオイルを配合。メイク中でも乾燥に負けず、長時間しっとりとしたお肌を保てます。スルスル広がり、溶け込むようにぴたっと密着するので、仕上がりも重くなりません。  さらにうるおい感を持続させるためには、下地として「メー...</td>\n",
              "      <td>【終了しました】しっとりなめらかな美肌を作る「ちふれ ベースメイクセット」を3名様にプレゼント</td>\n",
              "      <td>乾燥知らずのうるおい美肌を手に入れる「ちふれ ベースメイクセット」</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46f825b8-bc57-4d1c-b4e0-c6c4fa597b17')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-a7ef73b9-187a-4c52-ae6b-1d99795eb6d9\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7ef73b9-187a-4c52-ae6b-1d99795eb6d9')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a7ef73b9-187a-4c52-ae6b-1d99795eb6d9 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46f825b8-bc57-4d1c-b4e0-c6c4fa597b17 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46f825b8-bc57-4d1c-b4e0-c6c4fa597b17');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               content  \\\n",
              "0  「今日はそういう日だったのか！Googleロゴが変わっている理由」で紹介したように、Googleはたまにトップページのロゴを変える。今、Googleにアクセスすると、トップページのGoogleロゴが変わっているのに気づくだろう。  Googleロゴは、折り紙に見える。クリックすると、「吉澤章」という言葉が検索される。今日（3月14日）は、折り紙作家 吉澤章の誕生日だからだ。  吉澤章は、栃木県出身の折り紙作家だ。日本の創作折り紙の第一人者であるとともに、折り紙の世界的な普及に尽力したことで知られる。  興味を持った人は、インターネットで検索してみては如何だろうか。  ■Google     ■Googleに関連した記事をもっと見る ・今日はそういう日だったのか！Googleロゴが変わっている理由 ・女子のすこやかな成長を祈る日！Googleロゴがひな祭りのひな壇に ・今日はそういう日だったのか！Googleロゴが歌うカエルである理由 周波数の単位に関係あり！Googleロゴが波のアニメの理由  できるポケット Google スマートに使いこなす基本＆活用ワザ 180 ［グーグル...   \n",
              "1  ビデオサロン12月号の記事連動の動画です。今回のテーマは「音量の合わせ方」です。詳しくは2011年12月号本誌をご覧ください。 関連記事 【ビデオマイスター】養成講座の内田氏がビデオ編集講座   ■ビデオSALON　イベント・製品レポート 最新記事 ・11月号付録 キヤノン iVIS HF G10 Perfect Book 丸かじりダダウンロード ・【Special Report】ミドルレンジのビデオ制作者の有志が集まって結成したHD Usersが選んだATEM 1M/E Switcher ・第9回上映 中平悠里 監督作品『猫と少女の物語』 ・連載●After Effects 天国への階段第9回    ◆ビデオSALON2011年12月号 http://www.genkosha.co.jp/vs/backnumber/866.html  ■関連記事 ・ソニーNXCAMのファームアップ情報【ビデオSALONI】 ・今度の貞子はネットを使って呪いを拡散？　「リング」シリーズ最新作の構想が明らかに【話題】 ・土屋アンナが「今日はかわいく」紹介！　パナソニックが少数世帯向けのプチ家電を...   \n",
              "2  みずみずしい理想の肌に欠かせないのが”しっとり感”。しかし、冬が近づいてくると、乾燥によるカサカサ肌に悩む方も多いのではないでしょうか。乾燥に負けない理想のうるおい肌になるためには、自分にあった化粧品を惜しみなく使うことが大切です。  今、ちふれでは”自分にあった化粧品を、たっぷり使ってもらいたい”という想いのもと「なりたいきれい、好きなだけ。」キャンペーンを行なっています。女優の臼田あさみさんとモデルの雅姫さんをダブルで起用し、CMやキャンペーンサイトを展開。キャンペーンサイトでは、ちふれの人気アイテムが詰まった「なりたいきれい実感セット」が合計200名様に当たるプレゼントも12月27日まで実施中です。  この臼田あさみさんが出演するCMでもおなじみの「モイスチャー パウダー ファンデーション」は、3種の「しっとりやわらかパウダー」や、水分をつつみ込むダブルのうるおいオイルを配合。メイク中でも乾燥に負けず、長時間しっとりとしたお肌を保てます。スルスル広がり、溶け込むようにぴたっと密着するので、仕上がりも重くなりません。  さらにうるおい感を持続させるためには、下地として「メー...   \n",
              "\n",
              "                                             title  \\\n",
              "0                   なるほど、そういうことか！Googleロゴが折り紙である理由   \n",
              "1                  【記事連動】音の編集講座「音量の合わせ方」【ビデオSALON】   \n",
              "2  【終了しました】しっとりなめらかな美肌を作る「ちふれ ベースメイクセット」を3名様にプレゼント   \n",
              "\n",
              "                     generated_title  \n",
              "0    今日はそういう日だったのか!Googleロゴが変わっている理由  \n",
              "1                  音量の合わせ方【ビデオSALON】  \n",
              "2  乾燥知らずのうるおい美肌を手に入れる「ちふれ ベースメイクセット」  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# DataFrameのセル内の最大出力文字数を指定する\n",
        "pd.options.display.max_colwidth = 500\n",
        "# 記事、見出し、生成した見出しを表示する\n",
        "display(\n",
        "    pd.DataFrame(results)[:3][[\"content\", \"title\", \"generated_title\"]]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rczkje1BToJx"
      },
      "source": [
        "## 7.5 多様な生成方法による見出し生成"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOMHzvBlce1o",
        "outputId": "a3e15396-52ff-4ce2-b03e-1eb1a058caac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "記事: そろそろ梅雨がやってきます。この時期に多い女子のお悩みといえば、ヘアスタイルに関すること。  湿気で広がった髪がどうしてもまとまらず、とりあえず適当にまとめて家を飛び出したり、帽子を被ってごまかしたり……という経験、ありますよね。でも、じめじめした季節だってかわいく・オシャレに過ごしたいもの。  髪が広がりやすい梅雨の時期は、アレンジヘアで乗り切るのがベスト。「ヘアアレンジというと、難しそうな印象がありますが、ちょっとしたコツさえおさえれば、ぱぱっと簡単にできますよ！」と話してくれたのは、美容室「MADURiCA por DIFINO」のスタイリスト、山口祐亮さん。  ではさっそく、バクハツしがちなヘアをオシャレにごまかすアレンジテクニックを教えてください！  バクハツヘアもすっきり！ 楽ちんヘアアレンジ  元の髪はこんな感じ。ここからアレンジをしていきます。 基本の「まとまる」テクニック おしゃれなアレンジヘアに見せるポイントは、アレンジの前に毛先をアイロンやカーラーで巻いておくこと。このひと手間でまとまり方や仕上がりが変わります！  毛先に動きがあるだけで、ただのポニーテールもぐっとオシャレに見えるし、後れ毛だってアクセントに。コツは髪をいくつもの部分に分けて少しずつ巻くこと。慣れてしまえばそんなに時間はかかりません。  ＜簡単アレンジ1＞ ふんわりルーズ感がかわいい！ 簡単アップスタイル  完成形 蒸し暑い日はアップスタイルにしたいですよね。 このアレンジなら、編み込みがアクセントになって横顔美人！ 後ろから見たときも、ランダムに散らばった毛先がキュートです。下準備として、毛先はアイロンやカーラーで巻いておきましょう。   1.  前髪や顔周りの髪は残し、トップ〜耳上までの髪を前から見て約4：6くらいの割合になるように分け、多い方をざっくりと編み込みにして、ゴムで止めます。  編み込みを作ったら、少しずつ毛を引き出してルーズな感じにするとかわいい印象に。   2.  髪の毛の量が多い人は、後ろ髪を上下に分けて、下側の真ん中あたりの髪の一部を三つ編みにしてねじり、ピンで留めます。  これが土台になり、このあとピンをさしやすくなります。三つ編みにする量は自分の髪の量に合わせて調節してください。   3.  残った髪を何束かに分け、ねじって丸めながらバランスを見てピンで留めていきます。髪の広がりやバラつきが気になるところにどんどんピンをさしていってOK。  留めた髪の毛は毛先を広げたり逆毛を立てたり、ラフな感じに散らします。事前に毛先をアイロンなどで巻いておけば、適当にまとめてもかわいく仕上がります。  これで完成！ ＜ワンポイントアドバイス＞ 髪の量が多い人は、ピンを広げてたくさんの毛を一気に留めてしまいがち。 これではすぐにピンが外れてスタイルが崩れてしまいます。ピンは広げずにさして留めた方が取れにくいそう。今回のアレンジのように、髪をねじって留めるようにすれば、どこかがきちんと留まってさえいれば大丈夫です。  ＜簡単アレンジ2＞ オフィスでも、デートでも！ 万能ハーフアップ  完成形 簡単に作れる上品な印象のハーフアップ。後ろ姿もキレイです。きっちり作るとお堅い感じになってしまいがちですが、このアレンジならカジュアルなシーンにもぴったり。こちらも事前に毛先を巻いておくと、スタイルが決まります。   1.  好きな位置で分け目を作り、左右両方に、トップ〜耳の上の髪でやや細めの三つ編みを作っておきます。毛を引き出したりしてルーズな感じで作りましょう。  分け目を左右どちらかに寄せるとトップがふんわりして大人っぽくなります。    2.  片側に寄せてハーフアップを作り、ねじりながらまとめます。  ねじった髪の１番外側の髪（A）と、地肌の方に残っている髪（B）とをピンで縫うように留めます。ポイントは、Aの上からピンを差し込み、Bの髪をすくって上に向かってさすこと。ピンは2、3本留めておくとよいでしょう。    3.  1.で作った三つ編みを、ハーフアップをまとめたところに集めて、ピンで留めます。長さが届かなければ、先ほどのBの部分にピンで留めてまとめて。  これで完成！  どちらのスタイルも、完成後にあまり固めすぎないスプレーをしておくと、ふんわり感が持続し、崩れにくいですよ。  梅雨時の髪のクセ・広がりをおさえるテクニック  髪のクセや広がりをアレンジでごまかしちゃうのもいいけど、毎日のケアで気を付けることはあるのでしょうか？ 山口さんに聞いてみました。  テク1：髪の広がりを抑えるなら、とことん保湿！ 髪が広がる原因は、乾燥したところに水分が入り込むから。つまり、水分が入るスキマを作らないようにする必要があるのです。そのためにはお風呂上がりの保湿が重要。洗い流さないトリートメントを使って、きちんと保湿するようにしましょう。 テク2：クセを伸ばすには焦らず冷却時間をおく！ ブローしてもクセがなかなか直らない場合、きちんと冷やしていないことが原因かもしれません。ブローの際、クセを伸ばしたい部分を乾かしたら、ブラシで引っ張ったまましばらく待つか、ドライヤーの冷風モードを活用しましょう。「髪は冷えるときに固まるので、きちんと冷やさないとせっかくのスタイルが定着しません。ドライヤーの冷風モードはそのためにあるんですよ」と山口さん。涼むための機能ではなかったんですね〜。 梅雨どきのヘアスタイルにさんざん悩まされてきた女子は多いはず。今年は簡単アレンジやちょっとしたテクニックをマスターして、オシャレなスタイルで梅雨を乗り切りましょう！   MADURiCA por DIFINO お問い合わせ：03-6419-1921 営業時間： 月・木　11：00〜21：00 水・金　12：00〜22：00 土　10：00〜19：00 定休日：火曜日 住所：渋谷区神宮前5-51-8　ポルト青山8F アクセス：東京メトロ表参道駅B2出口より徒歩5分  ビルの8Fにあり、窓から表参道を見おろすことができる気持ちのいいロケーション。店内は隅々までオシャレな内装で、居心地バツグン。ゆったりくつろげる個室も用意されています。  ひとりひとりの髪質やくせに合わせ、毛束をひねりながら切っていく「Rカット」はこの美容室オリジナル。髪の悩みがある人は、一度相談してみては。 （写真：菅井淳子　文：K-Writer’s Club 八木友里恵）\n",
            "見出し: 湿気に負けない！ 梅雨の「楽カワ」ヘアアレンジ\n"
          ]
        }
      ],
      "source": [
        "content = dataset[\"test\"][434][\"content\"]\n",
        "title = dataset[\"test\"][434][\"title\"]\n",
        "print(f\"記事: {content}\")\n",
        "print(f\"見出し: {title}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S7RFTxEKzc4M"
      },
      "outputs": [],
      "source": [
        "from functools import partial\n",
        "from transformers import pipeline\n",
        "\n",
        "# 乱数シードを42に再設定する\n",
        "set_seed(42)\n",
        "\n",
        "# モデルを固定したpipelineを作成する\n",
        "fixed_model_pipeline = partial(\n",
        "    pipeline,\n",
        "    \"summarization\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    device=\"cuda:0\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ka-QIIjYTwaL"
      },
      "source": [
        "### 7.5.1 テキスト生成における探索アルゴリズム"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fnO7j5zxynPt"
      },
      "source": [
        "#### 貪欲法"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xQK4yiUce1o",
        "outputId": "a97c6686-e726-461e-969e-fecd4ff45888"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアスタイルは簡単アレンジで! 簡単アレンジで梅雨を乗り切りよう\n"
          ]
        }
      ],
      "source": [
        "print(fixed_model_pipeline()(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaKue0cSAvPw",
        "outputId": "e74f23b2-084c-44db-976f-d852e7332d51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアスタイルは簡単アレンジで! 簡単ヘアアレンジテクニック【ビューティー特集】\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(no_repeat_ngram_size=2)\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XH49y9yXyqiF"
      },
      "source": [
        "#### ビームサーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8WWVOjoKCZ5",
        "outputId": "b1557b9c-96fa-4065-cb8a-a29eb72232d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切ろう!\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(num_beams=3)\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "um5LZoK_KCX0",
        "outputId": "a5c336b4-5932-4394-aa08-c4c77dc9300e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切ろう!\n",
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切ろう\n",
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切りよう!\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    num_beams=3, num_return_sequences=3\n",
        ")\n",
        "for summary in summarization_pipeline(content):\n",
        "    print(summary[\"summary_text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPvrF3ItT3gm"
      },
      "source": [
        "### 7.5.2 サンプリングを用いたテキスト生成"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cc8pxiWEKEOM",
        "outputId": "b0186be0-4cbe-4039-8e58-a9e998aafba4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "うっすらパーマで崩れにくい!? 梅雨入りを涼しくパチリ!\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(do_sample=True, top_k=0)\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQkmB46JKCVT",
        "outputId": "045aa389-9e3f-434e-848c-6955be4dd896"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアアレンジは簡単! 簡単アレンジで梅雨を乗り切る【ビューティー特集\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    do_sample=True, top_k=0, temperature=0.5\n",
        ")\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeY8axtxKCTP",
        "outputId": "0de3a2ac-7e61-40f3-8533-82c92cfc50ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨の自然といえば〜!?セルフアレンジでボディメイク正解トレンドチェック【レポート】\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    do_sample=True, top_k=0, temperature=1.3\n",
        ")\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2np-dpVSyxFk"
      },
      "source": [
        "#### top-kサンプリング"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDucwfauKCRM",
        "outputId": "5862377c-64fd-4c1c-928c-bd98a6869880"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨はヘアスタイルで乗り切りましょう!梅雨のヘアスタイルはアレンジに気をつけたい! -\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    do_sample=True, top_k=10, temperature=1.3\n",
        ")\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zh-QAEz8y1J_"
      },
      "source": [
        "#### top-pサンプリング"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyFsmd4yKCDo",
        "outputId": "18859112-768f-49b3-d168-8fa0482b8f7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨時にこそ必要! 大人女子のための簡単アレンジ術\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    do_sample=True, top_k=0, top_p=0.5, temperature=1.3\n",
        ")\n",
        "print(summarization_pipeline(content)[0][\"summary_text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LW5Er2jvT-tS"
      },
      "source": [
        "### 7.5.3 長さを調整したテキスト生成"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUV8k5jNKH7y",
        "outputId": "668f5809-3864-4a06-bb3f-5caeac937f60"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Both `max_new_tokens` (=5) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアスタイルは\n",
            "梅雨のヘアスタイルを\n",
            "梅雨のヘアアレンジ\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    num_beams=3,\n",
        "    num_return_sequences=3,\n",
        "    min_new_tokens=5,\n",
        "    max_new_tokens=5,\n",
        ")\n",
        "for summary in summarization_pipeline(content):\n",
        "    print(summary[\"summary_text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdIqWivcKH5g",
        "outputId": "3a03b774-f87c-4aca-fab3-cc17d33e1822"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Both `max_new_tokens` (=35) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切ろう【オトナ女子のリアルな悩み解決術 vol.7】Presented\n",
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切ろう【オトナ女子のリアルな悩み解決術 vol.7】【ビューティー特集】\n",
            "梅雨のヘアスタイルをオシャレに! 簡単アレンジで梅雨を乗り切ろう【オトナ女子のリアルな悩み解決術 vol.7】【ビューティー特集】|\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    num_beams=3,\n",
        "    num_return_sequences=3,\n",
        "    min_new_tokens=35,\n",
        "    max_new_tokens=35,\n",
        ")\n",
        "for summary in summarization_pipeline(content):\n",
        "    print(summary[\"summary_text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPvN7vIuKH3N",
        "outputId": "eed09511-8681-4cd2-fce5-84d211fa8f64"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Both `max_new_tokens` (=35) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "梅雨の髪のクセ・広がりをおさえる! 簡単アレンジでオシャレなヘアに! 「MADURiCA por DIFINO」\n",
            "梅雨のヘアスタイルは簡単アレンジで決まり! 簡単アレンジ術をマスター【ビューティーマガジン】 vol.11『梅雨時の髪のクセや広がりをおさえる\n",
            "梅雨どきのヘアアレンジは簡単! 簡単アレンジで梅雨を乗り切りましょう【ビューティーマガジン vol.14】【ファッション特集】【ビューティー特集】|表参道/表参道サロン\n"
          ]
        }
      ],
      "source": [
        "summarization_pipeline = fixed_model_pipeline(\n",
        "    num_beams=3,\n",
        "    num_return_sequences=3,\n",
        "    min_new_tokens=35,\n",
        "    max_new_tokens=35,\n",
        "    do_sample=True,\n",
        "    temperature=1.3,\n",
        "    no_repeat_ngram_size=3,\n",
        ")\n",
        "for summary in summarization_pipeline(content):\n",
        "    print(summary[\"summary_text\"])"
      ]
    }
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