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Sync missing and misaligned values in Tagger loss (#6689)
Use `None` for both missing and misaligned annotation in `Tagger.get_loss`, reverting to the default missing value in the loss function.
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@ -256,8 +256,14 @@ class Tagger(TrainablePipe):
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DOCS: https://nightly.spacy.io/api/tagger#get_loss
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DOCS: https://nightly.spacy.io/api/tagger#get_loss
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"""
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"""
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validate_examples(examples, "Tagger.get_loss")
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validate_examples(examples, "Tagger.get_loss")
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loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, missing_value="")
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loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
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truths = [eg.get_aligned("TAG", as_string=True) for eg in examples]
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# Convert empty tag "" to missing value None so that both misaligned
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# tokens and tokens with missing annotation have the default missing
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# value None.
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truths = []
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for eg in examples:
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eg_truths = [tag if tag is not "" else None for tag in eg.get_aligned("TAG", as_string=True)]
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truths.append(eg_truths)
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d_scores, loss = loss_func(scores, 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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if self.model.ops.xp.isnan(loss):
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raise ValueError(Errors.E910.format(name=self.name))
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raise ValueError(Errors.E910.format(name=self.name))
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@ -37,7 +37,16 @@ TRAIN_DATA = [
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]
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]
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PARTIAL_DATA = [
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PARTIAL_DATA = [
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# partial annotation
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("I like green eggs", {"tags": ["", "V", "J", ""]}),
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("I like green eggs", {"tags": ["", "V", "J", ""]}),
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# misaligned partial annotation
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(
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"He hates green eggs",
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{
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"words": ["He", "hate", "s", "green", "eggs"],
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"tags": ["", "V", "S", "J", ""],
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},
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),
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]
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]
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