from tokenizers.pre_tokenizers import WhitespaceSplit,BertPreTokenizer# Text to pre-tokenizetext = ("this sentence's content includes: characters, spaces, and "\"punctuation.")# Instantiate pre-tokenizerbpt=BertPreTokenizer()# Pre-tokenize the textbpt.pre_tokenize_str(example_sentence) 结果如下:...
jinlist_1:sht_3[int(i),int(j)].color=(255,25,0)f()list_1=[]foriinrange(30):forjinr...
from transformers import AutoTokenizer # Text to pre-tokenize text = ("this sentence's content includes: characters, spaces, and " \ "punctuation.") # Instatiate the pre-tokenizers GPT2_PreTokenizer = AutoTokenizer.from_pretrained('gpt2').backend_tokenizer \ .pre_tokenizer Albert_PreTokenizer...
恰好open()函数返回TextIOWrapper的一个实例,其__enter__方法返回self。但在不同的类中,__enter__方法也可能返回其他对象,而不是上下文管理器实例。 无论以何种方式退出with块的控制流,__exit__方法都会在上下文管理器对象上调用,而不是在__enter__返回的任何对象上调用。 with语句的as子句是可选的。在open的...
stopwords (set of string) lemmatize (boolean) returns: list of string """ if lemmatize: stemmer = WordNetLemmatizer() tokens = [stemmer.lemmatize(w) for w in word_tokenize(sentence)] else: tokens = [w for w in word_tokenize(sentence)] ...
name=input("What is your name? ")print("Hello, "+name,"Time to play hangman!")time.sleep(1)print("Start guessing...\n")time.sleep(0.5)##AList Of Secret Words words=['python','programming','treasure','creative','medium','horror']word=random.choice(words)guesses=''turns=5whileturn...
string: 匹配时使用的文本。 re: 匹配时使用的Pattern对象。 pos: 文本中正则表达式开始搜索的索引。值与Pattern.match()和Pattern.seach()方法的同名参数相同。 endpos: 文本中正则表达式结束搜索的索引。值与Pattern.match()和Pattern.seach()方法的同名参数相同。
;\ Find last LF character in string, or return -1.: find-eol ( addr u -- eol-offset|-1 ) begin1- dup >=while 2dup + c@ 10 = if nip exit thenrepeat nip ;: main ( -- ) counts set-current \ Define into counts wordlist >r \ offset after remaining byte...
Sometimes strings go over several lines. Python provides us with various ways of entering them. In the next example, a sequence of two strings is joined into a single string. We need to use backslash①or parentheses②so that the interpreter knows that the statement is not complete after the...
import string def clean_descriptions(descriptions) : # prepare translation table for removing punctuation table = str.maketrans( '' , '' , string.punctuation) for key, desc_list in descriptions.items(): for i in range(len(desc_list)):desc = desc_list[i] # tokenize desc = desc.split()...