(text)) print('ooo---ooo') #2、用标点符号来拆分句子 #PunktSentenceTokenizer是标点符号分割器 from nltk.tokenize import PunktSentenceTokenizer PST=PunktSentenceTokenizer() print(PST.tokenize(text)) print('ooo---ooo') jupyter运行结果2 二、分割单词 #用句子tokenizer,将分割文本tokenize成单词 from nl...
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...
from tokenizers.pre_tokenizers import WhitespaceSplit, BertPreTokenizer# Text to normalizetext = ("this sentence's content includes: characters, spaces, and "\"punctuation.")#Definehelper function to display pre-tokenized outputdef print_pretokenized_str(pre_tokens):forpre_token in pre_tokens:pri...
get_tokenizer获取分词器,build_vocab_from_iterator则根据分词结果构建词汇表。 7. Pattern Pattern是一个非常实用的Python库,它主要用于Web挖掘、自然语言处理、机器学习等任务。Pattern提供了许多高级功能,如情感分析、网络爬虫等。 安装: pip install pattern 示例代码: from pattern.web import URL, DOM from ...
class TargetVocabularySizeError(Exception): def __init__(self, message): super().__init__(message) class BPE: '''An implementation of the Byte Pair Encoding tokenizer.''' def calculate_frequency(self, words): ''' Calculate the frequency for each word in a list of words. Take in a ...
{FNetTokenizer.backend_tokenizer.normalizer .normalize_str(text)}') print(f'CamemBERT Output: \ {CamembertTokenizer.backend_tokenizer.normalizer.normalize_str(text)}') print(f'BERT Output: \ {BertTokenizer.backend_tokenizer.normalizer.normalize_str(text)}') ...
tokenizer=word_tokenize 1. 2. 3. 5. 使用分词器进行分词 现在我们可以使用分词器对文本数据进行分词了。可以使用以下代码实现: tokens=tokenizer(text) 1. 6. 查看分词结果 分词完成后,我们可以查看分词的结果。可以使用以下代码输出分词结果: print(tokens) ...
Using TensorFlow backend.# 创建分词器 Tokenizer 对象>>>tokenizer=Tokenizer()# text>>>text=["今天 北京 下雨了","我 今天 加班"]# fit_on_texts 方法>>>tokenizer.fit_on_texts(text)# word_counts属性>>>tokenizer.word_countsOrderedDict([('今天',2),('北京',1),('下',1),('雨',1),('了...
fromnltk.tokenizeimportword_tokenize, regexp_tokenize, wordpunct_tokenize, blankline_tokenize 以下演示了如何使用word_tokenizer: print(word_tokenize(first_sentence)) ['We','are','seeking','developers','with','demonstrable','experience','in',':','ASP.NET',',','C','#',',','SQL','Serve...
word_index = tokenizer.word_index # 单词索引,就是词表字典啦,用这个就可以还原数据print(f'one_hot_results: shape={one_hot_results.shape}:\n', one_hot_results, ) print(f'Found {len(word_index)} unique tokens.', 'word_index:', word_index)...