Metadata-Version: 2.1
Name: quick_processor
Version: 0.3
Description-Content-Type: text/markdown

# quick_processor

## Overview

`quick_processor` is a Python-based text preprocessing tool designed to simplify and standardize text cleaning tasks for Natural Language Processing (NLP) applications. It leverages a variety of libraries, including NLTK, BeautifulSoup, and contractions, to provide a comprehensive suite of text cleaning functions.

## Features

- **Lowercase Conversion**: Converts all characters in a sentence to lowercase.
- **Email Removal**: Removes email addresses from the text.
- **Diacritic Removal**: Strips diacritics from characters.
- **HTML Cleaning**: Removes HTML tags from the text.
- **Repeated Character Replacement**: Replaces repeated punctuation marks with a single occurrence.
- **Emoji Translation**: Translates emojis into their textual representation.
- **Contraction Expansion**: Expands common contractions (e.g., "can't" to "cannot").
- **URL Removal**: Strips URLs from the text.
- **Possessive Removal**: Removes possessive forms from words.
- **Extra Space Removal**: Eliminates extra spaces.
- **Spelling Correction**: Corrects spelling errors.
- **Tokenization**: Splits text into tokens.
- **Stopword Removal**: Removes common stopwords.
- **Lemmatization**: Reduces words to their base or root form.
- **Emoticon Removal**: Removes emoticons from the text.
- **Non-Alphabetic Character Removal**: Strips non-alphabetic characters from the text.

## Usage

Here's an example of how to use `quick_processor` for text preprocessing:

```python
from quick_processor import Preprocessor

# Initialize the preprocessor
preprocessor = Preprocessor()

# Sample sentence
sentence = "This is a sample sentence with an email@example.com and a link http://example.com Ã°Å¸ËœÅ "

# Clean the sentence using default steps
cleaned_sentence = preprocessor.clean(sentence)

print(cleaned_sentence)
```

## Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

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With `quick_processor`, you can streamline your text preprocessing tasks, making your NLP pipeline more efficient and effective.
