06-自然语言处理/41-BERT创建训练数据(Tensorflow).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考链接：https://jalammar.github.io/illustrated-bert/    \n",
    "    https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/    \n",
    "        http://jalammar.github.io/illustrated-transformer/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `BERT`的输入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- BERT 是预训练的，**词汇表是固定的**   \n",
    "   \n",
    "   \n",
    "- 将未知单词拆分为 `subwords:embedding --> em, bed, ding`\n",
    "- 基本上每个字母都是一个 `subword`\n",
    "- 实际处理成 `embedding --> em, ##bed, ##ding`，处于一个单词中间的子词会添加`##`符号来标记"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 英文分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:56.850472Z",
     "start_time": "2020-05-08T03:05:53.922487Z"
    }
   },
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "\n",
    "with open('../models/bert/vocabulary.txt', 'w') as f:\n",
    "    for token in tokenizer.vocab.keys():\n",
    "        f.write(token + '\\n')\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:56.857367Z",
     "start_time": "2020-05-08T03:05:56.851740Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30522"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokenizer.vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前1000个标记大多数保留，形式为`[unused156]`，几个特殊符号\n",
    "```\n",
    "1-[pad]\n",
    "101-[UNK]\n",
    "102-[CLS]\n",
    "103-[SEP]\n",
    "104-[MASK]\n",
    "```\n",
    "1000-1996 为单字符     \n",
    "   \n",
    "1997开始为单词及字词，按词频排序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单字符的标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:56.865565Z",
     "start_time": "2020-05-08T03:05:56.858419Z"
    }
   },
   "outputs": [],
   "source": [
    "one_chars = []\n",
    "one_chars_hashes = []\n",
    "\n",
    "for token in tokenizer.vocab.keys():\n",
    "    if len(token) == 1:\n",
    "        one_chars.append(token)\n",
    "    elif len(token) == 3 and token[0:2] == '##':\n",
    "        one_chars_hashes.append(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:56.870008Z",
     "start_time": "2020-05-08T03:05:56.866497Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of single charater token:  997 \n",
      "\n",
      "! \" # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ [ \\ ] ^ _ ` a b\n",
      "c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ ¡ ¢ £ ¤ ¥ ¦ § ¨ © ª « ¬\n",
      "® ° ± ² ³ ´ µ ¶ · ¹ º » ¼ ½ ¾ ¿ × ß æ ð ÷ ø þ đ ħ ı ł ŋ œ ƒ ɐ ɑ ɒ ɔ ɕ ə ɛ ɡ ɣ ɨ\n",
      "ɪ ɫ ɬ ɯ ɲ ɴ ɹ ɾ ʀ ʁ ʂ ʃ ʉ ʊ ʋ ʌ ʎ ʐ ʑ ʒ ʔ ʰ ʲ ʳ ʷ ʸ ʻ ʼ ʾ ʿ ˈ ː ˡ ˢ ˣ ˤ α β γ δ\n",
      "ε ζ η θ ι κ λ μ ν ξ ο π ρ ς σ τ υ φ χ ψ ω а б в г д е ж з и к л м н о п р с т у\n",
      "ф х ц ч ш щ ъ ы ь э ю я ђ є і ј љ њ ћ ӏ ա բ գ դ ե թ ի լ կ հ մ յ ն ո պ ս վ տ ր ւ\n",
      "ք ־ א ב ג ד ה ו ז ח ט י ך כ ל ם מ ן נ ס ע ף פ ץ צ ק ר ש ת ، ء ا ب ة ت ث ج ح خ د\n",
      "ذ ر ز س ش ص ض ط ظ ع غ ـ ف ق ك ل م ن ه و ى ي ٹ پ چ ک گ ں ھ ہ ی ے अ आ उ ए क ख ग च\n",
      "ज ट ड ण त थ द ध न प ब भ म य र ल व श ष स ह ा ि ी ो । ॥ ং অ আ ই উ এ ও ক খ গ চ ছ জ\n",
      "ট ড ণ ত থ দ ধ ন প ব ভ ম য র ল শ ষ স হ া ি ী ে க ச ட த ந ன ப ம ய ர ல ள வ ா ி ு ே\n",
      "ை ನ ರ ಾ ක ය ර ල ව ා ก ง ต ท น พ ม ย ร ล ว ส อ า เ ་ ། ག ང ད ན པ བ མ འ ར ལ ས မ ა\n",
      "ბ გ დ ე ვ თ ი კ ლ მ ნ ო რ ს ტ უ ᄀ ᄂ ᄃ ᄅ ᄆ ᄇ ᄉ ᄊ ᄋ ᄌ ᄎ ᄏ ᄐ ᄑ ᄒ ᅡ ᅢ ᅥ ᅦ ᅧ ᅩ ᅪ ᅭ ᅮ\n",
      "ᅯ ᅲ ᅳ ᅴ ᅵ ᆨ ᆫ ᆯ ᆷ ᆸ ᆼ ᴬ ᴮ ᴰ ᴵ ᴺ ᵀ ᵃ ᵇ ᵈ ᵉ ᵍ ᵏ ᵐ ᵒ ᵖ ᵗ ᵘ ᵢ ᵣ ᵤ ᵥ ᶜ ᶠ ‐ ‑ ‒ – — ―\n",
      "‖ ‘ ’ ‚ “ ” „ † ‡ • … ‰ ′ ″ › ‿ ⁄ ⁰ ⁱ ⁴ ⁵ ⁶ ⁷ ⁸ ⁹ ⁺ ⁻ ⁿ ₀ ₁ ₂ ₃ ₄ ₅ ₆ ₇ ₈ ₉ ₊ ₍\n",
      "₎ ₐ ₑ ₒ ₓ ₕ ₖ ₗ ₘ ₙ ₚ ₛ ₜ ₤ ₩ € ₱ ₹ ℓ № ℝ ™ ⅓ ⅔ ← ↑ → ↓ ↔ ↦ ⇄ ⇌ ⇒ ∂ ∅ ∆ ∇ ∈ − ∗\n",
      "∘ √ ∞ ∧ ∨ ∩ ∪ ≈ ≡ ≤ ≥ ⊂ ⊆ ⊕ ⊗ ⋅ ─ │ ■ ▪ ● ★ ☆ ☉ ♠ ♣ ♥ ♦ ♭ ♯ ⟨ ⟩ ⱼ ⺩ ⺼ ⽥ 、 。 〈 〉\n",
      "《 》 「 」 『 』 〜 あ い う え お か き く け こ さ し す せ そ た ち っ つ て と な に ぬ ね の は ひ ふ へ ほ ま み\n",
      "む め も や ゆ よ ら り る れ ろ を ん ァ ア ィ イ ウ ェ エ オ カ キ ク ケ コ サ シ ス セ タ チ ッ ツ テ ト ナ ニ ノ ハ\n",
      "ヒ フ ヘ ホ マ ミ ム メ モ ャ ュ ョ ラ リ ル レ ロ ワ ン ・ ー 一 三 上 下 不 世 中 主 久 之 也 事 二 五 井 京 人 亻 仁\n",
      "介 代 仮 伊 会 佐 侍 保 信 健 元 光 八 公 内 出 分 前 劉 力 加 勝 北 区 十 千 南 博 原 口 古 史 司 合 吉 同 名 和 囗 四\n",
      "国 國 土 地 坂 城 堂 場 士 夏 外 大 天 太 夫 奈 女 子 学 宀 宇 安 宗 定 宣 宮 家 宿 寺 將 小 尚 山 岡 島 崎 川 州 巿 帝\n",
      "平 年 幸 广 弘 張 彳 後 御 德 心 忄 志 忠 愛 成 我 戦 戸 手 扌 政 文 新 方 日 明 星 春 昭 智 曲 書 月 有 朝 木 本 李 村\n",
      "東 松 林 森 楊 樹 橋 歌 止 正 武 比 氏 民 水 氵 氷 永 江 沢 河 治 法 海 清 漢 瀬 火 版 犬 王 生 田 男 疒 発 白 的 皇 目\n",
      "相 省 真 石 示 社 神 福 禾 秀 秋 空 立 章 竹 糹 美 義 耳 良 艹 花 英 華 葉 藤 行 街 西 見 訁 語 谷 貝 貴 車 軍 辶 道 郎\n",
      "郡 部 都 里 野 金 鈴 镇 長 門 間 阝 阿 陳 陽 雄 青 面 風 食 香 馬 高 龍 龸 ﬁ ﬂ ！ （ ） ， － ． ／ ： ？ ～\n"
     ]
    }
   ],
   "source": [
    "print(\"No of single charater token: \", len(one_chars), '\\n')\n",
    "\n",
    "for i in range(0, len(one_chars), 40):\n",
    "    end = min(i + 40, len(one_chars) + 1)\n",
    "    print(' '.join(one_chars[i:end]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:56.874706Z",
     "start_time": "2020-05-08T03:05:56.870724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of single charater tokens with hashes:  997 \n",
      "\n",
      "s a e i n o d r y t l m u h k c g p 2 z 1 b 3 f 4 6 7 x v 8 5 9 0 w j q ° ₂ а и\n",
      "² ₃ ı ₁ ⁺ ½ о ه ي α е د ن ν ø р ₄ ₀ ر я ³ ι ł н ᵢ ₙ ß ة ς م − т ː ل ь к ♭ η ی в\n",
      "ا × ¹ ы ה ɛ л ! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \\ ] ^ _ ` { | } ~ ¡\n",
      "¢ £ ¤ ¥ ¦ § ¨ © ª « ¬ ® ± ´ µ ¶ · º » ¼ ¾ ¿ æ ð ÷ þ đ ħ ŋ œ ƒ ɐ ɑ ɒ ɔ ɕ ə ɡ ɣ ɨ\n",
      "ɪ ɫ ɬ ɯ ɲ ɴ ɹ ɾ ʀ ʁ ʂ ʃ ʉ ʊ ʋ ʌ ʎ ʐ ʑ ʒ ʔ ʰ ʲ ʳ ʷ ʸ ʻ ʼ ʾ ʿ ˈ ˡ ˢ ˣ ˤ β γ δ ε ζ\n",
      "θ κ λ μ ξ ο π ρ σ τ υ φ χ ψ ω б г д ж з м п с у ф х ц ч ш щ ъ э ю ђ є і ј љ њ ћ\n",
      "ӏ ա բ գ դ ե թ ի լ կ հ մ յ ն ո պ ս վ տ ր ւ ք ־ א ב ג ד ו ז ח ט י ך כ ל ם מ ן נ ס\n",
      "ע ף פ ץ צ ק ר ש ת ، ء ب ت ث ج ح خ ذ ز س ش ص ض ط ظ ع غ ـ ف ق ك و ى ٹ پ چ ک گ ں ھ\n",
      "ہ ے अ आ उ ए क ख ग च ज ट ड ण त थ द ध न प ब भ म य र ल व श ष स ह ा ि ी ो । ॥ ং অ আ\n",
      "ই উ এ ও ক খ গ চ ছ জ ট ড ণ ত থ দ ধ ন প ব ভ ম য র ল শ ষ স হ া ি ী ে க ச ட த ந ன ப\n",
      "ம ய ர ல ள வ ா ி ு ே ை ನ ರ ಾ ක ය ර ල ව ා ก ง ต ท น พ ม ย ร ล ว ส อ า เ ་ ། ག ང ད\n",
      "ན པ བ མ འ ར ལ ས မ ა ბ გ დ ე ვ თ ი კ ლ მ ნ ო რ ს ტ უ ᄀ ᄂ ᄃ ᄅ ᄆ ᄇ ᄉ ᄊ ᄋ ᄌ ᄎ ᄏ ᄐ ᄑ\n",
      "ᄒ ᅡ ᅢ ᅥ ᅦ ᅧ ᅩ ᅪ ᅭ ᅮ ᅯ ᅲ ᅳ ᅴ ᅵ ᆨ ᆫ ᆯ ᆷ ᆸ ᆼ ᴬ ᴮ ᴰ ᴵ ᴺ ᵀ ᵃ ᵇ ᵈ ᵉ ᵍ ᵏ ᵐ ᵒ ᵖ ᵗ ᵘ ᵣ ᵤ\n",
      "ᵥ ᶜ ᶠ ‐ ‑ ‒ – — ― ‖ ‘ ’ ‚ “ ” „ † ‡ • … ‰ ′ ″ › ‿ ⁄ ⁰ ⁱ ⁴ ⁵ ⁶ ⁷ ⁸ ⁹ ⁻ ⁿ ₅ ₆ ₇ ₈\n",
      "₉ ₊ ₍ ₎ ₐ ₑ ₒ ₓ ₕ ₖ ₗ ₘ ₚ ₛ ₜ ₤ ₩ € ₱ ₹ ℓ № ℝ ™ ⅓ ⅔ ← ↑ → ↓ ↔ ↦ ⇄ ⇌ ⇒ ∂ ∅ ∆ ∇ ∈\n",
      "∗ ∘ √ ∞ ∧ ∨ ∩ ∪ ≈ ≡ ≤ ≥ ⊂ ⊆ ⊕ ⊗ ⋅ ─ │ ■ ▪ ● ★ ☆ ☉ ♠ ♣ ♥ ♦ ♯ ⟨ ⟩ ⱼ ⺩ ⺼ ⽥ 、 。 〈 〉\n",
      "《 》 「 」 『 』 〜 あ い う え お か き く け こ さ し す せ そ た ち っ つ て と な に ぬ ね の は ひ ふ へ ほ ま み\n",
      "む め も や ゆ よ ら り る れ ろ を ん ァ ア ィ イ ウ ェ エ オ カ キ ク ケ コ サ シ ス セ タ チ ッ ツ テ ト ナ ニ ノ ハ\n",
      "ヒ フ ヘ ホ マ ミ ム メ モ ャ ュ ョ ラ リ ル レ ロ ワ ン ・ ー 一 三 上 下 不 世 中 主 久 之 也 事 二 五 井 京 人 亻 仁\n",
      "介 代 仮 伊 会 佐 侍 保 信 健 元 光 八 公 内 出 分 前 劉 力 加 勝 北 区 十 千 南 博 原 口 古 史 司 合 吉 同 名 和 囗 四\n",
      "国 國 土 地 坂 城 堂 場 士 夏 外 大 天 太 夫 奈 女 子 学 宀 宇 安 宗 定 宣 宮 家 宿 寺 將 小 尚 山 岡 島 崎 川 州 巿 帝\n",
      "平 年 幸 广 弘 張 彳 後 御 德 心 忄 志 忠 愛 成 我 戦 戸 手 扌 政 文 新 方 日 明 星 春 昭 智 曲 書 月 有 朝 木 本 李 村\n",
      "東 松 林 森 楊 樹 橋 歌 止 正 武 比 氏 民 水 氵 氷 永 江 沢 河 治 法 海 清 漢 瀬 火 版 犬 王 生 田 男 疒 発 白 的 皇 目\n",
      "相 省 真 石 示 社 神 福 禾 秀 秋 空 立 章 竹 糹 美 義 耳 良 艹 花 英 華 葉 藤 行 街 西 見 訁 語 谷 貝 貴 車 軍 辶 道 郎\n",
      "郡 部 都 里 野 金 鈴 镇 長 門 間 阝 阿 陳 陽 雄 青 面 風 食 香 馬 高 龍 龸 ﬁ ﬂ ！ （ ） ， － ． ／ ： ？ ～\n"
     ]
    }
   ],
   "source": [
    "print(\"No of single charater tokens with hashes: \", len(one_chars_hashes),\n",
    "      '\\n')\n",
    "tokens = [token.replace(\"##\", '') for token in one_chars_hashes]\n",
    "for i in range(0, len(tokens), 40):\n",
    "    end = min(i + 40, len(tokens) + 1)\n",
    "    print(' '.join(tokens[i:end]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:56.877420Z",
     "start_time": "2020-05-08T03:05:56.875419Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are the two sets identical? True\n"
     ]
    }
   ],
   "source": [
    "print(\"Are the two sets identical?\", set(one_chars) == set(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标记长度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.187093Z",
     "start_time": "2020-05-08T03:05:56.878187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum token length:  18\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "token_length = [len(token) for token in tokenizer.vocab.keys()]\n",
    "\n",
    "sns.countplot(token_length)\n",
    "plt.title('Vocab Token Lengths')\n",
    "plt.xlabel('Token Length')\n",
    "plt.ylabel('# of tokens')\n",
    "\n",
    "print(\"Maximum token length: \", max(token_length))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字词的长度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.196484Z",
     "start_time": "2020-05-08T03:05:57.187910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subword: 5,828 of 30,522 \n",
      "19.1%\n"
     ]
    }
   ],
   "source": [
    "num_subwords = 0\n",
    "subword_lengths = []\n",
    "for token in tokenizer.vocab.keys():\n",
    "    if len(token) >= 2 and token[:2] == '##':\n",
    "        num_subwords += 1\n",
    "        length = len(token) - 2\n",
    "        subword_lengths.append(length)\n",
    "\n",
    "vocab_size = len(tokenizer.vocab.keys())\n",
    "print(\"Number of subword: {:,} of {:,} \".format(num_subwords, vocab_size))\n",
    "prcnt = float(num_subwords) / vocab_size * 100\n",
    "print(\"%.1f%%\" % prcnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.316999Z",
     "start_time": "2020-05-08T03:05:57.197752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum token length:  10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(subword_lengths)\n",
    "plt.title('Subword Token Lengths')\n",
    "plt.xlabel('Subword Length')\n",
    "plt.ylabel('No of Subwords')\n",
    "\n",
    "print(\"Maximum token length: \", max(subword_lengths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单词质量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.320122Z",
     "start_time": "2020-05-08T03:05:57.318012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'beginning' in tokenizer.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.323040Z",
     "start_time": "2020-05-08T03:05:57.320953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'begining' in tokenizer.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.325765Z",
     "start_time": "2020-05-08T03:05:57.323805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"can't\" in tokenizer.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.328392Z",
     "start_time": "2020-05-08T03:05:57.326511Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"cant\" in tokenizer.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.331015Z",
     "start_time": "2020-05-08T03:05:57.329141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'##ly' in tokenizer.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:57.333648Z",
     "start_time": "2020-05-08T03:05:57.331723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'ly' in tokenizer.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-03T00:51:02.784663Z",
     "start_time": "2020-05-03T00:51:02.779509Z"
    }
   },
   "source": [
    "## 中文分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.877399Z",
     "start_time": "2020-05-08T03:05:57.334395Z"
    }
   },
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer\n",
    "zh_tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.886280Z",
     "start_time": "2020-05-08T03:05:59.878287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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'##=', '##>', '##?', '##@', '##[', '##\\\\', '##]', '##^', '##_', '##`', '##{', '##|', '##}', '##~', '##¡', '##¢', '##£', '##¤', '##¥', '##¦', '##§', '##¨', '##©', '##ª', '##«', '##¬', '##®', '##±', '##´', '##µ', '##¶', '##·', '##º', '##»', '##¼', '##¾', '##¿', '##æ', '##ð', '##÷', '##þ', '##đ', '##ħ', '##ŋ', '##œ', '##ƒ', '##ɐ', '##ɑ', '##ɒ', '##ɔ', '##ɕ', '##ə', '##ɡ', '##ɣ', '##ɨ', '##ɪ', '##ɫ', '##ɬ', '##ɯ', '##ɲ', '##ɴ', '##ɹ', '##ɾ', '##ʀ', '##ʁ', '##ʂ', '##ʃ', '##ʉ', '##ʊ', '##ʋ', '##ʌ', '##ʎ', '##ʐ', '##ʑ', '##ʒ', '##ʔ', '##ʰ', '##ʲ', '##ʳ', '##ʷ', '##ʸ', '##ʻ', '##ʼ', '##ʾ', '##ʿ', '##ˈ', '##ˡ', '##ˢ', '##ˣ', '##ˤ', '##β', '##γ', '##δ', '##ε', '##ζ', '##θ', '##κ', '##λ', '##μ', '##ξ', '##ο', '##π', '##ρ', '##σ', '##τ', '##υ', '##φ', '##χ', '##ψ', '##ω', '##б', '##г', '##д', '##ж', '##з', '##м', '##п', '##с', '##у', '##ф', '##х', '##ц', '##ч', '##ш', '##щ', '##ъ', '##э', '##ю', '##ђ', '##є', '##і', '##ј', '##љ', '##њ', '##ћ', '##ӏ', '##ա', '##բ', '##գ', '##դ', '##ե', '##թ', '##ի', '##լ', '##կ', '##հ', '##մ', '##յ', '##ն', '##ո', '##պ', '##ս', '##վ', '##տ', '##ր', '##ւ', '##ք', '##־', '##א', '##ב', '##ג', '##ד', '##ו', '##ז', '##ח', '##ט', '##י', '##ך', '##כ', '##ל', '##ם', '##מ', '##ן', '##נ', '##ס', '##ע', '##ף', '##פ', '##ץ', '##צ', '##ק', '##ר', '##ש', '##ת', '##،', '##ء', '##ب', '##ت', '##ث', '##ج', '##ح', '##خ', '##ذ', '##ز', '##س', '##ش', '##ص', '##ض', '##ط', '##ظ', '##ع', '##غ', '##ـ', '##ف', '##ق', '##ك', '##و', '##ى', '##ٹ', '##پ', '##چ', '##ک', '##گ', '##ں', '##ھ', '##ہ', '##ے', '##अ', '##आ', '##उ', '##ए', '##क', '##ख', '##ग', '##च', '##ज', '##ट', '##ड', '##ण', '##त', '##थ', '##द', '##ध', '##न', '##प', '##ब', '##भ', '##म', '##य', '##र', '##ल', '##व', '##श', '##ष', '##स', '##ह', '##ा', '##ि', '##ी', '##ो', '##।', '##॥', '##ং', '##অ', '##আ', '##ই', '##উ', '##এ', '##ও', '##ক', '##খ', '##গ', '##চ', '##ছ', '##জ', '##ট', '##ড', '##ণ', '##ত', '##থ', '##দ', '##ধ', '##ন', '##প', '##ব', '##ভ', '##ম', '##য', '##র', '##ল', '##শ', '##ষ', '##স', '##হ', '##া', '##ি', '##ী', '##ে', '##க', '##ச', '##ட', '##த', '##ந', '##ன', '##ப', '##ம', '##ய', '##ர', '##ல', '##ள', '##வ', '##ா', '##ி', '##ு', '##ே', '##ை', '##ನ', '##ರ', '##ಾ', '##ක', '##ය', '##ර', '##ල', '##ව', '##ා', '##ก', '##ง', '##ต', '##ท', '##น', '##พ', '##ม', '##ย', '##ร', '##ล', '##ว', '##ส', '##อ', '##า', '##เ', '##་', '##།', '##ག', '##ང', '##ད', '##ན', '##པ', '##བ', '##མ', '##འ', '##ར', '##ལ', '##ས', '##မ', '##ა', '##ბ', '##გ', '##დ', '##ე', '##ვ', '##თ', '##ი', '##კ', '##ლ', '##მ', '##ნ', '##ო', '##რ', '##ს', '##ტ', '##უ', '##ᄀ', '##ᄂ', '##ᄃ', '##ᄅ', '##ᄆ', '##ᄇ', '##ᄉ', '##ᄊ', '##ᄋ', '##ᄌ', '##ᄎ', '##ᄏ', '##ᄐ', '##ᄑ', '##ᄒ', '##ᅡ', '##ᅢ', '##ᅥ', '##ᅦ', '##ᅧ', '##ᅩ', '##ᅪ', '##ᅭ', '##ᅮ', '##ᅯ', '##ᅲ', '##ᅳ', '##ᅴ', '##ᅵ', '##ᆨ', '##ᆫ', '##ᆯ', '##ᆷ', '##ᆸ', '##ᆼ', '##ᴬ', '##ᴮ', '##ᴰ', '##ᴵ', '##ᴺ', '##ᵀ', '##ᵃ', '##ᵇ', '##ᵈ', '##ᵉ', '##ᵍ', '##ᵏ', '##ᵐ', '##ᵒ', '##ᵖ', '##ᵗ', '##ᵘ', '##ᵣ', '##ᵤ', '##ᵥ', '##ᶜ', '##ᶠ', '##‐', '##‑', '##‒', '##–', '##—', '##―', '##‖', '##‘', '##’', '##‚', '##“', '##”', '##„', '##†', '##‡', '##•', '##…', '##‰', '##′', '##″', '##›', '##‿', '##⁄', '##⁰', '##ⁱ', '##⁴', '##⁵', '##⁶', '##⁷', '##⁸', '##⁹', '##⁻', '##ⁿ', '##₅', '##₆', '##₇', '##₈', '##₉', '##₊', '##₍', '##₎', '##ₐ', '##ₑ', '##ₒ', '##ₓ', '##ₕ', '##ₖ', '##ₗ', '##ₘ', '##ₚ', '##ₛ', '##ₜ', '##₤', '##₩', '##€', '##₱', '##₹', '##ℓ', '##№', '##ℝ', '##™', '##⅓', '##⅔', '##←', '##↑', '##→', '##↓', '##↔', '##↦', '##⇄', '##⇌', '##⇒', '##∂', '##∅', '##∆', '##∇', '##∈', '##∗', '##∘', '##√', '##∞', '##∧', '##∨', '##∩', '##∪', '##≈', '##≡', '##≤', '##≥', '##⊂', '##⊆', '##⊕', '##⊗', '##⋅', '##─', '##│', '##■', '##▪', '##●', '##★', '##☆', '##☉', '##♠', '##♣', '##♥', '##♦', '##♯', '##⟨', '##⟩', '##ⱼ', '##⺩', '##⺼', '##⽥', '##、', '##。', '##〈', '##〉', '##《', '##》', '##「', '##」', '##『', '##』', '##〜', '##あ', '##い', '##う', '##え', '##お', '##か', '##き', '##く', '##け', '##こ', '##さ', '##し', '##す', '##せ', '##そ', '##た', '##ち', '##っ', '##つ', '##て', '##と', '##な', '##に', '##ぬ', '##ね', '##の', '##は', '##ひ', '##ふ', '##へ', '##ほ', '##ま', '##み', '##む', '##め', '##も', '##や', '##ゆ', '##よ', '##ら', '##り', '##る', '##れ', '##ろ', '##を', '##ん', '##ァ', '##ア', '##ィ', '##イ', '##ウ', '##ェ', '##エ', '##オ', '##カ', '##キ', '##ク', '##ケ', '##コ', '##サ', '##シ', '##ス', '##セ', '##タ', '##チ', '##ッ', '##ツ', '##テ', '##ト', '##ナ', '##ニ', '##ノ', '##ハ', '##ヒ', '##フ', '##ヘ', '##ホ', '##マ', '##ミ', '##ム', '##メ', '##モ', '##ャ', '##ュ', '##ョ', '##ラ', '##リ', '##ル', '##レ', '##ロ', '##ワ', '##ン', '##・', '##ー', '##一', '##三', '##上', '##下', '##不', '##世', '##中', '##主', '##久', '##之', '##也', '##事', '##二', '##五', '##井', '##京', '##人', '##亻', '##仁', '##介', '##代', '##仮', '##伊', '##会', '##佐', '##侍', '##保', '##信', '##健', '##元', '##光', '##八', '##公', '##内', '##出', '##分', '##前', '##劉', '##力', '##加', '##勝', '##北', '##区', '##十', '##千', '##南', '##博', '##原', '##口', '##古', '##史', '##司', '##合', '##吉', '##同', '##名', '##和', '##囗', '##四', '##国', '##國', '##土', '##地', '##坂', '##城', '##堂', '##場', '##士', '##夏', '##外', '##大', '##天', '##太', '##夫', '##奈', '##女', '##子', '##学', '##宀', '##宇', '##安', '##宗', '##定', '##宣', '##宮', '##家', '##宿', '##寺', '##將', '##小', '##尚', '##山', '##岡', '##島', '##崎', '##川', '##州', '##巿', '##帝', '##平', '##年', '##幸', '##广', '##弘', '##張', '##彳', '##後', '##御', '##德', '##心', '##忄', '##志', '##忠', '##愛', '##成', '##我', '##戦', '##戸', '##手', '##扌', '##政', '##文', '##新', '##方', '##日', '##明', '##星', '##春', '##昭', '##智', '##曲', '##書', '##月', '##有', '##朝', '##木', '##本', '##李', '##村', '##東', '##松', '##林', '##森', '##楊', '##樹', '##橋', '##歌', '##止', '##正', '##武', '##比', '##氏', '##民', '##水', '##氵', '##氷', '##永', '##江', '##沢', '##河', '##治', '##法', '##海', '##清', '##漢', '##瀬', '##火', '##版', '##犬', '##王', '##生', '##田', '##男', '##疒', '##発', '##白', '##的', '##皇', '##目', '##相', '##省', '##真', '##石', '##示', '##社', '##神', '##福', '##禾', '##秀', '##秋', '##空', '##立', '##章', '##竹', '##糹', '##美', '##義', '##耳', '##良', '##艹', '##花', '##英', '##華', '##葉', '##藤', '##行', '##街', '##西', '##見', '##訁', '##語', '##谷', '##貝', '##貴', '##車', '##軍', '##辶', '##道', '##郎', '##郡', '##部', '##都', '##里', '##野', '##金', '##鈴', '##镇', '##長', '##門', '##間', '##阝', '##阿', '##陳', '##陽', '##雄', '##青', '##面', '##風', '##食', '##香', '##馬', '##高', '##龍', '##龸', '##ﬁ', '##ﬂ', '##！', '##（', '##）', '##，', '##－', '##．', '##／', '##：', '##？', '##～'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.vocab.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.889378Z",
     "start_time": "2020-05-08T03:05:59.887031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[CLS] [UNK] 埂 埂 [UNK] 国 [UNK] [SEP]'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zh_tokenizer.decode(tokenizer.encode(\"今天天气不错\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.892281Z",
     "start_time": "2020-05-08T03:05:59.890081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "791"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zh_tokenizer.vocab[\"今\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分词"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据给定的词汇表，如何将原始文本转换成满足模型输入形式的数据?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 词汇表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.895709Z",
     "start_time": "2020-05-08T03:05:59.893138Z"
    }
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "def load_vocab(vocab_file):\n",
    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
    "    vocab = collections.OrderedDict()\n",
    "    index = 0\n",
    "    with tf.io.gfile.GFile(vocab_file, \"r\") as reader:\n",
    "        while True:\n",
    "            token = convert_to_unicode(reader.readline())\n",
    "            if not token:\n",
    "                break\n",
    "            token = token.strip()\n",
    "            vocab[token] = index\n",
    "            index += 1\n",
    "    return vocab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分词器\n",
    "- 首先将文本按单词级别进行拆分，BasicTokenizer\n",
    "- 然后将拆分到的单词，拆分成子词语，WordpieceTokenizer\n",
    "\n",
    "    ```\n",
    "    text:\n",
    "    --> convert_to_unicode\n",
    "    --> _clean_text\n",
    "    --> _tokenize_chinese_chars:          # 中文字符两边添加空格\n",
    "    --> whitespace_tokenize               # 基于空格拆分单词\n",
    "\n",
    "    token 列表，对于每个 token：\n",
    "    --> _run_strip_accents                # 删除重音符号\n",
    "    --> do_lower_case\n",
    "    --> _run_split_on_punc                # 拆分标点符号\n",
    "\n",
    "    token 列表，对于每个 token，前向最大匹配拆分 子词：\n",
    "    --> wordpiece                         # 子词\n",
    "    ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.899403Z",
     "start_time": "2020-05-08T03:05:59.896432Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert_to_unicode(text):\n",
    "    \"\"\"转换成 Unicode\"\"\"\n",
    "    if six.PY3:\n",
    "        if isinstance(text, str):\n",
    "            return text\n",
    "        elif isinstance(text, bytes):\n",
    "            return text.decode(\"utf-8\", \"ignore\")\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n",
    "    elif six.PY2:\n",
    "        if isinstance(text, str):\n",
    "            return text.decode(\"utf-8\", \"ignore\")\n",
    "        elif isinstance(text, unicode):\n",
    "            return text\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported string type: %s\" % (type(text)))\n",
    "    else:\n",
    "        raise ValueError(\"Not running on Python2 or Python 3?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.903284Z",
     "start_time": "2020-05-08T03:05:59.900052Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert_by_vocab(vocab, items):\n",
    "    \"\"\"Converts a sequence of [tokens|ids] using the vocab.\"\"\"\n",
    "    output = []\n",
    "    for item in items:\n",
    "        output.append(vocab[item])\n",
    "    return output\n",
    "\n",
    "\n",
    "def convert_tokens_to_ids(vocab, tokens):\n",
    "    return convert_by_vocab(vocab, tokens)\n",
    "\n",
    "\n",
    "def convert_ids_to_tokens(inv_vocab, ids):\n",
    "    return convert_by_vocab(inv_vocab, ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.906428Z",
     "start_time": "2020-05-08T03:05:59.903940Z"
    }
   },
   "outputs": [],
   "source": [
    "def whitespace_tokenize(text):\n",
    "    \"\"\"按照空格，进行分词\"\"\"\n",
    "    text = text.strip()\n",
    "    if not text:\n",
    "        return []\n",
    "    tokens = text.split()\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.911215Z",
     "start_time": "2020-05-08T03:05:59.907174Z"
    }
   },
   "outputs": [],
   "source": [
    "class FullTokenizer(object):\n",
    "    \"\"\"Runs end-to-end tokenziation.\"\"\"\n",
    "    def __init__(self, vocab_file, do_lower_case=True):\n",
    "        self.vocab = load_vocab(vocab_file)\n",
    "        self.inv_vocab = {v: k for k, v in self.vocab.items()}\n",
    "        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n",
    "        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        # 先拆分成 单词，再将单词拆分成 子词\n",
    "        split_tokens = []\n",
    "        for token in self.basic_tokenizer.tokenize(text):\n",
    "            for sub_token in self.wordpiece_tokenizer.tokenize(token):\n",
    "                split_tokens.append(sub_token)\n",
    "\n",
    "        return split_tokens\n",
    "\n",
    "    def convert_tokens_to_ids(self, tokens):\n",
    "        # 标记 转换成 id \n",
    "        return convert_by_vocab(self.vocab, tokens)\n",
    "\n",
    "    def convert_ids_to_tokens(self, ids):\n",
    "        # id 转换成 标记\n",
    "        return convert_by_vocab(self.inv_vocab, ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.920902Z",
     "start_time": "2020-05-08T03:05:59.911930Z"
    }
   },
   "outputs": [],
   "source": [
    "class BasicTokenizer(object):\n",
    "    \"\"\"Runs basic tokenization (punctuation splitting, lower casing, etc.).\"\"\"\n",
    "    def __init__(self, do_lower_case=True):\n",
    "        \"\"\"Constructs a BasicTokenizer.\n",
    "\n",
    "    Args:\n",
    "      do_lower_case: Whether to lower case the input.\n",
    "    \"\"\"\n",
    "        self.do_lower_case = do_lower_case\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        \"\"\"Tokenizes a piece of text.\"\"\"\n",
    "        text = convert_to_unicode(text)  # 转换成 Unicode\n",
    "        text = self._clean_text(text)  # 文本清洗，删除空格\n",
    "\n",
    "        # This was added on November 1st, 2018 for the multilingual and Chinese\n",
    "        # models. This is also applied to the English models now, but it doesn't\n",
    "        # matter since the English models were not trained on any Chinese data\n",
    "        # and generally don't have any Chinese data in them (there are Chinese\n",
    "        # characters in the vocabulary because Wikipedia does have some Chinese\n",
    "        # words in the English Wikipedia.).\n",
    "        text = self._tokenize_chinese_chars(text) # 拆分中文字\n",
    "\n",
    "        orig_tokens = whitespace_tokenize(text) # 按空格拆分\n",
    "        split_tokens = []\n",
    "        for token in orig_tokens:\n",
    "            if self.do_lower_case:\n",
    "                token = token.lower()\n",
    "                token = self._run_strip_accents(token)\n",
    "            split_tokens.extend(self._run_split_on_punc(token))\n",
    "\n",
    "        output_tokens = whitespace_tokenize(\" \".join(split_tokens))\n",
    "        return output_tokens\n",
    "\n",
    "    def _run_strip_accents(self, text):\n",
    "        \"\"\"Strips accents from a piece of text.\"\"\"\n",
    "        text = unicodedata.normalize(\"NFD\", text)\n",
    "        output = []\n",
    "        for char in text:\n",
    "            cat = unicodedata.category(char)\n",
    "            if cat == \"Mn\":\n",
    "                continue\n",
    "            output.append(char)\n",
    "        return \"\".join(output)\n",
    "\n",
    "    def _run_split_on_punc(self, text):\n",
    "        \"\"\"按照标点符号拆分，将文本拆分成子句\"\"\"\n",
    "        chars = list(text)\n",
    "        i = 0\n",
    "        start_new_word = True\n",
    "        output = []\n",
    "        while i < len(chars):\n",
    "            char = chars[i]\n",
    "            if _is_punctuation(char):\n",
    "                output.append([char])\n",
    "                start_new_word = True\n",
    "            else:\n",
    "                if start_new_word:\n",
    "                    output.append([])\n",
    "                start_new_word = False\n",
    "                output[-1].append(char)\n",
    "            i += 1\n",
    "\n",
    "        return [\"\".join(x) for x in output]\n",
    "\n",
    "    def _tokenize_chinese_chars(self, text):\n",
    "        \"\"\"在每个中文字符两边添加空格\"\"\"\n",
    "        output = []\n",
    "        for char in text:\n",
    "            cp = ord(char)\n",
    "            if self._is_chinese_char(cp):\n",
    "                output.append(\" \")\n",
    "                output.append(char)\n",
    "                output.append(\" \")\n",
    "            else:\n",
    "                output.append(char)\n",
    "        return \"\".join(output)\n",
    "\n",
    "    def _is_chinese_char(self, cp):\n",
    "        \"\"\"Checks whether CP is the codepoint of a CJK character.\"\"\"\n",
    "        # This defines a \"chinese character\" as anything in the CJK Unicode block:\n",
    "        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)\n",
    "        \n",
    "        # 中文字符定义\n",
    "        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #\n",
    "            (cp >= 0x3400 and cp <= 0x4DBF) or  #\n",
    "            (cp >= 0x20000 and cp <= 0x2A6DF) or  #\n",
    "            (cp >= 0x2A700 and cp <= 0x2B73F) or  #\n",
    "            (cp >= 0x2B740 and cp <= 0x2B81F) or  #\n",
    "            (cp >= 0x2B820 and cp <= 0x2CEAF) or\n",
    "            (cp >= 0xF900 and cp <= 0xFAFF) or  #\n",
    "            (cp >= 0x2F800 and cp <= 0x2FA1F)):  #\n",
    "            return True\n",
    "\n",
    "        return False\n",
    "\n",
    "    def _clean_text(self, text):\n",
    "        \"\"\"删除空格及一些特殊字符\"\"\"\n",
    "        output = []\n",
    "        for char in text:\n",
    "            cp = ord(char)\n",
    "            if cp == 0 or cp == 0xfffd or _is_control(char):\n",
    "                continue\n",
    "            if _is_whitespace(char):\n",
    "                output.append(\" \")\n",
    "            else:\n",
    "                output.append(char)\n",
    "        return \"\".join(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.926595Z",
     "start_time": "2020-05-08T03:05:59.921673Z"
    }
   },
   "outputs": [],
   "source": [
    "class WordpieceTokenizer(object):\n",
    "    \"\"\"Runs WordPiece tokenziation.\"\"\"\n",
    "    def __init__(self, vocab, unk_token=\"[UNK]\", max_input_chars_per_word=200):\n",
    "        self.vocab = vocab\n",
    "        self.unk_token = unk_token\n",
    "        self.max_input_chars_per_word = max_input_chars_per_word\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        \"\"\"Tokenizes a piece of text into its word pieces.\n",
    "\n",
    "    This uses a greedy longest-match-first algorithm to perform tokenization\n",
    "    using the given vocabulary.\n",
    "\n",
    "    For example:\n",
    "      input = \"unaffable\"\n",
    "      output = [\"un\", \"##aff\", \"##able\"]\n",
    "\n",
    "    Args:\n",
    "      text: A single token or whitespace separated tokens. This should have\n",
    "        already been passed through `BasicTokenizer.\n",
    "\n",
    "    Returns:\n",
    "      A list of wordpiece tokens.\n",
    "    \"\"\"\n",
    "\n",
    "        text = convert_to_unicode(text)\n",
    "\n",
    "        output_tokens = []\n",
    "        for token in whitespace_tokenize(text):\n",
    "            chars = list(token)\n",
    "            \n",
    "            # 字符数超过最大值，替换成 [UNK]\n",
    "            if len(chars) > self.max_input_chars_per_word:\n",
    "                output_tokens.append(self.unk_token)\n",
    "                continue\n",
    "\n",
    "            is_bad = False\n",
    "            start = 0\n",
    "            sub_tokens = []\n",
    "            \n",
    "            # 最大前向匹配获取子词\n",
    "            while start < len(chars):\n",
    "                end = len(chars)\n",
    "                cur_substr = None\n",
    "                while start < end:\n",
    "                    substr = \"\".join(chars[start:end])\n",
    "                    if start > 0:\n",
    "                        substr = \"##\" + substr\n",
    "                        \n",
    "                    # 子词是否在词汇表中\n",
    "                    if substr in self.vocab:\n",
    "                        cur_substr = substr\n",
    "                        break\n",
    "                    end -= 1\n",
    "                if cur_substr is None:\n",
    "                    is_bad = True\n",
    "                    break\n",
    "                sub_tokens.append(cur_substr)\n",
    "                start = end\n",
    "\n",
    "            if is_bad:\n",
    "                output_tokens.append(self.unk_token)\n",
    "            else:\n",
    "                output_tokens.extend(sub_tokens)\n",
    "        return output_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字符类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.931436Z",
     "start_time": "2020-05-08T03:05:59.929331Z"
    }
   },
   "outputs": [],
   "source": [
    "def _is_whitespace(char):\n",
    "    \"\"\"Checks whether `chars` is a whitespace character.\"\"\"\n",
    "    # \\t, \\n, and \\r are technically contorl characters but we treat them\n",
    "    # as whitespace since they are generally considered as such.\n",
    "    if char == \" \" or char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n",
    "        return True\n",
    "    cat = unicodedata.category(char)\n",
    "    if cat == \"Zs\":\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.935194Z",
     "start_time": "2020-05-08T03:05:59.933031Z"
    }
   },
   "outputs": [],
   "source": [
    "def _is_control(char):\n",
    "    \"\"\"Checks whether `chars` is a control character.\"\"\"\n",
    "    # These are technically control characters but we count them as whitespace\n",
    "    # characters.\n",
    "    if char == \"\\t\" or char == \"\\n\" or char == \"\\r\":\n",
    "        return False\n",
    "    cat = unicodedata.category(char)\n",
    "    if cat in (\"Cc\", \"Cf\"):\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.938629Z",
     "start_time": "2020-05-08T03:05:59.936060Z"
    }
   },
   "outputs": [],
   "source": [
    "def _is_punctuation(char):\n",
    "    \"\"\"Checks whether `chars` is a punctuation character.\"\"\"\n",
    "    cp = ord(char)\n",
    "    # We treat all non-letter/number ASCII as punctuation.\n",
    "    # Characters such as \"^\", \"$\", and \"`\" are not in the Unicode\n",
    "    # Punctuation class but we treat them as punctuation anyways, for\n",
    "    # consistency.\n",
    "    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64)\n",
    "            or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):\n",
    "        return True\n",
    "    cat = unicodedata.category(char)\n",
    "    if cat.startswith(\"P\"):\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:05:59.961078Z",
     "start_time": "2020-05-08T03:05:59.939527Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "........s.\n",
      "----------------------------------------------------------------------\n",
      "Ran 10 tests in 0.005s\n",
      "\n",
      "OK (skipped=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.main.TestProgram at 0x7f577ac5bcd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import tempfile\n",
    "import six\n",
    "import unicodedata\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "class TokenizationTest(tf.test.TestCase):\n",
    "    \"\"\"Tokenization test.\n",
    "\n",
    "    The implementation is forked from\n",
    "    https://github.com/google-research/bert/blob/master/tokenization_test.py.\"\n",
    "  \"\"\"\n",
    "    def test_full_tokenizer(self):\n",
    "        vocab_tokens = [\n",
    "            \"[UNK]\", \"[CLS]\", \"[SEP]\", \"want\", \"##want\", \"##ed\", \"wa\", \"un\",\n",
    "            \"runn\", \"##ing\", \",\"\n",
    "        ]\n",
    "        with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:\n",
    "            if six.PY2:\n",
    "                vocab_writer.write(\"\".join([x + \"\\n\" for x in vocab_tokens]))\n",
    "            else:\n",
    "                vocab_writer.write(\"\".join([x + \"\\n\" for x in vocab_tokens\n",
    "                                            ]).encode(\"utf-8\"))\n",
    "\n",
    "            vocab_file = vocab_writer.name\n",
    "\n",
    "        tokenizer = FullTokenizer(vocab_file)\n",
    "        os.unlink(vocab_file)\n",
    "\n",
    "        tokens = tokenizer.tokenize(u\"UNwant\\u00E9d,running\")\n",
    "        self.assertAllEqual(tokens,\n",
    "                            [\"un\", \"##want\", \"##ed\", \",\", \"runn\", \"##ing\"])\n",
    "\n",
    "        self.assertAllEqual(tokenizer.convert_tokens_to_ids(tokens),\n",
    "                            [7, 4, 5, 10, 8, 9])\n",
    "\n",
    "    def test_chinese(self):\n",
    "        tokenizer = BasicTokenizer()\n",
    "\n",
    "        self.assertAllEqual(tokenizer.tokenize(u\"ah\\u535A\\u63A8zz\"),\n",
    "                            [u\"ah\", u\"\\u535A\", u\"\\u63A8\", u\"zz\"])\n",
    "\n",
    "    def test_basic_tokenizer_lower(self):\n",
    "        tokenizer = BasicTokenizer(do_lower_case=True)\n",
    "\n",
    "        self.assertAllEqual(tokenizer.tokenize(u\" \\tHeLLo!how  \\n Are yoU?  \"),\n",
    "                            [\"hello\", \"!\", \"how\", \"are\", \"you\", \"?\"])\n",
    "        self.assertAllEqual(tokenizer.tokenize(u\"H\\u00E9llo\"), [\"hello\"])\n",
    "\n",
    "    def test_basic_tokenizer_no_lower(self):\n",
    "        tokenizer = BasicTokenizer(do_lower_case=False)\n",
    "\n",
    "        self.assertAllEqual(tokenizer.tokenize(u\" \\tHeLLo!how  \\n Are yoU?  \"),\n",
    "                            [\"HeLLo\", \"!\", \"how\", \"Are\", \"yoU\", \"?\"])\n",
    "\n",
    "    def test_wordpiece_tokenizer(self):\n",
    "        vocab_tokens = [\n",
    "            \"[UNK]\", \"[CLS]\", \"[SEP]\", \"want\", \"##want\", \"##ed\", \"wa\", \"un\",\n",
    "            \"runn\", \"##ing\"\n",
    "        ]\n",
    "\n",
    "        vocab = {}\n",
    "        for (i, token) in enumerate(vocab_tokens):\n",
    "            vocab[token] = i\n",
    "        tokenizer = WordpieceTokenizer(vocab=vocab)\n",
    "\n",
    "        self.assertAllEqual(tokenizer.tokenize(\"\"), [])\n",
    "\n",
    "        self.assertAllEqual(tokenizer.tokenize(\"unwanted running\"),\n",
    "                            [\"un\", \"##want\", \"##ed\", \"runn\", \"##ing\"])\n",
    "\n",
    "        self.assertAllEqual(tokenizer.tokenize(\"unwantedX running\"),\n",
    "                            [\"[UNK]\", \"runn\", \"##ing\"])\n",
    "\n",
    "    def test_convert_tokens_to_ids(self):\n",
    "        vocab_tokens = [\n",
    "            \"[UNK]\", \"[CLS]\", \"[SEP]\", \"want\", \"##want\", \"##ed\", \"wa\", \"un\",\n",
    "            \"runn\", \"##ing\"\n",
    "        ]\n",
    "\n",
    "        vocab = {}\n",
    "        for (i, token) in enumerate(vocab_tokens):\n",
    "            vocab[token] = i\n",
    "\n",
    "        self.assertAllEqual(\n",
    "            convert_tokens_to_ids(vocab,\n",
    "                                  [\"un\", \"##want\", \"##ed\", \"runn\", \"##ing\"]),\n",
    "            [7, 4, 5, 8, 9])\n",
    "\n",
    "    def test_is_whitespace(self):\n",
    "        self.assertTrue(_is_whitespace(u\" \"))\n",
    "        self.assertTrue(_is_whitespace(u\"\\t\"))\n",
    "        self.assertTrue(_is_whitespace(u\"\\r\"))\n",
    "        self.assertTrue(_is_whitespace(u\"\\n\"))\n",
    "        self.assertTrue(_is_whitespace(u\"\\u00A0\"))\n",
    "\n",
    "        self.assertFalse(_is_whitespace(u\"A\"))\n",
    "        self.assertFalse(_is_whitespace(u\"-\"))\n",
    "\n",
    "    def test_is_control(self):\n",
    "        self.assertTrue(_is_control(u\"\\u0005\"))\n",
    "\n",
    "        self.assertFalse(_is_control(u\"A\"))\n",
    "        self.assertFalse(_is_control(u\" \"))\n",
    "        self.assertFalse(_is_control(u\"\\t\"))\n",
    "        self.assertFalse(_is_control(u\"\\r\"))\n",
    "        self.assertFalse(_is_control(u\"\\U0001F4A9\"))\n",
    "\n",
    "    def test_is_punctuation(self):\n",
    "        self.assertTrue(_is_punctuation(u\"-\"))\n",
    "        self.assertTrue(_is_punctuation(u\"$\"))\n",
    "        self.assertTrue(_is_punctuation(u\"`\"))\n",
    "        self.assertTrue(_is_punctuation(u\".\"))\n",
    "\n",
    "        self.assertFalse(_is_punctuation(u\"A\"))\n",
    "        self.assertFalse(_is_punctuation(u\" \"))\n",
    "\n",
    "\n",
    "import unittest\n",
    "unittest.main(argv=['first-arg-is-ignored'], exit=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建预训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:06:07.953711Z",
     "start_time": "2020-05-08T03:06:07.948805Z"
    }
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import random \n",
    "from absl import app\n",
    "from absl import flags\n",
    "from absl import logging\n",
    "import tensorflow as tf\n",
    "\n",
    "FLAGS = flags.FLAGS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义数据实例\n",
    "bert 预训练两个任务：\n",
    "- `MaskLM`，输入的话中随机选择一些词，用特殊符号 `[MASK]` 替换，让模型预测这些词\n",
    "- `Next Sentence Prediction`，输入的两句话，让模型判断是否是连续的两句话\n",
    "\n",
    "\n",
    "因此最终的数据实例包含：\n",
    "- `input_ids` ： 输入标记的 id 列表\n",
    "- `input_mask` ：记录输入标记是真实token，还是处理成等长时，在末尾填充的 0\n",
    "- `segment_ids` ：每个标记是属于句子 a, 还是句子 b\n",
    "- `masked_lm_positions` ：被遮挡的 token 的位置\n",
    "- `masked_lm_ids` ： 被遮挡待预测的 token 的索引。`MaskLM` 任务的标签\n",
    "- `masked_lm_weights` ： 被遮挡的 token 的权重\n",
    "- `next_sentence_labels` ： 句子 a-b 是真实连续的句子，还是不是？`NSP`分类任务的标签\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T07:29:22.619157Z",
     "start_time": "2020-05-08T07:29:22.613131Z"
    }
   },
   "outputs": [],
   "source": [
    "class TMP():\n",
    "    def printable_text(self, x):\n",
    "        if isinstance(x, str):\n",
    "            return x\n",
    "        elif isinstance(x, bytes):\n",
    "            return x.decode(\"utf-8\", \"ignore\")\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported string type: %s\" % (type(x)))\n",
    "\n",
    "\n",
    "tokenization = TMP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T07:29:23.230331Z",
     "start_time": "2020-05-08T07:29:23.223405Z"
    }
   },
   "outputs": [],
   "source": [
    "class TrainingInstance:\n",
    "    def __init__(self, tokens, segment_ids, masked_lm_positions,\n",
    "                 masked_lm_labels, is_random_next):\n",
    "        self.tokens = tokens\n",
    "        self.segment_ids = segment_ids\n",
    "        self.is_random_next = is_random_next\n",
    "        self.masked_lm_positions = masked_lm_positions\n",
    "        self.masked_lm_labels = masked_lm_labels\n",
    "\n",
    "    def __str__(self):\n",
    "        s = \"\"\n",
    "        s += \"tokens: %s\\n\" % (\" \".join(\n",
    "            [tokenization.printable_text(x) for x in self.tokens]))\n",
    "        s += \"segment_ids: %s\\n\" % (\" \".join(\n",
    "            [str(x) for x in self.segment_ids]))\n",
    "        s += \"is_random_next: %s\\n\" % self.is_random_next\n",
    "        s += \"masked_lm_positions: %s\\n\" % (\" \".join(\n",
    "            [str(x) for x in self.masked_lm_positions]))\n",
    "        s += \"masked_lm_labels: %s\\n\" % (\" \".join(\n",
    "            [tokenization.printable_text(x) for x in self.masked_lm_labels]))\n",
    "        s += \"\\n\"\n",
    "        return s\n",
    "\n",
    "    def __repr__(self):\n",
    "        return self.__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T07:21:14.879771Z",
     "start_time": "2020-05-08T07:21:14.873806Z"
    }
   },
   "outputs": [],
   "source": [
    "# 创建 tensorflow 特征\n",
    "\n",
    "def create_int_feature(values):\n",
    "    feature = tf.train.Feature(int64_list=tf.train.Int64List(\n",
    "        value=list(values)))\n",
    "    return feature\n",
    "\n",
    "\n",
    "def create_float_feature(values):\n",
    "    feature = tf.train.Feature(float_list=tf.train.FloatList(\n",
    "        value=list(values)))\n",
    "    return feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T07:21:18.061679Z",
     "start_time": "2020-05-08T07:21:18.050850Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建数据实例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建遮挡单词序列\n",
    "- 从 token 列表中选择一定比例的 token，作为待预测的数据\n",
    "- 记录被遮挡后的 token 列表，遮挡的位置，遮挡处的 token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:06:13.181615Z",
     "start_time": "2020-05-08T03:06:13.171612Z"
    }
   },
   "outputs": [],
   "source": [
    "MaskedLmInstance = collections.namedtuple(\"MaskedLmInstance\",\n",
    "                                          [\"index\", \"label\"])\n",
    "\n",
    "def create_masked_lm_predictions(tokens, masked_lm_prob,\n",
    "                                 max_predictions_per_seq, vocab_words, rng):\n",
    "    \"\"\"Creates the predictions for the masked LM objective.\"\"\"\n",
    "    \n",
    "    # 输入为添加了 [CLS] 和 [SEP] 的 tokens 索引列表\n",
    "    # 可以被选择的 token 列表\n",
    "    cand_indexes = []\n",
    "    for (i, token) in enumerate(tokens):\n",
    "        if token == \"[CLS]\" or token == \"[SEP]\":\n",
    "            continue\n",
    "        # Whole Word Masking means that if we mask all of the wordpieces\n",
    "        # corresponding to an original word. When a word has been split into\n",
    "        # WordPieces, the first token does not have any marker and any subsequence\n",
    "        # tokens are prefixed with ##. So whenever we see the ## token, we\n",
    "        # append it to the previous set of word indexes.\n",
    "        #\n",
    "        # Note that Whole Word Masking does *not* change the training code\n",
    "        # at all -- we still predict each WordPiece independently, softmaxed\n",
    "        # over the entire vocabulary.\n",
    "        # 全词预测，单词被拆分成子词后，若任一子词被遮挡，该单词所有子词都同时进行遮挡待预测\n",
    "        if (FLAGS.do_whole_word_mask and len(\n",
    "                cand_indexes) >= 1 and token.startswith(\"##\")):\n",
    "            cand_indexes[-1].append(i)\n",
    "        else:\n",
    "            cand_indexes.append([i])\n",
    "    \n",
    "    rng.shuffle(cand_indexes)\n",
    "    \n",
    "    output_tokens = list(tokens)\n",
    "    \n",
    "    # 被遮挡待预测的 token 数量\n",
    "    num_to_predict = min(max_predictions_per_seq,\n",
    "                         max(1, int(round(len(tokens) * masked_lm_prob))))\n",
    "    \n",
    "    masked_lms = []\n",
    "    covered_indexes = set()\n",
    "    for index_set in cand_indexes:\n",
    "        if len(masked_lms) >= num_to_predict:\n",
    "            break\n",
    "        # If adding a whole-word mask would exceed the maximum number of\n",
    "        # predictions, then just skip this candidate.\n",
    "        \n",
    "        # 当 子词列表 + 已选择token 的数量过多时，跳过该子词列表\n",
    "        if len(masked_lms) + len(index_set) > num_to_predict:\n",
    "            continue\n",
    "        is_any_index_covered = False\n",
    "        for index in index_set:\n",
    "            if index in covered_indexes:\n",
    "                is_any_index_covered = True\n",
    "                break\n",
    "        if is_any_index_covered:\n",
    "            continue\n",
    "        for index in index_set:\n",
    "            covered_indexes.add(index)\n",
    "            \n",
    "            masked_token = None\n",
    "            \n",
    "            # 80% 概率，将被选中的token，替换成 [MASK]\n",
    "            if rng.random() < 0.8:\n",
    "                masked_token = \"[MASK]\"\n",
    "            else:\n",
    "                # 10% 概率 不变\n",
    "                if rng.random() < 0.5:\n",
    "                    masked_token = tokens[index]\n",
    "                # 10% 概率 替换成 随机单词\n",
    "                else:\n",
    "                    masked_token = vocab_words[\n",
    "                        rng.randint(0, len(vocab_words) - 1)]\n",
    "            \n",
    "            output_tokens[index] = masked_token\n",
    "            \n",
    "            masked_lms.append(\n",
    "                MaskedLmInstance(index=index, label=tokens[index]))\n",
    "    assert len(masked_lms) <= num_to_predict\n",
    "    masked_lms = sorted(masked_lms, key=lambda x: x.index)\n",
    "    \n",
    "    masked_lm_positions = []\n",
    "    masked_lm_labels = []\n",
    "    for p in masked_lms:\n",
    "        masked_lm_positions.append(p.index)\n",
    "        masked_lm_labels.append(p.label)\n",
    "    \n",
    "    # 输出被遮挡后的token列表，被遮挡的位置，被遮挡的token\n",
    "    return (output_tokens, masked_lm_positions, masked_lm_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T03:48:00.464638Z",
     "start_time": "2020-05-08T03:48:00.456311Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokens before mask:\n",
      " [CLS] con ##vo ##lu ##tion ##al neural networks is very interesting and powerful . [SEP]\n",
      "================================================================================\n",
      "Tokens after mask:\n",
      " [CLS] con ##vo ##lu ##tion ##al neural [MASK] is very [MASK] [MASK] powerful [MASK] [SEP]\n",
      "7th token:networks --> [MASK]\n",
      "8th token:is --> is\n",
      "10th token:interesting --> [MASK]\n",
      "11th token:and --> [MASK]\n",
      "13th token:. --> [MASK]\n"
     ]
    }
   ],
   "source": [
    "def test_create_masked_lm():\n",
    "    class TMP:\n",
    "        def __init__(self):\n",
    "            pass\n",
    "\n",
    "    global FLAGS\n",
    "    FLAGS = TMP()\n",
    "    FLAGS.do_whole_word_mask = True\n",
    "\n",
    "    text = \"Convolutional neural networks is very interesting and powerful.\"\n",
    "    token_ids = tokenizer.encode(text)\n",
    "    tokens = tokenizer.convert_ids_to_tokens(token_ids)\n",
    "    print(\"Tokens before mask:\\n\", ' '.join(tokens))\n",
    "    output_tokens, masked_lm_positions, masked_lm_labels = create_masked_lm_predictions(\n",
    "        tokens=tokens,\n",
    "        masked_lm_prob=0.4,\n",
    "        max_predictions_per_seq=5,\n",
    "        vocab_words=list(tokenizer.vocab.keys()),\n",
    "        rng=random.Random(random.randint(1, 1000)),\n",
    "    )\n",
    "    print(\"=\" * 80)\n",
    "    print(\"Tokens after mask:\\n\", ' '.join(output_tokens))\n",
    "    for ind in masked_lm_positions:\n",
    "        print(f\"{ind}th token:{tokens[ind]} --> {output_tokens[ind]}\")\n",
    "\n",
    "\n",
    "test_create_masked_lm()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从单个文本中创建训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T07:07:30.839134Z",
     "start_time": "2020-05-08T07:07:30.832891Z"
    }
   },
   "outputs": [],
   "source": [
    "# 控制句子对的总长度\n",
    "# 并保持，每个句子单词数的平衡\n",
    "\n",
    "\n",
    "def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):\n",
    "    \"\"\"Truncates a pair of sequences to a maximum sequence length.\"\"\"\n",
    "    while True:\n",
    "        total_length = len(tokens_a) + len(tokens_b)\n",
    "        if total_length <= max_num_tokens:\n",
    "            break\n",
    "\n",
    "        trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b\n",
    "        assert len(trunc_tokens) >= 1\n",
    "\n",
    "        # We want to sometimes truncate from the front and sometimes from the\n",
    "        # back to add more randomness and avoid biases.\n",
    "        if rng.random() < 0.5:\n",
    "            del trunc_tokens[0]\n",
    "        else:\n",
    "            trunc_tokens.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T07:07:31.556380Z",
     "start_time": "2020-05-08T07:07:31.549998Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq pair before trancating:\n",
      " ['deep', 'learning', 'is', 'interesting'],['and', 'con', '##vo', '##lu', '##tion', '##al', 'neural', 'networks', 'is', 'very', 'powerful', 'for', 'images', '.']\n",
      "Seq pair after trancating:\n",
      " ['deep', 'learning', 'is', 'interesting'],['##tion', '##al', 'neural', 'networks', 'is', 'very']\n"
     ]
    }
   ],
   "source": [
    "def test_truncate_seq_pair():\n",
    "    text_a = \"Deep learning is interesting\"\n",
    "    text_b = \"and convolutional neural networks is very powerful for images.\"\n",
    "    tokens_a = tokenizer.tokenize(text_a)\n",
    "    tokens_b = tokenizer.tokenize(text_b)\n",
    "    print(f\"Seq pair before trancating:\\n {tokens_a},{tokens_b}\")\n",
    "    truncate_seq_pair(tokens_a,\n",
    "                      tokens_b,\n",
    "                      max_num_tokens=10,\n",
    "                      rng=random.Random(42))\n",
    "    print(f\"Seq pair after trancating:\\n {tokens_a},{tokens_b}\")\n",
    "\n",
    "\n",
    "test_truncate_seq_pair()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T08:03:17.159876Z",
     "start_time": "2020-05-08T08:03:17.140773Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_instances_from_document(all_documents, document_index,\n",
    "                                   max_seq_length, short_seq_prob,\n",
    "                                   masked_lm_prob, max_predictions_per_seq,\n",
    "                                   vocab_words, rng):\n",
    "    \"\"\"Creates `TrainingInstance`s for a single document.\"\"\"\n",
    "    document = all_documents[document_index]\n",
    "\n",
    "    # Account for [CLS], [SEP], [SEP]\n",
    "    max_num_tokens = max_seq_length - 3\n",
    "\n",
    "    # We *usually* want to fill up the entire sequence since we are padding\n",
    "    # to `max_seq_length` anyways, so short sequences are generally wasted\n",
    "    # computation. However, we *sometimes*\n",
    "    # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter\n",
    "    # sequences to minimize the mismatch between pre-training and fine-tuning.\n",
    "    # The `target_seq_length` is just a rough target however, whereas\n",
    "    # `max_seq_length` is a hard limit.\n",
    "    target_seq_length = max_num_tokens\n",
    "    if rng.random() < short_seq_prob:\n",
    "        target_seq_length = rng.randint(2, max_num_tokens)\n",
    "\n",
    "    # We DON'T just concatenate all of the tokens from a document into a long\n",
    "    # sequence and choose an arbitrary split point because this would make the\n",
    "    # next sentence prediction task too easy. Instead, we split the input into\n",
    "    # segments \"A\" and \"B\" based on the actual \"sentences\" provided by the user\n",
    "    # input.\n",
    "    instances = []\n",
    "    current_chunk = []\n",
    "    current_length = 0\n",
    "    i = 0\n",
    "    while i < len(document):\n",
    "        \n",
    "        # 依次选择 target_seq_length 个标记添加到 current_chunk 中\n",
    "        segment = document[i]\n",
    "        current_chunk.append(segment)\n",
    "        current_length += len(segment) \n",
    "        \n",
    "        # 将 current_chunk 拆分成 a，b 两个句子\n",
    "        if i == len(document) - 1 or current_length >= target_seq_length:\n",
    "            if current_chunk:\n",
    "\n",
    "                # 为句子 a 随机选择一个长度\n",
    "                a_end = 1\n",
    "                if len(current_chunk) >= 2:\n",
    "                    a_end = rng.randint(1, len(current_chunk) - 1)\n",
    "\n",
    "                tokens_a = []\n",
    "                for j in range(a_end):\n",
    "                    tokens_a.extend(current_chunk[j])\n",
    "\n",
    "                tokens_b = []\n",
    "                \n",
    "                # 句子 b 为语料其它部分随机选择的序列\n",
    "                is_random_next = False\n",
    "                if len(current_chunk) == 1 or rng.random() < 0.5:\n",
    "                    is_random_next = True\n",
    "                    target_b_length = target_seq_length - len(tokens_a)\n",
    "\n",
    "                    # This should rarely go for more than one iteration for large\n",
    "                    # corpora. However, just to be careful, we try to make sure that\n",
    "                    # the random document is not the same as the document\n",
    "                    # we're processing.\n",
    "                    for _ in range(10):\n",
    "                        random_document_index = rng.randint(\n",
    "                            0,\n",
    "                            len(all_documents) - 1)\n",
    "                        if random_document_index != document_index:\n",
    "                            break\n",
    "\n",
    "                    random_document = all_documents[random_document_index]\n",
    "                    random_start = rng.randint(0, len(random_document) - 1)\n",
    "                    for j in range(random_start, len(random_document)):\n",
    "                        tokens_b.extend(random_document[j])\n",
    "                        if len(tokens_b) >= target_b_length:\n",
    "                            break\n",
    "                    # We didn't actually use these segments so we \"put them back\" so\n",
    "                    # they don't go to waste.\n",
    "                    num_unused_segments = len(current_chunk) - a_end\n",
    "                    i -= num_unused_segments\n",
    "                    \n",
    "                # 句子 b 为 current_chunk 中剩下的的部分\n",
    "                else:\n",
    "                    is_random_next = False\n",
    "                    for j in range(a_end, len(current_chunk)):\n",
    "                        tokens_b.extend(current_chunk[j])\n",
    "                        \n",
    "                # 控制句子对总长，并保持两者单词数相当\n",
    "                truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)\n",
    "\n",
    "                assert len(tokens_a) >= 1\n",
    "                assert len(tokens_b) >= 1\n",
    "\n",
    "                # 合并句子对，标记属于句子 a 还是句子 b\n",
    "                tokens = []\n",
    "                segment_ids = []\n",
    "                tokens.append(\"[CLS]\") \n",
    "                segment_ids.append(0) \n",
    "                for token in tokens_a:\n",
    "                    tokens.append(token)\n",
    "                    segment_ids.append(0)\n",
    "\n",
    "                tokens.append(\"[SEP]\")\n",
    "                segment_ids.append(0)\n",
    "\n",
    "                for token in tokens_b:\n",
    "                    tokens.append(token)\n",
    "                    segment_ids.append(1)\n",
    "                tokens.append(\"[SEP]\")\n",
    "                segment_ids.append(1)\n",
    "                \n",
    "                # 创建 被遮挡待预测 的 token 序列\n",
    "                (tokens, masked_lm_positions,\n",
    "                 masked_lm_labels) = create_masked_lm_predictions(\n",
    "                     tokens, masked_lm_prob, max_predictions_per_seq,\n",
    "                     vocab_words, rng)\n",
    "                \n",
    "                # 创建训练数据实例\n",
    "                instance = TrainingInstance(\n",
    "                    tokens=tokens,\n",
    "                    segment_ids=segment_ids,\n",
    "                    is_random_next=is_random_next,\n",
    "                    masked_lm_positions=masked_lm_positions,\n",
    "                    masked_lm_labels=masked_lm_labels)\n",
    "                instances.append(instance)\n",
    "            current_chunk = []\n",
    "            current_length = 0\n",
    "        i += 1\n",
    "\n",
    "    return instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T08:03:22.267023Z",
     "start_time": "2020-05-08T08:03:22.257597Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tokens: [CLS] b e r t [SEP] d # # i r e # # c t i o n # # a l [MASK] n # # c [MASK] d e # [MASK] r r e p r e s e n t a t i o n s [MASK] r o m t r a n s f o r m e r s ) i s a r e c e n t p a p e r p u b l i s h e d b y r e s e a r c h [SEP]\n",
      "segment_ids: 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      "is_random_next: False\n",
      "masked_lm_positions: 17 23 28 32 49\n",
      "masked_lm_labels: o e o # f\n",
      "\n",
      "\n",
      "================================================================================\n",
      "tokens: [CLS] a t g headed [MASK] g l e a [MASK] [SEP] [MASK] a n g u a g [MASK] . [SEP]\n",
      "segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1\n",
      "is_random_next: False\n",
      "masked_lm_positions: 4 5 10 12 19\n",
      "masked_lm_labels: o o i l e\n",
      "\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "def test_create_instances_from_document():\n",
    "    import tempfile\n",
    "    text = \"BERT (Bidirectional Encoder Representations from Transformers) is a recent paper published by researchers at Google AI Language.\\n It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering (SQuAD v1.1), Natural Language Inference (MNLI), and others.\"\n",
    "    all_documents = [tokenizer.tokenize(doc) for doc in text.split(\"\\n\")]\n",
    "    instances = create_instances_from_document(\n",
    "        all_documents,\n",
    "        document_index=0,\n",
    "        max_seq_length=100,\n",
    "        short_seq_prob=0.1,\n",
    "        masked_lm_prob=0.3,\n",
    "        max_predictions_per_seq=5,\n",
    "        vocab_words=list(tokenizer.vocab.keys()),\n",
    "        rng=random.Random(42),\n",
    "    )\n",
    "    for instance in instances:\n",
    "        print(instance)\n",
    "        print(\"=\" * 80)\n",
    "\n",
    "\n",
    "test_create_instances_from_document()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从文件中创建训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T08:03:30.052017Z",
     "start_time": "2020-05-08T08:03:30.044034Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_training_instances(input_files, tokenizer, max_seq_length,\n",
    "                              dupe_factor, short_seq_prob, masked_lm_prob,\n",
    "                              max_predictions_per_seq, rng):\n",
    "    \"\"\"Create `TrainingInstance`s from raw text.\"\"\"\n",
    "    all_documents = [[]]\n",
    "    \n",
    "    # Input file format:\n",
    "    # (1) One sentence per line. These should ideally be actual sentences, not\n",
    "    # entire paragraphs or arbitrary spans of text. (Because we use the\n",
    "    # sentence boundaries for the \"next sentence prediction\" task).\n",
    "    # (2) Blank lines between documents. Document boundaries are needed so\n",
    "    # that the \"next sentence prediction\" task doesn't span between documents.\n",
    "    for input_file in input_files:\n",
    "        with tf.io.gfile.GFile(input_file, \"rb\") as reader:\n",
    "            while True:\n",
    "                line = tokenization.convert_to_unicode(reader.readline())\n",
    "                if not line:\n",
    "                    break\n",
    "                line = line.strip()\n",
    "                \n",
    "                # Empty lines are used as document delimiters\n",
    "                if not line:\n",
    "                    all_documents.append([])\n",
    "                tokens = tokenizer.tokenize(line)\n",
    "                if tokens:\n",
    "                    all_documents[-1].append(tokens)\n",
    "    \n",
    "    # Remove empty documents\n",
    "    all_documents = [x for x in all_documents if x]\n",
    "    rng.shuffle(all_documents)\n",
    "    \n",
    "    vocab_words = list(tokenizer.vocab.keys())\n",
    "    instances = []\n",
    "    for _ in range(dupe_factor):\n",
    "        for document_index in range(len(all_documents)):\n",
    "            instances.extend(\n",
    "                create_instances_from_document(all_documents, document_index,\n",
    "                                               max_seq_length, short_seq_prob,\n",
    "                                               masked_lm_prob,\n",
    "                                               max_predictions_per_seq,\n",
    "                                               vocab_words, rng))\n",
    "    \n",
    "    rng.shuffle(instances)\n",
    "    return instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T09:15:05.339279Z",
     "start_time": "2020-05-08T09:15:05.316648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw text:\n",
      " This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত\n",
      "Text should be one-sentence-per-line, with empty lines between documents.\n",
      "This sample text is public domain and was rand\n",
      "================================================================================\n",
      "tokens: [CLS] text should be [MASK] [MASK] sentence - per - line , with empty lines between documents . [SEP] . mr . cass ##ius crossed the highway , and stopped suddenly . something [MASK] [MASK] in the nearest red pool before him . gold , surely ! but , wonderful to relate , not an irregular , shape ##less fragment of crude ore , fresh from nature [MASK] s cr ##ucible , but a bit of jewel ##er ' s hand ##ic ##raf ##t [MASK] the [MASK] of a plain gold ring . looking at it more at [MASK] [MASK] [MASK] he saw that it bore [MASK] inscription , [MASK] may to cass . \" like [MASK] of his fellow gold [MASK] seekers , cass was super [SEP]\n",
      "segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      "is_random_next: True\n",
      "masked_lm_positions: 4 5 9 33 34 45 67 84 86 97 98 99 100 103 106 108 109 116 121 123\n",
      "masked_lm_labels: one - - glitter ##ed surely ' in form at ##ten ##tively , that the , \" most - ,\n",
      "\n",
      "\n",
      "tokens: [CLS] a fallen star filthy i am [MASK] sir ! ' continued he [MASK] pens ##ively , [MASK] [MASK] lean stomach - - ' a fallen star ! - - [MASK] , if the [MASK] of philosophy will allow of the simi ##le , among the hog ##s of the lower world - - [MASK] [MASK] even into [MASK] hog - bucket itself . well , after all , i will show [MASK] the way to the archbishop ' s [MASK] [SEP] [MASK] this may have been [MASK] reason why early rise ##rs in that locality ##loading during the rainy season , adopted a [MASK] habit of body , and seldom lifted [MASK] eyes to [MASK] rift ##ed or [MASK] - ink washed skies above them simpler [SEP]\n",
      "segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      "is_random_next: True\n",
      "masked_lm_positions: 4 7 13 17 18 30 34 54 55 58 72 80 82 87 96 104 112 115 119 126\n",
      "masked_lm_labels: , , , stroking his fallen dignity indeed , the you . possibly the , thoughtful their the india .\n",
      "\n",
      "\n",
      "tokens: [CLS] of the laura - - for [MASK] which knew him and smiled upon him ; but it was too late mt turn back now . downloaded guide held on for [MASK] than a mile up the great main street , crossed in [MASK] centre of [MASK] city , at right dollar , by one [MASK] [MASK] , [MASK] each end of which , miles away , appeared , dim and distant over [MASK] [MASK] of [unused424] living stream of passengers , [MASK] yellow sand - hills of the [MASK] ; while at the end of [MASK] vista in front of them gleamed the blue harbour , through a [MASK] of [SEP] like most of his fellow gold - seekers , cass was super ##sti ##tious . [SEP]\n",
      "segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      "is_random_next: True\n",
      "masked_lm_positions: 7 13 21 26 31 43 46 49 51 54 55 56 58 73 74 76 82 89 96 109\n",
      "masked_lm_labels: faces upon to his more the the at angles one equally magnificent at the heads the the desert the network\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def test_create_training_instances():\n",
    "    tokenization.convert_to_unicode = convert_to_unicode\n",
    "\n",
    "    input_file = \"../datasets/sample_text.txt\"\n",
    "    with open(input_file, 'r') as f:\n",
    "        print(\"Raw text:\\n\", f.read()[:200])\n",
    "    print(\"=\" * 80)\n",
    "\n",
    "    input_files = []\n",
    "    for input_pattern in input_file.split(\",\"):\n",
    "        input_files.extend(tf.io.gfile.glob(input_pattern))\n",
    "\n",
    "    rng = random.Random(43)\n",
    "    instances = create_training_instances(\n",
    "        input_files,\n",
    "        tokenizer,\n",
    "        max_seq_length=128,\n",
    "        dupe_factor=2,\n",
    "        short_seq_prob=0.1,\n",
    "        masked_lm_prob=0.3,\n",
    "        max_predictions_per_seq=20,\n",
    "        rng=rng,\n",
    "    )\n",
    "    for i in range(3):\n",
    "        instance = random.choice(instances)\n",
    "        print(instance)\n",
    "\n",
    "\n",
    "test_create_training_instances()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生成最终数值数据，并保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_instance_to_example_files(instances, tokenizer, max_seq_length,\n",
    "                                    max_predictions_per_seq, output_files):\n",
    "    \"\"\"Create TF example files from `TrainingInstance`s.\"\"\"\n",
    "    # 将实例保存到 TFRecord 文件\n",
    "    writers = []\n",
    "    for output_file in output_files:\n",
    "        writers.append(\n",
    "            tf.io.TFRecordWriter(\n",
    "                output_file, options=\"GZIP\" if FLAGS.gzip_compress else \"\"))\n",
    "\n",
    "    writer_index = 0\n",
    "\n",
    "    total_written = 0\n",
    "    for (inst_index, instance) in enumerate(instances):\n",
    "        # tokens 的索引列表，对应的填充 mask，属于哪个句子的列表，都必须等长 ：max_seq_length\n",
    "        input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)\n",
    "        input_mask = [1] * len(input_ids)\n",
    "        segment_ids = list(instance.segment_ids)\n",
    "        assert len(input_ids) <= max_seq_length\n",
    "\n",
    "        while len(input_ids) < max_seq_length:\n",
    "            input_ids.append(0)\n",
    "            input_mask.append(0)\n",
    "            segment_ids.append(0)\n",
    "\n",
    "        assert len(input_ids) == max_seq_length\n",
    "        assert len(input_mask) == max_seq_length\n",
    "        assert len(segment_ids) == max_seq_length\n",
    "\n",
    "        # 被遮挡待预测单词的 索引，标签，权重，且填充为等长：max_predictions_per_seq\n",
    "        masked_lm_positions = list(instance.masked_lm_positions)\n",
    "        masked_lm_ids = tokenizer.convert_tokens_to_ids(\n",
    "            instance.masked_lm_labels)\n",
    "        masked_lm_weights = [1.0] * len(masked_lm_ids)\n",
    "\n",
    "        while len(masked_lm_positions) < max_predictions_per_seq:\n",
    "            masked_lm_positions.append(0)\n",
    "            masked_lm_ids.append(0)\n",
    "            masked_lm_weights.append(0.0)\n",
    "            \n",
    "        # 两个子句是随机组合的，还是连续的两个子句\n",
    "        next_sentence_label = 1 if instance.is_random_next else 0\n",
    "\n",
    "        # 将列表都转换成 tensorflow 特征\n",
    "        features = collections.OrderedDict()\n",
    "        features[\"input_ids\"] = create_int_feature(input_ids)\n",
    "        features[\"input_mask\"] = create_int_feature(input_mask)\n",
    "        features[\"segment_ids\"] = create_int_feature(segment_ids)\n",
    "        features[\"masked_lm_positions\"] = create_int_feature(\n",
    "            masked_lm_positions)\n",
    "        features[\"masked_lm_ids\"] = create_int_feature(masked_lm_ids)\n",
    "        features[\"masked_lm_weights\"] = create_float_feature(masked_lm_weights)\n",
    "        features[\"next_sentence_labels\"] = create_int_feature(\n",
    "            [next_sentence_label])\n",
    "\n",
    "        # 将所有输入组合起来，并保存\n",
    "        tf_example = tf.train.Example(features=tf.train.Features(\n",
    "            feature=features))\n",
    "\n",
    "        writers[writer_index].write(tf_example.SerializeToString())\n",
    "        writer_index = (writer_index + 1) % len(writers)\n",
    "\n",
    "        total_written += 1\n",
    "\n",
    "        # 日志\n",
    "        if inst_index < 20:\n",
    "            logging.info(\"*** Example ***\")\n",
    "            logging.info(\n",
    "                \"tokens: %s\", \" \".join(\n",
    "                    [tokenization.printable_text(x) for x in instance.tokens]))\n",
    "\n",
    "            for feature_name in features.keys():\n",
    "                feature = features[feature_name]\n",
    "                values = []\n",
    "                if feature.int64_list.value:\n",
    "                    values = feature.int64_list.value\n",
    "                elif feature.float_list.value:\n",
    "                    values = feature.float_list.value\n",
    "                logging.info(\"%s: %s\", feature_name,\n",
    "                             \" \".join([str(x) for x in values]))\n",
    "\n",
    "    for writer in writers:\n",
    "        writer.close()\n",
    "\n",
    "    logging.info(\"Wrote %d total instances\", total_written)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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