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Adriane Boyd 2020-10-09 14:42:07 +02:00
parent 39aabf50ab
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website/docs/usage/embeddings-transformers.md
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@ -514,7 +514,7 @@ Many neural network models are able to use word vector tables as additional
features, which sometimes results in significant improvements in accuracy. features, which sometimes results in significant improvements in accuracy.
spaCy's built-in embedding layer, spaCy's built-in embedding layer,
[MultiHashEmbed](/api/architectures#MultiHashEmbed), can be configured to use [MultiHashEmbed](/api/architectures#MultiHashEmbed), can be configured to use
word vector tables using the `also_use_static_vectors` flag. This setting is word vector tables using the `include_static_vectors` flag. This setting is
also available on the [MultiHashEmbedCNN](/api/architectures#MultiHashEmbedCNN) also available on the [MultiHashEmbedCNN](/api/architectures#MultiHashEmbedCNN)
layer, which builds the default token-to-vector encoding architecture. layer, which builds the default token-to-vector encoding architecture.
@ -522,9 +522,9 @@ layer, which builds the default token-to-vector encoding architecture.
[tagger.model.tok2vec.embed] [tagger.model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1" @architectures = "spacy.MultiHashEmbed.v1"
width = 128 width = 128
rows = 7000 attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
also_embed_subwords = true rows = [5000,2500,2500,2500]
also_use_static_vectors = true include_static_vectors = true
``` ```
<Infobox title="How it works" emoji="💡"> <Infobox title="How it works" emoji="💡">