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Update v2-2.md [ci skip]
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@ -334,6 +334,11 @@ check if all of your models are up to date, you can run the
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the `Vocab` and serialized with it. This means that serialized objects (`nlp`,
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the `Vocab` and serialized with it. This means that serialized objects (`nlp`,
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pipeline components, vocab) will now include additional data, and models
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pipeline components, vocab) will now include additional data, and models
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written to disk will include additional files.
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written to disk will include additional files.
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- The [`Lemmatizer`](/api/lemmatizer) class is now initialized with an instance
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of [`Lookups`](/api/lookups) containing the rules and tables, instead of dicts
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as separate arguments. This makes it easier to share data tables and modify
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them at runtime. This is mostly internals, but if you've been implementing a
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custom `Lemmatizer`, you'll need to update your code.
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- The [Dutch model](/models/nl) has been trained on a new NER corpus (custom
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- The [Dutch model](/models/nl) has been trained on a new NER corpus (custom
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labelled UD instead of WikiNER), so their predictions may be very different
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labelled UD instead of WikiNER), so their predictions may be very different
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compared to the previous version. The results should be significantly better
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compared to the previous version. The results should be significantly better
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@ -399,6 +404,29 @@ don't explicitly install the lookups data, that `nlp` object won't have any
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lemmatization rules available. spaCy will now show you a warning when you train
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lemmatization rules available. spaCy will now show you a warning when you train
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a new part-of-speech tagger and the vocab has no lookups available.
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a new part-of-speech tagger and the vocab has no lookups available.
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#### Lemmatizer initialization
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This is mainly internals and should hopefully not affect your code. But if
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you've been creating custom [`Lemmatizers`](/api/lemmatizer), you'll need to
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update how they're initialized and pass in an instance of
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[`Lookups`](/api/lookups) with the (optional) tables `lemma_index`, `lemma_exc`,
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`lemma_rules` and `lemma_lookup`.
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```diff
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from spacy.lemmatizer import Lemmatizer
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+ from spacy.lookups import Lookups
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lemma_index = {"verb": ("cope", "cop")}
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lemma_exc = {"verb": {"coping": ("cope",)}}
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lemma_rules = {"verb": [["ing", ""]]}
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- lemmatizer = Lemmatizer(lemma_index, lemma_exc, lemma_rules)
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+ lookups = Lookups()
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+ lookups.add_table("lemma_index", lemma_index)
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+ lookups.add_table("lemma_exc", lemma_exc)
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+ lookups.add_table("lemma_rules", lemma_rules)
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+ lemmatizer = Lemmatizer(lookups)
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```
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#### Converting entity offsets to BILUO tags
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#### Converting entity offsets to BILUO tags
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If you've been using the
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If you've been using the
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