449 lines
14 KiB
Plaintext
449 lines
14 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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"vscode": {
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"languageId": "plaintext"
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}
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},
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"source": [
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"# 0-Tokenizer\n",
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"\n",
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"Tokenizer（分词器）在 NLP 领域扮演着基础且关键的作用，它将文本分割成单词或子词并转化为数组编号，为模型提供可处理的输入，在文本预处理、语义理解及适配不同语言和任务等方面奠定基础，是连接自然语言文本与计算机可处理数据的重要桥梁."
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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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"vscode": {
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"languageId": "plaintext"
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}
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},
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"source": [
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"## 子词分词算法\n",
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"\n",
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"常见的子词分词算法有三种：\n",
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"\n",
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"1. 字节对编码（Byte Pair Encoding，BPE）\n",
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"2. WordPiece\n",
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"3. Unigram\n",
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"\n",
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"### BPE\n",
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"\n",
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"BPE 是一种简单的数据压缩技术，它会迭代地替换序列中最频繁出现的字节对。BPE 依赖一个预分词器，该预分词器会将训练数据分割成单词（在本项目中，我们使用按空格分词的方法作为预分词方法）.\n",
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"\n",
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"在预分词之后，会创建一组唯一的单词，并确定它们在数据中的出现频率。接下来，BPE 会创建一个基础词表，该词表包含预分词器最初生成的数据中所有唯一单词的符号。然后，会将这对符号从词表中移除，新形成的符号将加入词表。在迭代过程中，BPE 算法会合并频繁出现的符号对.\n",
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"\n",
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"给定词表的大小，BPE（字节对编码）算法最终会合并出现频率最高的符号对，直到收敛到该大小.\n",
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"\n",
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"### WordPiece\n",
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"\n",
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"WordPiece 算法与 BPE 非常相似。WordPiece 首先将词表初始化为包含训练数据中出现的每个字符，然后逐步学习给定数量的合并规则. 与 BPE 不同的是，WordPiece 并不选择最频繁出现的符号对，而是选择那个加入词表后能使训练数据出现的可能性最大化的符号对.\n",
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"\n",
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"### Unigram\n",
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"\n",
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"Unigram 算法将其基础词表初始化为大量的符号，然后逐步削减每个符号，以获得一个更小的词表。它会在训练数据上定义一个对数似然损失，以此来确定是否从词表中移除某个符号."
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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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"## 训练一个最简单的分词器\n",
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"\n",
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"在本节中，我们将学习基于 transformers 库来训练你自己的分词器."
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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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"### 初始化\n",
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"\n",
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"首先，我们应该初始化我们的分词器，并确定选择哪种方法。我们将使用字节对编码（BPE）算法."
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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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"from tokenizers import (\n",
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" decoders,\n",
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" models,\n",
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" normalizers,\n",
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" pre_tokenizers,\n",
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" processors,\n",
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" trainers,\n",
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" Tokenizer,\n",
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")\n",
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"\n",
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"tokenizer = Tokenizer(models.BPE())\n",
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"tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)"
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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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"### 定义特殊标记\n",
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"\n",
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"数据集中存在一些我们不希望被分词的特殊标记，我们会将这些标记定义为特殊标记，并将它们传递给分词器训练器，以防止出现错误的分词情况."
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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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"source": [
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"special_tokens = [\"<unk>\", \"<s>\", \"</s>\"]\n",
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"\n",
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"trainer = trainers.BpeTrainer(\n",
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" vocab_size=256,\n",
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" special_tokens=special_tokens,\n",
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" show_progress=True,\n",
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" initial_alphabet=pre_tokenizers.ByteLevel.alphabet()\n",
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")"
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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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"### 从文件中读取数据\n",
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"\n",
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"在本次实验中，我们使用 JSON Lines（jsonl）格式来存储 Tokenizer 训练数据，分词器内置的训练函数要求训练数据以迭代器的形式传入，因此，我们首先获取一个数据读取的生成器."
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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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"import json\n",
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"\n",
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"def read_texts_from_jsonl(file_path):\n",
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" with open(file_path, 'r', encoding='utf-8') as f:\n",
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" for line in f:\n",
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" data = json.loads(line)\n",
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" yield data['text']\n",
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"\n",
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"data_path = './demo/tokenizer_data.jsonl'"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Row 1: <s>近年来，人工智能技术迅速发展，深刻改变了各行各业的面貌。机器学习、自然语言处理、计算机视觉等领域的突破性进展，使得智能产品和服务越来越普及。从智能家居到自动驾驶，再到智能医疗，AI的应用场景正在快速拓展。随着技术的不断进步，未来的人工智能将更加智能、更加贴近人类生活。</s>\n"
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]
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}
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],
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"source": [
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"data_iter = read_texts_from_jsonl(data_path)\n",
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"print(f'Row 1: {next(data_iter)}')"
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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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"### 开始训练!\n",
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"\n",
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"我们使用分词器的内置函数 `tokenizer.train_from_iterator` 来训练分词器."
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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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"tokenizer.train_from_iterator(data_iter, trainer=trainer)"
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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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"### 设置解码器"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer.decoder = decoders.ByteLevel()"
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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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"接下来，检查一下特殊标记是否得到了妥善处理。"
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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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"source": [
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"assert tokenizer.token_to_id('<unk>') == 0\n",
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"assert tokenizer.token_to_id('<s>') == 1\n",
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"assert tokenizer.token_to_id('</s>') == 2"
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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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"### 将训练好的分词器保存到磁盘"
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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": 8,
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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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"['./demo/tokenizer_demo\\\\vocab.json', './demo/tokenizer_demo\\\\merges.txt']"
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]
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},
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"execution_count": 8,
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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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"import os\n",
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"\n",
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"tokenizer_dir = \"./demo/tokenizer_demo\"\n",
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"os.makedirs(tokenizer_dir, exist_ok=True)\n",
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"tokenizer.save(os.path.join(tokenizer_dir, \"tokenizer.json\")) # At this point, you will see a file named tokenizer.json under tokenizer_dir\n",
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"tokenizer.model.save(tokenizer_dir) # generate vocab.json & merges.txt"
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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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"### 手动创建一份配置文件"
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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": 9,
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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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"Tokenizer training completed and saved.\n"
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]
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}
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],
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"source": [
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"config = {\n",
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" \"add_bos_token\": False,\n",
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" \"add_eos_token\": False,\n",
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" \"add_prefix_space\": False,\n",
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" \"added_tokens_decoder\": {\n",
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" \"0\": {\n",
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" \"content\": \"<unk>\",\n",
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" \"lstrip\": False,\n",
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" \"normalized\": False,\n",
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" \"rstrip\": False,\n",
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" \"single_word\": False,\n",
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" \"special\": True\n",
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" },\n",
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" \"1\": {\n",
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" \"content\": \"<s>\",\n",
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" \"lstrip\": False,\n",
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" \"normalized\": False,\n",
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" \"rstrip\": False,\n",
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" \"single_word\": False,\n",
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" \"special\": True\n",
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" },\n",
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" \"2\": {\n",
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" \"content\": \"</s>\",\n",
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" \"lstrip\": False,\n",
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" \"normalized\": False,\n",
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" \"rstrip\": False,\n",
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" \"single_word\": False,\n",
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" \"special\": True\n",
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" }\n",
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" },\n",
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" \"additional_special_tokens\": [],\n",
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" \"bos_token\": \"<s>\",\n",
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" \"clean_up_tokenization_spaces\": False,\n",
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" \"eos_token\": \"</s>\",\n",
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" \"legacy\": True,\n",
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" \"model_max_length\": 32768,\n",
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" \"pad_token\": \"<unk>\",\n",
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" \"sp_model_kwargs\": {},\n",
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" \"spaces_between_special_tokens\": False,\n",
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" \"tokenizer_class\": \"PreTrainedTokenizerFast\",\n",
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" \"unk_token\": \"<unk>\",\n",
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" \"chat_template\": \"{{ '<s>' + messages[0]['text'] + '</s>' }}\"\n",
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"}\n",
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"\n",
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"with open(os.path.join(tokenizer_dir, \"tokenizer_config.json\"), \"w\", encoding=\"utf-8\") as config_file:\n",
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" json.dump(config, config_file, ensure_ascii=False, indent=4)\n",
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"\n",
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"print(\"Tokenizer training completed and saved.\")"
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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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"现在我们已经训练了一个简单的分词器，并将其进行保存，接下来，我们试着加载它，并使用其帮助我们对文本进行编解码."
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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": 10,
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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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"原始文本：[{'text': '失去的东西就要学着去接受，学着放下。'}]\n",
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"修改文本：[{'text': '失去的东西就要学着去接受，学着放下。'}] (添加自定义聊天模板)\n"
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]
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}
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],
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"source": [
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"from transformers import AutoTokenizer\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"./demo/tokenizer_demo\")\n",
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"msg = [{\"text\": \"失去的东西就要学着去接受，学着放下。\"}]\n",
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"new_msg = tokenizer.apply_chat_template(\n",
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" msg,\n",
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" tokenize=False\n",
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")\n",
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"print(f'原始文本：{msg}')\n",
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"print(f'修改文本：{msg} (添加自定义聊天模板)')"
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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": 11,
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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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"分词器词表大小：259\n"
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]
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}
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],
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"source": [
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"print(f'分词器词表大小：{tokenizer.vocab_size}')"
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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": 12,
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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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"查看分词结果：\n",
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"{'input_ids': [1, 164, 100, 112, 164, 239, 122, 166, 251, 229, 163, 119, 253, 167, 101, 126, 164, 111, 112, 167, 102, 226, 164, 258, 102, 166, 254, 225, 164, 239, 122, 165, 239, 101, 164, 240, 248, 174, 123, 237, 164, 258, 102, 166, 254, 225, 165, 245, 125, 163, 119, 236, 162, 225, 227, 2], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
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]
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}
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],
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"source": [
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"model_inputs = tokenizer(new_msg)\n",
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"print(f'查看分词结果：\\n{model_inputs}')"
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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": 13,
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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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"对分词结果进行解码：<s>失去的东西就要学着去接受，学着放下。</s> (保留特殊字符)\n"
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]
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}
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],
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"source": [
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"response = tokenizer.decode(model_inputs['input_ids'], skip_special_tokens=False)\n",
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"print(f'对分词结果进行解码：{response} (保留特殊字符)' )"
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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": 14,
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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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"对分词结果进行解码：失去的东西就要学着去接受，学着放下。 (移除特殊字符)\n"
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]
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}
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],
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"source": [
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"response = tokenizer.decode(model_inputs['input_ids'], skip_special_tokens=True)\n",
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"print(f'对分词结果进行解码：{response} (移除特殊字符)' )"
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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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"## 参考资料\n",
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"\n",
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"- [Hugging Face NLP Course](https://huggingface.co/learn/nlp-course/zh-CN/chapter2/4)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.14"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|