Use NORM attribute, not LOWER
@@ -13,7 +13,7 @@ from thinc import describe
from thinc.describe import Dimension, Synapses, Biases, Gradient
from thinc.neural._classes.affine import _set_dimensions_if_needed
-from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP
+from .attrs import ID, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP
from .tokens.doc import Doc
import numpy
@@ -131,14 +131,14 @@ class PrecomputableMaxouts(Model):
return Yfp, backward
def Tok2Vec(width, embed_size, preprocess=None):
- cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE]
+ cols = [ID, NORM, PREFIX, SUFFIX, SHAPE]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
- lower = get_col(cols.index(LOWER)) >> HashEmbed(width, embed_size, name='embed_lower')
+ norm = get_col(cols.index(NORM)) >> HashEmbed(width, embed_size, name='embed_lower')
prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size//2, name='embed_prefix')
suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2, name='embed_suffix')
shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size//2, name='embed_shape')
- embed = (lower | prefix | suffix | shape )
+ embed = (norm | prefix | suffix | shape )
tok2vec = (
with_flatten(
asarray(Model.ops, dtype='uint64')
@@ -148,7 +148,7 @@ def Tok2Vec(width, embed_size, preprocess=None):
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)),
- pad=4, ndim=5)
+ pad=4)
)
if preprocess not in (False, None):
tok2vec = preprocess >> tok2vec
@@ -243,7 +243,7 @@ def zero_init(model):
def doc2feats(cols=None):
- cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE]
+ cols = [ID, NORM, PREFIX, SUFFIX, SHAPE]
def forward(docs, drop=0.):
feats = []
for doc in docs:
