Merge pull request #6231 from adrianeboyd/feature/include-static-vectors

spacy/ml/models/tok2vec.py

@@ -177,7 +177,7 @@ def CharacterEmbed(
     rows: int,
     nM: int,
     nC: int,
-    also_use_static_vectors: bool,
+    include_static_vectors: bool,
     feature: Union[int, str] = "LOWER",
 ) -> Model[List[Doc], List[Floats2d]]:
     """Construct an embedded representation based on character embeddings, using
@@ -204,13 +204,13 @@ def CharacterEmbed(
     nC (int): The number of UTF-8 bytes to embed per word. Recommended values
         are between 3 and 8, although it may depend on the length of words in the
         language.
-    also_use_static_vectors (bool): Whether to also use static word vectors.
+    include_static_vectors (bool): Whether to also use static word vectors.
         Requires a vectors table to be loaded in the Doc objects' vocab.
     """
     feature = intify_attr(feature)
     if feature is None:
         raise ValueError(Errors.E911(feat=feature))
-    if also_use_static_vectors:
+    if include_static_vectors:
         model = chain(
             concatenate(
                 chain(_character_embed.CharacterEmbed(nM=nM, nC=nC), list2ragged()),

spacy/pipeline/morphologizer.pyx

@@ -32,7 +32,7 @@ width = 128
 rows = 7000
 nM = 64
 nC = 8
-also_use_static_vectors = false
+include_static_vectors = false
 
 [model.tok2vec.encode]
 @architectures = "spacy.MaxoutWindowEncoder.v1"

spacy/tests/pipeline/test_tok2vec.py

@@ -63,8 +63,8 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
     [
         (8, MultiHashEmbed, {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
         (8, MultiHashEmbed, {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
-        (8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "also_use_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
-        (8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "also_use_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 3}),
+        (8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
+        (8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 3}),
     ],
 )
 # fmt: on

website/docs/usage/embeddings-transformers.md

@@ -516,7 +516,7 @@ Many neural network models are able to use word vector tables as additional
 features, which sometimes results in significant improvements in accuracy.
 spaCy's built-in embedding layer,
 [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)
 layer, which builds the default token-to-vector encoding architecture.
 
@@ -524,9 +524,9 @@ layer, which builds the default token-to-vector encoding architecture.
 [tagger.model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v1"
 width = 128
-rows = 7000
-also_embed_subwords = true
-also_use_static_vectors = true
+attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,2500,2500,2500]
+include_static_vectors = true
 ```
 
 <Infobox title="How it works" emoji="💡">