mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 20:28:20 +03:00
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
|
|
import pytest
|
|
import io
|
|
|
|
from thinc.api import Config
|
|
from spacy.language import Language
|
|
from spacy.training import Example
|
|
from spacy.training.loop import train
|
|
from spacy.lang.en import English
|
|
from spacy.util import registry, load_model_from_config
|
|
|
|
|
|
@pytest.fixture
|
|
def config_str():
|
|
return """
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["sentencizer","assert_sents"]
|
|
disabled = []
|
|
before_creation = null
|
|
after_creation = null
|
|
after_pipeline_creation = null
|
|
batch_size = 1000
|
|
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
|
|
|
|
[components]
|
|
|
|
[components.assert_sents]
|
|
factory = "assert_sents"
|
|
|
|
[components.sentencizer]
|
|
factory = "sentencizer"
|
|
punct_chars = null
|
|
|
|
[training]
|
|
dev_corpus = "corpora.dev"
|
|
train_corpus = "corpora.train"
|
|
annotating_components = ["sentencizer"]
|
|
max_steps = 2
|
|
|
|
[corpora]
|
|
|
|
[corpora.dev]
|
|
@readers = "unannotated_corpus"
|
|
|
|
[corpora.train]
|
|
@readers = "unannotated_corpus"
|
|
"""
|
|
|
|
|
|
def test_annotates_on_update():
|
|
# The custom component checks for sentence annotation
|
|
@Language.factory("assert_sents", default_config={})
|
|
def assert_sents(nlp, name):
|
|
return AssertSents(name)
|
|
|
|
class AssertSents:
|
|
def __init__(self, name, **cfg):
|
|
self.name = name
|
|
pass
|
|
|
|
def __call__(self, doc):
|
|
if not doc.has_annotation("SENT_START"):
|
|
raise ValueError("No sents")
|
|
return doc
|
|
|
|
def update(self, examples, *, drop=0.0, sgd=None, losses=None):
|
|
for example in examples:
|
|
if not example.predicted.has_annotation("SENT_START"):
|
|
raise ValueError("No sents")
|
|
return {}
|
|
|
|
nlp = English()
|
|
nlp.add_pipe("sentencizer")
|
|
nlp.add_pipe("assert_sents")
|
|
|
|
# When the pipeline runs, annotations are set
|
|
doc = nlp("This is a sentence.")
|
|
|
|
examples = []
|
|
for text in ["a a", "b b", "c c"]:
|
|
examples.append(Example(nlp.make_doc(text), nlp(text)))
|
|
|
|
for example in examples:
|
|
assert not example.predicted.has_annotation("SENT_START")
|
|
|
|
# If updating without setting annotations, assert_sents will raise an error
|
|
with pytest.raises(ValueError):
|
|
nlp.update(examples)
|
|
|
|
# Updating while setting annotations for the sentencizer succeeds
|
|
nlp.update(examples, annotates=["sentencizer"])
|
|
|
|
|
|
def test_annotating_components_from_config(config_str):
|
|
@registry.readers("unannotated_corpus")
|
|
def create_unannotated_corpus() -> Callable[[Language], Iterable[Example]]:
|
|
return UnannotatedCorpus()
|
|
|
|
class UnannotatedCorpus:
|
|
def __call__(self, nlp: Language) -> Iterator[Example]:
|
|
for text in ["a a", "b b", "c c"]:
|
|
doc = nlp.make_doc(text)
|
|
yield Example(doc, doc)
|
|
|
|
orig_config = Config().from_str(config_str)
|
|
nlp = load_model_from_config(orig_config, auto_fill=True, validate=True)
|
|
assert nlp.config["training"]["annotating_components"] == ["sentencizer"]
|
|
train(nlp)
|
|
|
|
nlp.config["training"]["annotating_components"] = []
|
|
with pytest.raises(ValueError):
|
|
train(nlp)
|