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Description:

preProcessingGEP 1.0.5

Usage of PreProcessingService
You can access following functions:
The provided package is a collection of functions for text preprocessing and cleaning. Here is a brief explanation of each function:
_en_stopwords: Returns a list of English stopwords.
strip_numeric: Removes numbers from a string.
strip_punctuation: Removes all punctuation marks from a string.
remove_stopwords: Removes stopwords from a string.
strip_multiple_whitespaces: Removes excess whitespace from a string.
strip_short: Removes words with a length less than a specified size from a string.
remove_alpha_num: Removes alphanumeric characters from a string.
lemmatize: Lemmatizes a list of tokens.
nltk_pos: Performs Part-of-Speech (POS) tagging using NLTK.
stanford_pos: Performs POS tagging using Stanford POS Tagger.
filter_pos: Filters words based on specific POS tags.
clean_text: Cleans the text by applying various cleaning functions.
preprocess_string: Preprocesses a string by applying a list of cleaning functions.
preprocess_postags: Applies a list of filtering functions to a list of strings.
guided_buy_preprocess: Preprocesses a list of strings for guided buying purposes.
dump_interim_steps: Creates a DataFrame with the intermediate steps of preprocessing.
lemmatization: Performs lemmatization on a string.
cold_start_preprocess: Preprocesses a list of words for cold start purposes.
unique_value_col: Splits words based on '|'.
convert_synonyms:A collection of synonym mappings
clean_description: Includes all above cleaning steps
Example usage:

pip install preProcessingGEP==1.0.4

import preProcessingGEP
print(preProcessingGEP.PreProcessingService.clean_description(inputText))
General Syntax to use any function
preProcessingGEP.PreProcessingService.function_name()