Universe

phruzz-matcher

Phrase matcher using RapidFuzz

Combination of the RapidFuzz library with Spacy PhraseMatcher The goal of this component is to find matches when there were NO "perfect matches" due to typos or abbreviations between a Spacy doc and a list of phrases.

Example

import spacy from spacy.language import Language from phruzz_matcher.phrase_matcher import PhruzzMatcher famous_people = [ "Brad Pitt", "Demi Moore", "Bruce Willis", "Jim Carrey", ] @Language.factory("phrase_matcher") def phrase_matcher(nlp: Language, name: str): return PhruzzMatcher(nlp, famous_people, "FAMOUS_PEOPLE", 85) nlp = spacy.blank('es') nlp.add_pipe("phrase_matcher") doc = nlp("El otro día fui a un bar donde vi a brad pit y a Demi Moore, estaban tomando unas cervezas mientras charlaban de sus asuntos.") print(f"doc.ents: {doc.ents}") #OUTPUT #doc.ents: (brad pit, Demi Moore)
Author info

Martin Vallone

GitHubmjvallone/phruzz-matcher

Categories pipeline research standalone

Submit your project

If you have a project that you want the spaCy community to make use of, you can suggest it by submitting a pull request to the spaCy website repository. The Universe database is open-source and collected in a simple JSON file. For more details on the formats and available fields, see the documentation. Looking for inspiration your own spaCy plugin or extension? Check out the project idea label on the issue tracker.

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