Source code for vnlp.named_entity_recognizer.named_entity_recognizer

from typing import List, Tuple

from .charner import CharNER
from .spu_context_ner import SPUContextNER

[docs]class NamedEntityRecognizer: """ Main API class for Named Entity Recognizer implementations. Available models: ['SPUContextNER', 'CharNER'] In order to evaluate, initialize the class with "evaluate = True" argument. This will load the model weights that are not trained on test sets. """ def __init__(self, model="SPUContextNER", evaluate=False): self.models = ["SPUContextNER", "CharNER"] self.evaluate = evaluate if model == "SPUContextNER": self.instance = SPUContextNER(evaluate) elif model == "CharNER": self.instance = CharNER(evaluate) else: raise ValueError( f"{model} is not a valid model. Try one of {self.models}" )
[docs] def predict( self, sentence: str, displacy_format: bool = False ) -> List[Tuple[str, str]]: """ High level user API for Named Entity Recognition. Args: sentence: Input sentence/text. displacy_format: When set True, returns the result in spacy.displacy format to allow visualization. Returns: NER result as pairs of (token, entity). Example:: from vnlp import NamedEntityRecognizer ner = NamedEntityRecognizer() ner.predict("Benim adım Melikşah, 29 yaşındayım, İstanbul'da ikamet ediyorum ve VNGRS AI Takımı'nda çalışıyorum.") [('Benim', 'O'), ('adım', 'O'), ('Melikşah', 'PER'), (',', 'O'), ('29', 'O'), ('yaşındayım', 'O'), (',', 'O'), ("İstanbul'da", 'LOC'), ('ikamet', 'O'), ('ediyorum', 'O'), ('ve', 'O'), ('VNGRS', 'ORG'), ('AI', 'ORG'), ("Takımı'nda", 'ORG'), ('çalışıyorum', 'O'), ('.', 'O')] # Visualization with Spacy: import spacy from vnlp import NamedEntityRecognizer ner = NamedEntityRecognizer() result = ner.predict("İstanbul'dan Foça'ya giderken Zeynep ile Bursa'ya uğradık.", displacy_format = True) spacy.displacy.render(result, style="ent", manual = True) """ return self.instance.predict(sentence, displacy_format)