importtokenizewithtokenize.open('hello.py')asf:tokens=tokenize.generate_tokens(f.readline)fortokenintokens:print(token) 结果如下,可见用generate_tokens()是得不到ENCODING的 TokenInfo(type=1(NAME),string='def',start=(1,0),end=(1,3),line='def say_hello():\n')TokenInfo(type=1(NAME),strin...
wss=WhitespaceSplit()bpt=BertPreTokenizer()# Pre-tokenize the textprint('Whitespace Pre-Tokenizer:')print_pretokenized_str(wss.pre_tokenize_str(text))#Whitespace Pre-Tokenizer:#"this","sentence's","content","includes:","characters,","spaces,",#"and","punctuation.",print('\n\nBERT Pre-T...
像tokenize() 一样,readline 参数需要可调用,并且返回输入的一行,但是需要返回 str 对象,而不是 bytes。 返回的结果是一个迭代器,返回的具名元祖和tokenize()的完全一样。只不过没有ENCODING(一种表示标识类型的常量)类型的标记。(tokenize()第一个返回的就是ENCODING标记的内容) ENCODING和OP一样是常量,还有很多,...
from tokenize import tokenize, untokenize, NUMBER, STRING, NAME, OP from io import BytesIO def decistmt(s): """Substitute Decimals for floats in a string of statements. >>> from decimal import Decimal >>> s = 'print(+21.3e-5*-.1234/81.7)' >>> decistmt(s) "print (+Decimal ('...
import string # 下载必要的资源 nltk.download('punkt') nltk.download('stopwords') # 示例文本 text = "Natural Language Processing (NLP) is an exciting field of artificial intelligence." # 分词 tokens = word_tokenize(text) print("Tokens:", tokens) ...
# Pre-tokenize the text print('Whitespace Pre-Tokenizer:') print_pretokenized_str(wss.pre_tokenize_str(text)) #Whitespace Pre-Tokenizer: #"this", "sentence's", "content", "includes:", "characters,", "spaces,", #"and", "punctuation.", ...
nltk.download('punkt')fromnltk.tokenizeimportword_tokenize text ="Hello Mr. Smith, how are you doing today?"tokens = word_tokenize(text)print(tokens) 通过使用这些库,Python 程序员能够执行各种文本处理任务,从简单的字符串操作到复杂的文本分析和处理。根据项目的具体需求,正确选择合适的库对于提高效率和...
print_pretokenized_str(wss.pre_tokenize_str(text))#Whitespace Pre-Tokenizer:#"this","sentence's","content","includes:","characters,","spaces,",#"and","punctuation.",print('\n\nBERT Pre-Tokenizer:') print_pretokenized_str(bpt.pre_tokenize_str(text))#BERT Pre-Tokenizer:#"this","senten...
# Pre-tokenize the text print('Whitespace Pre-Tokenizer:') print_pretokenized_str(wss.pre_tokenize_str(text)) #Whitespace Pre-Tokenizer: #"this", "sentence's", "content", "includes:", "characters,", "spaces,", #"and", "punctuation.", ...