Metadata-Version: 2.1
Name: replacy
Version: 0.46.0
Summary: ReplaCy = spaCy Matcher + pyInflect. Create rules, correct sentences.
Home-page: https://github.com/Qordobacode/replaCy
Author: Qordoba
Author-email: Sam Havens <sam.havens@qordoba.com>, Melisa Stal <melisa@qordoba.com>
License: MIT
Description: <p align="center">
        <img src="./docs/replacy_logo.png" align="center" />
        </p>
        
        # replaCy: match & replace with spaCy
        
        We found that in multiple projects we had duplicate code for using spaCy’s blazing fast matcher to do the same thing: Match-Replace-Grammaticalize. So we wrote replaCy!
        
        - Match - spaCy’s matcher is great, and lets you match on text, shape, POS, dependency parse, and other features. We extended this with “match hooks”, predicates that get used in the callback function to further refine a match.
        - Replace - Not built into spaCy’s matcher syntax, but easily added. You often want to replace a matched word with some other term.
        - Grammaticalize - If you match on ”LEMMA”: “dance”, and replace with suggestions: ["sing"], but the actual match is danced, you need to conjugate “sing” appropriately. This is the “killer feature” of replaCy
        
        [![spaCy](https://img.shields.io/badge/made%20with%20❤%20and-spaCy-09a3d5.svg)](https://spacy.io)
        [![pypi Version](https://img.shields.io/pypi/v/replacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/replacy/)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)
        
        <p align="center">
        <img src="./docs/replacy_ex.png" align="center" />
        </p>
        
        
        ## Requirements
        
        - `spacy >= 2.0` (not installed by default, but replaCy needs to be instantiated with an `nlp` object)
        
        ## Installation
        
        `pip install replacy`
        
        ## Quick start
        
        ```python
        from replacy import ReplaceMatcher
        from replacy.db import load_json
        import spacy
        
        
        match_dict = load_json('/path/to/your/match/dict.json')
        # load nlp spacy model of your choice
        nlp = spacy.load("en_core_web_sm")
        
        rmatcher = ReplaceMatcher(nlp, match_dict=match_dict)
        
        # get inflected suggestions
        # look up the first suggestion
        span = rmatcher("She extracts revenge.")[0]
        span._.suggestions
        # >>> ['exacts']
        ```
        
        ## Input
        
        ReplaceMatcher accepts both text and spaCy doc.
        
        ```python
        # text is ok
        span = r_matcher("She extracts revenge.")[0]
        
        # doc is ok too
        doc = nlp("She extracts revenge.")
        span = r_matcher(doc)[0]
        ```
        
        ## match_dict.json format
        
        Here is a minimal `match_dict.json`:
        
        ```json
        {
          "extract-revenge": {
            "patterns": [
              {
                "LEMMA": "extract",
                "TEMPLATE_ID": 1
              }
            ],
            "suggestions": [
              [
                {
                  "TEXT": "exact",
                  "FROM_TEMPLATE_ID": 1
                }
              ]
            ],
            "match_hook": [
              {
                "name": "succeeded_by_phrase",
                "args": "revenge",
                "match_if_predicate_is": true
              }
            ],
            "test": {
              "positive": [
                "And at the same time extract revenge on those he so despises?",
                "Watch as Tampa Bay extracts revenge against his former Los Angeles Rams team."
              ],
              "negative": ["Mother flavours her custards with lemon extract."]
            }
          }
        }
        ```
        For more information how to compose `match_dict` see our [wiki](https://github.com/Qordobacode/replaCy/wiki/match_dict.json-format): 
        
        
        # Citing
        
        If you use replaCy in your research, please cite with the following BibText
        
        ```bibtext
        @misc{havens2019replacy,
            title  = {SpaCy match and replace, maintaining conjugation},
            author = {Sam Havens and Aneta Stal},
            url    = {https://github.com/Qordobacode/replaCy},
            year   = {2019}
        }
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Typing :: Typed
Requires-Python: >=3.5
Description-Content-Type: text/markdown
