BPE分词

BPE(Byte Pair Encoding)是一种基于统计的无监督分词算法,常用于自然语言处理任务中,如机器翻译、文本生成等。BPE算法通过将原始文本逐步拆分为子词或字符,从而实现分词的目的。

以下是BPE分词算法的详细说明:

  1. 数据预处理: BPE算法首先对输入的训练语料进行预处理,将每个词按字符切分为序列,加上特殊符号(如开始符号和结束符号)。

  2. 构建词表: BPE算法通过统计训练语料中字符或子词的频率来构建词表。初始时,将训练语料中的字符或子词作为词表中的初始词汇。

  3. 计算频率: 统计训练语料中字符或子词的出现频率,并按照频率排序。

  4. 合并操作: 选择最频繁出现的一对相邻字符或子词进行合并,形成一个新的字符或子词,并更新词表和频率统计。

  5. 重复合并操作: 重复进行合并操作,直到达到预设的合并次数或无法再合并为止。

  6. 分词: 使用最终的词表,将输入文本进行分词。分词时,优先匹配较长的子词,当无法继续匹配时,再匹配较短的子词。

  7. 恢复原始文本: 将分词结果中的特殊符号去除,并将字符或子词连接起来,恢复为原始的文本形式。

BPE分词算法的优点是可以自动构建词表,并且能够处理未登录词(Out-of-Vocabulary,OOV)问题。它能够灵活地识别和生成复杂的词组,适用于不同领域和语种的文本处理任务。

以下是一个使用Python实现BPE分词算法的示例代码:

from collections import defaultdict

def learn_bpe(data, num_merges):
    # 初始化词表,将每个字符作为初始词汇
    vocab = defaultdict(int)
    for word in data:
        for char in word:
            vocab[char] += 1
    
    # 进行合并操作
    merges = []
    for _ in range(num_merges):
        # 统计词频
        pairs = defaultdict(int)
        for word in data:
            symbols = word.split()
            for i in range(len(symbols)-1):
                pairs[symbols[i],symbols[i+1]] += 1
        
        # 找到最频繁的一对相邻字符或子词
        best = max(pairs, key=pairs.get)
        merges.append(best)
        
        # 更新词表
        new_vocab = defaultdict(int)
        for word in data:
            # 合并最频繁的一对相邻字符或子词
            new_word = word.replace(' '.join(best), ''.join(best))
            new_vocab[new_word] += 1
        vocab = new_vocab
    
    return merges, vocab

def segment_text(text, merges):
    # 恢复分词结果
    segments = []
    for word in text.split():
        for merge in merges:
            if merge in word:
                word = word.replace(merge, ' '.join(merge))
        segments.extend(word.split())
    
    return segments

# 示例使用
data = ["low", "lower", "newest", "widest", "special", "specials"]
merges, vocab = learn_bpe(data, 5)
print("Merges:", merges)
print("Vocabulary:", dict(vocab))

text = "lowest specials"
segments = segment_text(text, merges)
print("Segments:", segments)

c++实现:

#include <iostream>
#include <unordered_map>
#include <vector>
#include <algorithm>

std::unordered_map<std::string, int> learn_bpe(const std::vector<std::string>& data, int num_merges) {
    std::unordered_map<std::string, int> vocab;
    for (const std::string& word : data) {
        for (char c : word) {
            std::string charStr(1, c);
            vocab[charStr]++;
        }
    }

    std::unordered_map<std::pair<std::string, std::string>, int> pairs;
    for (const std::string& word : data) {
        std::vector<std::string> symbols;
        size_t len = word.length();
        for (size_t i = 0; i < len - 1; ++i) {
            std::string sym = word.substr(i, 2);
            pairs[std::make_pair(sym.substr(0, 1), sym.substr(1, 1))]++;
        }
    }

    std::vector<std::pair<std::string, std::string>> merges;
    for (int i = 0; i < num_merges; ++i) {
        auto best = std::max_element(pairs.begin(), pairs.end(),
            [](const auto& a, const auto& b) {
                return a.second < b.second;
            });

        std::pair<std::string, std::string> merge = best->first;
        merges.push_back(merge);

        std::unordered_map<std::string, int> new_vocab;
        for (const std::string& word : data) {
            std::string new_word = word;
            size_t index = 0;
            while ((index = new_word.find(merge.first + merge.second, index)) != std::string::npos) {
                new_word.replace(index, 2, merge.first + merge.second);
                index += merge.first.length();
            }
            new_vocab[new_word]++;
        }
        vocab = new_vocab;

        pairs.erase(best);
    }

    return vocab;
}

std::vector<std::string> segment_text(const std::string& text, const std::vector<std::pair<std::string, std::string>>& merges) {
    std::vector<std::string> segments;
    std::string word = text;
    size_t len = merges.size();
    for (size_t i = 0; i < len; ++i) {
        const auto& merge = merges[i];
        size_t index = 0;
        while ((index = word.find(merge.first + merge.second, index)) != std::string::npos) {
            word.replace(index, 2, merge.first + " " + merge.second);
            index += merge.first.length() + 1;
        }
    }
    
    size_t startIndex = 0;
    size_t endIndex = word.find(' ');
    while (endIndex != std::string::npos) {
        segments.push_back(word.substr(startIndex, endIndex - startIndex));
        startIndex = endIndex + 1;
        endIndex = word.find(' ', startIndex);
    }
    segments.push_back(word.substr(startIndex));

    return segments;
}

int main() {
    std::vector<std::string> data = {"low", "lower", "newest", "widest", "special", "specials"};
    int num_merges = 5;
    std::unordered_map<std::string, int> vocab = learn_bpe(data, num_merges);

    std::cout << "Vocabulary:" << std::endl;
    for (const auto& entry : vocab) {
        std::cout << entry.first << ": " << entry.second << std::endl;
    }

    std::string text = "lowest specials";
    std::vector<std::pair<std::string, std::string>> merges;
    for (int i = 0; i < num_merges; ++i) {
        merges.push_back(std::make_pair("", ""));
    }
    std::vector<std::string> segments = segment_text(text, merges);

    std::cout << "Segments:" << std::endl;
    for (const std::string& segment : segments) {
        std::cout << segment << std::endl;
    }

    return 0;
}

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转载自blog.csdn.net/qq_36541069/article/details/132335949
BPE