Allow CharacterEmbed to specify feature

spacy/ml/models/tok2vec.py

@@ -1,4 +1,4 @@
-from typing import Optional, List
+from typing import Optional, List, Union
 from thinc.api import chain, clone, concatenate, with_array, with_padded
 from thinc.api import Model, noop, list2ragged, ragged2list
 from thinc.api import FeatureExtractor, HashEmbed
@@ -165,7 +165,8 @@ def MultiHashEmbed(
 
 @registry.architectures.register("spacy.CharacterEmbed.v1")
 def CharacterEmbed(
-    width: int, rows: int, nM: int, nC: int, also_use_static_vectors: bool
+    width: int, rows: int, nM: int, nC: int, also_use_static_vectors: bool,
+    feature: Union[int, str]="NORM"
 ):
     """Construct an embedded representation based on character embeddings, using
     a feed-forward network. A fixed number of UTF-8 byte characters are used for
@@ -183,7 +184,8 @@ def CharacterEmbed(
     also concatenated on, and the result is then passed through a feed-forward
     network to construct a single vector to represent the information.
 
-    width (int): The width of the output vector and the NORM hash embedding.
+    feature (int or str): An attribute to embed, to concatenate with the characters.
+    width (int): The width of the output vector and the feature embedding.
     rows (int): The number of rows in the NORM hash embedding table.
     nM (int): The dimensionality of the character embeddings. Recommended values
         are between 16 and 64.
@@ -193,12 +195,15 @@ def CharacterEmbed(
     also_use_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("Invalid feature: Must be a token attribute.")
     if also_use_static_vectors:
         model = chain(
             concatenate(
                 chain(_character_embed.CharacterEmbed(nM=nM, nC=nC), list2ragged()),
                 chain(
-                    FeatureExtractor([NORM]),
+                    FeatureExtractor([feature]),
                     list2ragged(),
                     with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5)),
                 ),
@@ -214,7 +219,7 @@ def CharacterEmbed(
             concatenate(
                 chain(_character_embed.CharacterEmbed(nM=nM, nC=nC), list2ragged()),
                 chain(
-                    FeatureExtractor([NORM]),
+                    FeatureExtractor([feature]),
                     list2ragged(),
                     with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5)),
                 ),