nlp-text-clean 0.1.2

A simple and configurable text preprocessing library for NLP tasks.

NLP Text Cleaner

A lightweight, configurable Python library for text preprocessing in NLP tasks.
It helps you clean raw text by lowercasing, removing noise, lemmatizing, and more — ready for machine learning or deep learning models.

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✨ Features

• Convert text to lowercase
• Remove special characters
• Remove punctuation (optional)
• Remove numbers or replace them with <NUM> token
• Remove extra spaces
• Lemmatization (default) or stemming (optional)
• Stopword removal (customizable)
• Multi-language support (via NLTK / spaCy)
• Add your own custom stopwords
• Remove Html Tags
• Remove URLs

Installation

From PyPI:

pip install nlp-text-clean
From TestPyPI (for testing):

🚀 Usage
python

from nlp_text_clean.cleaner import TextCleaner

# Initialize with custom options
cleaner = TextCleaner(
    remove_numbers=True,
    remove_punctuation=True,
    remove_special_chars=True,
    use_stemming=False,
    use_lemmatization=True,
    language="english",
    custom_stopwords=None,
    preserve_num_token=False,
    remove_html_tags=False,
    remove_urls=False
)

text = "He scored 100 marks!!! Running better than others."
cleaned = cleaner.clean_text(text)

print(cleaned)
# Output: "score <NUM> mark run well"

Parameters

Parameter Name	Type	Default	Description
remove_numbers	bool	False	Remove numbers from text
preserve_num_token	bool	False	Replace numbers with <NUM> token
remove_punctuation	bool	True	Remove punctuation marks
remove_special_chars	bool	True	Remove non-alphanumeric characters
use_lemmatization	bool	True	Apply lemmatization
use_stemming	bool	False	Apply stemming instead of lemmatization
stopwords_language	str	"english"	Language for stopwords
custom_stopwords	list[str]	None	Add your own stopwords
remove_html_tags	bool	False	Remove HTML tags
remove_urls	bool	False	Remove Urls

• Running Tests

pytest

Why use this package?

There are other preprocessing tools (like NLTK or clean-text), but nlp-cleaner is designed to be:

1. Lightweight & Easy to Use → Just one class, minimal setup

2. Highly Configurable → Toggle lemmatization, stemming, number handling, punctuation, etc.

3. Production Ready → Clean and consistent outputs for ML pipelines

4. Customizable → Extend with your own stopwords or language models

5. Multi-language → Works beyond English when supported by spaCy/NLTK

If you want a plug-and-play solution to clean messy text quickly, this is the right tool.