edsnlp.pipelines.trainable.nested_ner.factory

create_component(nlp, name, model, ent_labels=None, spans_labels=None, scorer=None)

Initialize a general named entity recognizer (with or without nested or overlapping entities).

PARAMETER	DESCRIPTION
nlp	The current nlp object TYPE: Language
name	Name of the component TYPE: str
model	The model to extract the spans TYPE: Model
ent_labels	list of labels to filter entities for in doc.ents DEFAULT: None
spans_labels	Mapping from span group names to list of labels to look for entities and assign the predicted entities DEFAULT: None
scorer	Method to call to score predictions DEFAULT: None

Source code in edsnlp/pipelines/trainable/nested_ner/factory.py

@Language.factory(
    "nested_ner",
    default_config=NESTED_NER_DEFAULTS,
    requires=["doc.ents", "doc.spans"],
    assigns=["doc.ents", "doc.spans"],
    default_score_weights={
        "ents_f": 1.0,
        "ents_p": 0.0,
        "ents_r": 0.0,
    },
)
def create_component(
    nlp: Language,
    name: str,
    model: Model,
    ent_labels=None,
    spans_labels=None,
    scorer=None,
):
    """
    Initialize a general named entity recognizer (with or without nested or
    overlapping entities).

    Parameters
    ----------
    nlp: Language
        The current nlp object
    name: str
        Name of the component
    model: Model
        The model to extract the spans
    ent_labels: Iterable[str]
        list of labels to filter entities for in `doc.ents`
    spans_labels: Mapping[str, Iterable[str]]
        Mapping from span group names to list of labels to look for entities
        and assign the predicted entities
    scorer: Optional[Callable]
        Method to call to score predictions
    """
    return TrainableNer(
        vocab=nlp.vocab,
        model=model,
        name=name,
        ent_labels=ent_labels,
        spans_labels=spans_labels,
        scorer=scorer,
    )