{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b954633",
   "metadata": {
    "id": "p6yklm2xWn9f"
   },
   "source": [
    "## Part I: Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718b33b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:39.949755Z",
     "iopub.status.busy": "2026-10-04T09:33:39.949755Z",
     "iopub.status.idle": "2026-10-04T09:33:41.806198Z",
     "shell.execute_reply": "2026-10-04T09:33:41.806198Z"
    },
    "id": "YycoIJomXwqH"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9339c38d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:41.814726Z",
     "iopub.status.busy": "2026-10-04T09:33:41.814726Z",
     "iopub.status.idle": "2026-10-04T09:33:42.205456Z",
     "shell.execute_reply": "2026-10-04T09:33:42.205456Z"
    },
    "id": "KKAXuhZIUxD8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloaded questions-words.txt\n"
     ]
    }
   ],
   "source": [
    "# [Data loading changed] 規定允許修改資料載入部分。\n",
    "# 原本是 `!wget ...`。Windows 沒有 wget，改用 Python 內建的 urllib 下載同一個網址。\n",
    "import urllib.request\n",
    "urllib.request.urlretrieve(\"http://download.tensorflow.org/data/questions-words.txt\", \"questions-words.txt\")\n",
    "print(\"downloaded questions-words.txt\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0003c26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.210460Z",
     "iopub.status.busy": "2026-10-04T09:33:42.210460Z",
     "iopub.status.idle": "2026-10-04T09:33:42.224221Z",
     "shell.execute_reply": "2026-10-04T09:33:42.223632Z"
    },
    "id": "xue5pVFLVNQQ"
   },
   "outputs": [],
   "source": [
    "# Preprocess the dataset\n",
    "file_name = \"questions-words\"\n",
    "with open(f\"{file_name}.txt\", \"r\") as f:\n",
    "    data = f.read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "430d2d79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.231227Z",
     "iopub.status.busy": "2026-10-04T09:33:42.231227Z",
     "iopub.status.idle": "2026-10-04T09:33:42.235197Z",
     "shell.execute_reply": "2026-10-04T09:33:42.235197Z"
    },
    "id": "h7dOdJOsZzAF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ": capital-common-countries\n",
      "Athens Greece Baghdad Iraq\n",
      "Athens Greece Bangkok Thailand\n",
      "Athens Greece Beijing China\n",
      "Athens Greece Berlin Germany\n",
      "Athens Greece Bern Switzerland\n",
      "Athens Greece Cairo Egypt\n",
      "Athens Greece Canberra Australia\n",
      "Athens Greece Hanoi Vietnam\n",
      "Athens Greece Havana Cuba\n"
     ]
    }
   ],
   "source": [
    "# check data from the first 10 entries\n",
    "for entry in data[:10]:\n",
    "    print(entry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9a4218a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.242711Z",
     "iopub.status.busy": "2026-10-04T09:33:42.241207Z",
     "iopub.status.idle": "2026-10-04T09:33:42.250725Z",
     "shell.execute_reply": "2026-10-04T09:33:42.250725Z"
    },
    "id": "wmYQ0IWZZxf3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19544 questions\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# TODO1: Write your code here for processing data to pd.DataFrame\n",
    "# Please note that the first five mentions of \": \" indicate `semantic`,\n",
    "# and the remaining nine belong to the `syntatic` category.\n",
    "questions, categories, sub_categories = [], [], []\n",
    "n_headers = 0          # 目前讀到第幾個 \": \" 標題\n",
    "current_sub = None     # 目前所在的小類，例如 \": capital-common-countries\"\n",
    "for line in data:\n",
    "    if line.startswith(\": \"):          # 這一行是小類標題，不是題目\n",
    "        n_headers += 1\n",
    "        current_sub = line\n",
    "        continue\n",
    "    questions.append(line)\n",
    "    categories.append(\"Semantic\" if n_headers <= 5 else \"Syntactic\")\n",
    "    sub_categories.append(current_sub)\n",
    "print(len(questions), \"questions\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fd622cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.256531Z",
     "iopub.status.busy": "2026-10-04T09:33:42.256531Z",
     "iopub.status.idle": "2026-10-04T09:33:42.261676Z",
     "shell.execute_reply": "2026-10-04T09:33:42.261676Z"
    },
    "id": "_bKA05rVZb_i"
   },
   "outputs": [],
   "source": [
    "# Create the dataframe\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"Question\": questions,\n",
    "        \"Category\": categories,\n",
    "        \"SubCategory\": sub_categories,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7db2883d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.267710Z",
     "iopub.status.busy": "2026-10-04T09:33:42.267710Z",
     "iopub.status.idle": "2026-10-04T09:33:42.280360Z",
     "shell.execute_reply": "2026-10-04T09:33:42.280360Z"
    },
    "id": "UN2FBcicZmpV"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Question</th>\n",
       "      <th>Category</th>\n",
       "      <th>SubCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Athens Greece Baghdad Iraq</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>: capital-common-countries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Athens Greece Bangkok Thailand</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>: capital-common-countries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Athens Greece Beijing China</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>: capital-common-countries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Athens Greece Berlin Germany</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>: capital-common-countries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Athens Greece Bern Switzerland</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>: capital-common-countries</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Question  Category                 SubCategory\n",
       "0      Athens Greece Baghdad Iraq  Semantic  : capital-common-countries\n",
       "1  Athens Greece Bangkok Thailand  Semantic  : capital-common-countries\n",
       "2     Athens Greece Beijing China  Semantic  : capital-common-countries\n",
       "3    Athens Greece Berlin Germany  Semantic  : capital-common-countries\n",
       "4  Athens Greece Bern Switzerland  Semantic  : capital-common-countries"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ea0e141",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.285878Z",
     "iopub.status.busy": "2026-10-04T09:33:42.285878Z",
     "iopub.status.idle": "2026-10-04T09:33:42.334111Z",
     "shell.execute_reply": "2026-10-04T09:33:42.333606Z"
    },
    "id": "nMGvoDeiZhbp"
   },
   "outputs": [],
   "source": [
    "df.to_csv(f\"{file_name}.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b958132f",
   "metadata": {
    "id": "Zi2SNNuHWiZO"
   },
   "source": [
    "## Part II: Use pre-trained word embeddings\n",
    "- After finish Part I, you can run Part II code blocks only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "659e79d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:42.342116Z",
     "iopub.status.busy": "2026-10-04T09:33:42.342116Z",
     "iopub.status.idle": "2026-10-04T09:33:55.969206Z",
     "shell.execute_reply": "2026-10-04T09:33:55.969206Z"
    },
    "id": "yB4rpJymXiSN"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gensim.downloader\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5d5ab22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:55.975675Z",
     "iopub.status.busy": "2026-10-04T09:33:55.974669Z",
     "iopub.status.idle": "2026-10-04T09:33:55.997840Z",
     "shell.execute_reply": "2026-10-04T09:33:55.997840Z"
    },
    "id": "-pGLoyKSHXuQ"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"questions-words.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a3adfbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:33:56.003351Z",
     "iopub.status.busy": "2026-10-04T09:33:56.003351Z",
     "iopub.status.idle": "2026-10-04T09:34:30.874917Z",
     "shell.execute_reply": "2026-10-04T09:34:30.874917Z"
    },
    "id": "YWa_1hF3aZHO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Gensim model loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"glove-wiki-gigaword-100\"\n",
    "# You can try other models.\n",
    "# https://radimrehurek.com/gensim/models/word2vec.html#pretrained-models\n",
    "\n",
    "# Load the pre-trained model (using GloVe vectors here)\n",
    "model = gensim.downloader.load(MODEL_NAME)\n",
    "print(\"The Gensim model loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c14d0de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:34:30.880922Z",
     "iopub.status.busy": "2026-10-04T09:34:30.880922Z",
     "iopub.status.idle": "2026-10-04T09:37:12.917575Z",
     "shell.execute_reply": "2026-10-04T09:37:12.917057Z"
    },
    "id": "YTsqJcP1WSTH"
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    {
     "name": "stdout",
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     "text": [
      "OOV questions: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# Do predictions and preserve the gold answers (word_D)\n",
    "preds = []\n",
    "golds = []\n",
    "\n",
    "for analogy in tqdm(data[\"Question\"]):\n",
    "      # TODO2: Write your code here to use pre-trained word embeddings for getting predictions of the analogy task.\n",
    "      # You should also preserve the gold answers during iterations for evaluations later.\n",
    "      # GloVe 的字全部是小寫，所以題目也先轉小寫，否則 \"Athens\" 會查不到。\n",
    "      word_a, word_b, word_c, word_d = analogy.lower().split()\n",
    "      golds.append(word_d)\n",
    "      if all(w in model.key_to_index for w in (word_a, word_b, word_c)):\n",
    "          # b - a + c：most_similar 會自動排除 a、b、c 這三個字本身\n",
    "          preds.append(model.most_similar(positive=[word_b, word_c], negative=[word_a], topn=1)[0][0])\n",
    "      else:\n",
    "          preds.append(None)   # 題目裡有字典沒有的字（OOV），直接算答錯\n",
    "print(\"OOV questions:\", sum(p is None for p in preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd35d5c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:12.971197Z",
     "iopub.status.busy": "2026-10-04T09:37:12.971197Z",
     "iopub.status.idle": "2026-10-04T09:37:13.019494Z",
     "shell.execute_reply": "2026-10-04T09:37:13.019494Z"
    },
    "id": "xG7vcPXAW6uT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category: Semantic, Accuracy: 65.3399481339497%\n",
      "Category: Syntactic, Accuracy: 61.255269320843084%\n",
      "Sub-Category: capital-common-countries, Accuracy: 93.87351778656127%\n",
      "Sub-Category: capital-world, Accuracy: 88.94783377541998%\n",
      "Sub-Category: currency, Accuracy: 14.203233256351039%\n",
      "Sub-Category: city-in-state, Accuracy: 30.806647750304013%\n",
      "Sub-Category: family, Accuracy: 81.62055335968378%\n",
      "Sub-Category: gram1-adjective-to-adverb, Accuracy: 24.39516129032258%\n",
      "Sub-Category: gram2-opposite, Accuracy: 20.073891625615765%\n",
      "Sub-Category: gram3-comparative, Accuracy: 79.12912912912913%\n",
      "Sub-Category: gram4-superlative, Accuracy: 54.278074866310156%\n",
      "Sub-Category: gram5-present-participle, Accuracy: 69.50757575757575%\n",
      "Sub-Category: gram6-nationality-adjective, Accuracy: 87.86741713570981%\n",
      "Sub-Category: gram7-past-tense, Accuracy: 55.44871794871795%\n",
      "Sub-Category: gram8-plural, Accuracy: 71.996996996997%\n",
      "Sub-Category: gram9-plural-verbs, Accuracy: 58.39080459770115%\n"
     ]
    }
   ],
   "source": [
    "# Perform evaluations. You do not need to modify this block!!\n",
    "\n",
    "def calculate_accuracy(gold: np.ndarray, pred: np.ndarray) -> float:\n",
    "    return np.mean(gold == pred)\n",
    "\n",
    "golds_np, preds_np = np.array(golds), np.array(preds)\n",
    "data = pd.read_csv(\"questions-words.csv\")\n",
    "\n",
    "# Evaluation: categories\n",
    "for category in data[\"Category\"].unique():\n",
    "    mask = data[\"Category\"] == category\n",
    "    golds_cat, preds_cat = golds_np[mask], preds_np[mask]\n",
    "    acc_cat = calculate_accuracy(golds_cat, preds_cat)\n",
    "    print(f\"Category: {category}, Accuracy: {acc_cat * 100}%\")\n",
    "\n",
    "# Evaluation: sub-categories\n",
    "for sub_category in data[\"SubCategory\"].unique():\n",
    "    mask = data[\"SubCategory\"] == sub_category\n",
    "    golds_subcat, preds_subcat = golds_np[mask], preds_np[mask]\n",
    "    acc_subcat = calculate_accuracy(golds_subcat, preds_subcat)\n",
    "    print(f\"Sub-Category{sub_category}, Accuracy: {acc_subcat * 100}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f75a7d9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:13.063877Z",
     "iopub.status.busy": "2026-10-04T09:37:13.063877Z",
     "iopub.status.idle": "2026-10-04T09:37:13.611275Z",
     "shell.execute_reply": "2026-10-04T09:37:13.611275Z"
    },
    "id": "7_z6CybBXKZu"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# Collect words from Google Analogy dataset\n",
    "SUB_CATEGORY = \": family\"\n",
    "\n",
    "# TODO3: Plot t-SNE for the words in the SUB_CATEGORY `: family`\n",
    "family_words = sorted({w for q in data[data[\"SubCategory\"] == SUB_CATEGORY][\"Question\"]\n",
    "                       for w in q.lower().split()})\n",
    "family_words = [w for w in family_words if w in model.key_to_index]\n",
    "vectors = np.array([model[w] for w in family_words])\n",
    "# t-SNE：把 100 維壓成 2 維。字只有幾十個，所以 perplexity 要調小（必須小於樣本數）。\n",
    "points = TSNE(n_components=2, perplexity=15, random_state=42, init=\"pca\").fit_transform(vectors)\n",
    "plt.figure(figsize=(12, 9))\n",
    "plt.scatter(points[:, 0], points[:, 1], s=15)\n",
    "for (x, y), w in zip(points, family_words):\n",
    "    plt.annotate(w, (x, y), fontsize=9)\n",
    "\n",
    "\n",
    "plt.title(\"Word Relationships from Google Analogy Task\")\n",
    "plt.show()\n",
    "plt.savefig(\"word_relationships.png\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306d371b",
   "metadata": {
    "id": "DKRPJxgKXH4j"
   },
   "source": [
    "### Part III: Train your own word embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11609b3",
   "metadata": {
    "id": "VC_0fE1UzL8T"
   },
   "source": [
    "### Get the latest English Wikipedia articles and do sampling.\n",
    "- Usually, we start from Wikipedia dump (https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2). However, the downloading step will take very long. Also, the cleaning step for the Wikipedia corpus ([`gensim.corpora.wikicorpus.WikiCorpus`](https://radimrehurek.com/gensim/corpora/wikicorpus.html#gensim.corpora.wikicorpus.WikiCorpus)) will take much time. Therefore, we provide cleaned files for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53ac0928",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:13.656654Z",
     "iopub.status.busy": "2026-10-04T09:37:13.655666Z",
     "iopub.status.idle": "2026-10-04T09:37:13.761685Z",
     "shell.execute_reply": "2026-10-04T09:37:13.761685Z"
    },
    "id": "FkubArwCCYxR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\wiki_texts_part_0.txt.gz 1.51 GB\n",
      "data\\wiki_texts_part_1.txt.gz 0.84 GB\n",
      "data\\wiki_texts_part_2.txt.gz 0.67 GB\n",
      "data\\wiki_texts_part_3.txt.gz 0.60 GB\n",
      "data\\wiki_texts_part_4.txt.gz 0.57 GB\n",
      "data\\wiki_texts_part_5.txt.gz 0.58 GB\n",
      "data\\wiki_texts_part_6.txt.gz 0.56 GB\n",
      "data\\wiki_texts_part_7.txt.gz 0.53 GB\n",
      "data\\wiki_texts_part_8.txt.gz 0.55 GB\n",
      "data\\wiki_texts_part_9.txt.gz 0.55 GB\n",
      "data\\wiki_texts_part_10.txt.gz 0.00 GB\n"
     ]
    }
   ],
   "source": [
    "# [Data loading changed] 規定允許修改資料載入部分。\n",
    "# 原本是 11 行 `!gdown --id ...`。改成 Python 迴圈：檔案已經存在就跳過，斷線後可以重跑。\n",
    "# Each file contain 562365 lines (articles), except the last file.\n",
    "import os, gdown\n",
    "DATA_DIR = \"data\"\n",
    "os.makedirs(DATA_DIR, exist_ok=True)\n",
    "WIKI_IDS = [\n",
    "    \"1jiu9E1NalT2Y8EIuWNa1xf2Tw1f1XuGd\", \"1ABblLRd9HXdXvaNv8H9fFq984bhnowoG\",\n",
    "    \"1z2VFNhpPvCejTP5zyejzKj5YjI_Bn42M\", \"1VKjded9BxADRhIoCzXy_W8uzVOTWIf0g\",\n",
    "    \"16mBeG26m9LzHXdPe8UrijUIc6sHxhknz\", \"17JFvxOH-kc-VmvGkhG7p3iSZSpsWdgJI\",\n",
    "    \"19IvB2vOJRGlrYulnTXlZECR8zT5v550P\", \"1sjwO8A2SDOKruv6-8NEq7pEIuQ50ygVV\",\n",
    "    \"1s7xKWJmyk98Jbq6Fi1scrHy7fr_ellUX\", \"17eQXcrvY1cfpKelLbP2BhQKrljnFNykr\",\n",
    "    \"1J5TAN6bNBiSgTIYiPwzmABvGhAF58h62\",\n",
    "]\n",
    "for i, fid in enumerate(WIKI_IDS):\n",
    "    path = os.path.join(DATA_DIR, f\"wiki_texts_part_{i}.txt.gz\")\n",
    "    if not os.path.exists(path):\n",
    "        gdown.download(id=fid, output=path, quiet=True)\n",
    "    print(path, f\"{os.path.getsize(path)/1e9:.2f} GB\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a4e02bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:13.808922Z",
     "iopub.status.busy": "2026-10-04T09:37:13.808922Z",
     "iopub.status.idle": "2026-10-04T09:37:13.814294Z",
     "shell.execute_reply": "2026-10-04T09:37:13.813284Z"
    },
    "id": "8S3ibNT3C8Xk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all parts downloaded\n"
     ]
    }
   ],
   "source": [
    "# [Data loading changed] 規定允許修改資料載入部分。\n",
    "# 上一格已經把 11 個檔都下載完（模板原本把下載拆成兩格）。\n",
    "print(\"all parts downloaded\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca61ea5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:13.857976Z",
     "iopub.status.busy": "2026-10-04T09:37:13.857976Z",
     "iopub.status.idle": "2026-10-04T09:37:13.863029Z",
     "shell.execute_reply": "2026-10-04T09:37:13.862498Z"
    },
    "id": "DUg_c79BC7OL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 parts: ['wiki_texts_part_0.txt.gz', 'wiki_texts_part_1.txt.gz', 'wiki_texts_part_2.txt.gz', 'wiki_texts_part_3.txt.gz', 'wiki_texts_part_4.txt.gz', 'wiki_texts_part_5.txt.gz', 'wiki_texts_part_6.txt.gz', 'wiki_texts_part_7.txt.gz', 'wiki_texts_part_8.txt.gz', 'wiki_texts_part_9.txt.gz', 'wiki_texts_part_10.txt.gz']\n"
     ]
    }
   ],
   "source": [
    "# [Data loading changed] 規定允許修改資料載入部分。\n",
    "# 原本是 `!gunzip -k`（解壓縮並保留壓縮檔）＋ 下一格 `!cat`（合併成一個大檔）。\n",
    "# 這樣同一份資料會在硬碟上存三份（壓縮檔＋解壓檔＋合併檔），本機硬碟吃不消。\n",
    "# 改成：不解壓、不合併，下一步直接用 gzip 一行一行讀壓縮檔。結果完全一樣。\n",
    "import glob, gzip, re\n",
    "wiki_parts = sorted(glob.glob(os.path.join(DATA_DIR, \"wiki_texts_part_*.txt.gz\")),\n",
    "                    key=lambda p: int(re.search(r\"part_(\\d+)\", p).group(1)))\n",
    "print(len(wiki_parts), \"parts:\", [os.path.basename(p) for p in wiki_parts])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c8a1b395",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:13.904915Z",
     "iopub.status.busy": "2026-10-04T09:37:13.904915Z",
     "iopub.status.idle": "2026-10-04T09:37:13.908867Z",
     "shell.execute_reply": "2026-10-04T09:37:13.908867Z"
    },
    "id": "7duk2RbYDB02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total compressed size: 6.93 GB\n"
     ]
    }
   ],
   "source": [
    "# [Data loading changed] 規定允許修改資料載入部分。\n",
    "# 不需要合併：wiki_parts 這個清單按 0 到 10 的順序排好，依序讀就等於讀合併檔。\n",
    "print(\"total compressed size:\", round(sum(os.path.getsize(p) for p in wiki_parts) / 1e9, 2), \"GB\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbadd605",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:13.954126Z",
     "iopub.status.busy": "2026-10-04T09:37:13.952977Z",
     "iopub.status.idle": "2026-10-04T09:37:13.966924Z",
     "shell.execute_reply": "2026-10-04T09:37:13.966924Z"
    },
    "id": "givLH7NrDs6X"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 anarchism is political philosophy and movement that is against all forms of authority and seeks to abolish the institutions it claims maintain unnecessary coercion and hierarchy typically including th ...\n",
      "1 albedo change in greenland the map shows the difference between the amount of sunlight greenland reflected in the summer of versus the average percent it reflected between and some areas reflect close ...\n",
      "2 or is the first letter and the first vowel letter of the latin alphabet used in the modern english alphabet and others worldwide its name in english is pronounced plural aes it is similar in shape to ...\n",
      "3 alabama is state in the southeastern region of the united states it borders tennessee to the north georgia to the east florida and the gulf of mexico to the south and mississippi to the west alabama i ...\n",
      "4 in greek mythology achilles or achilleus was hero of the trojan war who was known as being the greatest of all the greek warriors the central character in homer iliad he was the son of the nereid thet ...\n",
      "5 abraham lincoln february april was an american lawyer politician and statesman who served as the th president of the united states from until his assassination in he led the united states through the ...\n",
      "6 aristotle aristotélēs bc was an ancient greek philosopher and polymath his writings cover broad range of subjects spanning the natural sciences philosophy linguistics economics politics psychology and ...\n",
      "7 an american in paris is jazz influenced symphonic poem or tone poem for orchestra by american composer george gershwin first performed in it was inspired by the time that gershwin had spent in paris a ...\n",
      "8 the academy award for best production design recognizes achievement for art direction in film the category original name was best art direction but was changed to its current name in for the th academ ...\n",
      "9 the academy awards of merit commonly known as the oscars or academy awards are awards for artistic and technical merit given for excellence within the american and international film industry they are ...\n"
     ]
    }
   ],
   "source": [
    "# [Data loading changed] 規定允許修改資料載入部分。\n",
    "# 原本是 `!head -n 10`。每一行是一整篇文章，很長，所以每行只印前 200 個字元。\n",
    "with gzip.open(wiki_parts[0], \"rt\", encoding=\"utf-8\") as f:\n",
    "    for i, line in enumerate(f):\n",
    "        print(i, line[:200].strip(), \"...\")\n",
    "        if i == 9:\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e27ce2",
   "metadata": {
    "id": "Hfwx92QCEhrq"
   },
   "source": [
    "Please note that we used the default parameters of [`gensim.corpora.wikicorpus.WikiCorpus`](https://radimrehurek.com/gensim/corpora/wikicorpus.html#gensim.corpora.wikicorpus.WikiCorpus) for cleaning the Wiki raw file. Thus, words with one character were discarded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9619e13f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:37:14.014232Z",
     "iopub.status.busy": "2026-10-04T09:37:14.013156Z",
     "iopub.status.idle": "2026-10-04T09:39:22.632374Z",
     "shell.execute_reply": "2026-10-04T09:39:22.632374Z"
    },
    "id": "vUAzButoP03w"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/11 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  9%|▉         | 1/11 [00:28<04:41, 28.18s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|█▊        | 2/11 [00:42<02:58, 19.80s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 27%|██▋       | 3/11 [00:53<02:06, 15.82s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36%|███▋      | 4/11 [01:03<01:34, 13.47s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 45%|████▌     | 5/11 [01:14<01:16, 12.68s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 55%|█████▍    | 6/11 [01:25<01:01, 12.30s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 64%|██████▎   | 7/11 [01:36<00:46, 11.64s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 73%|███████▎  | 8/11 [01:47<00:35, 11.67s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 82%|████████▏ | 9/11 [01:59<00:23, 11.62s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 91%|█████████ | 10/11 [02:08<00:10, 10.86s/it]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|██████████| 11/11 [02:08<00:00, 11.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "articles: total 5,623,655, sampled 1,124,733 (20.00%)\n",
      "wiki_sampled_5.txt 1.04 GB\n",
      "wiki_sampled_10.txt 2.08 GB\n",
      "wiki_sampled_20.txt 4.16 GB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# Now you need to do sampling because the corpus is too big.\n",
    "# You can further perform analysis with a greater sampling ratio.\n",
    "\n",
    "import random\n",
    "\n",
    "wiki_txt_path = \"wiki_texts_combined.txt\"\n",
    "# wiki_texts_combined.txt is a text file separated by linebreaks (\\n).\n",
    "# Each row in wiki_texts_combined.txt indicates a Wikipedia article.\n",
    "# [Data loading changed] 實際讀的是上面的 wiki_parts（11 個壓縮檔依序讀），內容等同 wiki_texts_combined.txt。\n",
    "\n",
    "SAMPLE_RATIO = 0.20\n",
    "output_path = \"wiki_sampled_20.txt\"\n",
    "random.seed(42)   # 固定亂數種子，重跑會抽到同一批文章\n",
    "\n",
    "# 順便在同一趟裡做出報告第 2 題要的 5%、10%：同一個亂數 r < 0.05 的文章，一定也 < 0.10、< 0.20，\n",
    "# 所以 5% ⊂ 10% ⊂ 20%，只是資料量不同，比較才公平。讀一趟壓縮檔要十幾分鐘，這樣省掉兩趟。\n",
    "extra_outputs = {0.05: open(\"wiki_sampled_5.txt\", \"w\", encoding=\"utf-8\", newline=\"\\n\"),\n",
    "                 0.10: open(\"wiki_sampled_10.txt\", \"w\", encoding=\"utf-8\", newline=\"\\n\")}\n",
    "n_total = n_kept = 0\n",
    "with open(output_path, \"w\", encoding=\"utf-8\", newline=\"\\n\") as output_file:\n",
    "    # TODO4: Sample `20%` Wikipedia articles\n",
    "    # Write your code here\n",
    "    for part in tqdm(wiki_parts):\n",
    "        with gzip.open(part, \"rt\", encoding=\"utf-8\") as f:\n",
    "            for line in f:                      # 一次只讀一行（一篇文章），記憶體不會爆\n",
    "                n_total += 1\n",
    "                r = random.random()\n",
    "                if r < SAMPLE_RATIO:\n",
    "                    output_file.write(line)\n",
    "                    n_kept += 1\n",
    "                for ratio, fh in extra_outputs.items():\n",
    "                    if r < ratio:\n",
    "                        fh.write(line)\n",
    "for fh in extra_outputs.values():\n",
    "    fh.close()\n",
    "print(f\"articles: total {n_total:,}, sampled {n_kept:,} ({n_kept / n_total:.2%})\")\n",
    "for p in [\"wiki_sampled_5.txt\", \"wiki_sampled_10.txt\", output_path]:\n",
    "    print(p, round(os.path.getsize(p) / 1e9, 2), \"GB\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7cbf24a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T09:39:22.674151Z",
     "iopub.status.busy": "2026-10-04T09:39:22.673154Z",
     "iopub.status.idle": "2026-10-04T10:19:41.876802Z",
     "shell.execute_reply": "2026-10-04T10:19:41.876802Z"
    },
    "id": "G7q1Xzunxkdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20% wiki tokens after preprocessing: 666,053,126\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained on wiki_sampled_20_clean.txt: 36.9 min, vocab 299,405\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# TODO5: Train your own word embeddings with the sampled articles\n",
    "# https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec\n",
    "# Hint: You should perform some pre-processing before training.\n",
    "import time\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "# 前處理：助教給的資料已經是小寫、去標點、斷好字（gensim WikiCorpus 預設）。\n",
    "# 這裡只做「只留純英文字母的字」。刻意「不」做的兩件事（理由寫在報告第 1 題）：\n",
    "#   - 不移除停用詞：he / she / his / her 是 family 類的考題答案\n",
    "#   - 不做詞形還原：biggest -> big 會讓比較級、最高級、複數、過去式的答案消失\n",
    "TOKEN_RE = re.compile(r\"^[a-z]+$\")\n",
    "\n",
    "def preprocess_file(src, dst):\n",
    "    n_tokens = 0\n",
    "    with open(src, encoding=\"utf-8\") as fin, open(dst, \"w\", encoding=\"utf-8\", newline=\"\\n\") as fout:\n",
    "        for line in fin:\n",
    "            tokens = [w for w in line.split() if TOKEN_RE.match(w)]\n",
    "            n_tokens += len(tokens)\n",
    "            fout.write(\" \".join(tokens) + \"\\n\")\n",
    "    return n_tokens\n",
    "\n",
    "# 超參數：向量長度跟 GloVe 一樣是 100，比較才公平；skip-gram 是老師投影片的重點。\n",
    "# max_final_vocab：只留最常見的 30 萬個字（老師建議的「只保留高頻字」）。\n",
    "W2V_PARAMS = dict(vector_size=100, window=5, sg=1, negative=5, sample=1e-3,\n",
    "                  min_count=5, max_final_vocab=300_000, epochs=5, workers=15, seed=42)\n",
    "\n",
    "def train_w2v(clean_path):\n",
    "    t = time.time()\n",
    "    m = Word2Vec(corpus_file=clean_path, **W2V_PARAMS)\n",
    "    print(f\"trained on {clean_path}: {(time.time() - t) / 60:.1f} min, vocab {len(m.wv.key_to_index):,}\")\n",
    "    return m\n",
    "\n",
    "clean_path = \"wiki_sampled_20_clean.txt\"\n",
    "n_tokens = preprocess_file(output_path, clean_path)\n",
    "print(f\"20% wiki tokens after preprocessing: {n_tokens:,}\")\n",
    "my_model = train_w2v(clean_path)\n",
    "my_model.save(\"w2v_wiki20.model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e833c849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:19:41.921222Z",
     "iopub.status.busy": "2026-10-04T10:19:41.921222Z",
     "iopub.status.idle": "2026-10-04T10:19:41.941593Z",
     "shell.execute_reply": "2026-10-04T10:19:41.940496Z"
    },
    "id": "qWiQF70izxP7"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"questions-words.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a54ad97f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:19:41.981149Z",
     "iopub.status.busy": "2026-10-04T10:19:41.981149Z",
     "iopub.status.idle": "2026-10-04T10:21:28.957980Z",
     "shell.execute_reply": "2026-10-04T10:21:28.957980Z"
    },
    "id": "q6xpqgdIy5x1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/19544 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "text": [
      "OOV questions: 0\n",
      "Category: Semantic, Accuracy: 55.59815086255496%\n",
      "Category: Syntactic, Accuracy: 42.21077283372365%\n",
      "Sub-Category: capital-common-countries, Accuracy: 73.71541501976284%\n",
      "Sub-Category: capital-world, Accuracy: 71.3527851458886%\n",
      "Sub-Category: currency, Accuracy: 9.815242494226329%\n",
      "Sub-Category: city-in-state, Accuracy: 35.22496959870288%\n",
      "Sub-Category: family, Accuracy: 74.30830039525692%\n",
      "Sub-Category: gram1-adjective-to-adverb, Accuracy: 16.733870967741936%\n",
      "Sub-Category: gram2-opposite, Accuracy: 16.25615763546798%\n",
      "Sub-Category: gram3-comparative, Accuracy: 54.87987987987988%\n",
      "Sub-Category: gram4-superlative, Accuracy: 25.40106951871658%\n",
      "Sub-Category: gram5-present-participle, Accuracy: 33.04924242424242%\n",
      "Sub-Category: gram6-nationality-adjective, Accuracy: 81.05065666041276%\n",
      "Sub-Category: gram7-past-tense, Accuracy: 41.28205128205128%\n",
      "Sub-Category: gram8-plural, Accuracy: 41.14114114114114%\n",
      "Sub-Category: gram9-plural-verbs, Accuracy: 40.804597701149426%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# Do predictions and preserve the gold answers (word_D)\n",
    "preds = []\n",
    "golds = []\n",
    "my_wv = my_model.wv\n",
    "\n",
    "for analogy in tqdm(data[\"Question\"]):\n",
    "      # TODO6: Write your code here to use your trained word embeddings for getting predictions of the analogy task.\n",
    "      # You should also preserve the gold answers during iterations for evaluations later.\n",
    "      word_a, word_b, word_c, word_d = analogy.lower().split()\n",
    "      golds.append(word_d)\n",
    "      if all(w in my_wv.key_to_index for w in (word_a, word_b, word_c)):\n",
    "          preds.append(my_wv.most_similar(positive=[word_b, word_c], negative=[word_a], topn=1)[0][0])\n",
    "      else:\n",
    "          preds.append(None)\n",
    "print(\"OOV questions:\", sum(p is None for p in preds))\n",
    "\n",
    "# 模板在 TODO6 後面沒有評分格，所以這裡用 Part II 的 calculate_accuracy 印出成績。\n",
    "golds_np, preds_np = np.array(golds), np.array(preds)\n",
    "for category in data[\"Category\"].unique():\n",
    "    mask = data[\"Category\"] == category\n",
    "    print(f\"Category: {category}, Accuracy: {calculate_accuracy(golds_np[mask], preds_np[mask]) * 100}%\")\n",
    "for sub_category in data[\"SubCategory\"].unique():\n",
    "    mask = data[\"SubCategory\"] == sub_category\n",
    "    print(f\"Sub-Category{sub_category}, Accuracy: {calculate_accuracy(golds_np[mask], preds_np[mask]) * 100}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77265659",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:21:29.024337Z",
     "iopub.status.busy": "2026-10-04T10:21:29.024337Z",
     "iopub.status.idle": "2026-10-04T10:21:29.430636Z",
     "shell.execute_reply": "2026-10-04T10:21:29.430636Z"
    },
    "id": "AjZ14dQL0mhf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# Collect words from Google Analogy dataset\n",
    "SUB_CATEGORY = \": family\"\n",
    "\n",
    "# TODO7: Plot t-SNE for the words in the SUB_CATEGORY `: family`\n",
    "family_words = sorted({w for q in data[data[\"SubCategory\"] == SUB_CATEGORY][\"Question\"]\n",
    "                       for w in q.lower().split()})\n",
    "family_words = [w for w in family_words if w in my_wv.key_to_index]\n",
    "vectors = np.array([my_wv[w] for w in family_words])\n",
    "# t-SNE：把 100 維壓成 2 維。字只有幾十個，所以 perplexity 要調小（必須小於樣本數）。\n",
    "points = TSNE(n_components=2, perplexity=15, random_state=42, init=\"pca\").fit_transform(vectors)\n",
    "plt.figure(figsize=(12, 9))\n",
    "plt.scatter(points[:, 0], points[:, 1], s=15)\n",
    "for (x, y), w in zip(points, family_words):\n",
    "    plt.annotate(w, (x, y), fontsize=9)\n",
    "\n",
    "\n",
    "plt.title(\"Word Relationships from Google Analogy Task\")\n",
    "plt.show()\n",
    "plt.savefig(\"word_relationships.png\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d4052e",
   "metadata": {},
   "source": [
    "> **示範版報告**：由 Claude 撰寫，用來示範「每一題寫到什麼程度」。不是要繳交的版本，請用自己的結果與文字重寫。\n",
    "\n",
    "Running environment: Local — Windows 11 (10.0.26200), CPU: Intel Core i7-11800H (8 cores / 16 threads), RAM 16 GB\n",
    "\n",
    "Python version: 3.12.3\n",
    "\n",
    "##### 1. Which embedding model do you use? What are the pre-processing steps? What are the hyperparameter settings? (5%)\n",
    "\n",
    "Answer:\n",
    "\n",
    "**模型**\n",
    "- Part II：`glove-wiki-gigaword-100`（Stanford GloVe，維基百科＋Gigaword 新聞約 60 億字訓練，40 萬字、100 維、全部小寫）。\n",
    "- Part III：Gensim `Word2Vec`（Skip-gram），用 20% 維基百科自己訓練。\n",
    "\n",
    "**抽樣**：以文章為單位，`random.seed(42)`，每篇擲一次亂數，`r < 0.2` 就留下（1,124,733 / 5,623,655 篇）。同一個亂數同時產生 5%、10% 子集，所以 5% ⊂ 10% ⊂ 20%。直接串流讀 11 個 `.gz`，不解壓、不合併，記憶體只放一篇文章。\n",
    "\n",
    "**前處理**\n",
    "- 做了：只保留 `[a-z]+` 的字（助教資料已小寫、去標點；非英文字只占約 0.4%）；字典只留最常見的 30 萬字（`max_final_vocab`）。\n",
    "- **刻意沒做：移除停用詞**——考卷有 206 題含 NLTK 停用詞（family 的 he/she/his/her、currency 的 won）；第 5 題實驗顯示移除後 family 從 67.19% 掉到 50.20%。\n",
    "- **刻意沒做：詞形還原**——語法類考的就是字形變化（bigger、biggest、cars），還原會讓答案消失。\n",
    "\n",
    "**超參數**：`vector_size=100`（與 GloVe-100 相同，方便公平比較）、`window=5`、`sg=1`（Skip-gram）、`negative=5`、`sample=1e-3`、`min_count=5`、`max_final_vocab=300000`、`epochs=5`、`workers=15`、`seed=42`。20% 維基共 666,053,126 字，訓練 36.9 分鐘，字典 299,405 字。\n",
    "\n",
    "**答題**：3CosAdd（`most_similar(positive=[b, c], negative=[a])`，自動排除題目三個字），題目先轉小寫；題目字不在字典中則算答錯（本次 0 題）。\n",
    "\n",
    "##### 2. What will the performance be like if you sample 5%, 10% and 20% of wiki text in TODO4? (10%, 3% for each)\n",
    "\n",
    "Answer:\n",
    "\n",
    "| | 5% | 10% | 20% |\n",
    "|---|---|---|---|\n",
    "| 字數 | 1.66 億 | 3.33 億 | 6.66 億 |\n",
    "| 訓練時間 | 9.1 min | 18.4 min | 36.9 min |\n",
    "| **Overall** | 45.22 | 46.87 | **48.29** |\n",
    "| Semantic | 53.93 | 54.26 | 55.60 |\n",
    "| Syntactic | 37.99 | 40.74 | 42.21 |\n",
    "| superlative | 16.76 | 22.82 | 25.40 |\n",
    "| comparative | 43.09 | 51.13 | 54.88 |\n",
    "| OOV 題數 | 107 | 0 | 0 |\n",
    "\n",
    "- **越多越好，但邊際效益遞減**：資料 ×4、時間 ×4，整體只 +3.07。常見字在 5% 時座標就已穩定，多出的資料主要幫到少見字。\n",
    "- **語法類進步比語意類大**（+4.22 vs +1.67），最高級 +8.6：biggest、worst 等字形本來就少見，需要更多出現次數。\n",
    "- **5% 有 107 題 OOV**：部分字出現不到 `min_count=5` 次。\n",
    "- 部分小類不單調（capital-common-countries 79.25→73.72）。同設定重跑 20% 兩次，整體 48.21 vs 48.29，但 comparative 45.95 vs 54.88——多執行緒訓練有隨機性，題數少的小類波動大，**1～2 點的差距不應過度解讀**。\n",
    "\n",
    "##### 3. What is the performance for different categories or sub-categories when trained on different corpora? (15%)\n",
    "\n",
    "3.1. Present your results. (5%)\n",
    "\n",
    "Answer:\n",
    "\n",
    "語料：SemEval 2016/2017 Task 3 論壇問答（Qatar Living）。為了把「資料量」和「內容種類」分開，另從 5% 維基切出**同樣字數（7,292 萬字）**的對照組，兩者用完全相同的參數訓練。\n",
    "\n",
    "| | Wiki control | Forum |\n",
    "|---|---|---|\n",
    "| **Overall** | **41.66** | 26.05 |\n",
    "| Semantic | 46.07 | 12.37 |\n",
    "| Syntactic | 38.00 | 37.41 |\n",
    "| capital-world | 54.47 | 12.00 |\n",
    "| city-in-state | 34.29 | 2.51 |\n",
    "| nationality-adjective | 75.48 | 22.01 |\n",
    "| family | 63.24 | 60.67 |\n",
    "| comparative | 53.45 | **65.54** |\n",
    "| superlative | 19.25 | **43.58** |\n",
    "| present-participle | 31.16 | **52.18** |\n",
    "| plural-verbs | 34.71 | **46.32** |\n",
    "| OOV 題數 | 169 | 3,510 |\n",
    "\n",
    "答案覆蓋率（四個字都在字典中的比例）：capital-world 98.3% vs **48.6%**、currency 86.8% vs **39.0%**、city-in-state 100% vs 68.3%。\n",
    "\n",
    "3.2. Introduce the corpus you selected and explain what are the differences between the Wikipedia corpus and your corpus. (including data size, topic difference, structural difference … ) (5%)\n",
    "\n",
    "Answer:\n",
    "\n",
    "- **是什麼**：卡達生活論壇的發問與留言，189,941 個討論串、2,118,254 則貼文；只取標題、內文、留言三個欄位，刪除 HTML 與網址後，用與維基相同的規則（小寫、2～15 字母）斷詞。\n",
    "- **資料量**：72,919,300 字，約為 20% 維基的 1/9；因此另做同字數的維基對照組。字典 100,310 字（對照組 224,050）。\n",
    "- **主題**：維基涵蓋歷史、地理、人物、科學；論壇集中在簽證、工作、薪水、租屋、購物等在地生活。\n",
    "- **結構與文體**：維基是編輯校稿過的長篇說明文；論壇是短貼文、口語、問句多、錯字與縮寫多（salary 的鄰居是 slary、salry、sallary；visa 的鄰居是 iqama、rp）。\n",
    "\n",
    "3.3. Explain why the performance increases or decreases. (5%)\n",
    "\n",
    "Answer:\n",
    "\n",
    "在**資料量相同**的前提下，差異來自內容：\n",
    "\n",
    "- **語意類大輸（46.07 → 12.37）**：論壇很少談世界首都、美國州名、國籍。每百萬字出現次數：capital 160.4 vs 43.2、illinois 90.0 vs 1.1、albanian 13.4 vs 0.8。很多答案根本不在字典（覆蓋率 48.6%）；就算在字典中，也很少出現在「X 是 Y 的首都」這種前後文，關係學不起來。\n",
    "- **語法類打平，但比較級、最高級、-ing、第三人稱動詞反而贏**：網友常寫 better（8.5 倍）、cheapest（28.6 倍）、looking（12.1 倍）、going（8.8 倍）、goes（3.5 倍）。字形出現越多，座標越準。\n",
    "- **錯字成為「最像的字」**：錯字與正確字的前後文完全相同，而詞向量只看前後文——這說明詞向量學的是分布，而非字義。\n",
    "- 結論：**語料的主題與文體決定詞向量學到哪些關係**；要讓某類關係學好，語料裡就要有大量該類的用法。\n",
    "\n",
    "##### 4. Select a few words and use their embeddings to retrieve the five most similar words. What do you observe?  (10%)\n",
    "\n",
    "Answer:\n",
    "\n",
    "挑字原則：考卷字（king）、老師提到的例子（cat）、一字多義（apple、bank）、反義測試（good）、專有名詞（taiwan）、一般名詞（computer）。\n",
    "\n",
    "| 字 | GloVe | Wiki 20% |\n",
    "|---|---|---|\n",
    "| king | prince, queen, son, brother, monarch | nangklao, prince, queen, suriyothai, throne |\n",
    "| cat | dog, rabbit, cats, monkey, pet | dog, rabbit, pet, sourpuss, mouse |\n",
    "| apple | microsoft, ibm, intel, software, dell | blackberry, iphone, tvos, raspberry, xelibri |\n",
    "| bank | banks, banking, credit, investment, financial | savings, bancorp, guaranty, dfcu, bancorporation |\n",
    "| good | better, sure, really, kind, very | sure, **bad**, tough, better, decent |\n",
    "| taiwan | mainland, china, taiwanese, taipei, hong | china, guangdong, taipei, hainan, japan |\n",
    "| computer | computers, software, technology, pc, hardware | computers, software, computing, mainframe, hardware |\n",
    "\n",
    "觀察：\n",
    "- **反義詞很近**：good 的第二名是 bad——兩者前後文相同（「a ___ movie」），詞向量分不出正反。\n",
    "- **一字多義被混在一起**：apple 同時靠近手機（blackberry、iphone）與水果（raspberry）；bank 只剩金融義，河岸義被較常見的用法蓋過。每個字只有一個向量，是靜態詞向量的根本限制（BERT 等上下文模型才能解決）。\n",
    "- **語料偏差直接反映在鄰居**：king 的第一名是泰國國王 nangklao；cat 的鄰居 sourpuss，回查語料發現是卡通貓角色（「sourpuss cranky cat who hates mondays」）——驗證了老師說的「貓的鄰居不一定是貓」。\n",
    "- GloVe 的相似度普遍較高（0.8～0.9 vs 0.7～0.8），鄰居也較「一般」，反映其訓練資料量大約 9 倍。\n",
    "\n",
    "##### 5. … Anything that can strengthen your report.  (5%)\n",
    "\n",
    "Answer:\n",
    "\n",
    "**(a) 移除停用詞的實驗（5% 維基）**：整體 45.22 → 45.03 幾乎不變，但 family 67.19 → 50.20、plural-verbs 38.16 → 27.01、comparative 43.09 → 37.46；地理類反而上升（nationality 76.99 → 82.05）。整體分數掩蓋了小類的此消彼長：停用詞既是 family 的答案、也是語法線索；移除後首都與國家在視窗中更靠近，地理關係反而好學。**前處理沒有絕對好壞，取決於要評估的任務。**\n",
    "\n",
    "**(b) 超參數（5% 維基，一次只改一個）**\n",
    "\n",
    "| 設定 | Overall | Semantic | Syntactic |\n",
    "|---|---|---|---|\n",
    "| 基準（Skip-gram, window 5, 100 維） | 45.22 | 53.93 | 37.99 |\n",
    "| CBOW | 52.71 | 59.16 | 47.34 |\n",
    "| window=10 | 41.86 | 50.70 | 34.51 |\n",
    "| vector_size=300 | **56.95** | **68.16** | 47.63 |\n",
    "\n",
    "- 300 維用 5% 資料就贏過 100 維的 20%（48.29），語意類甚至超過 GloVe-100（65.34）：在此規模下，**向量容量比資料量更關鍵**。\n",
    "- CBOW 在語法類大勝（comparative 43→68、superlative 17→37）：CBOW 以上下文預測中間字，對「此位置該放哪種字形」特別敏感。\n",
    "- window 變大反而變差（family −12.5）：視窗越大越偏向主題相關（king–kingdom），而非功能相似（king–queen）。\n",
    "- 這些差距（7～12 點）遠大於隨機波動（整體約 0.1 點），結論可信。\n",
    "\n",
    "**(c) Top-k 命中率**：Wiki 20% 的 top-1/3/5/10 為 48.29 / 62.27 / 67.01 / 72.77（GloVe 63.11 / 73.68 / 77.73 / 82.01）。很多錯題只是「差一點」。\n",
    "\n",
    "**(d) 錯題分析（Wiki 20%，10,107 題錯）**：0 題 OOV、4,785 題（47%）正解在第 2～10 名。錯法：拼法變體（argentinian vs argentinean、belarusian vs belorussian——其實是考卷的拼法限制）、方向相反（large → smaller）、同類選錯（Bishkek → tajikistan）、近親字（father → stepmother）、語料偏差（child → abusers）。\n",
    "\n",
    "**(e) 3CosMul**：同一份 Wiki 20% 向量改用 3CosMul，整體 48.29 → 44.61、各小類皆下降；文獻中 3CosMul 常較好，但在本設定不成立。\n",
    "\n",
    "**(f) PCA vs t-SNE**：PCA 的第二主成分大致是性別軸（女性詞在上、男性詞在下，she/her 例外地靠近 he/his）；t-SNE 則把每對（boy/girl、groom/bride）貼得很緊、分群清楚。PCA 保留全域方向，t-SNE 保留局部鄰居。\n",
    "\n",
    "**(g) 隨機性**：同設定兩次訓練，整體差 0.08，但 comparative 差 8.9 點，比較實驗時需注意。\n",
    "\n",
    "##### 6. Generative AI Usage\n",
    "\n",
    "Answer:\n",
    "\n",
    "本示範筆記本的程式（標有 `# [Generative AI]` 的每一格）與本報告，皆由 Claude（Anthropic）產生並在本機實際執行；所有數字與圖表皆來自本筆記本的執行輸出。這份是學習用示範，實際繳交時應由學生自行重做並如實說明 AI 的使用範圍。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b2c4fd",
   "metadata": {},
   "source": [
    "## Code For 3. What is the performance for different categories or sub-categories when trained on different corpora? (15%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "40a1b9ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:21:29.503496Z",
     "iopub.status.busy": "2026-10-04T10:21:29.503496Z",
     "iopub.status.idle": "2026-10-04T10:25:53.192082Z",
     "shell.execute_reply": "2026-10-04T10:25:53.191076Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GloVe (pre-trained): overall 63.11%  (OOV questions 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 20%: overall 48.29%  (OOV questions 0)\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 共用工具：用任何一份詞向量考一次試。\n",
    "# 每題一次拿前 10 名候選字，記下正確答案排第幾名（之後算 top-k、做錯題分析都用得到）。\n",
    "results, details, models = {}, {}, {}\n",
    "\n",
    "def evaluate(wv, name, method=\"add\", max_k=10):\n",
    "    sim = wv.most_similar if method == \"add\" else wv.most_similar_cosmul\n",
    "    ranks, preds, oov = [], [], 0\n",
    "    for q in data[\"Question\"]:\n",
    "        a, b, c, d = q.lower().split()\n",
    "        if all(w in wv.key_to_index for w in (a, b, c)):\n",
    "            cands = [w for w, _ in sim(positive=[b, c], negative=[a], topn=max_k)]\n",
    "            preds.append(cands[0])\n",
    "            ranks.append(cands.index(d) + 1 if d in cands else None)\n",
    "        else:\n",
    "            preds.append(None)\n",
    "            ranks.append(None)\n",
    "            oov += 1\n",
    "    hits = np.array([r == 1 for r in ranks])\n",
    "    res = {\"Overall\": hits.mean() * 100}\n",
    "    for col in [\"Category\", \"SubCategory\"]:\n",
    "        for v in data[col].unique():\n",
    "            res[v] = hits[(data[col] == v).values].mean() * 100\n",
    "    res[\"OOV questions\"] = oov\n",
    "    results[name], details[name] = res, {\"ranks\": ranks, \"preds\": preds}\n",
    "    print(f\"{name}: overall {res['Overall']:.2f}%  (OOV questions {oov})\")\n",
    "    return res\n",
    "\n",
    "def answer_coverage(wv):\n",
    "    # 每個小類裡，四個字全部都在字典裡的題目佔幾 %（答案不在字典裡，再聰明也答不對）\n",
    "    ok = data[\"Question\"].map(lambda q: all(w in wv.key_to_index for w in q.lower().split()))\n",
    "    return ok.groupby(data[\"SubCategory\"], sort=False).mean().mul(100).round(1)\n",
    "\n",
    "models[\"GloVe (pre-trained)\"], models[\"Wiki 20%\"] = model, my_wv\n",
    "_ = evaluate(model, \"GloVe (pre-trained)\")\n",
    "_ = evaluate(my_wv, \"Wiki 20%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "79375586",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:25:53.273651Z",
     "iopub.status.busy": "2026-10-04T10:25:53.272651Z",
     "iopub.status.idle": "2026-10-04T10:25:53.837292Z",
     "shell.execute_reply": "2026-10-04T10:25:53.836283Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys: ['RelComments', 'RelQuestion', 'THREAD_SEQUENCE'] | question keys: ['RELQ_DATE', 'RELQ_ID', 'RelQSubject', 'RELQ_USERNAME', 'RELQ_USERID', 'RelQBody', 'RELQ_CATEGORY']\n",
      "subject: Thailand:IT Minsitry blocks CNN; Facebook;\n",
      "first comment: have they blocked porn??? <img src=\"http://www.qatarliving.com/files/images/Da.gif\">\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 3 題：換一份「不是維基百科」的語料。\n",
    "# 選的是 SemEval 2016/2017 Task 3 的論壇問答：卡達生活論壇（Qatar Living）網友的發問與留言，gensim-data 可以直接下載。\n",
    "# 注意：gensim 幫這份資料附的讀檔程式用了舊版 smart_open 的寫法，新版會 ImportError。\n",
    "#       所以只用 gensim 下載（return_path=True 只回傳檔案位置），讀檔自己來：每一行是一個討論串的 JSON。\n",
    "import gensim.downloader, itertools, json\n",
    "from gensim.utils import simple_preprocess\n",
    "\n",
    "FORUM_PATH = gensim.downloader.load(\"semeval-2016-2017-task3-subtaskA-unannotated\", return_path=True)\n",
    "\n",
    "def iter_threads():\n",
    "    with gzip.open(FORUM_PATH, \"rt\", encoding=\"utf-8\") as f:\n",
    "        for line in f:\n",
    "            yield json.loads(line)\n",
    "\n",
    "def thread_texts(thread):\n",
    "    # 只拿真正的文字：發問標題、發問內容、每一則留言。使用者名稱、日期、編號都不要。\n",
    "    q = thread.get(\"RelQuestion\", {})\n",
    "    yield q.get(\"RelQSubject\") or \"\"\n",
    "    yield q.get(\"RelQBody\") or \"\"\n",
    "    for c in thread.get(\"RelComments\", []):\n",
    "        yield c.get(\"RelCText\") or \"\"\n",
    "\n",
    "first = next(iter_threads())\n",
    "print(\"keys:\", list(first.keys()), \"| question keys:\", list(first[\"RelQuestion\"].keys()))\n",
    "print(\"subject:\", first[\"RelQuestion\"].get(\"RelQSubject\"))\n",
    "print(\"first comment:\", first[\"RelComments\"][0][\"RelCText\"][:200])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44a30ebc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:25:53.925344Z",
     "iopub.status.busy": "2026-10-04T10:25:53.924342Z",
     "iopub.status.idle": "2026-10-04T10:28:24.191416Z",
     "shell.execute_reply": "2026-10-04T10:28:24.191416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forum: 189,941 threads, 2,118,254 posts, 72,919,300 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5% wiki tokens: 166,194,401\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wiki control: 72,920,923 tokens\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 用跟維基百科一樣的規則前處理：先拿掉網頁語法和網址，再小寫、只留 2～15 個字母的英文字（WikiCorpus 的預設）。\n",
    "HTML_URL_RE = re.compile(r\"<[^>]+>|https?://\\S+|www\\.\\S+\")\n",
    "forum_tokens = forum_lines = n_threads = 0\n",
    "with open(\"forum_clean.txt\", \"w\", encoding=\"utf-8\", newline=\"\\n\") as fout:\n",
    "    for thread in iter_threads():\n",
    "        n_threads += 1\n",
    "        for text in thread_texts(thread):\n",
    "            text = HTML_URL_RE.sub(\" \", text)\n",
    "            toks = [w for w in simple_preprocess(text, min_len=2, max_len=15) if TOKEN_RE.match(w)]\n",
    "            if len(toks) >= 3:\n",
    "                fout.write(\" \".join(toks) + \"\\n\")\n",
    "                forum_tokens += len(toks)\n",
    "                forum_lines += 1\n",
    "print(f\"forum: {n_threads:,} threads, {forum_lines:,} posts, {forum_tokens:,} tokens\")\n",
    "\n",
    "# 對照組：從維基 5% 裡依序拿文章，拿到「字數跟論壇一樣多」就停。\n",
    "# 這樣兩份資料一樣大，分數的差別就只剩「內容種類」造成的。\n",
    "n5 = preprocess_file(\"wiki_sampled_5.txt\", \"wiki_sampled_5_clean.txt\")\n",
    "print(f\"5% wiki tokens: {n5:,}\")\n",
    "ctrl_tokens = 0\n",
    "with open(\"wiki_sampled_5_clean.txt\", encoding=\"utf-8\") as fin, open(\"wiki_control_clean.txt\", \"w\", encoding=\"utf-8\", newline=\"\\n\") as fout:\n",
    "    for line in fin:\n",
    "        if ctrl_tokens >= forum_tokens:\n",
    "            break\n",
    "        fout.write(line)\n",
    "        ctrl_tokens += len(line.split())\n",
    "print(f\"wiki control: {ctrl_tokens:,} tokens\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "60c0242a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:28:24.267958Z",
     "iopub.status.busy": "2026-10-04T10:28:24.266124Z",
     "iopub.status.idle": "2026-10-04T10:37:58.150898Z",
     "shell.execute_reply": "2026-10-04T10:37:58.150315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained on forum_clean.txt: 3.9 min, vocab 100,310\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained on wiki_control_clean.txt: 4.2 min, vocab 224,050\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forum (same size): overall 26.05%  (OOV questions 3510)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki control (same size): overall 41.66%  (OOV questions 169)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>Wiki control (same size)</th>\n",
       "      <th>Forum (same size)</th>\n",
       "      <th>Wiki 20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>41.66</td>\n",
       "      <td>26.05</td>\n",
       "      <td>48.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semantic</th>\n",
       "      <td>46.07</td>\n",
       "      <td>12.37</td>\n",
       "      <td>55.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syntactic</th>\n",
       "      <td>38.00</td>\n",
       "      <td>37.41</td>\n",
       "      <td>42.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>77.47</td>\n",
       "      <td>34.78</td>\n",
       "      <td>73.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>54.47</td>\n",
       "      <td>12.00</td>\n",
       "      <td>71.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>7.39</td>\n",
       "      <td>1.04</td>\n",
       "      <td>9.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>34.29</td>\n",
       "      <td>2.51</td>\n",
       "      <td>35.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>63.24</td>\n",
       "      <td>60.67</td>\n",
       "      <td>74.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>11.19</td>\n",
       "      <td>12.80</td>\n",
       "      <td>16.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>9.73</td>\n",
       "      <td>14.04</td>\n",
       "      <td>16.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>53.45</td>\n",
       "      <td>65.54</td>\n",
       "      <td>54.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>19.25</td>\n",
       "      <td>43.58</td>\n",
       "      <td>25.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>31.16</td>\n",
       "      <td>52.18</td>\n",
       "      <td>33.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>75.48</td>\n",
       "      <td>22.01</td>\n",
       "      <td>81.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>41.22</td>\n",
       "      <td>35.38</td>\n",
       "      <td>41.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>34.31</td>\n",
       "      <td>40.02</td>\n",
       "      <td>41.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>34.71</td>\n",
       "      <td>46.32</td>\n",
       "      <td>40.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOV questions</th>\n",
       "      <td>169.00</td>\n",
       "      <td>3510.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Wiki control (same size)  Forum (same size)  \\\n",
       "Overall                                           41.66              26.05   \n",
       "Semantic                                          46.07              12.37   \n",
       "Syntactic                                         38.00              37.41   \n",
       ": capital-common-countries                        77.47              34.78   \n",
       ": capital-world                                   54.47              12.00   \n",
       ": currency                                         7.39               1.04   \n",
       ": city-in-state                                   34.29               2.51   \n",
       ": family                                          63.24              60.67   \n",
       ": gram1-adjective-to-adverb                       11.19              12.80   \n",
       ": gram2-opposite                                   9.73              14.04   \n",
       ": gram3-comparative                               53.45              65.54   \n",
       ": gram4-superlative                               19.25              43.58   \n",
       ": gram5-present-participle                        31.16              52.18   \n",
       ": gram6-nationality-adjective                     75.48              22.01   \n",
       ": gram7-past-tense                                41.22              35.38   \n",
       ": gram8-plural                                    34.31              40.02   \n",
       ": gram9-plural-verbs                              34.71              46.32   \n",
       "OOV questions                                    169.00            3510.00   \n",
       "\n",
       "                               Wiki 20%  \n",
       "Overall                           48.29  \n",
       "Semantic                          55.60  \n",
       "Syntactic                         42.21  \n",
       ": capital-common-countries        73.72  \n",
       ": capital-world                   71.35  \n",
       ": currency                         9.82  \n",
       ": city-in-state                   35.22  \n",
       ": family                          74.31  \n",
       ": gram1-adjective-to-adverb       16.73  \n",
       ": gram2-opposite                  16.26  \n",
       ": gram3-comparative               54.88  \n",
       ": gram4-superlative               25.40  \n",
       ": gram5-present-participle        33.05  \n",
       ": gram6-nationality-adjective     81.05  \n",
       ": gram7-past-tense                41.28  \n",
       ": gram8-plural                    41.14  \n",
       ": gram9-plural-verbs              40.80  \n",
       "OOV questions                      0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>coverage: wiki control</th>\n",
       "      <th>coverage: forum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubCategory</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>98.3</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>86.8</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>100.0</td>\n",
       "      <td>68.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>100.0</td>\n",
       "      <td>91.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>94.1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>100.0</td>\n",
       "      <td>95.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               coverage: wiki control  coverage: forum\n",
       "SubCategory                                                           \n",
       ": capital-common-countries                      100.0            100.0\n",
       ": capital-world                                  98.3             48.6\n",
       ": currency                                       86.8             39.0\n",
       ": city-in-state                                 100.0             68.3\n",
       ": family                                        100.0             91.3\n",
       ": gram1-adjective-to-adverb                     100.0            100.0\n",
       ": gram2-opposite                                100.0            100.0\n",
       ": gram3-comparative                             100.0            100.0\n",
       ": gram4-superlative                              94.1            100.0\n",
       ": gram5-present-participle                      100.0            100.0\n",
       ": gram6-nationality-adjective                   100.0             95.1\n",
       ": gram7-past-tense                              100.0            100.0\n",
       ": gram8-plural                                  100.0            100.0\n",
       ": gram9-plural-verbs                            100.0            100.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "forum_model = train_w2v(\"forum_clean.txt\")\n",
    "ctrl_model = train_w2v(\"wiki_control_clean.txt\")\n",
    "models[\"Forum (same size)\"], models[\"Wiki control (same size)\"] = forum_model.wv, ctrl_model.wv\n",
    "evaluate(forum_model.wv, \"Forum (same size)\")\n",
    "evaluate(ctrl_model.wv, \"Wiki control (same size)\")\n",
    "\n",
    "cols = [\"Wiki control (same size)\", \"Forum (same size)\", \"Wiki 20%\"]\n",
    "display(pd.DataFrame({k: results[k] for k in cols}).round(2))\n",
    "# 答案覆蓋率：題目四個字都在字典裡的比例。覆蓋率低 = 很多題連答的機會都沒有。\n",
    "display(pd.DataFrame({\"coverage: wiki control\": answer_coverage(ctrl_model.wv),\n",
    "                      \"coverage: forum\": answer_coverage(forum_model.wv)}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f9e1109",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:37:58.227033Z",
     "iopub.status.busy": "2026-10-04T10:37:58.227033Z",
     "iopub.status.idle": "2026-10-04T10:37:58.270490Z",
     "shell.execute_reply": "2026-10-04T10:37:58.270490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>word</th>\n",
       "      <th>corpus</th>\n",
       "      <th>top 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>visa</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>visas, passport, bdtc, passports, zwartendijk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>visa</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>rp, iqama, viza, residence, residance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>salary</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>salaries, repayment, pay, wages, paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>salary</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>slary, salry, sallary, salery, salaray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>doha</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>manama, abbasiyyin, qatar, dubai, shaab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>doha</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>qatar, doah, qata, maseeid, alkhore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>family</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>relatives, father, families, mother, grandfather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>family</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>familly, rescidence, fmaily, pernament, famliy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>king</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>prince, queen, monarch, harthacnut, harefoot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>king</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>edshel, koil, ypsilanti, edsel, bhumibol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>paris</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>brussels, marseille, fontainebleau, universell...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>paris</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>amsterdam, france, cancun, london, tokyo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>computer</td>\n",
       "      <td>Wiki control (same size)</td>\n",
       "      <td>computers, software, mainframe, hardware, comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>computer</td>\n",
       "      <td>Forum (same size)</td>\n",
       "      <td>soundcard, desktop, hardware, pc, computers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        word                    corpus  \\\n",
       "0       visa  Wiki control (same size)   \n",
       "1       visa         Forum (same size)   \n",
       "2     salary  Wiki control (same size)   \n",
       "3     salary         Forum (same size)   \n",
       "4       doha  Wiki control (same size)   \n",
       "5       doha         Forum (same size)   \n",
       "6     family  Wiki control (same size)   \n",
       "7     family         Forum (same size)   \n",
       "8       king  Wiki control (same size)   \n",
       "9       king         Forum (same size)   \n",
       "10     paris  Wiki control (same size)   \n",
       "11     paris         Forum (same size)   \n",
       "12  computer  Wiki control (same size)   \n",
       "13  computer         Forum (same size)   \n",
       "\n",
       "                                                top 5  \n",
       "0       visas, passport, bdtc, passports, zwartendijk  \n",
       "1               rp, iqama, viza, residence, residance  \n",
       "2               salaries, repayment, pay, wages, paid  \n",
       "3              slary, salry, sallary, salery, salaray  \n",
       "4             manama, abbasiyyin, qatar, dubai, shaab  \n",
       "5                 qatar, doah, qata, maseeid, alkhore  \n",
       "6    relatives, father, families, mother, grandfather  \n",
       "7      familly, rescidence, fmaily, pernament, famliy  \n",
       "8        prince, queen, monarch, harthacnut, harefoot  \n",
       "9            edshel, koil, ypsilanti, edsel, bhumibol  \n",
       "10  brussels, marseille, fontainebleau, universell...  \n",
       "11           amsterdam, france, cancun, london, tokyo  \n",
       "12  computers, software, mainframe, hardware, comp...  \n",
       "13        soundcard, desktop, hardware, pc, computers  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 同一個字，在論壇和維基（一樣大小）學到的鄰居差在哪\n",
    "rows = []\n",
    "for w in [\"visa\", \"salary\", \"doha\", \"family\", \"king\", \"paris\", \"computer\"]:\n",
    "    for name in [\"Wiki control (same size)\", \"Forum (same size)\"]:\n",
    "        wv = models[name]\n",
    "        rows.append({\"word\": w, \"corpus\": name,\n",
    "                     \"top 5\": \", \".join(x for x, _ in wv.most_similar(w, topn=5)) if w in wv.key_to_index else \"(not in vocab)\"})\n",
    "display(pd.DataFrame(rows))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "502fbfde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T12:12:56.388827Z",
     "iopub.status.busy": "2026-10-04T12:12:56.387827Z",
     "iopub.status.idle": "2026-10-04T12:13:10.600908Z",
     "shell.execute_reply": "2026-10-04T12:13:10.599903Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki control (per 1M): 72,920,923 tokens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forum (per 1M): 72,919,300 tokens\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wiki control (per 1M)</th>\n",
       "      <th>Forum (per 1M)</th>\n",
       "      <th>forum / wiki</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>better</th>\n",
       "      <td>116.98</td>\n",
       "      <td>990.40</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best</th>\n",
       "      <td>553.01</td>\n",
       "      <td>988.65</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cheaper</th>\n",
       "      <td>7.78</td>\n",
       "      <td>80.27</td>\n",
       "      <td>10.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cheapest</th>\n",
       "      <td>0.96</td>\n",
       "      <td>27.50</td>\n",
       "      <td>28.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bigger</th>\n",
       "      <td>13.12</td>\n",
       "      <td>53.58</td>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biggest</th>\n",
       "      <td>37.26</td>\n",
       "      <td>59.97</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>looking</th>\n",
       "      <td>56.31</td>\n",
       "      <td>680.74</td>\n",
       "      <td>12.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>going</th>\n",
       "      <td>104.22</td>\n",
       "      <td>914.05</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working</th>\n",
       "      <td>187.26</td>\n",
       "      <td>583.48</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>costs</th>\n",
       "      <td>43.99</td>\n",
       "      <td>77.13</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goes</th>\n",
       "      <td>66.47</td>\n",
       "      <td>235.33</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wife</th>\n",
       "      <td>212.38</td>\n",
       "      <td>605.50</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>husband</th>\n",
       "      <td>104.85</td>\n",
       "      <td>418.87</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he</th>\n",
       "      <td>5122.78</td>\n",
       "      <td>3669.63</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>she</th>\n",
       "      <td>1266.80</td>\n",
       "      <td>1885.25</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital</th>\n",
       "      <td>160.38</td>\n",
       "      <td>43.23</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illinois</th>\n",
       "      <td>90.02</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>albanian</th>\n",
       "      <td>13.37</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brazilian</th>\n",
       "      <td>42.43</td>\n",
       "      <td>33.46</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kwanza</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Wiki control (per 1M)  Forum (per 1M)  forum / wiki\n",
       "better                    116.98          990.40          8.47\n",
       "best                      553.01          988.65          1.79\n",
       "cheaper                     7.78           80.27         10.32\n",
       "cheapest                    0.96           27.50         28.64\n",
       "bigger                     13.12           53.58          4.08\n",
       "biggest                    37.26           59.97          1.61\n",
       "looking                    56.31          680.74         12.09\n",
       "going                     104.22          914.05          8.77\n",
       "working                   187.26          583.48          3.12\n",
       "costs                      43.99           77.13          1.75\n",
       "goes                       66.47          235.33          3.54\n",
       "wife                      212.38          605.50          2.85\n",
       "husband                   104.85          418.87          3.99\n",
       "he                       5122.78         3669.63          0.72\n",
       "she                      1266.80         1885.25          1.49\n",
       "capital                   160.38           43.23          0.27\n",
       "illinois                   90.02            1.10          0.01\n",
       "albanian                   13.37            0.75          0.06\n",
       "brazilian                  42.43           33.46          0.79\n",
       "kwanza                      0.19            0.01          0.07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 3 題補充證據：同樣的字，在兩份一樣大的教材裡各出現幾次（每 100 萬字）。\n",
    "# 這一格是事後補跑的：不依賴前面任何變數，只讀 forum_clean.txt 和 wiki_control_clean.txt。\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "\n",
    "probe_words = [\"better\", \"best\", \"cheaper\", \"cheapest\", \"bigger\", \"biggest\", \"looking\", \"going\",\n",
    "               \"working\", \"costs\", \"goes\", \"wife\", \"husband\", \"he\", \"she\",\n",
    "               \"capital\", \"illinois\", \"albanian\", \"brazilian\", \"kwanza\"]\n",
    "wanted = set(probe_words)\n",
    "freq = {}\n",
    "for name, path in [(\"Wiki control (per 1M)\", \"wiki_control_clean.txt\"), (\"Forum (per 1M)\", \"forum_clean.txt\")]:\n",
    "    counts, total = Counter(), 0\n",
    "    with open(path, encoding=\"utf-8\") as f:\n",
    "        for line in f:\n",
    "            toks = line.split()\n",
    "            total += len(toks)\n",
    "            counts.update(t for t in toks if t in wanted)\n",
    "    print(f\"{name}: {total:,} tokens\")\n",
    "    freq[name] = {w: counts[w] / total * 1e6 for w in probe_words}\n",
    "table = pd.DataFrame(freq)\n",
    "table[\"forum / wiki\"] = table[\"Forum (per 1M)\"] / table[\"Wiki control (per 1M)\"]\n",
    "display(table.round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2427b60",
   "metadata": {},
   "source": [
    "## Extra code for report question 2 (5% / 10% / 20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e8cd7044",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T10:37:58.346681Z",
     "iopub.status.busy": "2026-10-04T10:37:58.346681Z",
     "iopub.status.idle": "2026-10-04T11:10:16.344199Z",
     "shell.execute_reply": "2026-10-04T11:10:16.344199Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% wiki tokens: 333,052,711\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained on wiki_sampled_5_clean.txt: 9.1 min, vocab 278,954\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5%: overall 45.22%  (OOV questions 107)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained on wiki_sampled_10_clean.txt: 18.4 min, vocab 294,612\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 10%: overall 46.87%  (OOV questions 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>Wiki 5%</th>\n",
       "      <th>Wiki 10%</th>\n",
       "      <th>Wiki 20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>45.22</td>\n",
       "      <td>46.87</td>\n",
       "      <td>48.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semantic</th>\n",
       "      <td>53.93</td>\n",
       "      <td>54.26</td>\n",
       "      <td>55.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syntactic</th>\n",
       "      <td>37.99</td>\n",
       "      <td>40.74</td>\n",
       "      <td>42.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>79.25</td>\n",
       "      <td>75.69</td>\n",
       "      <td>73.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>67.68</td>\n",
       "      <td>69.65</td>\n",
       "      <td>71.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>7.16</td>\n",
       "      <td>9.01</td>\n",
       "      <td>9.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>37.21</td>\n",
       "      <td>33.44</td>\n",
       "      <td>35.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>67.19</td>\n",
       "      <td>74.11</td>\n",
       "      <td>74.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>13.21</td>\n",
       "      <td>10.69</td>\n",
       "      <td>16.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>10.96</td>\n",
       "      <td>14.66</td>\n",
       "      <td>16.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>43.09</td>\n",
       "      <td>51.13</td>\n",
       "      <td>54.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>16.76</td>\n",
       "      <td>22.82</td>\n",
       "      <td>25.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>29.73</td>\n",
       "      <td>35.32</td>\n",
       "      <td>33.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>76.99</td>\n",
       "      <td>75.98</td>\n",
       "      <td>81.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>44.87</td>\n",
       "      <td>44.68</td>\n",
       "      <td>41.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>37.24</td>\n",
       "      <td>41.14</td>\n",
       "      <td>41.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>38.16</td>\n",
       "      <td>40.69</td>\n",
       "      <td>40.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOV questions</th>\n",
       "      <td>107.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Wiki 5%  Wiki 10%  Wiki 20%\n",
       "Overall                          45.22     46.87     48.29\n",
       "Semantic                         53.93     54.26     55.60\n",
       "Syntactic                        37.99     40.74     42.21\n",
       ": capital-common-countries       79.25     75.69     73.72\n",
       ": capital-world                  67.68     69.65     71.35\n",
       ": currency                        7.16      9.01      9.82\n",
       ": city-in-state                  37.21     33.44     35.22\n",
       ": family                         67.19     74.11     74.31\n",
       ": gram1-adjective-to-adverb      13.21     10.69     16.73\n",
       ": gram2-opposite                 10.96     14.66     16.26\n",
       ": gram3-comparative              43.09     51.13     54.88\n",
       ": gram4-superlative              16.76     22.82     25.40\n",
       ": gram5-present-participle       29.73     35.32     33.05\n",
       ": gram6-nationality-adjective    76.99     75.98     81.05\n",
       ": gram7-past-tense               44.87     44.68     41.28\n",
       ": gram8-plural                   37.24     41.14     41.14\n",
       ": gram9-plural-verbs             38.16     40.69     40.80\n",
       "OOV questions                   107.00      0.00      0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 2 題：5%、10%、20%（助教說這題不用附程式，這裡附上是為了示範）\n",
    "n10 = preprocess_file(\"wiki_sampled_10.txt\", \"wiki_sampled_10_clean.txt\")\n",
    "print(f\"10% wiki tokens: {n10:,}\")\n",
    "for pct in [5, 10]:\n",
    "    m = train_w2v(f\"wiki_sampled_{pct}_clean.txt\")\n",
    "    models[f\"Wiki {pct}%\"] = m.wv\n",
    "    evaluate(m.wv, f\"Wiki {pct}%\")\n",
    "display(pd.DataFrame({k: results[k] for k in [\"Wiki 5%\", \"Wiki 10%\", \"Wiki 20%\"]}).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51bcfb5",
   "metadata": {},
   "source": [
    "## Extra code for report question 4 (most similar words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "94adebc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T11:10:16.449651Z",
     "iopub.status.busy": "2026-10-04T11:10:16.448663Z",
     "iopub.status.idle": "2026-10-04T11:10:16.542288Z",
     "shell.execute_reply": "2026-10-04T11:10:16.542288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>word</th>\n",
       "      <th>model</th>\n",
       "      <th>top 5 (similarity)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>king</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>prince (0.77), queen (0.75), son (0.70), brother (0.70), monarch (0.70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>king</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>nangklao (0.74), prince (0.74), queen (0.72), suriyothai (0.72), throne (0.72)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>dog (0.88), rabbit (0.74), cats (0.73), monkey (0.73), pet (0.72)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>dog (0.74), rabbit (0.74), pet (0.72), sourpuss (0.71), mouse (0.71)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apple</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>microsoft (0.74), ibm (0.68), intel (0.68), software (0.68), dell (0.67)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>apple</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>blackberry (0.77), iphone (0.70), tvos (0.68), raspberry (0.67), xelibri (0.66)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bank</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>banks (0.81), banking (0.75), credit (0.70), investment (0.69), financial (0.68)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bank</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>savings (0.74), bancorp (0.72), guaranty (0.71), dfcu (0.70), bancorporation (0.70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>good</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>better (0.89), sure (0.83), really (0.83), kind (0.83), very (0.83)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>good</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>sure (0.74), bad (0.73), tough (0.72), better (0.72), decent (0.71)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>taiwan</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>mainland (0.86), china (0.83), taiwanese (0.79), taipei (0.79), hong (0.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>taiwan</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>china (0.85), guangdong (0.80), taipei (0.80), hainan (0.80), japan (0.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>computer</td>\n",
       "      <td>GloVe (pre-trained)</td>\n",
       "      <td>computers (0.88), software (0.84), technology (0.76), pc (0.74), hardware (0.73)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>computer</td>\n",
       "      <td>Wiki 20%</td>\n",
       "      <td>computers (0.82), software (0.81), computing (0.81), mainframe (0.79), hardware (0.76)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        word                model  \\\n",
       "0       king  GloVe (pre-trained)   \n",
       "1       king             Wiki 20%   \n",
       "2        cat  GloVe (pre-trained)   \n",
       "3        cat             Wiki 20%   \n",
       "4      apple  GloVe (pre-trained)   \n",
       "5      apple             Wiki 20%   \n",
       "6       bank  GloVe (pre-trained)   \n",
       "7       bank             Wiki 20%   \n",
       "8       good  GloVe (pre-trained)   \n",
       "9       good             Wiki 20%   \n",
       "10    taiwan  GloVe (pre-trained)   \n",
       "11    taiwan             Wiki 20%   \n",
       "12  computer  GloVe (pre-trained)   \n",
       "13  computer             Wiki 20%   \n",
       "\n",
       "                                                                        top 5 (similarity)  \n",
       "0                  prince (0.77), queen (0.75), son (0.70), brother (0.70), monarch (0.70)  \n",
       "1           nangklao (0.74), prince (0.74), queen (0.72), suriyothai (0.72), throne (0.72)  \n",
       "2                        dog (0.88), rabbit (0.74), cats (0.73), monkey (0.73), pet (0.72)  \n",
       "3                     dog (0.74), rabbit (0.74), pet (0.72), sourpuss (0.71), mouse (0.71)  \n",
       "4                 microsoft (0.74), ibm (0.68), intel (0.68), software (0.68), dell (0.67)  \n",
       "5          blackberry (0.77), iphone (0.70), tvos (0.68), raspberry (0.67), xelibri (0.66)  \n",
       "6         banks (0.81), banking (0.75), credit (0.70), investment (0.69), financial (0.68)  \n",
       "7      savings (0.74), bancorp (0.72), guaranty (0.71), dfcu (0.70), bancorporation (0.70)  \n",
       "8                      better (0.89), sure (0.83), really (0.83), kind (0.83), very (0.83)  \n",
       "9                      sure (0.74), bad (0.73), tough (0.72), better (0.72), decent (0.71)  \n",
       "10             mainland (0.86), china (0.83), taiwanese (0.79), taipei (0.79), hong (0.77)  \n",
       "11              china (0.85), guangdong (0.80), taipei (0.80), hainan (0.80), japan (0.78)  \n",
       "12        computers (0.88), software (0.84), technology (0.76), pc (0.74), hardware (0.73)  \n",
       "13  computers (0.82), software (0.81), computing (0.81), mainframe (0.79), hardware (0.76)  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 4 題：挑字，各找 5 個最像的字。同時看現成的 GloVe 和自己訓練的 Wiki 20%。\n",
    "# 挑字的原則：有一個字兩種意思（apple、bank）、反義詞（good）、專有名詞（taiwan）、一般名詞（cat、computer）、考卷裡的字（king）\n",
    "probe = [\"king\", \"cat\", \"apple\", \"bank\", \"good\", \"taiwan\", \"computer\"]\n",
    "rows = []\n",
    "for w in probe:\n",
    "    for name in [\"GloVe (pre-trained)\", \"Wiki 20%\"]:\n",
    "        wv = models[name]\n",
    "        if w in wv.key_to_index:\n",
    "            rows.append({\"word\": w, \"model\": name,\n",
    "                         \"top 5 (similarity)\": \", \".join(f\"{x} ({s:.2f})\" for x, s in wv.most_similar(w, topn=5))})\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "display(pd.DataFrame(rows))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddefd764",
   "metadata": {},
   "source": [
    "## Extra code for report question 5 (anything that strengthens the report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4c0ec9c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T11:10:16.614827Z",
     "iopub.status.busy": "2026-10-04T11:10:16.614827Z",
     "iopub.status.idle": "2026-10-04T11:10:16.631845Z",
     "shell.execute_reply": "2026-10-04T11:10:16.631845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>top-1</th>\n",
       "      <th>top-3</th>\n",
       "      <th>top-5</th>\n",
       "      <th>top-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GloVe (pre-trained)</th>\n",
       "      <td>63.11</td>\n",
       "      <td>73.68</td>\n",
       "      <td>77.73</td>\n",
       "      <td>82.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wiki 20%</th>\n",
       "      <td>48.29</td>\n",
       "      <td>62.27</td>\n",
       "      <td>67.01</td>\n",
       "      <td>72.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     top-1  top-3  top-5  top-10\n",
       "GloVe (pre-trained)  63.11  73.68  77.73   82.01\n",
       "Wiki 20%             48.29  62.27  67.01   72.77"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (a)：前 k 名有猜中就算對，正確率會變多少？（老師說 queen 可能不是第一名，而是第二、三名）\n",
    "rows = {}\n",
    "for name in [\"GloVe (pre-trained)\", \"Wiki 20%\"]:\n",
    "    ranks = details[name][\"ranks\"]\n",
    "    rows[name] = {f\"top-{k}\": np.mean([r is not None and r <= k for r in ranks]) * 100 for k in [1, 3, 5, 10]}\n",
    "display(pd.DataFrame(rows).T.round(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "746a4b1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T11:10:16.734183Z",
     "iopub.status.busy": "2026-10-04T11:10:16.734183Z",
     "iopub.status.idle": "2026-10-04T11:10:16.846530Z",
     "shell.execute_reply": "2026-10-04T11:10:16.846023Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrong answers: 10107 of 19544\n",
      "  OOV (question word not in vocab): 0\n",
      "  gold answer was 2nd-10th: 4785\n",
      "\n",
      ": capital-world\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>gold</th>\n",
       "      <th>pred</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Abuja Nigeria Amman Jordan</td>\n",
       "      <td>jordan</td>\n",
       "      <td>kuwait</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Abuja Nigeria Antananarivo Madagascar</td>\n",
       "      <td>madagascar</td>\n",
       "      <td>senegal</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Abuja Nigeria Asmara Eritrea</td>\n",
       "      <td>eritrea</td>\n",
       "      <td>indonesia</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>Abuja Nigeria Bamako Mali</td>\n",
       "      <td>mali</td>\n",
       "      <td>faso</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>Abuja Nigeria Bern Switzerland</td>\n",
       "      <td>switzerland</td>\n",
       "      <td>aarau</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Abuja Nigeria Bishkek Kyrgyzstan</td>\n",
       "      <td>kyrgyzstan</td>\n",
       "      <td>tajikistan</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Question         gold        pred  rank\n",
       "508             Abuja Nigeria Amman Jordan       jordan      kuwait   NaN\n",
       "510  Abuja Nigeria Antananarivo Madagascar   madagascar     senegal   8.0\n",
       "513           Abuja Nigeria Asmara Eritrea      eritrea   indonesia   5.0\n",
       "518              Abuja Nigeria Bamako Mali         mali        faso   NaN\n",
       "526         Abuja Nigeria Bern Switzerland  switzerland       aarau   2.0\n",
       "527       Abuja Nigeria Bishkek Kyrgyzstan   kyrgyzstan  tajikistan   2.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": currency\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>gold</th>\n",
       "      <th>pred</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>Algeria dinar Angola kwanza</td>\n",
       "      <td>kwanza</td>\n",
       "      <td>banknote</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5032</th>\n",
       "      <td>Algeria dinar Armenia dram</td>\n",
       "      <td>dram</td>\n",
       "      <td>hryvnia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>Algeria dinar Brazil real</td>\n",
       "      <td>real</td>\n",
       "      <td>peso</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5034</th>\n",
       "      <td>Algeria dinar Bulgaria lev</td>\n",
       "      <td>lev</td>\n",
       "      <td>hryvnia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5035</th>\n",
       "      <td>Algeria dinar Cambodia riel</td>\n",
       "      <td>riel</td>\n",
       "      <td>kyat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5036</th>\n",
       "      <td>Algeria dinar Canada dollar</td>\n",
       "      <td>dollar</td>\n",
       "      <td>toonie</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Question    gold      pred  rank\n",
       "5030  Algeria dinar Angola kwanza  kwanza  banknote   NaN\n",
       "5032   Algeria dinar Armenia dram    dram   hryvnia   NaN\n",
       "5033    Algeria dinar Brazil real    real      peso   NaN\n",
       "5034   Algeria dinar Bulgaria lev     lev   hryvnia   NaN\n",
       "5035  Algeria dinar Cambodia riel    riel      kyat   NaN\n",
       "5036  Algeria dinar Canada dollar  dollar    toonie   NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": family\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>gold</th>\n",
       "      <th>pred</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8363</th>\n",
       "      <td>boy girl brother sister</td>\n",
       "      <td>sister</td>\n",
       "      <td>cousin</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8366</th>\n",
       "      <td>boy girl father mother</td>\n",
       "      <td>mother</td>\n",
       "      <td>stepmother</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8376</th>\n",
       "      <td>boy girl nephew niece</td>\n",
       "      <td>niece</td>\n",
       "      <td>cousin</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8381</th>\n",
       "      <td>boy girl stepbrother stepsister</td>\n",
       "      <td>stepsister</td>\n",
       "      <td>stepmother</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8382</th>\n",
       "      <td>boy girl stepfather stepmother</td>\n",
       "      <td>stepmother</td>\n",
       "      <td>husband</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8391</th>\n",
       "      <td>brother sister groom bride</td>\n",
       "      <td>bride</td>\n",
       "      <td>bridesmaid</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Question        gold        pred  rank\n",
       "8363          boy girl brother sister      sister      cousin   4.0\n",
       "8366           boy girl father mother      mother  stepmother   2.0\n",
       "8376            boy girl nephew niece       niece      cousin   8.0\n",
       "8381  boy girl stepbrother stepsister  stepsister  stepmother   5.0\n",
       "8382   boy girl stepfather stepmother  stepmother     husband   2.0\n",
       "8391       brother sister groom bride       bride  bridesmaid   2.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": gram3-comparative\n"
     ]
    },
    {
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       "      <th>Question</th>\n",
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       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10673</th>\n",
       "      <td>bad worse big bigger</td>\n",
       "      <td>bigger</td>\n",
       "      <td>sooner</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10677</th>\n",
       "      <td>bad worse cool cooler</td>\n",
       "      <td>cooler</td>\n",
       "      <td>better</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10686</th>\n",
       "      <td>bad worse hot hotter</td>\n",
       "      <td>hotter</td>\n",
       "      <td>peaking</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10687</th>\n",
       "      <td>bad worse large larger</td>\n",
       "      <td>larger</td>\n",
       "      <td>smaller</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10688</th>\n",
       "      <td>bad worse long longer</td>\n",
       "      <td>longer</td>\n",
       "      <td>shorter</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10690</th>\n",
       "      <td>bad worse low lower</td>\n",
       "      <td>lower</td>\n",
       "      <td>higher</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Question    gold     pred  rank\n",
       "10673    bad worse big bigger  bigger   sooner   2.0\n",
       "10677   bad worse cool cooler  cooler   better   6.0\n",
       "10686    bad worse hot hotter  hotter  peaking   NaN\n",
       "10687  bad worse large larger  larger  smaller   2.0\n",
       "10688   bad worse long longer  longer  shorter   4.0\n",
       "10690     bad worse low lower   lower   higher   NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": gram6-nationality-adjective\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>gold</th>\n",
       "      <th>pred</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14183</th>\n",
       "      <td>Albania Albanian Argentina Argentinean</td>\n",
       "      <td>argentinean</td>\n",
       "      <td>argentinian</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14186</th>\n",
       "      <td>Albania Albanian Belarus Belorussian</td>\n",
       "      <td>belorussian</td>\n",
       "      <td>belarusian</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14192</th>\n",
       "      <td>Albania Albanian Colombia Colombian</td>\n",
       "      <td>colombian</td>\n",
       "      <td>honduran</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14201</th>\n",
       "      <td>Albania Albanian India Indian</td>\n",
       "      <td>indian</td>\n",
       "      <td>oriya</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14217</th>\n",
       "      <td>Albania Albanian Slovakia Slovakian</td>\n",
       "      <td>slovakian</td>\n",
       "      <td>slovak</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14224</th>\n",
       "      <td>Argentina Argentinean Belarus Belorussian</td>\n",
       "      <td>belorussian</td>\n",
       "      <td>belarusian</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Question         gold         pred  \\\n",
       "14183     Albania Albanian Argentina Argentinean  argentinean  argentinian   \n",
       "14186       Albania Albanian Belarus Belorussian  belorussian   belarusian   \n",
       "14192        Albania Albanian Colombia Colombian    colombian     honduran   \n",
       "14201              Albania Albanian India Indian       indian        oriya   \n",
       "14217        Albania Albanian Slovakia Slovakian    slovakian       slovak   \n",
       "14224  Argentina Argentinean Belarus Belorussian  belorussian   belarusian   \n",
       "\n",
       "       rank  \n",
       "14183   8.0  \n",
       "14186   NaN  \n",
       "14192   2.0  \n",
       "14201   4.0  \n",
       "14217   5.0  \n",
       "14224   NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ": gram8-plural\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "      <th>gold</th>\n",
       "      <th>pred</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17342</th>\n",
       "      <td>banana bananas bird birds</td>\n",
       "      <td>birds</td>\n",
       "      <td>waterfowl</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17344</th>\n",
       "      <td>banana bananas building buildings</td>\n",
       "      <td>buildings</td>\n",
       "      <td>renovating</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17345</th>\n",
       "      <td>banana bananas car cars</td>\n",
       "      <td>cars</td>\n",
       "      <td>truck</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17346</th>\n",
       "      <td>banana bananas cat cats</td>\n",
       "      <td>cats</td>\n",
       "      <td>rabbits</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17347</th>\n",
       "      <td>banana bananas child children</td>\n",
       "      <td>children</td>\n",
       "      <td>abusers</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17348</th>\n",
       "      <td>banana bananas cloud clouds</td>\n",
       "      <td>clouds</td>\n",
       "      <td>lidar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Question       gold        pred  rank\n",
       "17342          banana bananas bird birds      birds   waterfowl   7.0\n",
       "17344  banana bananas building buildings  buildings  renovating   2.0\n",
       "17345            banana bananas car cars       cars       truck   2.0\n",
       "17346            banana bananas cat cats       cats     rabbits   5.0\n",
       "17347      banana bananas child children   children     abusers   NaN\n",
       "17348        banana bananas cloud clouds     clouds       lidar   NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (b)：錯題分析——電腦答錯時，它到底猜了什麼？\n",
    "d20 = details[\"Wiki 20%\"]\n",
    "err = data.assign(pred=d20[\"preds\"], rank=d20[\"ranks\"],\n",
    "                  gold=data[\"Question\"].str.lower().str.split().str[3])\n",
    "wrong = err[err[\"pred\"] != err[\"gold\"]]\n",
    "print(\"wrong answers:\", len(wrong), \"of\", len(err))\n",
    "print(\"  OOV (question word not in vocab):\", int(wrong[\"pred\"].isna().sum()))\n",
    "print(\"  gold answer was 2nd-10th:\", int(wrong[\"rank\"].notna().sum()))\n",
    "for sub in [\": capital-world\", \": currency\", \": family\", \": gram3-comparative\", \": gram6-nationality-adjective\", \": gram8-plural\"]:\n",
    "    print(\"\\n\" + sub)\n",
    "    display(wrong[(wrong[\"SubCategory\"] == sub) & wrong[\"pred\"].notna()][[\"Question\", \"gold\", \"pred\", \"rank\"]].head(6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ee0e0f5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T11:10:16.920876Z",
     "iopub.status.busy": "2026-10-04T11:10:16.920876Z",
     "iopub.status.idle": "2026-10-04T11:18:59.584095Z",
     "shell.execute_reply": "2026-10-04T11:18:59.583090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "questions containing a stop word: 206\n",
      "SubCategory\n",
      ": family                       86\n",
      ": gram1-adjective-to-adverb    62\n",
      ": currency                     58\n",
      "Name: count, dtype: int64\n",
      "['Algeria dinar Korea won', 'Angola kwanza Korea won', 'Argentina peso Korea won', 'Armenia dram Korea won', 'Brazil real Korea won']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained on wiki_sampled_5_nostop.txt: 6.8 min, vocab 278,810\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% (stop words removed): overall 45.03%  (OOV questions 284)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Wiki 5%</th>\n",
       "      <th>Wiki 5% (stop words removed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>45.22</td>\n",
       "      <td>45.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semantic</th>\n",
       "      <td>53.93</td>\n",
       "      <td>54.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syntactic</th>\n",
       "      <td>37.99</td>\n",
       "      <td>37.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>79.25</td>\n",
       "      <td>81.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>67.68</td>\n",
       "      <td>70.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>7.16</td>\n",
       "      <td>7.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>37.21</td>\n",
       "      <td>36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>67.19</td>\n",
       "      <td>50.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>13.21</td>\n",
       "      <td>12.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>10.96</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>43.09</td>\n",
       "      <td>37.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>16.76</td>\n",
       "      <td>16.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>29.73</td>\n",
       "      <td>31.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>76.99</td>\n",
       "      <td>82.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>44.87</td>\n",
       "      <td>40.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>37.24</td>\n",
       "      <td>42.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>38.16</td>\n",
       "      <td>27.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOV questions</th>\n",
       "      <td>107.00</td>\n",
       "      <td>284.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Wiki 5%  Wiki 5% (stop words removed)\n",
       "Overall                          45.22                         45.03\n",
       "Semantic                         53.93                         54.59\n",
       "Syntactic                        37.99                         37.09\n",
       ": capital-common-countries       79.25                         81.82\n",
       ": capital-world                  67.68                         70.73\n",
       ": currency                        7.16                          7.27\n",
       ": city-in-state                  37.21                         36.93\n",
       ": family                         67.19                         50.20\n",
       ": gram1-adjective-to-adverb      13.21                         12.90\n",
       ": gram2-opposite                 10.96                          8.62\n",
       ": gram3-comparative              43.09                         37.46\n",
       ": gram4-superlative              16.76                         16.40\n",
       ": gram5-present-participle       29.73                         31.63\n",
       ": gram6-nationality-adjective    76.99                         82.05\n",
       ": gram7-past-tense               44.87                         40.64\n",
       ": gram8-plural                   37.24                         42.27\n",
       ": gram9-plural-verbs             38.16                         27.01\n",
       "OOV questions                   107.00                        284.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (c)：為什麼不移除停用詞？先看考卷，再真的做實驗比較。\n",
    "import nltk\n",
    "nltk.download(\"stopwords\", quiet=True)\n",
    "from nltk.corpus import stopwords\n",
    "sw = set(stopwords.words(\"english\"))\n",
    "has_sw = data[\"Question\"].map(lambda q: any(w in sw for w in q.lower().split()))\n",
    "print(\"questions containing a stop word:\", int(has_sw.sum()))\n",
    "print(data[has_sw][\"SubCategory\"].value_counts())\n",
    "print(data[has_sw][\"Question\"].head(5).tolist())\n",
    "\n",
    "# 實驗：同樣是 5% 維基、同樣的參數，只差在「有沒有移除停用詞」\n",
    "with open(\"wiki_sampled_5_clean.txt\", encoding=\"utf-8\") as fin, open(\"wiki_sampled_5_nostop.txt\", \"w\", encoding=\"utf-8\", newline=\"\\n\") as fout:\n",
    "    for line in fin:\n",
    "        fout.write(\" \".join(w for w in line.split() if w not in sw) + \"\\n\")\n",
    "m = train_w2v(\"wiki_sampled_5_nostop.txt\")\n",
    "models[\"Wiki 5% (stop words removed)\"] = m.wv\n",
    "evaluate(m.wv, \"Wiki 5% (stop words removed)\")\n",
    "cols = [\"Wiki 5%\", \"Wiki 5% (stop words removed)\"]\n",
    "display(pd.DataFrame({k: results[k] for k in cols}).round(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1a7326b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T11:18:59.667152Z",
     "iopub.status.busy": "2026-10-04T11:18:59.667152Z",
     "iopub.status.idle": "2026-10-04T12:06:38.163296Z",
     "shell.execute_reply": "2026-10-04T12:06:38.163296Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% CBOW (sg=0): trained in 9.6 min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% CBOW (sg=0): overall 52.71%  (OOV questions 107)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% window=10: trained in 15.1 min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% window=10: overall 41.86%  (OOV questions 107)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% vector_size=300: trained in 16.6 min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 5% vector_size=300: overall 56.95%  (OOV questions 107)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Wiki 5%</th>\n",
       "      <th>Wiki 5% CBOW (sg=0)</th>\n",
       "      <th>Wiki 5% window=10</th>\n",
       "      <th>Wiki 5% vector_size=300</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>45.22</td>\n",
       "      <td>52.71</td>\n",
       "      <td>41.86</td>\n",
       "      <td>56.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semantic</th>\n",
       "      <td>53.93</td>\n",
       "      <td>59.16</td>\n",
       "      <td>50.70</td>\n",
       "      <td>68.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syntactic</th>\n",
       "      <td>37.99</td>\n",
       "      <td>47.34</td>\n",
       "      <td>34.51</td>\n",
       "      <td>47.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>79.25</td>\n",
       "      <td>80.04</td>\n",
       "      <td>75.89</td>\n",
       "      <td>94.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>67.68</td>\n",
       "      <td>73.21</td>\n",
       "      <td>66.20</td>\n",
       "      <td>80.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>7.16</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.62</td>\n",
       "      <td>8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>37.21</td>\n",
       "      <td>43.17</td>\n",
       "      <td>31.41</td>\n",
       "      <td>59.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>67.19</td>\n",
       "      <td>78.06</td>\n",
       "      <td>54.74</td>\n",
       "      <td>78.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>13.21</td>\n",
       "      <td>19.05</td>\n",
       "      <td>13.10</td>\n",
       "      <td>13.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>10.96</td>\n",
       "      <td>11.95</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>43.09</td>\n",
       "      <td>68.09</td>\n",
       "      <td>33.56</td>\n",
       "      <td>59.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>16.76</td>\n",
       "      <td>36.54</td>\n",
       "      <td>10.25</td>\n",
       "      <td>26.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>29.73</td>\n",
       "      <td>40.72</td>\n",
       "      <td>27.46</td>\n",
       "      <td>37.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>76.99</td>\n",
       "      <td>76.30</td>\n",
       "      <td>79.80</td>\n",
       "      <td>86.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>44.87</td>\n",
       "      <td>46.47</td>\n",
       "      <td>36.03</td>\n",
       "      <td>49.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>37.24</td>\n",
       "      <td>50.15</td>\n",
       "      <td>36.26</td>\n",
       "      <td>54.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>38.16</td>\n",
       "      <td>46.90</td>\n",
       "      <td>36.32</td>\n",
       "      <td>52.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOV questions</th>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Wiki 5%  Wiki 5% CBOW (sg=0)  \\\n",
       "Overall                          45.22                52.71   \n",
       "Semantic                         53.93                59.16   \n",
       "Syntactic                        37.99                47.34   \n",
       ": capital-common-countries       79.25                80.04   \n",
       ": capital-world                  67.68                73.21   \n",
       ": currency                        7.16                 8.08   \n",
       ": city-in-state                  37.21                43.17   \n",
       ": family                         67.19                78.06   \n",
       ": gram1-adjective-to-adverb      13.21                19.05   \n",
       ": gram2-opposite                 10.96                11.95   \n",
       ": gram3-comparative              43.09                68.09   \n",
       ": gram4-superlative              16.76                36.54   \n",
       ": gram5-present-participle       29.73                40.72   \n",
       ": gram6-nationality-adjective    76.99                76.30   \n",
       ": gram7-past-tense               44.87                46.47   \n",
       ": gram8-plural                   37.24                50.15   \n",
       ": gram9-plural-verbs             38.16                46.90   \n",
       "OOV questions                   107.00               107.00   \n",
       "\n",
       "                               Wiki 5% window=10  Wiki 5% vector_size=300  \n",
       "Overall                                    41.86                    56.95  \n",
       "Semantic                                   50.70                    68.16  \n",
       "Syntactic                                  34.51                    47.63  \n",
       ": capital-common-countries                 75.89                    94.47  \n",
       ": capital-world                            66.20                    80.37  \n",
       ": currency                                  7.62                     8.31  \n",
       ": city-in-state                            31.41                    59.30  \n",
       ": family                                   54.74                    78.26  \n",
       ": gram1-adjective-to-adverb                13.10                    13.21  \n",
       ": gram2-opposite                            8.00                    15.39  \n",
       ": gram3-comparative                        33.56                    59.53  \n",
       ": gram4-superlative                        10.25                    26.92  \n",
       ": gram5-present-participle                 27.46                    37.31  \n",
       ": gram6-nationality-adjective              79.80                    86.74  \n",
       ": gram7-past-tense                         36.03                    49.10  \n",
       ": gram8-plural                             36.26                    54.80  \n",
       ": gram9-plural-verbs                       36.32                    52.53  \n",
       "OOV questions                             107.00                   107.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (d)：超參數比較（都用 5% 維基，一次只改一個設定，其他跟主模型一樣）\n",
    "variants = {\n",
    "    \"Wiki 5% CBOW (sg=0)\": dict(sg=0),\n",
    "    \"Wiki 5% window=10\": dict(window=10),\n",
    "    \"Wiki 5% vector_size=300\": dict(vector_size=300),\n",
    "}\n",
    "for name, change in variants.items():\n",
    "    t = time.time()\n",
    "    m = Word2Vec(corpus_file=\"wiki_sampled_5_clean.txt\", **{**W2V_PARAMS, **change})\n",
    "    print(f\"{name}: trained in {(time.time() - t) / 60:.1f} min\")\n",
    "    models[name] = m.wv\n",
    "    evaluate(m.wv, name)\n",
    "cols = [\"Wiki 5%\"] + list(variants)\n",
    "display(pd.DataFrame({k: results[k] for k in cols}).round(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c36241d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T12:06:38.246251Z",
     "iopub.status.busy": "2026-10-04T12:06:38.246251Z",
     "iopub.status.idle": "2026-10-04T12:11:51.967925Z",
     "shell.execute_reply": "2026-10-04T12:11:51.967925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki 20% (3CosMul): overall 44.61%  (OOV questions 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>Wiki 20%</th>\n",
       "      <th>Wiki 20% (3CosMul)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>48.29</td>\n",
       "      <td>44.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semantic</th>\n",
       "      <td>55.60</td>\n",
       "      <td>52.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syntactic</th>\n",
       "      <td>42.21</td>\n",
       "      <td>38.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>73.72</td>\n",
       "      <td>73.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>71.35</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>9.82</td>\n",
       "      <td>9.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>35.22</td>\n",
       "      <td>32.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>74.31</td>\n",
       "      <td>71.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>16.73</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>16.26</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>54.88</td>\n",
       "      <td>49.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>25.40</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>33.05</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>81.05</td>\n",
       "      <td>77.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>41.28</td>\n",
       "      <td>37.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>41.14</td>\n",
       "      <td>35.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>40.80</td>\n",
       "      <td>38.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOV questions</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Wiki 20%  Wiki 20% (3CosMul)\n",
       "Overall                           48.29               44.61\n",
       "Semantic                          55.60               52.54\n",
       "Syntactic                         42.21               38.02\n",
       ": capital-common-countries        73.72               73.32\n",
       ": capital-world                   71.35               67.04\n",
       ": currency                         9.82                9.58\n",
       ": city-in-state                   35.22               32.83\n",
       ": family                          74.31               71.74\n",
       ": gram1-adjective-to-adverb       16.73               12.30\n",
       ": gram2-opposite                  16.26               11.82\n",
       ": gram3-comparative               54.88               49.85\n",
       ": gram4-superlative               25.40               23.71\n",
       ": gram5-present-participle        33.05               26.70\n",
       ": gram6-nationality-adjective     81.05               77.42\n",
       ": gram7-past-tense                41.28               37.69\n",
       ": gram8-plural                    41.14               35.06\n",
       ": gram9-plural-verbs              40.80               38.62\n",
       "OOV questions                      0.00                0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (e)：換一種「算答案」的公式。3CosAdd 是 b - a + c；3CosMul（Levy & Goldberg 2014）用乘除，\n",
    "# 比較不會被「某一個字特別像」帶走。同一份 Wiki 20% 詞向量，只換公式。\n",
    "evaluate(my_wv, \"Wiki 20% (3CosMul)\", method=\"cosmul\")\n",
    "display(pd.DataFrame({k: results[k] for k in [\"Wiki 20%\", \"Wiki 20% (3CosMul)\"]}).round(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e05738aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T12:11:52.047344Z",
     "iopub.status.busy": "2026-10-04T12:11:52.046282Z",
     "iopub.status.idle": "2026-10-04T12:11:52.515989Z",
     "shell.execute_reply": "2026-10-04T12:11:52.515989Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (f)：同一批 family 字，用 PCA 和 t-SNE 兩種方法壓成 2 維，圖長得不一樣嗎？\n",
    "from sklearn.decomposition import PCA\n",
    "words = sorted({w for q in data[data[\"SubCategory\"] == \": family\"][\"Question\"] for w in q.lower().split()})\n",
    "words = [w for w in words if w in my_wv.key_to_index]\n",
    "vecs = np.array([my_wv[w] for w in words])\n",
    "fig, axes = plt.subplots(1, 2, figsize=(18, 8))\n",
    "for ax, (title, pts) in zip(axes, [\n",
    "        (\"PCA\", PCA(n_components=2, random_state=42).fit_transform(vecs)),\n",
    "        (\"t-SNE\", TSNE(n_components=2, perplexity=15, random_state=42, init=\"pca\").fit_transform(vecs))]):\n",
    "    ax.scatter(pts[:, 0], pts[:, 1], s=12)\n",
    "    for (x, y), w in zip(pts, words):\n",
    "        ax.annotate(w, (x, y), fontsize=8)\n",
    "    ax.set_title(f\"{title} (Wiki 20%, family)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f1e2464d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T12:11:52.588364Z",
     "iopub.status.busy": "2026-10-04T12:11:52.588030Z",
     "iopub.status.idle": "2026-10-04T12:11:52.632633Z",
     "shell.execute_reply": "2026-10-04T12:11:52.632633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GloVe</th>\n",
       "      <th>Wiki 20%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubCategory</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               GloVe  Wiki 20%\n",
       "SubCategory                                   \n",
       ": capital-common-countries     100.0     100.0\n",
       ": capital-world                100.0     100.0\n",
       ": currency                     100.0     100.0\n",
       ": city-in-state                100.0     100.0\n",
       ": family                       100.0     100.0\n",
       ": gram1-adjective-to-adverb    100.0     100.0\n",
       ": gram2-opposite               100.0     100.0\n",
       ": gram3-comparative            100.0     100.0\n",
       ": gram4-superlative            100.0     100.0\n",
       ": gram5-present-participle     100.0     100.0\n",
       ": gram6-nationality-adjective  100.0     100.0\n",
       ": gram7-past-tense             100.0     100.0\n",
       ": gram8-plural                 100.0     100.0\n",
       ": gram9-plural-verbs           100.0     100.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 第 5 題 (g)：各小類「四個字都在字典裡」的比例：GloVe vs Wiki 20%\n",
    "display(pd.DataFrame({\"GloVe\": answer_coverage(model), \"Wiki 20%\": answer_coverage(my_wv)}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd54832",
   "metadata": {},
   "source": [
    "## Summary of all results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "22deea59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T12:11:52.702450Z",
     "iopub.status.busy": "2026-10-04T12:11:52.702450Z",
     "iopub.status.idle": "2026-10-04T12:11:52.715443Z",
     "shell.execute_reply": "2026-10-04T12:11:52.715115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>GloVe (pre-trained)</th>\n",
       "      <th>Wiki 20%</th>\n",
       "      <th>Forum (same size)</th>\n",
       "      <th>Wiki control (same size)</th>\n",
       "      <th>Wiki 5%</th>\n",
       "      <th>Wiki 10%</th>\n",
       "      <th>Wiki 5% (stop words removed)</th>\n",
       "      <th>Wiki 5% CBOW (sg=0)</th>\n",
       "      <th>Wiki 5% window=10</th>\n",
       "      <th>Wiki 5% vector_size=300</th>\n",
       "      <th>Wiki 20% (3CosMul)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>63.11</td>\n",
       "      <td>48.29</td>\n",
       "      <td>26.05</td>\n",
       "      <td>41.66</td>\n",
       "      <td>45.22</td>\n",
       "      <td>46.87</td>\n",
       "      <td>45.03</td>\n",
       "      <td>52.71</td>\n",
       "      <td>41.86</td>\n",
       "      <td>56.95</td>\n",
       "      <td>44.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semantic</th>\n",
       "      <td>65.34</td>\n",
       "      <td>55.60</td>\n",
       "      <td>12.37</td>\n",
       "      <td>46.07</td>\n",
       "      <td>53.93</td>\n",
       "      <td>54.26</td>\n",
       "      <td>54.59</td>\n",
       "      <td>59.16</td>\n",
       "      <td>50.70</td>\n",
       "      <td>68.16</td>\n",
       "      <td>52.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syntactic</th>\n",
       "      <td>61.26</td>\n",
       "      <td>42.21</td>\n",
       "      <td>37.41</td>\n",
       "      <td>38.00</td>\n",
       "      <td>37.99</td>\n",
       "      <td>40.74</td>\n",
       "      <td>37.09</td>\n",
       "      <td>47.34</td>\n",
       "      <td>34.51</td>\n",
       "      <td>47.63</td>\n",
       "      <td>38.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-common-countries</th>\n",
       "      <td>93.87</td>\n",
       "      <td>73.72</td>\n",
       "      <td>34.78</td>\n",
       "      <td>77.47</td>\n",
       "      <td>79.25</td>\n",
       "      <td>75.69</td>\n",
       "      <td>81.82</td>\n",
       "      <td>80.04</td>\n",
       "      <td>75.89</td>\n",
       "      <td>94.47</td>\n",
       "      <td>73.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: capital-world</th>\n",
       "      <td>88.95</td>\n",
       "      <td>71.35</td>\n",
       "      <td>12.00</td>\n",
       "      <td>54.47</td>\n",
       "      <td>67.68</td>\n",
       "      <td>69.65</td>\n",
       "      <td>70.73</td>\n",
       "      <td>73.21</td>\n",
       "      <td>66.20</td>\n",
       "      <td>80.37</td>\n",
       "      <td>67.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: currency</th>\n",
       "      <td>14.20</td>\n",
       "      <td>9.82</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.39</td>\n",
       "      <td>7.16</td>\n",
       "      <td>9.01</td>\n",
       "      <td>7.27</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.62</td>\n",
       "      <td>8.31</td>\n",
       "      <td>9.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: city-in-state</th>\n",
       "      <td>30.81</td>\n",
       "      <td>35.22</td>\n",
       "      <td>2.51</td>\n",
       "      <td>34.29</td>\n",
       "      <td>37.21</td>\n",
       "      <td>33.44</td>\n",
       "      <td>36.93</td>\n",
       "      <td>43.17</td>\n",
       "      <td>31.41</td>\n",
       "      <td>59.30</td>\n",
       "      <td>32.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: family</th>\n",
       "      <td>81.62</td>\n",
       "      <td>74.31</td>\n",
       "      <td>60.67</td>\n",
       "      <td>63.24</td>\n",
       "      <td>67.19</td>\n",
       "      <td>74.11</td>\n",
       "      <td>50.20</td>\n",
       "      <td>78.06</td>\n",
       "      <td>54.74</td>\n",
       "      <td>78.26</td>\n",
       "      <td>71.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram1-adjective-to-adverb</th>\n",
       "      <td>24.40</td>\n",
       "      <td>16.73</td>\n",
       "      <td>12.80</td>\n",
       "      <td>11.19</td>\n",
       "      <td>13.21</td>\n",
       "      <td>10.69</td>\n",
       "      <td>12.90</td>\n",
       "      <td>19.05</td>\n",
       "      <td>13.10</td>\n",
       "      <td>13.21</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram2-opposite</th>\n",
       "      <td>20.07</td>\n",
       "      <td>16.26</td>\n",
       "      <td>14.04</td>\n",
       "      <td>9.73</td>\n",
       "      <td>10.96</td>\n",
       "      <td>14.66</td>\n",
       "      <td>8.62</td>\n",
       "      <td>11.95</td>\n",
       "      <td>8.00</td>\n",
       "      <td>15.39</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram3-comparative</th>\n",
       "      <td>79.13</td>\n",
       "      <td>54.88</td>\n",
       "      <td>65.54</td>\n",
       "      <td>53.45</td>\n",
       "      <td>43.09</td>\n",
       "      <td>51.13</td>\n",
       "      <td>37.46</td>\n",
       "      <td>68.09</td>\n",
       "      <td>33.56</td>\n",
       "      <td>59.53</td>\n",
       "      <td>49.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram4-superlative</th>\n",
       "      <td>54.28</td>\n",
       "      <td>25.40</td>\n",
       "      <td>43.58</td>\n",
       "      <td>19.25</td>\n",
       "      <td>16.76</td>\n",
       "      <td>22.82</td>\n",
       "      <td>16.40</td>\n",
       "      <td>36.54</td>\n",
       "      <td>10.25</td>\n",
       "      <td>26.92</td>\n",
       "      <td>23.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram5-present-participle</th>\n",
       "      <td>69.51</td>\n",
       "      <td>33.05</td>\n",
       "      <td>52.18</td>\n",
       "      <td>31.16</td>\n",
       "      <td>29.73</td>\n",
       "      <td>35.32</td>\n",
       "      <td>31.63</td>\n",
       "      <td>40.72</td>\n",
       "      <td>27.46</td>\n",
       "      <td>37.31</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram6-nationality-adjective</th>\n",
       "      <td>87.87</td>\n",
       "      <td>81.05</td>\n",
       "      <td>22.01</td>\n",
       "      <td>75.48</td>\n",
       "      <td>76.99</td>\n",
       "      <td>75.98</td>\n",
       "      <td>82.05</td>\n",
       "      <td>76.30</td>\n",
       "      <td>79.80</td>\n",
       "      <td>86.74</td>\n",
       "      <td>77.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram7-past-tense</th>\n",
       "      <td>55.45</td>\n",
       "      <td>41.28</td>\n",
       "      <td>35.38</td>\n",
       "      <td>41.22</td>\n",
       "      <td>44.87</td>\n",
       "      <td>44.68</td>\n",
       "      <td>40.64</td>\n",
       "      <td>46.47</td>\n",
       "      <td>36.03</td>\n",
       "      <td>49.10</td>\n",
       "      <td>37.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram8-plural</th>\n",
       "      <td>72.00</td>\n",
       "      <td>41.14</td>\n",
       "      <td>40.02</td>\n",
       "      <td>34.31</td>\n",
       "      <td>37.24</td>\n",
       "      <td>41.14</td>\n",
       "      <td>42.27</td>\n",
       "      <td>50.15</td>\n",
       "      <td>36.26</td>\n",
       "      <td>54.80</td>\n",
       "      <td>35.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>: gram9-plural-verbs</th>\n",
       "      <td>58.39</td>\n",
       "      <td>40.80</td>\n",
       "      <td>46.32</td>\n",
       "      <td>34.71</td>\n",
       "      <td>38.16</td>\n",
       "      <td>40.69</td>\n",
       "      <td>27.01</td>\n",
       "      <td>46.90</td>\n",
       "      <td>36.32</td>\n",
       "      <td>52.53</td>\n",
       "      <td>38.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOV questions</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3510.00</td>\n",
       "      <td>169.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>284.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               GloVe (pre-trained)  Wiki 20%  \\\n",
       "Overall                                      63.11     48.29   \n",
       "Semantic                                     65.34     55.60   \n",
       "Syntactic                                    61.26     42.21   \n",
       ": capital-common-countries                   93.87     73.72   \n",
       ": capital-world                              88.95     71.35   \n",
       ": currency                                   14.20      9.82   \n",
       ": city-in-state                              30.81     35.22   \n",
       ": family                                     81.62     74.31   \n",
       ": gram1-adjective-to-adverb                  24.40     16.73   \n",
       ": gram2-opposite                             20.07     16.26   \n",
       ": gram3-comparative                          79.13     54.88   \n",
       ": gram4-superlative                          54.28     25.40   \n",
       ": gram5-present-participle                   69.51     33.05   \n",
       ": gram6-nationality-adjective                87.87     81.05   \n",
       ": gram7-past-tense                           55.45     41.28   \n",
       ": gram8-plural                               72.00     41.14   \n",
       ": gram9-plural-verbs                         58.39     40.80   \n",
       "OOV questions                                 0.00      0.00   \n",
       "\n",
       "                               Forum (same size)  Wiki control (same size)  \\\n",
       "Overall                                    26.05                     41.66   \n",
       "Semantic                                   12.37                     46.07   \n",
       "Syntactic                                  37.41                     38.00   \n",
       ": capital-common-countries                 34.78                     77.47   \n",
       ": capital-world                            12.00                     54.47   \n",
       ": currency                                  1.04                      7.39   \n",
       ": city-in-state                             2.51                     34.29   \n",
       ": family                                   60.67                     63.24   \n",
       ": gram1-adjective-to-adverb                12.80                     11.19   \n",
       ": gram2-opposite                           14.04                      9.73   \n",
       ": gram3-comparative                        65.54                     53.45   \n",
       ": gram4-superlative                        43.58                     19.25   \n",
       ": gram5-present-participle                 52.18                     31.16   \n",
       ": gram6-nationality-adjective              22.01                     75.48   \n",
       ": gram7-past-tense                         35.38                     41.22   \n",
       ": gram8-plural                             40.02                     34.31   \n",
       ": gram9-plural-verbs                       46.32                     34.71   \n",
       "OOV questions                            3510.00                    169.00   \n",
       "\n",
       "                               Wiki 5%  Wiki 10%  \\\n",
       "Overall                          45.22     46.87   \n",
       "Semantic                         53.93     54.26   \n",
       "Syntactic                        37.99     40.74   \n",
       ": capital-common-countries       79.25     75.69   \n",
       ": capital-world                  67.68     69.65   \n",
       ": currency                        7.16      9.01   \n",
       ": city-in-state                  37.21     33.44   \n",
       ": family                         67.19     74.11   \n",
       ": gram1-adjective-to-adverb      13.21     10.69   \n",
       ": gram2-opposite                 10.96     14.66   \n",
       ": gram3-comparative              43.09     51.13   \n",
       ": gram4-superlative              16.76     22.82   \n",
       ": gram5-present-participle       29.73     35.32   \n",
       ": gram6-nationality-adjective    76.99     75.98   \n",
       ": gram7-past-tense               44.87     44.68   \n",
       ": gram8-plural                   37.24     41.14   \n",
       ": gram9-plural-verbs             38.16     40.69   \n",
       "OOV questions                   107.00      0.00   \n",
       "\n",
       "                               Wiki 5% (stop words removed)  \\\n",
       "Overall                                               45.03   \n",
       "Semantic                                              54.59   \n",
       "Syntactic                                             37.09   \n",
       ": capital-common-countries                            81.82   \n",
       ": capital-world                                       70.73   \n",
       ": currency                                             7.27   \n",
       ": city-in-state                                       36.93   \n",
       ": family                                              50.20   \n",
       ": gram1-adjective-to-adverb                           12.90   \n",
       ": gram2-opposite                                       8.62   \n",
       ": gram3-comparative                                   37.46   \n",
       ": gram4-superlative                                   16.40   \n",
       ": gram5-present-participle                            31.63   \n",
       ": gram6-nationality-adjective                         82.05   \n",
       ": gram7-past-tense                                    40.64   \n",
       ": gram8-plural                                        42.27   \n",
       ": gram9-plural-verbs                                  27.01   \n",
       "OOV questions                                        284.00   \n",
       "\n",
       "                               Wiki 5% CBOW (sg=0)  Wiki 5% window=10  \\\n",
       "Overall                                      52.71              41.86   \n",
       "Semantic                                     59.16              50.70   \n",
       "Syntactic                                    47.34              34.51   \n",
       ": capital-common-countries                   80.04              75.89   \n",
       ": capital-world                              73.21              66.20   \n",
       ": currency                                    8.08               7.62   \n",
       ": city-in-state                              43.17              31.41   \n",
       ": family                                     78.06              54.74   \n",
       ": gram1-adjective-to-adverb                  19.05              13.10   \n",
       ": gram2-opposite                             11.95               8.00   \n",
       ": gram3-comparative                          68.09              33.56   \n",
       ": gram4-superlative                          36.54              10.25   \n",
       ": gram5-present-participle                   40.72              27.46   \n",
       ": gram6-nationality-adjective                76.30              79.80   \n",
       ": gram7-past-tense                           46.47              36.03   \n",
       ": gram8-plural                               50.15              36.26   \n",
       ": gram9-plural-verbs                         46.90              36.32   \n",
       "OOV questions                               107.00             107.00   \n",
       "\n",
       "                               Wiki 5% vector_size=300  Wiki 20% (3CosMul)  \n",
       "Overall                                          56.95               44.61  \n",
       "Semantic                                         68.16               52.54  \n",
       "Syntactic                                        47.63               38.02  \n",
       ": capital-common-countries                       94.47               73.32  \n",
       ": capital-world                                  80.37               67.04  \n",
       ": currency                                        8.31                9.58  \n",
       ": city-in-state                                  59.30               32.83  \n",
       ": family                                         78.26               71.74  \n",
       ": gram1-adjective-to-adverb                      13.21               12.30  \n",
       ": gram2-opposite                                 15.39               11.82  \n",
       ": gram3-comparative                              59.53               49.85  \n",
       ": gram4-superlative                              26.92               23.71  \n",
       ": gram5-present-participle                       37.31               26.70  \n",
       ": gram6-nationality-adjective                    86.74               77.42  \n",
       ": gram7-past-tense                               49.10               37.69  \n",
       ": gram8-plural                                   54.80               35.06  \n",
       ": gram9-plural-verbs                             52.53               38.62  \n",
       "OOV questions                                   107.00                0.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "# 全部結果總表（報告引用的數字都來自這裡）\n",
    "pd.set_option(\"display.max_columns\", 30)\n",
    "display(pd.DataFrame(results).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b692ba01",
   "metadata": {},
   "source": [
    "## Running environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fb12330f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-04T12:11:52.784035Z",
     "iopub.status.busy": "2026-10-04T12:11:52.783035Z",
     "iopub.status.idle": "2026-10-04T12:11:52.787054Z",
     "shell.execute_reply": "2026-10-04T12:11:52.787054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.3 (tags/v3.12.3:f6650f9, Apr  9 2024, 14:05:25) [MSC v.1938 64 bit (AMD64)]\n",
      "Windows-11-10.0.26200-SP0 | Intel64 Family 6 Model 141 Stepping 1, GenuineIntel\n",
      "gensim 4.4.0 | numpy 2.5.3 | pandas 3.0.6 | scikit-learn 1.9.1\n"
     ]
    }
   ],
   "source": [
    "# [Generative AI] 本格程式由 Claude（Anthropic）產生，作為示範；不是要繳交的版本。\n",
    "import sys, platform, gensim, sklearn\n",
    "print(\"Python\", sys.version)\n",
    "print(platform.platform(), \"|\", platform.processor())\n",
    "print(\"gensim\", gensim.__version__, \"| numpy\", np.__version__, \"| pandas\", pd.__version__, \"| scikit-learn\", sklearn.__version__)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}