454 lines
15 KiB
Plaintext
454 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fdaad6d5",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"# 文本预处理\n",
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":label:`sec_text_preprocessing`\n",
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"\n",
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"对于序列数据处理问题，我们在 :numref:`sec_sequence`中\n",
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"评估了所需的统计工具和预测时面临的挑战。\n",
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"这样的数据存在许多种形式，文本是最常见例子之一。\n",
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"例如，一篇文章可以被简单地看作一串单词序列，甚至是一串字符序列。\n",
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"本节中，我们将解析文本的常见预处理步骤。\n",
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"这些步骤通常包括：\n",
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"\n",
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"1. 将文本作为字符串加载到内存中。\n",
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"1. 将字符串拆分为词元（如单词和字符）。\n",
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"1. 建立一个词表，将拆分的词元映射到数字索引。\n",
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"1. 将文本转换为数字索引序列，方便模型操作。\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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"id": "bb8907ca",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:02:24.243885Z",
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"iopub.status.busy": "2023-08-18T07:02:24.243343Z",
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"iopub.status.idle": "2023-08-18T07:02:26.213654Z",
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"shell.execute_reply": "2023-08-18T07:02:26.212745Z"
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},
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import collections\n",
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"import re\n",
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"from d2l import torch as d2l"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e987bf4c",
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"metadata": {
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"origin_pos": 5
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},
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"source": [
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"## 读取数据集\n",
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"\n",
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"首先，我们从H.G.Well的[时光机器](https://www.gutenberg.org/ebooks/35)中加载文本。\n",
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"这是一个相当小的语料库，只有30000多个单词，但足够我们小试牛刀，\n",
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"而现实中的文档集合可能会包含数十亿个单词。\n",
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"下面的函数(**将数据集读取到由多条文本行组成的列表中**)，其中每条文本行都是一个字符串。\n",
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"为简单起见，我们在这里忽略了标点符号和字母大写。\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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"id": "ac0f9f0d",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:02:26.218338Z",
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"iopub.status.busy": "2023-08-18T07:02:26.217685Z",
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"iopub.status.idle": "2023-08-18T07:02:26.304928Z",
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"shell.execute_reply": "2023-08-18T07:02:26.304151Z"
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},
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"origin_pos": 6,
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"tab": [
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"pytorch"
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]
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},
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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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"Downloading ../data/timemachine.txt from http://d2l-data.s3-accelerate.amazonaws.com/timemachine.txt...\n",
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"# 文本总行数: 3221\n",
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"the time machine by h g wells\n",
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"twinkled and his usually pale face was flushed and animated the\n"
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]
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}
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],
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"source": [
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"#@save\n",
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"d2l.DATA_HUB['time_machine'] = (d2l.DATA_URL + 'timemachine.txt',\n",
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" '090b5e7e70c295757f55df93cb0a180b9691891a')\n",
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"\n",
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"def read_time_machine(): #@save\n",
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" \"\"\"将时间机器数据集加载到文本行的列表中\"\"\"\n",
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" with open(d2l.download('time_machine'), 'r') as f:\n",
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" lines = f.readlines()\n",
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" return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]\n",
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"\n",
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"lines = read_time_machine()\n",
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"print(f'# 文本总行数: {len(lines)}')\n",
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"print(lines[0])\n",
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"print(lines[10])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c34664d1",
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"metadata": {
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"origin_pos": 7
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},
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"source": [
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"## 词元化\n",
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"\n",
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"下面的`tokenize`函数将文本行列表（`lines`）作为输入，\n",
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"列表中的每个元素是一个文本序列（如一条文本行）。\n",
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"[**每个文本序列又被拆分成一个词元列表**]，*词元*（token）是文本的基本单位。\n",
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"最后，返回一个由词元列表组成的列表，其中的每个词元都是一个字符串（string）。\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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"id": "afd6a9df",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:02:26.308604Z",
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"iopub.status.busy": "2023-08-18T07:02:26.308048Z",
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"iopub.status.idle": "2023-08-18T07:02:26.317083Z",
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"shell.execute_reply": "2023-08-18T07:02:26.316264Z"
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},
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"origin_pos": 8,
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"tab": [
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"pytorch"
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]
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},
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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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"['the', 'time', 'machine', 'by', 'h', 'g', 'wells']\n",
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"[]\n",
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"[]\n",
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"[]\n",
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"[]\n",
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"['i']\n",
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"[]\n",
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"[]\n",
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"['the', 'time', 'traveller', 'for', 'so', 'it', 'will', 'be', 'convenient', 'to', 'speak', 'of', 'him']\n",
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"['was', 'expounding', 'a', 'recondite', 'matter', 'to', 'us', 'his', 'grey', 'eyes', 'shone', 'and']\n",
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"['twinkled', 'and', 'his', 'usually', 'pale', 'face', 'was', 'flushed', 'and', 'animated', 'the']\n"
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]
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}
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],
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"source": [
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"def tokenize(lines, token='word'): #@save\n",
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" \"\"\"将文本行拆分为单词或字符词元\"\"\"\n",
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" if token == 'word':\n",
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" return [line.split() for line in lines]\n",
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" elif token == 'char':\n",
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" return [list(line) for line in lines]\n",
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" else:\n",
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" print('错误：未知词元类型：' + token)\n",
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"\n",
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"tokens = tokenize(lines)\n",
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"for i in range(11):\n",
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" print(tokens[i])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e61c06e8",
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"metadata": {
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"origin_pos": 9
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},
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"source": [
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"## 词表\n",
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"\n",
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"词元的类型是字符串，而模型需要的输入是数字，因此这种类型不方便模型使用。\n",
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"现在，让我们[**构建一个字典，通常也叫做*词表*（vocabulary），\n",
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"用来将字符串类型的词元映射到从$0$开始的数字索引中**]。\n",
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"我们先将训练集中的所有文档合并在一起，对它们的唯一词元进行统计，\n",
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"得到的统计结果称之为*语料*（corpus）。\n",
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"然后根据每个唯一词元的出现频率，为其分配一个数字索引。\n",
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"很少出现的词元通常被移除，这可以降低复杂性。\n",
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"另外，语料库中不存在或已删除的任何词元都将映射到一个特定的未知词元“<unk>”。\n",
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"我们可以选择增加一个列表，用于保存那些被保留的词元，\n",
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"例如：填充词元（“<pad>”）；\n",
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"序列开始词元（“<bos>”）；\n",
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"序列结束词元（“<eos>”）。\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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"id": "16db7dad",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:02:26.320587Z",
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"iopub.status.busy": "2023-08-18T07:02:26.320050Z",
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"iopub.status.idle": "2023-08-18T07:02:26.330519Z",
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"shell.execute_reply": "2023-08-18T07:02:26.329736Z"
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},
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"origin_pos": 10,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"class Vocab: #@save\n",
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" \"\"\"文本词表\"\"\"\n",
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" def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):\n",
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" if tokens is None:\n",
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" tokens = []\n",
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" if reserved_tokens is None:\n",
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" reserved_tokens = []\n",
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" # 按出现频率排序\n",
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" counter = count_corpus(tokens)\n",
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" self._token_freqs = sorted(counter.items(), key=lambda x: x[1],\n",
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" reverse=True)\n",
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" # 未知词元的索引为0\n",
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" self.idx_to_token = ['<unk>'] + reserved_tokens\n",
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" self.token_to_idx = {token: idx\n",
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" for idx, token in enumerate(self.idx_to_token)}\n",
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" for token, freq in self._token_freqs:\n",
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" if freq < min_freq:\n",
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" break\n",
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" if token not in self.token_to_idx:\n",
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" self.idx_to_token.append(token)\n",
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" self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.idx_to_token)\n",
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"\n",
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" def __getitem__(self, tokens):\n",
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" if not isinstance(tokens, (list, tuple)):\n",
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" return self.token_to_idx.get(tokens, self.unk)\n",
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" return [self.__getitem__(token) for token in tokens]\n",
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"\n",
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" def to_tokens(self, indices):\n",
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" if not isinstance(indices, (list, tuple)):\n",
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" return self.idx_to_token[indices]\n",
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" return [self.idx_to_token[index] for index in indices]\n",
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"\n",
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" @property\n",
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" def unk(self): # 未知词元的索引为0\n",
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" return 0\n",
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"\n",
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" @property\n",
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" def token_freqs(self):\n",
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" return self._token_freqs\n",
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"\n",
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"def count_corpus(tokens): #@save\n",
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" \"\"\"统计词元的频率\"\"\"\n",
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" # 这里的tokens是1D列表或2D列表\n",
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" if len(tokens) == 0 or isinstance(tokens[0], list):\n",
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" # 将词元列表展平成一个列表\n",
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" tokens = [token for line in tokens for token in line]\n",
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" return collections.Counter(tokens)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d7fde2e0",
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"metadata": {
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"origin_pos": 11
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},
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"source": [
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"我们首先使用时光机器数据集作为语料库来[**构建词表**]，然后打印前几个高频词元及其索引。\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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"id": "1501d478",
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"metadata": {
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"execution": {
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||
"iopub.execute_input": "2023-08-18T07:02:26.333942Z",
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"iopub.status.busy": "2023-08-18T07:02:26.333382Z",
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"iopub.status.idle": "2023-08-18T07:02:26.346927Z",
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||
"shell.execute_reply": "2023-08-18T07:02:26.346182Z"
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},
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"origin_pos": 12,
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"tab": [
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"pytorch"
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]
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},
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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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"[('<unk>', 0), ('the', 1), ('i', 2), ('and', 3), ('of', 4), ('a', 5), ('to', 6), ('was', 7), ('in', 8), ('that', 9)]\n"
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]
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}
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],
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"source": [
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"vocab = Vocab(tokens)\n",
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"print(list(vocab.token_to_idx.items())[:10])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ce7b78a3",
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"metadata": {
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"origin_pos": 13
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},
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"source": [
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"现在，我们可以(**将每一条文本行转换成一个数字索引列表**)。\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": 6,
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"id": "f0244f09",
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"metadata": {
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"execution": {
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||
"iopub.execute_input": "2023-08-18T07:02:26.350343Z",
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||
"iopub.status.busy": "2023-08-18T07:02:26.349779Z",
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||
"iopub.status.idle": "2023-08-18T07:02:26.354215Z",
|
||
"shell.execute_reply": "2023-08-18T07:02:26.353468Z"
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},
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"origin_pos": 14,
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"tab": [
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"pytorch"
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]
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},
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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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"文本: ['the', 'time', 'machine', 'by', 'h', 'g', 'wells']\n",
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"索引: [1, 19, 50, 40, 2183, 2184, 400]\n",
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"文本: ['twinkled', 'and', 'his', 'usually', 'pale', 'face', 'was', 'flushed', 'and', 'animated', 'the']\n",
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"索引: [2186, 3, 25, 1044, 362, 113, 7, 1421, 3, 1045, 1]\n"
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]
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}
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],
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"source": [
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"for i in [0, 10]:\n",
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" print('文本:', tokens[i])\n",
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" print('索引:', vocab[tokens[i]])"
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]
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},
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{
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"cell_type": "markdown",
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||
"id": "e84c1a2a",
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||
"metadata": {
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||
"origin_pos": 15
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},
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"source": [
|
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"## 整合所有功能\n",
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"\n",
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"在使用上述函数时，我们[**将所有功能打包到`load_corpus_time_machine`函数中**]，\n",
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"该函数返回`corpus`（词元索引列表）和`vocab`（时光机器语料库的词表）。\n",
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"我们在这里所做的改变是：\n",
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"\n",
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"1. 为了简化后面章节中的训练，我们使用字符（而不是单词）实现文本词元化；\n",
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"1. 时光机器数据集中的每个文本行不一定是一个句子或一个段落，还可能是一个单词，因此返回的`corpus`仅处理为单个列表，而不是使用多词元列表构成的一个列表。\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": 7,
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||
"id": "578ed76f",
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||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-08-18T07:02:26.357414Z",
|
||
"iopub.status.busy": "2023-08-18T07:02:26.357141Z",
|
||
"iopub.status.idle": "2023-08-18T07:02:26.470812Z",
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||
"shell.execute_reply": "2023-08-18T07:02:26.470008Z"
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},
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"origin_pos": 16,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
|
||
{
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||
"data": {
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"text/plain": [
|
||
"(170580, 28)"
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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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"def load_corpus_time_machine(max_tokens=-1): #@save\n",
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" \"\"\"返回时光机器数据集的词元索引列表和词表\"\"\"\n",
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" lines = read_time_machine()\n",
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" tokens = tokenize(lines, 'char')\n",
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" vocab = Vocab(tokens)\n",
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" # 因为时光机器数据集中的每个文本行不一定是一个句子或一个段落，\n",
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" # 所以将所有文本行展平到一个列表中\n",
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" corpus = [vocab[token] for line in tokens for token in line]\n",
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" if max_tokens > 0:\n",
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" corpus = corpus[:max_tokens]\n",
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" return corpus, vocab\n",
|
||
"\n",
|
||
"corpus, vocab = load_corpus_time_machine()\n",
|
||
"len(corpus), len(vocab)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "28620a4d",
|
||
"metadata": {
|
||
"origin_pos": 17
|
||
},
|
||
"source": [
|
||
"## 小结\n",
|
||
"\n",
|
||
"* 文本是序列数据的一种最常见的形式之一。\n",
|
||
"* 为了对文本进行预处理，我们通常将文本拆分为词元，构建词表将词元字符串映射为数字索引，并将文本数据转换为词元索引以供模型操作。\n",
|
||
"\n",
|
||
"## 练习\n",
|
||
"\n",
|
||
"1. 词元化是一个关键的预处理步骤，它因语言而异。尝试找到另外三种常用的词元化文本的方法。\n",
|
||
"1. 在本节的实验中，将文本词元为单词和更改`Vocab`实例的`min_freq`参数。这对词表大小有何影响？\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "17f3b26f",
|
||
"metadata": {
|
||
"origin_pos": 19,
|
||
"tab": [
|
||
"pytorch"
|
||
]
|
||
},
|
||
"source": [
|
||
"[Discussions](https://discuss.d2l.ai/t/2094)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"language_info": {
|
||
"name": "python"
|
||
},
|
||
"required_libs": []
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
} |