mirror of
https://github.com/explosion/spaCy.git
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7f5715a081
* setting KB in the EL constructor, similar to how the model is passed on * removing wikipedia example files - moved to projects * throw an error when nlp.update is called with 2 positional arguments * rewriting the config logic in create pipe to accomodate for other objects (e.g. KB) in the config * update config files with new parameters * avoid training pipeline components that don't have a model (like sentencizer) * various small fixes + UX improvements * small fixes * set thinc to 8.0.0a9 everywhere * remove outdated comment
34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
from pathlib import Path
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from thinc.api import chain, clone, list2ragged, reduce_mean, residual
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from thinc.api import Model, Maxout, Linear
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from ...util import registry
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from ...kb import KnowledgeBase
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from ...vocab import Vocab
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@registry.architectures.register("spacy.EntityLinker.v1")
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def build_nel_encoder(tok2vec, nO=None):
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with Model.define_operators({">>": chain, "**": clone}):
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token_width = tok2vec.get_dim("nO")
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output_layer = Linear(nO=nO, nI=token_width)
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model = (
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tok2vec
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>> list2ragged()
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>> reduce_mean()
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>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
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>> output_layer
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)
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model.set_ref("output_layer", output_layer)
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model.set_ref("tok2vec", tok2vec)
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return model
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@registry.assets.register("spacy.KBFromFile.v1")
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def load_kb(nlp_path, kb_path) -> KnowledgeBase:
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vocab = Vocab().from_disk(Path(nlp_path) / "vocab")
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kb = KnowledgeBase(vocab=vocab)
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kb.load_bulk(kb_path)
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return kb
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