577 lines
20 KiB
Plaintext
577 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 文本分類任務\n",
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"\n",
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"如前所述，我們將專注於基於 **AG_NEWS** 數據集的簡單文本分類任務，目的是將新聞標題分類為以下四個類別之一：國際、體育、商業和科技。\n",
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"\n",
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"## 數據集\n",
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"\n",
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"此數據集已內建於 [`torchtext`](https://github.com/pytorch/text) 模組中，因此我們可以輕鬆存取它。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torchtext\n",
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"import os\n",
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"import collections\n",
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"os.makedirs('./data',exist_ok=True)\n",
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"train_dataset, test_dataset = torchtext.datasets.AG_NEWS(root='./data')\n",
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"classes = ['World', 'Sports', 'Business', 'Sci/Tech']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"在這裡，`train_dataset` 和 `test_dataset` 包含分別返回標籤（類別數字）和文本的集合，例如：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(3,\n",
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" \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\")"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(train_dataset)[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"所以，讓我們列印出資料集中前10個新的標題：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"**Sci/Tech** -> Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.\n",
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"**Sci/Tech** -> Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private investment firm Carlyle Group,\\which has a reputation for making well-timed and occasionally\\controversial plays in the defense industry, has quietly placed\\its bets on another part of the market.\n",
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"**Sci/Tech** -> Oil and Economy Cloud Stocks' Outlook (Reuters) Reuters - Soaring crude prices plus worries\\about the economy and the outlook for earnings are expected to\\hang over the stock market next week during the depth of the\\summer doldrums.\n",
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"**Sci/Tech** -> Iraq Halts Oil Exports from Main Southern Pipeline (Reuters) Reuters - Authorities have halted oil export\\flows from the main pipeline in southern Iraq after\\intelligence showed a rebel militia could strike\\infrastructure, an oil official said on Saturday.\n",
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"**Sci/Tech** -> Oil prices soar to all-time record, posing new menace to US economy (AFP) AFP - Tearaway world oil prices, toppling records and straining wallets, present a new economic menace barely three months before the US presidential elections.\n"
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]
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}
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],
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"source": [
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"for i,x in zip(range(5),train_dataset):\n",
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" print(f\"**{classes[x[0]]}** -> {x[1]}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"因為數據集是迭代器，如果我們想多次使用數據，我們需要將其轉換為列表：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dataset, test_dataset = torchtext.datasets.AG_NEWS(root='./data')\n",
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"train_dataset = list(train_dataset)\n",
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"test_dataset = list(test_dataset)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 分詞\n",
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"\n",
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"現在我們需要將文本轉換成可以表示為張量的**數字**。如果我們想要詞級表示，需要完成以下兩件事：\n",
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"* 使用**分詞器**將文本拆分成**詞元**\n",
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"* 建立這些詞元的**詞彙表**。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['he', 'said', 'hello']"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tokenizer = torchtext.data.utils.get_tokenizer('basic_english')\n",
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"tokenizer('He said: hello')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"counter = collections.Counter()\n",
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"for (label, line) in train_dataset:\n",
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" counter.update(tokenizer(line))\n",
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"vocab = torchtext.vocab.vocab(counter, min_freq=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"使用詞彙，我們可以輕鬆地將標記化的字串編碼為一組數字：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Vocab size if 95810\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[599, 3279, 97, 1220, 329, 225, 7368]"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vocab_size = len(vocab)\n",
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"print(f\"Vocab size if {vocab_size}\")\n",
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"\n",
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"stoi = vocab.get_stoi() # dict to convert tokens to indices\n",
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"\n",
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"def encode(x):\n",
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" return [stoi[s] for s in tokenizer(x)]\n",
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"\n",
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"encode('I love to play with my words')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 詞袋文字表示法\n",
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"\n",
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"由於文字代表了意義，有時我們可以僅通過查看單個詞語來理解文本的含義，而不考慮它們在句子中的順序。例如，在分類新聞時，像 *天氣*、*雪* 這樣的詞語可能表明是 *天氣預報*，而像 *股票*、*美元* 這樣的詞語則可能屬於 *財經新聞*。\n",
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"\n",
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"**詞袋** (BoW) 向量表示法是最常用的傳統向量表示法。每個詞語都與一個向量索引相關聯，向量元素包含某個詞語在特定文檔中出現的次數。\n",
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"\n",
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" \n",
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"\n",
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"> **Note**: 你也可以將 BoW 理解為文本中每個詞語的單熱編碼向量的總和。\n",
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"\n",
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"以下是一個使用 Scikit Learn Python 庫生成詞袋表示法的範例：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 1, 0, 2, 0, 0, 0, 0, 0]], dtype=int64)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"vectorizer = CountVectorizer()\n",
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"corpus = [\n",
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" 'I like hot dogs.',\n",
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" 'The dog ran fast.',\n",
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" 'Its hot outside.',\n",
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" ]\n",
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"vectorizer.fit_transform(corpus)\n",
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"vectorizer.transform(['My dog likes hot dogs on a hot day.']).toarray()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"要從我們的 AG_NEWS 數據集的向量表示計算詞袋向量，可以使用以下函數：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([2., 1., 2., ..., 0., 0., 0.])\n"
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]
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}
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],
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"source": [
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"vocab_size = len(vocab)\n",
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"\n",
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"def to_bow(text,bow_vocab_size=vocab_size):\n",
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" res = torch.zeros(bow_vocab_size,dtype=torch.float32)\n",
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" for i in encode(text):\n",
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" if i<bow_vocab_size:\n",
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" res[i] += 1\n",
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" return res\n",
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"\n",
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"print(to_bow(train_dataset[0][1]))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> **注意：** 這裡我們使用全域變數 `vocab_size` 來指定詞彙表的預設大小。由於詞彙表的大小通常相當大，我們可以將詞彙表的大小限制為最常出現的詞彙。嘗試降低 `vocab_size` 的值並執行下面的程式碼，看看它如何影響準確性。你應該預期準確性會有所下降，但不會太劇烈，以換取更高的效能。\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 訓練 BoW 分類器\n",
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"\n",
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"現在我們已經學會如何建立文字的詞袋表示法，接下來讓我們在其基礎上訓練一個分類器。首先，我們需要將數據集轉換為適合訓練的格式，將所有位置向量表示轉換為詞袋表示法。這可以通過將 `bowify` 函數作為標準 torch `DataLoader` 的 `collate_fn` 參數來實現：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"from torch.utils.data import DataLoader\n",
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"import numpy as np \n",
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"\n",
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"# this collate function gets list of batch_size tuples, and needs to \n",
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"# return a pair of label-feature tensors for the whole minibatch\n",
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"def bowify(b):\n",
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" return (\n",
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" torch.LongTensor([t[0]-1 for t in b]),\n",
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" torch.stack([to_bow(t[1]) for t in b])\n",
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" )\n",
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"\n",
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"train_loader = DataLoader(train_dataset, batch_size=16, collate_fn=bowify, shuffle=True)\n",
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"test_loader = DataLoader(test_dataset, batch_size=16, collate_fn=bowify, shuffle=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"現在讓我們定義一個簡單的分類器神經網絡，其中包含一個線性層。輸入向量的大小等於 `vocab_size`，輸出大小對應於類別數量（4）。由於我們正在解決分類任務，最終的激活函數是 `LogSoftmax()`。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"net = torch.nn.Sequential(torch.nn.Linear(vocab_size,4),torch.nn.LogSoftmax(dim=1))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"現在我們將定義標準的 PyTorch 訓練迴圈。由於我們的數據集相當大，為了教學目的，我們只訓練一個 epoch，有時甚至少於一個 epoch（指定 `epoch_size` 參數可以限制訓練）。我們還會在訓練過程中報告累積的訓練準確率；報告的頻率是使用 `report_freq` 參數指定的。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_epoch(net,dataloader,lr=0.01,optimizer=None,loss_fn = torch.nn.NLLLoss(),epoch_size=None, report_freq=200):\n",
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" optimizer = optimizer or torch.optim.Adam(net.parameters(),lr=lr)\n",
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" net.train()\n",
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" total_loss,acc,count,i = 0,0,0,0\n",
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" for labels,features in dataloader:\n",
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" optimizer.zero_grad()\n",
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" out = net(features)\n",
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" loss = loss_fn(out,labels) #cross_entropy(out,labels)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" total_loss+=loss\n",
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" _,predicted = torch.max(out,1)\n",
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" acc+=(predicted==labels).sum()\n",
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" count+=len(labels)\n",
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" i+=1\n",
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" if i%report_freq==0:\n",
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" print(f\"{count}: acc={acc.item()/count}\")\n",
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" if epoch_size and count>epoch_size:\n",
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" break\n",
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" return total_loss.item()/count, acc.item()/count"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3200: acc=0.8028125\n",
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"6400: acc=0.8371875\n",
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"9600: acc=0.8534375\n",
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"12800: acc=0.85765625\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"(0.026090790722161722, 0.8620069296375267)"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_epoch(net,train_loader,epoch_size=15000)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 雙詞組、三詞組與 N 詞組\n",
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"\n",
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"詞袋模型的一個限制是，有些詞是多詞表達的一部分。例如，「熱狗」這個詞的意思與「熱」和「狗」在其他語境中的意思完全不同。如果我們總是用相同的向量來表示「熱」和「狗」，可能會讓模型感到困惑。\n",
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"\n",
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"為了解決這個問題，**N 詞組表示法**經常被用於文件分類的方法中，其中每個單詞、雙詞或三詞的頻率是訓練分類器的一個有用特徵。例如，在雙詞組表示法中，除了原始單詞之外，我們還會將所有的單詞對加入詞彙表中。\n",
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"\n",
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"以下是一個使用 Scikit Learn 生成雙詞組詞袋表示法的範例：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Vocabulary:\n",
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" {'i': 7, 'like': 11, 'hot': 4, 'dogs': 2, 'i like': 8, 'like hot': 12, 'hot dogs': 5, 'the': 16, 'dog': 0, 'ran': 14, 'fast': 3, 'the dog': 17, 'dog ran': 1, 'ran fast': 15, 'its': 9, 'outside': 13, 'its hot': 10, 'hot outside': 6}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[1, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
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" dtype=int64)"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
||
"bigram_vectorizer = CountVectorizer(ngram_range=(1, 2), token_pattern=r'\\b\\w+\\b', min_df=1)\n",
|
||
"corpus = [\n",
|
||
" 'I like hot dogs.',\n",
|
||
" 'The dog ran fast.',\n",
|
||
" 'Its hot outside.',\n",
|
||
" ]\n",
|
||
"bigram_vectorizer.fit_transform(corpus)\n",
|
||
"print(\"Vocabulary:\\n\",bigram_vectorizer.vocabulary_)\n",
|
||
"bigram_vectorizer.transform(['My dog likes hot dogs on a hot day.']).toarray()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"N-gram 方法的主要缺點是詞彙量會迅速增長。在實際應用中，我們需要將 N-gram 表示法與一些降維技術結合使用，例如 *嵌入*，我們會在下一單元中討論。\n",
|
||
"\n",
|
||
"要在我們的 **AG News** 數據集中使用 N-gram 表示法，我們需要建立專門的 ngram 詞彙：\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Bigram vocabulary length = 1308842\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"counter = collections.Counter()\n",
|
||
"for (label, line) in train_dataset:\n",
|
||
" l = tokenizer(line)\n",
|
||
" counter.update(torchtext.data.utils.ngrams_iterator(l,ngrams=2))\n",
|
||
" \n",
|
||
"bi_vocab = torchtext.vocab.vocab(counter, min_freq=1)\n",
|
||
"\n",
|
||
"print(\"Bigram vocabulary length = \",len(bi_vocab))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"我們可以使用上述相同的程式碼來訓練分類器，但這樣做會非常佔用記憶體。在下一單元中，我們將使用嵌入來訓練雙詞組分類器。\n",
|
||
"\n",
|
||
"> **注意：** 你只能保留那些在文本中出現次數超過指定數量的 ngrams。這樣可以確保罕見的雙詞組會被省略，並顯著減少維度。為此，將 `min_freq` 參數設置為更高的值，並觀察詞彙表長度的變化。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 詞頻-逆文檔頻率 TF-IDF\n",
|
||
"\n",
|
||
"在 BoW 表示法中，詞的出現次數被均等地加權，而不考慮詞本身的特性。然而，很明顯像 *a*、*in* 等這些常見詞對分類的影響遠不如專業術語重要。事實上，在大多數 NLP 任務中，有些詞比其他詞更具相關性。\n",
|
||
"\n",
|
||
"**TF-IDF** 代表 **詞頻–逆文檔頻率**。它是袋子模型（BoW）的變體，與使用二進制 0/1 值表示詞在文檔中的出現不同，TF-IDF 使用浮點值，該值與詞在語料庫中的出現頻率相關。\n",
|
||
"\n",
|
||
"更正式地說，詞 $i$ 在文檔 $j$ 中的權重 $w_{ij}$ 定義如下：\n",
|
||
"$$\n",
|
||
"w_{ij} = tf_{ij}\\times\\log({N\\over df_i})\n",
|
||
"$$\n",
|
||
"其中：\n",
|
||
"* $tf_{ij}$ 是詞 $i$ 在文檔 $j$ 中的出現次數，也就是我們之前看到的 BoW 值\n",
|
||
"* $N$ 是語料庫中的文檔總數\n",
|
||
"* $df_i$ 是包含詞 $i$ 的文檔數量\n",
|
||
"\n",
|
||
"TF-IDF 值 $w_{ij}$ 與詞在文檔中出現的次數成正比，但會根據語料庫中包含該詞的文檔數量進行調整。這有助於平衡某些詞比其他詞更頻繁出現的情況。例如，如果某個詞出現在語料庫的*每一個*文檔中，則 $df_i=N$，而 $w_{ij}=0$，這些詞將被完全忽略。\n",
|
||
"\n",
|
||
"您可以使用 Scikit Learn 輕鬆地生成文本的 TF-IDF 向量化：\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[0.43381609, 0. , 0.43381609, 0. , 0.65985664,\n",
|
||
" 0.43381609, 0. , 0. , 0. , 0. ,\n",
|
||
" 0. , 0. , 0. , 0. , 0. ,\n",
|
||
" 0. ]])"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||
"vectorizer = TfidfVectorizer(ngram_range=(1,2))\n",
|
||
"vectorizer.fit_transform(corpus)\n",
|
||
"vectorizer.transform(['My dog likes hot dogs on a hot day.']).toarray()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 結論\n",
|
||
"\n",
|
||
"然而，儘管 TF-IDF 表示法為不同的詞提供了頻率權重，但它無法表達詞義或順序。正如著名語言學家 J. R. Firth 在1935年所說：「詞語的完整意義總是與上下文相關，任何脫離上下文的意義研究都不應被認真對待。」在課程的後續部分，我們將學習如何通過語言建模從文本中捕捉上下文信息。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n---\n\n**免責聲明**： \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。\n"
|
||
]
|
||
}
|
||
],
|
||
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|
||
"interpreter": {
|
||
"hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f"
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|
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|
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|
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|
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|
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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