mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 03:19:13 +03:00
Tagger label smoothing (#12293)
* add label smoothing * use True/False instead of floats * add entropy to debug data * formatting * docs * change test to check difference in distributions * Update website/docs/api/tagger.mdx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/tagger.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * bool -> float * update docs * fix seed * black * update tests to use label_smoothing = 0.0 * set default to 0.0, update quickstart * Update spacy/pipeline/tagger.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * update morphologizer, tagger test * fix morph docs * add url to docs --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
This commit is contained in:
Vinit Ravishankar
GitHub
parent
b479f8bfa5
commit
28de85737f
|
|
@ -7,6 +7,7 @@ import srsly
|
|||
from wasabi import Printer, MESSAGES, msg
|
||||
import typer
|
||||
import math
|
||||
import numpy
|
||||
|
||||
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
|
||||
from ._util import import_code, debug_cli, _format_number
|
||||
|
|
@ -521,9 +522,13 @@ def debug_data(
|
|||
|
||||
if "tagger" in factory_names:
|
||||
msg.divider("Part-of-speech Tagging")
|
||||
label_list = [label for label in gold_train_data["tags"]]
|
||||
model_labels = _get_labels_from_model(nlp, "tagger")
|
||||
label_list, counts = zip(*gold_train_data["tags"].items())
|
||||
msg.info(f"{len(label_list)} label(s) in train data")
|
||||
p = numpy.array(counts)
|
||||
p = p / p.sum()
|
||||
norm_entropy = (-p * numpy.log2(p)).sum() / numpy.log2(len(label_list))
|
||||
msg.info(f"{norm_entropy} is the normalised label entropy")
|
||||
model_labels = _get_labels_from_model(nlp, "tagger")
|
||||
labels = set(label_list)
|
||||
missing_labels = model_labels - labels
|
||||
if missing_labels:
|
||||
|
|
|
|||
|
|
@ -331,6 +331,7 @@ maxout_pieces = 3
|
|||
{% if "morphologizer" in components %}
|
||||
[components.morphologizer]
|
||||
factory = "morphologizer"
|
||||
label_smoothing = 0.05
|
||||
|
||||
[components.morphologizer.model]
|
||||
@architectures = "spacy.Tagger.v2"
|
||||
|
|
@ -344,6 +345,7 @@ width = ${components.tok2vec.model.encode.width}
|
|||
{% if "tagger" in components %}
|
||||
[components.tagger]
|
||||
factory = "tagger"
|
||||
label_smoothing = 0.05
|
||||
|
||||
[components.tagger.model]
|
||||
@architectures = "spacy.Tagger.v2"
|
||||
|
|
|
|||
|
|
@ -52,7 +52,8 @@ DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
|
|||
@Language.factory(
|
||||
"morphologizer",
|
||||
assigns=["token.morph", "token.pos"],
|
||||
default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False, "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}},
|
||||
default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False,
|
||||
"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}, "label_smoothing": 0.0},
|
||||
default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
|
||||
)
|
||||
def make_morphologizer(
|
||||
|
|
@ -61,9 +62,10 @@ def make_morphologizer(
|
|||
name: str,
|
||||
overwrite: bool,
|
||||
extend: bool,
|
||||
label_smoothing: float,
|
||||
scorer: Optional[Callable],
|
||||
):
|
||||
return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, scorer=scorer)
|
||||
return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, label_smoothing=label_smoothing, scorer=scorer)
|
||||
|
||||
|
||||
def morphologizer_score(examples, **kwargs):
|
||||
|
|
@ -94,6 +96,7 @@ class Morphologizer(Tagger):
|
|||
*,
|
||||
overwrite: bool = BACKWARD_OVERWRITE,
|
||||
extend: bool = BACKWARD_EXTEND,
|
||||
label_smoothing: float = 0.0,
|
||||
scorer: Optional[Callable] = morphologizer_score,
|
||||
):
|
||||
"""Initialize a morphologizer.
|
||||
|
|
@ -121,6 +124,7 @@ class Morphologizer(Tagger):
|
|||
"labels_pos": {},
|
||||
"overwrite": overwrite,
|
||||
"extend": extend,
|
||||
"label_smoothing": label_smoothing,
|
||||
}
|
||||
self.cfg = dict(sorted(cfg.items()))
|
||||
self.scorer = scorer
|
||||
|
|
@ -270,7 +274,8 @@ class Morphologizer(Tagger):
|
|||
DOCS: https://spacy.io/api/morphologizer#get_loss
|
||||
"""
|
||||
validate_examples(examples, "Morphologizer.get_loss")
|
||||
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
|
||||
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False,
|
||||
label_smoothing=self.cfg["label_smoothing"])
|
||||
truths = []
|
||||
for eg in examples:
|
||||
eg_truths = []
|
||||
|
|
|
|||
|
|
@ -45,7 +45,7 @@ DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
|
|||
@Language.factory(
|
||||
"tagger",
|
||||
assigns=["token.tag"],
|
||||
default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!"},
|
||||
default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!", "label_smoothing": 0.0},
|
||||
default_score_weights={"tag_acc": 1.0},
|
||||
)
|
||||
def make_tagger(
|
||||
|
|
@ -55,6 +55,7 @@ def make_tagger(
|
|||
overwrite: bool,
|
||||
scorer: Optional[Callable],
|
||||
neg_prefix: str,
|
||||
label_smoothing: float,
|
||||
):
|
||||
"""Construct a part-of-speech tagger component.
|
||||
|
||||
|
|
@ -63,7 +64,7 @@ def make_tagger(
|
|||
in size, and be normalized as probabilities (all scores between 0 and 1,
|
||||
with the rows summing to 1).
|
||||
"""
|
||||
return Tagger(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer, neg_prefix=neg_prefix)
|
||||
return Tagger(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer, neg_prefix=neg_prefix, label_smoothing=label_smoothing)
|
||||
|
||||
|
||||
def tagger_score(examples, **kwargs):
|
||||
|
|
@ -89,6 +90,7 @@ class Tagger(TrainablePipe):
|
|||
overwrite=BACKWARD_OVERWRITE,
|
||||
scorer=tagger_score,
|
||||
neg_prefix="!",
|
||||
label_smoothing=0.0,
|
||||
):
|
||||
"""Initialize a part-of-speech tagger.
|
||||
|
||||
|
|
@ -105,7 +107,7 @@ class Tagger(TrainablePipe):
|
|||
self.model = model
|
||||
self.name = name
|
||||
self._rehearsal_model = None
|
||||
cfg = {"labels": [], "overwrite": overwrite, "neg_prefix": neg_prefix}
|
||||
cfg = {"labels": [], "overwrite": overwrite, "neg_prefix": neg_prefix, "label_smoothing": label_smoothing}
|
||||
self.cfg = dict(sorted(cfg.items()))
|
||||
self.scorer = scorer
|
||||
|
||||
|
|
@ -256,7 +258,7 @@ class Tagger(TrainablePipe):
|
|||
DOCS: https://spacy.io/api/tagger#get_loss
|
||||
"""
|
||||
validate_examples(examples, "Tagger.get_loss")
|
||||
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, neg_prefix=self.cfg["neg_prefix"])
|
||||
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, neg_prefix=self.cfg["neg_prefix"], label_smoothing=self.cfg["label_smoothing"])
|
||||
# Convert empty tag "" to missing value None so that both misaligned
|
||||
# tokens and tokens with missing annotation have the default missing
|
||||
# value None.
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import pytest
|
||||
from numpy.testing import assert_equal
|
||||
from numpy.testing import assert_equal, assert_almost_equal
|
||||
|
||||
from spacy import util
|
||||
from spacy.training import Example
|
||||
|
|
@ -19,6 +19,8 @@ def test_label_types():
|
|||
morphologizer.add_label(9)
|
||||
|
||||
|
||||
TAGS = ["Feat=N", "Feat=V", "Feat=J"]
|
||||
|
||||
TRAIN_DATA = [
|
||||
(
|
||||
"I like green eggs",
|
||||
|
|
@ -32,6 +34,29 @@ TRAIN_DATA = [
|
|||
]
|
||||
|
||||
|
||||
def test_label_smoothing():
|
||||
nlp = Language()
|
||||
morph_no_ls = nlp.add_pipe("morphologizer", "no_label_smoothing")
|
||||
morph_ls = nlp.add_pipe(
|
||||
"morphologizer", "label_smoothing", config=dict(label_smoothing=0.05)
|
||||
)
|
||||
train_examples = []
|
||||
losses = {}
|
||||
for tag in TAGS:
|
||||
morph_no_ls.add_label(tag)
|
||||
morph_ls.add_label(tag)
|
||||
for t in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
||||
|
||||
nlp.initialize(get_examples=lambda: train_examples)
|
||||
tag_scores, bp_tag_scores = morph_ls.model.begin_update(
|
||||
[eg.predicted for eg in train_examples]
|
||||
)
|
||||
no_ls_grads = morph_no_ls.get_loss(train_examples, tag_scores)[1][0]
|
||||
ls_grads = morph_ls.get_loss(train_examples, tag_scores)[1][0]
|
||||
assert_almost_equal(ls_grads / no_ls_grads, 0.94285715)
|
||||
|
||||
|
||||
def test_no_label():
|
||||
nlp = Language()
|
||||
nlp.add_pipe("morphologizer")
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import pytest
|
||||
from numpy.testing import assert_equal
|
||||
from numpy.testing import assert_equal, assert_almost_equal
|
||||
from spacy.attrs import TAG
|
||||
|
||||
from spacy import util
|
||||
|
|
@ -67,6 +67,29 @@ PARTIAL_DATA = [
|
|||
]
|
||||
|
||||
|
||||
def test_label_smoothing():
|
||||
nlp = Language()
|
||||
tagger_no_ls = nlp.add_pipe("tagger", "no_label_smoothing")
|
||||
tagger_ls = nlp.add_pipe(
|
||||
"tagger", "label_smoothing", config=dict(label_smoothing=0.05)
|
||||
)
|
||||
train_examples = []
|
||||
losses = {}
|
||||
for tag in TAGS:
|
||||
tagger_no_ls.add_label(tag)
|
||||
tagger_ls.add_label(tag)
|
||||
for t in TRAIN_DATA:
|
||||
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
||||
|
||||
nlp.initialize(get_examples=lambda: train_examples)
|
||||
tag_scores, bp_tag_scores = tagger_ls.model.begin_update(
|
||||
[eg.predicted for eg in train_examples]
|
||||
)
|
||||
no_ls_grads = tagger_no_ls.get_loss(train_examples, tag_scores)[1][0]
|
||||
ls_grads = tagger_ls.get_loss(train_examples, tag_scores)[1][0]
|
||||
assert_almost_equal(ls_grads / no_ls_grads, 0.925)
|
||||
|
||||
|
||||
def test_no_label():
|
||||
nlp = Language()
|
||||
nlp.add_pipe("tagger")
|
||||
|
|
|
|||
|
|
@ -42,12 +42,13 @@ architectures and their arguments and hyperparameters.
|
|||
> nlp.add_pipe("morphologizer", config=config)
|
||||
> ```
|
||||
|
||||
| Setting | Description |
|
||||
| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `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~~ |
|
||||
| `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]~~ |
|
||||
| Setting | Description |
|
||||
| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `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~~ |
|
||||
| `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]~~ |
|
||||
| `label_smoothing` <Tag variant="new">3.6</Tag> | [Label smoothing](https://arxiv.org/abs/1906.02629) factor. Defaults to `0.0`. ~~float~~ |
|
||||
|
||||
```python
|
||||
%%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx
|
||||
|
|
|
|||
|
|
@ -40,12 +40,13 @@ architectures and their arguments and hyperparameters.
|
|||
> nlp.add_pipe("tagger", config=config)
|
||||
> ```
|
||||
|
||||
| Setting | Description |
|
||||
| ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~ |
|
||||
| `neg_prefix` <Tag variant="new">3.2.1</Tag> | The prefix used to specify incorrect tags while training. The tagger will learn not to predict exactly this tag. Defaults to `!`. ~~str~~ |
|
||||
| Setting | Description |
|
||||
| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"tag"`. ~~Optional[Callable]~~ |
|
||||
| `neg_prefix` <Tag variant="new">3.2.1</Tag> | The prefix used to specify incorrect tags while training. The tagger will learn not to predict exactly this tag. Defaults to `!`. ~~str~~ |
|
||||
| `label_smoothing` <Tag variant="new">3.6</Tag> | [Label smoothing](https://arxiv.org/abs/1906.02629) factor. Defaults to `0.0`. ~~float~~ |
|
||||
|
||||
```python
|
||||
%%GITHUB_SPACY/spacy/pipeline/tagger.pyx
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user