{CamembertTokenizer.backend_tokenizer.normalizer.normalize_str(text)}')print(f'BERT Output: \ {BertTokenizer.backend_tokenizer.normalizer.normalize_str(text)}')#FNet Output:ThÍs is áNExaMPlé sÉnteNCE #CamemBERT Output:ThÍs is áNExaMPlé sÉnteNCE #BERTOutput:thisis an example sentence 2、...
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:...
importstring# load doc into memorydefload_doc(filename):# open the file as read onlyfile = open(filename,'r')# read all texttext = file.read()# close the filefile.close()returntext# extract descriptions for imagesdefload_descriptions(doc):mapping = dict()# process linesforlineindoc.sp...
str = "this is string example...wow!!! this is really string" print (str.replace("is", "was")) print (str.replace("is", "was", 3)) 当运行上面的程序时,它会产生以下结果 - thwas was string example...wow!!! thwas was really string thwas was string example...wow!!! thwas i...
Tokenizer.backend_tokenizer.normalizer.normalize_str(text)}') print(f'BERT Output: \ {BertTokenizer.backend_tokenizer.normalizer.normalize_str(text)}') #FNet Output: ThÍs is áN ExaMPlé sÉnteNCE #CamemBERT Output: ThÍs is áN ExaMPlé sÉnteNCE #BERT Output: this is an example sentence...
#BERT: this is an example sentence 下面的示例可以看到,只有NFC删除了不必要的空白。 from transformers import FNetTokenizerFast, CamembertTokenizerFast, \ BertTokenizerFast # Text to normalize text = 'ThÍs is áN ExaMPlé sÉnteNCE' # Instantiate tokenizers ...
from tokenizers.pre_tokenizers import WhitespaceSplit, BertPreTokenizer # Text to pre-tokenize text = ("this sentence's content includes: characters, spaces, and " \ "punctuation.") # Instantiate pre-tokenizer bpt = BertPreTokenizer() # Pre-tokenize the text bpt.pre_tokenize_str(example_sent...
extend([ (NAME, 'Decimal'), (OP, '('), (STRING, repr(tokval)), (OP, ')') ]) else: result.append((toknum, tokval)) return untokenize(result).decode('utf-8') Example of tokenizing from the command line. The script: def say_hello(): print("Hello, World!") say_hello() ...
第三部分:使用 PyTorch 1.x 的实际 NLP 应用 在本节中,我们将使用 PyTorch 中可用的各种自然语言处理(NLP)技术来构建各种实际 -使用 PyTorch 的世界应用。 情感分析,文本摘要,文本分类以及使用 PyTorch 构建聊天机器人应用是本节将介绍的一些任务。 本节包含以下章节: “第 5 章”,“循环神经网络和情感分析”...
I am trying to run the NGen on UAHPC cluster and getting python error during runtime of NGen example. List of Module compilers/gcc/5.4.0 cmake/3.20.1 boost/1.72.0 python/python3/3.9.6 compilers/gcc/9.1.0 mpi/openmpi/gcc/4.1.1 Compilation...