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https://github.com/explosion/spaCy.git
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95c0833656
* Add training option to set annotations on update Add a `[training]` option called `set_annotations_on_update` to specify a list of components for which the predicted annotations should be set on `example.predicted` immediately after that component has been updated. The predicted annotations can be accessed by later components in the pipeline during the processing of the batch in the same `update` call. * Rename to annotates / annotating_components * Add test for `annotating_components` when training from config * Add documentation
114 lines
3.1 KiB
Python
114 lines
3.1 KiB
Python
from typing import Callable, Iterable, Iterator
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import pytest
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import io
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from thinc.api import Config
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from spacy.language import Language
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from spacy.training import Example
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from spacy.training.loop import train
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from spacy.lang.en import English
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from spacy.util import registry, load_model_from_config
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@pytest.fixture
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def config_str():
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return """
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[nlp]
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lang = "en"
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pipeline = ["sentencizer","assert_sents"]
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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batch_size = 1000
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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[components]
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[components.assert_sents]
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factory = "assert_sents"
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[components.sentencizer]
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factory = "sentencizer"
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punct_chars = null
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[training]
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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annotating_components = ["sentencizer"]
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max_steps = 2
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[corpora]
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[corpora.dev]
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@readers = "unannotated_corpus"
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[corpora.train]
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@readers = "unannotated_corpus"
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"""
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def test_annotates_on_update():
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# The custom component checks for sentence annotation
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@Language.factory("assert_sents", default_config={})
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def assert_sents(nlp, name):
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return AssertSents(name)
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class AssertSents:
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def __init__(self, name, **cfg):
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self.name = name
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pass
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def __call__(self, doc):
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if not doc.has_annotation("SENT_START"):
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raise ValueError("No sents")
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return doc
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def update(self, examples, *, drop=0.0, sgd=None, losses=None):
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for example in examples:
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if not example.predicted.has_annotation("SENT_START"):
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raise ValueError("No sents")
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return {}
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nlp = English()
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nlp.add_pipe("sentencizer")
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nlp.add_pipe("assert_sents")
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# When the pipeline runs, annotations are set
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doc = nlp("This is a sentence.")
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examples = []
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for text in ["a a", "b b", "c c"]:
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examples.append(Example(nlp.make_doc(text), nlp(text)))
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for example in examples:
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assert not example.predicted.has_annotation("SENT_START")
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# If updating without setting annotations, assert_sents will raise an error
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with pytest.raises(ValueError):
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nlp.update(examples)
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# Updating while setting annotations for the sentencizer succeeds
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nlp.update(examples, annotates=["sentencizer"])
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def test_annotating_components_from_config(config_str):
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@registry.readers("unannotated_corpus")
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def create_unannotated_corpus() -> Callable[[Language], Iterable[Example]]:
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return UnannotatedCorpus()
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class UnannotatedCorpus:
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def __call__(self, nlp: Language) -> Iterator[Example]:
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for text in ["a a", "b b", "c c"]:
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doc = nlp.make_doc(text)
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yield Example(doc, doc)
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orig_config = Config().from_str(config_str)
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nlp = load_model_from_config(orig_config, auto_fill=True, validate=True)
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assert nlp.config["training"]["annotating_components"] == ["sentencizer"]
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train(nlp)
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nlp.config["training"]["annotating_components"] = []
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with pytest.raises(ValueError):
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train(nlp)
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