TextProcessor+string text+list tokenize()+list remove_stopwords()Tokenizer+list word_tokenize(string text)StopWordFilter+set stop_words+list filter(list words) 在这个类图中: TextProcessor类负责处理文本,进行分词和去除停用词。 Tokenizer类用于实现文本的分词功能。 StopWordFilter类则负责定义并实施停用词的...
AI检测代码解析 # 加载停用词defload_stop_words(file_path):withopen(file_path,'r',encoding='utf-8')asf:returnset(f.read().splitlines())# 去除停用词defremove_stop_words(text,stop_words):tokens=text.split()return' '.join(wordforwordintokensifwordnotinstop_words) 1. 2. 3. 4. 5. 6....
``` # Python script to remove duplicates from data import pandas as pd def remove_duplicates(data_frame): cleaned_data = data_frame.drop_duplicates() return cleaned_data ``` 说明: 此Python脚本能够利用 pandas 从数据集中删除重复行,这是确保数据完整性和改进数据分析的简单而有效的方法。 11.2数据...
IDF hashingTF = HashingTF(inputCol="filtered", outputCol="rawFeatures", numFeatures=10000) idf = IDF(inputCol="rawFeatures", outputCol="features", minDocFreq=5) #minDocFreq: remove sparse terms pipeline = Pipeline(stages=[regexTokenizer, stopwordsRemover, hashingTF, idf, label_stringIdx]) ...
# Methood 1 : Regex # Remove the special charaters from the read string. no_specials_string ...
format(re.escape(string.punctuation))) filtered_tokens = filter(None,[pattern.sub('',token) for token in tokens]) filtered_text = ' '.join(filtered_tokens) return filtered_text # 去除停用词 def remove_stopwords(text): tokens = tokenize_text(text) filtered_tokens = [token for token in ...
(line) <2:continue# take the first token as the image id, the rest as the descriptionimage_id, image_desc = tokens[0], tokens[1:]# remove filename from image idimage_id = image_id.split('.')[0]# convert description tokens back to stringimage_desc =' '.join(image_desc)# ...
| Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | | split(...) | S.split(sep=None, maxsplit=-1) -> list of strings | | Return a list of the words in S, using sep as the ...
Return a copy of the string S with leading and trailing whitespace removed. If chars is given and not None, remove characters in chars instead. >>>str1=" hello world ">>>str2="hello world ">>>str1.strip()'hello world'>>>str2.strip()'hello world' ...
language String 翻译语种。 begin_time Long 句子开始时间,单位为ms。 end_time Long 句子结束时间,单位为ms。 text String 识别文本。 words List<Word> 字时间戳信息。 is_sentence_end Bool 当前文本是否构成完整的句子。 True:当前文本构成完整句子,已结束,翻译结果为最终结果。 False:当前文本未构成完整句子...