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af125875cf
* Fix `get_loss` to use NER annotation * Add labels as part of cfg * Add simple overfitting test
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
from spacy.lang.en import English
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from spacy.gold import Example
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from spacy import util
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from ..util import make_tempdir
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TRAIN_DATA = [
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("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
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("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
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]
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the SimpleNER component - ensuring the ML models work correctly
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nlp = English()
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ner = nlp.add_pipe("simple_ner")
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for ent in annotations.get("entities"):
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ner.add_label(ent[2])
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optimizer = nlp.begin_training()
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["ner"] < 0.0001
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# test the trained model
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test_text = "I like London."
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doc = nlp(test_text)
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ents = doc.ents
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assert len(ents) == 1
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assert ents[0].text == "London"
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assert ents[0].label_ == "LOC"
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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ents2 = doc2.ents
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assert len(ents2) == 1
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assert ents2[0].text == "London"
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assert ents2[0].label_ == "LOC"
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