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03fefa37e2
* Add overwrite settings for more components For pipeline components where it's relevant and not already implemented, add an explicit `overwrite` setting that controls whether `set_annotations` overwrites existing annotation. For the `morphologizer`, add an additional setting `extend`, which controls whether the existing features are preserved. * +overwrite, +extend: overwrite values of existing features, add any new features * +overwrite, -extend: overwrite completely, removing any existing features * -overwrite, +extend: keep values of existing features, add any new features * -overwrite, -extend: do not modify the existing value if set In all cases an unset value will be set by `set_annotations`. Preserve current overwrite defaults: * True: morphologizer, entity linker * False: tagger, sentencizer, senter * Add backwards compat overwrite settings * Put empty line back Removed by accident in last commit * Set backwards-compatible defaults in __init__ Because the `TrainablePipe` serialization methods update `cfg`, there's no straightforward way to detect whether models serialized with a previous version are missing the overwrite settings. It would be possible in the sentencizer due to its separate serialization methods, however to keep the changes parallel, this also sets the default in `__init__`. * Remove traces Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com>
306 lines
12 KiB
Cython
306 lines
12 KiB
Cython
# cython: infer_types=True, profile=True, binding=True
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from typing import Optional, Union, Dict, Callable
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import srsly
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from thinc.api import SequenceCategoricalCrossentropy, Model, Config
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from itertools import islice
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from ..tokens.doc cimport Doc
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from ..vocab cimport Vocab
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from ..morphology cimport Morphology
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from ..parts_of_speech import IDS as POS_IDS
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from ..symbols import POS
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from ..language import Language
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from ..errors import Errors
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from .pipe import deserialize_config
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from .tagger import Tagger
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from .. import util
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from ..scorer import Scorer
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from ..training import validate_examples, validate_get_examples
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from ..util import registry
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# See #9050
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BACKWARD_OVERWRITE = True
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BACKWARD_EXTEND = False
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default_model_config = """
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[model]
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@architectures = "spacy.Tagger.v1"
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[model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[model.tok2vec.embed]
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@architectures = "spacy.CharacterEmbed.v2"
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width = 128
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rows = 7000
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nM = 64
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nC = 8
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include_static_vectors = false
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[model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 128
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depth = 4
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window_size = 1
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maxout_pieces = 3
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"""
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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_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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nlp: Language,
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model: Model,
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name: str,
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overwrite: bool,
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extend: bool,
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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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def morphologizer_score(examples, **kwargs):
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def morph_key_getter(token, attr):
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return getattr(token, attr).key
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results = {}
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results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
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results.update(Scorer.score_token_attr(examples, "morph", getter=morph_key_getter, **kwargs))
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results.update(Scorer.score_token_attr_per_feat(examples,
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"morph", getter=morph_key_getter, **kwargs))
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return results
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@registry.scorers("spacy.morphologizer_scorer.v1")
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def make_morphologizer_scorer():
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return morphologizer_score
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class Morphologizer(Tagger):
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POS_FEAT = "POS"
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def __init__(
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self,
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vocab: Vocab,
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model: Model,
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name: str = "morphologizer",
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*,
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overwrite: bool = BACKWARD_OVERWRITE,
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extend: bool = BACKWARD_EXTEND,
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scorer: Optional[Callable] = morphologizer_score,
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):
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"""Initialize a morphologizer.
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vocab (Vocab): The shared vocabulary.
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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scorer (Optional[Callable]): The scoring method. Defaults to
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Scorer.score_token_attr for the attributes "pos" and "morph" and
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Scorer.score_token_attr_per_feat for the attribute "morph".
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DOCS: https://spacy.io/api/morphologizer#init
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"""
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self.vocab = vocab
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self.model = model
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self.name = name
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self._rehearsal_model = None
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# to be able to set annotations without string operations on labels,
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# store mappings from morph+POS labels to token-level annotations:
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# 1) labels_morph stores a mapping from morph+POS->morph
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# 2) labels_pos stores a mapping from morph+POS->POS
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cfg = {
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"labels_morph": {},
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"labels_pos": {},
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"overwrite": overwrite,
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"extend": extend,
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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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@property
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def labels(self):
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"""RETURNS (Tuple[str]): The labels currently added to the component."""
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return tuple(self.cfg["labels_morph"].keys())
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@property
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def label_data(self) -> Dict[str, Dict[str, Union[str, float, int, None]]]:
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"""A dictionary with all labels data."""
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return {"morph": self.cfg["labels_morph"], "pos": self.cfg["labels_pos"]}
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def add_label(self, label):
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"""Add a new label to the pipe.
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label (str): The label to add.
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RETURNS (int): 0 if label is already present, otherwise 1.
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DOCS: https://spacy.io/api/morphologizer#add_label
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"""
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if not isinstance(label, str):
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raise ValueError(Errors.E187)
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if label in self.labels:
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return 0
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self._allow_extra_label()
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# normalize label
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norm_label = self.vocab.morphology.normalize_features(label)
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# extract separate POS and morph tags
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label_dict = Morphology.feats_to_dict(label)
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pos = label_dict.get(self.POS_FEAT, "")
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if self.POS_FEAT in label_dict:
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label_dict.pop(self.POS_FEAT)
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# normalize morph string and add to morphology table
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norm_morph = self.vocab.strings[self.vocab.morphology.add(label_dict)]
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# add label mappings
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if norm_label not in self.cfg["labels_morph"]:
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self.cfg["labels_morph"][norm_label] = norm_morph
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self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
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return 1
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def initialize(self, get_examples, *, nlp=None, labels=None):
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"""Initialize the pipe for training, using a representative set
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of data examples.
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get_examples (Callable[[], Iterable[Example]]): Function that
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returns a representative sample of gold-standard Example objects.
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nlp (Language): The current nlp object the component is part of.
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DOCS: https://spacy.io/api/morphologizer#initialize
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"""
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validate_get_examples(get_examples, "Morphologizer.initialize")
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util.check_lexeme_norms(self.vocab, "morphologizer")
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if labels is not None:
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self.cfg["labels_morph"] = labels["morph"]
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self.cfg["labels_pos"] = labels["pos"]
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else:
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# First, fetch all labels from the data
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for example in get_examples():
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for i, token in enumerate(example.reference):
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pos = token.pos_
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# if both are unset, annotation is missing, so do not add
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# an empty label
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if pos == "" and not token.has_morph():
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continue
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morph = str(token.morph)
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# create and add the combined morph+POS label
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morph_dict = Morphology.feats_to_dict(morph)
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if pos:
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morph_dict[self.POS_FEAT] = pos
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norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
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# add label->morph and label->POS mappings
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if norm_label not in self.cfg["labels_morph"]:
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self.cfg["labels_morph"][norm_label] = morph
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self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
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if len(self.labels) < 1:
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raise ValueError(Errors.E143.format(name=self.name))
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doc_sample = []
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label_sample = []
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for example in islice(get_examples(), 10):
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gold_array = []
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for i, token in enumerate(example.reference):
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pos = token.pos_
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morph = str(token.morph)
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morph_dict = Morphology.feats_to_dict(morph)
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if pos:
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morph_dict[self.POS_FEAT] = pos
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norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
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gold_array.append([1.0 if label == norm_label else 0.0 for label in self.labels])
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doc_sample.append(example.x)
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label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
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assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
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assert len(label_sample) > 0, Errors.E923.format(name=self.name)
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self.model.initialize(X=doc_sample, Y=label_sample)
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def set_annotations(self, docs, batch_tag_ids):
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"""Modify a batch of documents, using pre-computed scores.
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docs (Iterable[Doc]): The documents to modify.
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batch_tag_ids: The IDs to set, produced by Morphologizer.predict.
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DOCS: https://spacy.io/api/morphologizer#set_annotations
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"""
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if isinstance(docs, Doc):
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docs = [docs]
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cdef Doc doc
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cdef Vocab vocab = self.vocab
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cdef bint overwrite = self.cfg["overwrite"]
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cdef bint extend = self.cfg["extend"]
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for i, doc in enumerate(docs):
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doc_tag_ids = batch_tag_ids[i]
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if hasattr(doc_tag_ids, "get"):
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doc_tag_ids = doc_tag_ids.get()
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for j, tag_id in enumerate(doc_tag_ids):
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morph = self.labels[tag_id]
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# set morph
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if doc.c[j].morph == 0 or overwrite or extend:
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if overwrite and extend:
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# morphologizer morph overwrites any existing features
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# while extending
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extended_morph = Morphology.feats_to_dict(self.vocab.strings[doc.c[j].morph])
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extended_morph.update(Morphology.feats_to_dict(self.cfg["labels_morph"].get(morph, 0)))
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doc.c[j].morph = self.vocab.morphology.add(extended_morph)
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elif extend:
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# existing features are preserved and any new features
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# are added
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extended_morph = Morphology.feats_to_dict(self.cfg["labels_morph"].get(morph, 0))
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extended_morph.update(Morphology.feats_to_dict(self.vocab.strings[doc.c[j].morph]))
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doc.c[j].morph = self.vocab.morphology.add(extended_morph)
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else:
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# clobber
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doc.c[j].morph = self.vocab.morphology.add(self.cfg["labels_morph"].get(morph, 0))
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# set POS
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if doc.c[j].pos == 0 or overwrite:
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doc.c[j].pos = self.cfg["labels_pos"].get(morph, 0)
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def get_loss(self, examples, scores):
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"""Find the loss and gradient of loss for the batch of documents and
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their predicted scores.
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examples (Iterable[Examples]): The batch of examples.
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scores: Scores representing the model's predictions.
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RETURNS (Tuple[float, float]): The loss and the gradient.
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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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truths = []
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for eg in examples:
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eg_truths = []
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pos_tags = eg.get_aligned("POS", as_string=True)
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morphs = eg.get_aligned("MORPH", as_string=True)
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for i in range(len(morphs)):
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pos = pos_tags[i]
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morph = morphs[i]
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# POS may align (same value for multiple tokens) when morph
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# doesn't, so if either is misaligned (None), treat the
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# annotation as missing so that truths doesn't end up with an
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# unknown morph+POS combination
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if pos is None or morph is None:
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label = None
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# If both are unset, the annotation is missing (empty morph
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# converted from int is "_" rather than "")
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elif pos == "" and morph == "":
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label = None
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# Otherwise, generate the combined label
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else:
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label_dict = Morphology.feats_to_dict(morph)
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if pos:
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label_dict[self.POS_FEAT] = pos
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label = self.vocab.strings[self.vocab.morphology.add(label_dict)]
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# As a fail-safe, skip any unrecognized labels
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if label not in self.labels:
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label = None
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eg_truths.append(label)
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truths.append(eg_truths)
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d_scores, loss = loss_func(scores, truths)
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if self.model.ops.xp.isnan(loss):
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raise ValueError(Errors.E910.format(name=self.name))
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return float(loss), d_scores
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