Use 0-vector for OOV lexemes
@@ -3,7 +3,7 @@ from thinc.api import chain, Maxout, LayerNorm, Softmax, Linear, zero_init, Mode
from thinc.api import MultiSoftmax, list2array
from thinc.api import to_categorical, CosineDistance, L2Distance
-from ...util import registry
+from ...util import registry, OOV_RANK
from ...errors import Errors
from ...attrs import ID
@@ -70,6 +70,7 @@ def get_vectors_loss(ops, docs, prediction, distance):
# and look them up all at once. This prevents data copying.
ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
target = docs[0].vocab.vectors.data[ids]
+ target[ids == OOV_RANK] = 0
d_target, loss = distance(prediction, target)
return loss, d_target
