Metadata-Version: 2.1
Name: polyfuzz
Version: 0.0.1
Summary: PolyFuzz performs fuzzy string matching, grouping, and evaluation.
Home-page: https://github.com/MaartenGr/PolyFuzz
Author: Maarten Grootendorst
Author-email: maartengrootendorst@gmail.com
License: UNKNOWN
Description: <img src="images/logo.png" width="70%" height="70%"/>
        
        [![PyPI - Python](https://img.shields.io/badge/python-3.6%20|%203.7%20|%203.8-blue.svg)](https://pypi.org/project/keybert/)
        [![PyPI - License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/MaartenGr/keybert/blob/master/LICENSE)
        [![PyPI - PyPi](https://img.shields.io/pypi/v/polyfuzz)](https://pypi.org/project/polyfuzz/)
        [![Build](https://img.shields.io/github/workflow/status/MaartenGr/polyfuzz/Code%20Checks/master)](https://pypi.org/project/polyfuzz/)
        
        **`PolyFuzz`** performs fuzzy string matching, string grouping, and contains extensive evaluation functions. 
        PolyFuzz is meant to bring fuzzy string matching techniques together within a single framework.
        
        Currently, methods include Levenshtein distance with RapidFuzz, a character-based n-gram TF-IDF, word embedding
        techniques such as FastText and GloVe, and finally ðŸ¤— transformers embeddings. 
        
        You can use your own **custom models** for both the fuzzy string matching as well as the string grouping. 
        
        Corresponding medium post can be found [here]().
        
        
        <a name="gettingstarted"/></a>
        ## Getting Started
        [Back to ToC](#toc)
        
        ```python
        from polyfuzz import PolyFuzz
        
        from_list = ["apple", "apples", "appl", "recal", "house", "similarity"]
        to_list = ["apple", "apples", "mouse"]
        
        model = PolyFuzz("TF-IDF").match(from_list, to_list)
        
        ```  
        
        The resulting string matches can be accessed through `model.get_matches()`:
        
        ```python
        >>> model.get_matches()
              From      To  Similarity
             apple   apple    1.000000
            apples  apples    1.000000
              appl   apple    0.783751
             recal    None    0.000000
             house   mouse    0.587927
        similarity    None    0.000000
        
        ``` 
        
        
        ## References
Keywords: nlp string matching embeddings levenshtein tfidf bert
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: docs
Provides-Extra: dev
