求推荐替代Spacy的轻量分词工具：需保留Token索引且性能更优

一、优化Spacy的使用方式 #

1. 使用纯空白模型（仅保留分词器） #

直接创建仅包含分词器的空白模型，比加载轻量模型再排除组件更高效，完全避免冗余组件的性能损耗：

import spacy
# 创建仅含英文分词器的空白模型
nlp = spacy.blank("en")
text = "I eat mangoes and I eat oranges"
doc = nlp(text)
for token in doc:
    print(token, "-->", token.i)

2. 转换为轻量数据结构 #

后续操作无需携带完整的Doc对象，提前将token的文本和索引转为普通元组列表，消除复杂对象的额外开销：

import itertools

# 分词后转换为(文本, 索引)的轻量列表
tokens = [(token.text, token.i) for token in doc]

# 改写后续操作函数，基于普通列表处理
def possible_long_forms(tokens_list):    
    window_list_3 = []
    for i in range(len(tokens_list)):
        if (i <= (len(tokens_list) - 5)) or (i == 0):
            window = tokens_list[i:i+5]
            window_list_3.append(window)
    
    window_list_3 = list(map(lambda x: list(itertools.combinations(x, 3)), window_list_3))
    return window_list_3

# 测试
text_new = "I like mobiles and laptops"
doc_new = nlp(text_new)
tokens_new = [(t.text, t.i) for t in doc_new]
results = possible_long_forms(tokens_new)
print(results, '\n')
first_item = results[0][0][0]
print("Check index :", first_item[0], "-->", first_item[1])

二、替代Spacy的分词工具 #

1. NLTK（分词精度接近，轻量易用） #

NLTK的word_tokenize分词精度与Spacy相当，支持处理缩写、标点等场景，通过enumerate直接获取token索引：

import nltk
import itertools
from nltk.tokenize import word_tokenize
# 首次使用需下载分词模型
nltk.download('punkt')

text = "I eat mangoes and I eat oranges"
tokens = word_tokenize(text)
for idx, token in enumerate(tokens):
    print(token, "-->", idx)

# 适配后续操作
def possible_long_forms_nltk(tokens_list):    
    window_list_3 = []
    for i in range(len(tokens_list)):
        if (i <= (len(tokens_list) - 5)) or (i == 0):
            window = tokens_list[i:i+5]
            window_list_3.append(window)
    
    window_list_3 = list(map(lambda x: list(itertools.combinations(x, 3)), window_list_3))
    return window_list_3

tokens_new = list(enumerate(word_tokenize("I like mobiles and laptops")))
tokens_new = [(t, idx) for idx, t in tokens_new]
results = possible_long_forms_nltk(tokens_new)
print(results, '\n')
first_item = results[0][0][0]
print("Check index :", first_item[0], "-->", first_item[1])

2. Hugging Face Tokenizers（高性能，匹配Spacy分词规则） #

该库支持构建与Spacy对齐的分词逻辑，速度远快于Spacy，同时保证分词精度：

import itertools
from tokenizers import Tokenizer
from tokenizers.models import WordLevel
from tokenizers.pre_tokenizers import WhitespaceSplit, Punctuation
from tokenizers.processors import TemplateProcessing

# 构建与Spacy对齐的分词器
tokenizer = Tokenizer(WordLevel(unk_token="[UNK]"))
tokenizer.pre_tokenizer = WhitespaceSplit() + Punctuation()
tokenizer.post_processor = TemplateProcessing(
    single="$A",
    special_tokens=[("[UNK]", tokenizer.token_to_id("[UNK]"))],
)

text = "I eat mangoes and I eat oranges"
output = tokenizer.encode(text)
for idx, token in enumerate(output.tokens):
    print(token, "-->", idx)

# 适配后续操作
tokens_new = list(enumerate(tokenizer.encode("I like mobiles and laptops").tokens))
tokens_new = [(t, idx) for idx, t in tokens_new]
results = possible_long_forms_nltk(tokens_new)
print(results, '\n')
first_item = results[0][0][0]
print("Check index :", first_item[0], "-->", first_item[1])

关键说明 #

• 核心优化思路是避免携带复杂对象（如Spacy Doc）进行后续计算，转换为普通元组/列表能大幅提升性能。
• 上述替代工具的分词精度在英文场景下与Spacy基本一致，可满足需求。

内容的提问来源于stack exchange，提问作者newbie101