Source code for vnlp.dependency_parser.dependency_parser

from typing import List, Tuple

from .spu_context_dp import SPUContextDP
from .treestack_dp import TreeStackDP

[docs]class DependencyParser: """ Main API class for Dependency Parser implementations. Available models: ['SPUContextDP', 'TreeStackDP'] 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="SPUContextDP", evaluate=False): self.models = ["SPUContextDP", "TreeStackDP"] self.evaluate = evaluate if model == "SPUContextDP": self.instance = SPUContextDP(evaluate) elif model == "TreeStackDP": self.instance = TreeStackDP(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, pos_result: List[Tuple[str, str]] = None, ) -> List[Tuple[int, str, int, str]]: """ High level user API for Dependency Parsing. Args: sentence: Input sentence. displacy_format: When set True, returns the result in spacy.displacy format to allow visualization. pos_result: Part of Speech tags. To be used when displacy_format = True. Returns: List of (token_index, token, arc, label). Raises: ValueError: Sentence is too long. Try again by splitting it into smaller pieces. Example:: from vnlp import DependencyParser dependency_parser = DependencyParser() dependency_parser.predict("Onun için yol arkadaşlarımızı titizlikle seçer, kendilerini iyice sınarız.") [(1, 'Onun', 6, 'obl'), (2, 'için', 1, 'case'), (3, 'yol', 4, 'nmod'), (4, 'arkadaşlarımızı', 6, 'obj'), (5, 'titizlikle', 6, 'obl'), (6, 'seçer', 10, 'parataxis'), (7, ',', 6, 'punct'), (8, 'kendilerini', 10, 'obj'), (9, 'iyice', 10, 'advmod'), (10, 'sınarız', 0, 'root'), (11, '.', 10, 'punct')] # Visualization with Spacy: import spacy from vnlp import DependencyParser dependency_parser = DependencyParser() result = dependency_parser.predict(Oğuz'un kırmızı bir Astra'sı vardı.", displacy_format = True) spacy.displacy.render(result, style="dep", manual = True) """ return self.instance.predict(sentence, displacy_format, pos_result)
# this is called when an attribute is not found: def __getattr__(self, name): return self.instance.__getattribute__(name)