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update morphologizer, tagger test
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vinit
parent
0b3480cf3f
commit
36634d2adf
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@ -69,7 +69,6 @@ grad_factor = 1.0
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{% if "tagger" in components %}
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[components.tagger]
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factory = "tagger"
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label_smoothing = 0.05
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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@ -287,6 +286,7 @@ maxout_pieces = 3
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{% if "morphologizer" in components %}
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[components.morphologizer]
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factory = "morphologizer"
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label_smoothing = 0.05
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[components.morphologizer.model]
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@architectures = "spacy.Tagger.v2"
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@ -300,6 +300,7 @@ width = ${components.tok2vec.model.encode.width}
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{% if "tagger" in components %}
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[components.tagger]
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factory = "tagger"
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label_smoothing = 0.05
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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@ -52,7 +52,8 @@ DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"morphologizer",
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assigns=["token.morph", "token.pos"],
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default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False, "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}},
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default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False,
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"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}, "label_smoothing": 0.0},
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default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
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)
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def make_morphologizer(
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@ -61,9 +62,10 @@ def make_morphologizer(
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name: str,
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overwrite: bool,
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extend: bool,
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label_smoothing: float,
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scorer: Optional[Callable],
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):
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return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, scorer=scorer)
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return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, label_smoothing=label_smoothing, scorer=scorer)
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def morphologizer_score(examples, **kwargs):
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@ -94,6 +96,7 @@ class Morphologizer(Tagger):
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*,
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overwrite: bool = BACKWARD_OVERWRITE,
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extend: bool = BACKWARD_EXTEND,
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label_smoothing: float = 0.0,
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scorer: Optional[Callable] = morphologizer_score,
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):
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"""Initialize a morphologizer.
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@ -121,6 +124,7 @@ class Morphologizer(Tagger):
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"labels_pos": {},
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"overwrite": overwrite,
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"extend": extend,
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"label_smoothing": label_smoothing,
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}
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self.cfg = dict(sorted(cfg.items()))
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self.scorer = scorer
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@ -270,7 +274,8 @@ class Morphologizer(Tagger):
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DOCS: https://spacy.io/api/morphologizer#get_loss
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"""
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validate_examples(examples, "Morphologizer.get_loss")
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loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
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loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False,
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label_smoothing=self.cfg["label_smoothing"])
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truths = []
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for eg in examples:
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eg_truths = []
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@ -1,5 +1,5 @@
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import pytest
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from numpy.testing import assert_equal
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from numpy.testing import assert_equal, assert_almost_equal
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from spacy import util
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from spacy.training import Example
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@ -19,6 +19,8 @@ def test_label_types():
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morphologizer.add_label(9)
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TAGS = ["Feat=N", "Feat=V", "Feat=J"]
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TRAIN_DATA = [
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(
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"I like green eggs",
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@ -32,6 +34,29 @@ TRAIN_DATA = [
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]
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def test_label_smoothing():
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nlp = Language()
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morph_no_ls = nlp.add_pipe("morphologizer", "no_label_smoothing")
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morph_ls = nlp.add_pipe(
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"morphologizer", "label_smoothing", config=dict(label_smoothing=0.05)
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)
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train_examples = []
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losses = {}
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for tag in TAGS:
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morph_no_ls.add_label(tag)
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morph_ls.add_label(tag)
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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nlp.initialize(get_examples=lambda: train_examples)
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tag_scores, bp_tag_scores = morph_ls.model.begin_update(
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[eg.predicted for eg in train_examples]
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)
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no_ls_grads = morph_no_ls.get_loss(train_examples, tag_scores)[1][0]
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ls_grads = morph_ls.get_loss(train_examples, tag_scores)[1][0]
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assert_almost_equal(ls_grads / no_ls_grads, 0.94285715)
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def test_no_label():
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nlp = Language()
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nlp.add_pipe("morphologizer")
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@ -1,5 +1,5 @@
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import pytest
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from numpy.testing import assert_equal, assert_array_almost_equal
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from numpy.testing import assert_equal, assert_almost_equal
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from spacy.attrs import TAG
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from spacy import util
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@ -68,7 +68,6 @@ PARTIAL_DATA = [
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def test_label_smoothing():
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util.fix_random_seed()
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nlp = Language()
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tagger_no_ls = nlp.add_pipe("tagger", "no_label_smoothing")
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tagger_ls = nlp.add_pipe(
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@ -88,7 +87,7 @@ def test_label_smoothing():
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)
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no_ls_grads = tagger_no_ls.get_loss(train_examples, tag_scores)[1][0]
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ls_grads = tagger_ls.get_loss(train_examples, tag_scores)[1][0]
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assert_array_almost_equal((ls_grads - no_ls_grads)[0], [0.05, -0.025, -0.025])
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assert_almost_equal(ls_grads / no_ls_grads, 0.925)
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def test_no_label():
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@ -42,12 +42,13 @@ architectures and their arguments and hyperparameters.
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> nlp.add_pipe("morphologizer", config=config)
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> ```
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| Setting | Description |
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| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
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| `extend` <Tag variant="new">3.2</Tag> | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
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| Setting | Description |
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| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
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| `extend` <Tag variant="new">3.2</Tag> | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
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| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
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| `label_smoothing` <Tag variant="new">3.6</Tag> | Whether or not to use label smoothing. Defaults to `False`. ~~bool~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx
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