from collections import defaultdict from spacy.ml.models.defaults import default_ner from spacy.pipeline import EntityRecognizer from spacy.lang.en import English from spacy.tokens import Span def test_issue4313(): """ This should not crash or exit with some strange error code """ beam_width = 16 beam_density = 0.0001 nlp = English() ner = EntityRecognizer(nlp.vocab, default_ner()) ner.add_label("SOME_LABEL") ner.begin_training([]) nlp.add_pipe(ner) # add a new label to the doc doc = nlp("What do you think about Apple ?") assert len(ner.labels) == 1 assert "SOME_LABEL" in ner.labels apple_ent = Span(doc, 5, 6, label="MY_ORG") doc.ents = list(doc.ents) + [apple_ent] # ensure the beam_parse still works with the new label docs = [doc] beams = nlp.entity.beam_parse( docs, beam_width=beam_width, beam_density=beam_density ) for doc, beam in zip(docs, beams): entity_scores = defaultdict(float) for score, ents in nlp.entity.moves.get_beam_parses(beam): for start, end, label in ents: entity_scores[(start, end, label)] += score