from spacy.pipeline.ner import DEFAULT_NER_MODEL from spacy.training import Example from spacy.pipeline import EntityRecognizer from spacy.tokens import Span, Doc from spacy import registry import pytest def _ner_example(ner): doc = Doc( ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"], ) gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]} return Example.from_dict(doc, gold) def test_doc_add_entities_set_ents_iob(en_vocab): text = ["This", "is", "a", "lion"] doc = Doc(en_vocab, words=text) config = { "learn_tokens": False, "min_action_freq": 30, "update_with_oracle_cut_size": 100, } cfg = {"model": DEFAULT_NER_MODEL} model = registry.resolve(cfg, validate=True)["model"] ner = EntityRecognizer(en_vocab, model, **config) ner.initialize(lambda: [_ner_example(ner)]) ner(doc) doc.ents = [("ANIMAL", 3, 4)] assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"] doc.ents = [("WORD", 0, 2)] assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"] def test_ents_reset(en_vocab): """Ensure that resetting doc.ents does not change anything""" text = ["This", "is", "a", "lion"] doc = Doc(en_vocab, words=text) config = { "learn_tokens": False, "min_action_freq": 30, "update_with_oracle_cut_size": 100, } cfg = {"model": DEFAULT_NER_MODEL} model = registry.resolve(cfg, validate=True)["model"] ner = EntityRecognizer(en_vocab, model, **config) ner.initialize(lambda: [_ner_example(ner)]) ner(doc) orig_iobs = [t.ent_iob_ for t in doc] doc.ents = list(doc.ents) assert [t.ent_iob_ for t in doc] == orig_iobs def test_add_overlapping_entities(en_vocab): text = ["Louisiana", "Office", "of", "Conservation"] doc = Doc(en_vocab, words=text) entity = Span(doc, 0, 4, label=391) doc.ents = [entity] new_entity = Span(doc, 0, 1, label=392) with pytest.raises(ValueError): doc.ents = list(doc.ents) + [new_entity]