From 05596159bfb8e50e4ecbf0f5841aa709a9a71f5d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 15:33:27 -0500
Subject: [PATCH] Fix serialization when pre-trained vectors

---
 spacy/pipeline.pyx | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index a7ff90174..f5b2db55a 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -145,8 +145,8 @@ class BaseThincComponent(object):
 
         deserialize = OrderedDict((
             ('cfg', lambda b: self.cfg.update(ujson.loads(b))),
-            ('model', load_model),
             ('vocab', lambda b: self.vocab.from_bytes(b))
+            ('model', load_model),
         ))
         util.from_bytes(bytes_data, deserialize, exclude)
         return self
@@ -154,8 +154,8 @@ class BaseThincComponent(object):
     def to_disk(self, path, **exclude):
         serialize = OrderedDict((
             ('cfg', lambda p: p.open('w').write(json_dumps(self.cfg))),
+            ('vocab', lambda p: self.vocab.to_disk(p)),
             ('model', lambda p: p.open('wb').write(self.model.to_bytes())),
-            ('vocab', lambda p: self.vocab.to_disk(p))
         ))
         util.to_disk(path, serialize, exclude)
 
@@ -168,8 +168,8 @@ class BaseThincComponent(object):
 
         deserialize = OrderedDict((
             ('cfg', lambda p: self.cfg.update(_load_cfg(p))),
-            ('model', load_model),
             ('vocab', lambda p: self.vocab.from_disk(p)),
+            ('model', load_model),
         ))
         util.from_disk(path, deserialize, exclude)
         return self
@@ -289,6 +289,7 @@ class TokenVectorEncoder(BaseThincComponent):
         pipeline (list): The pipeline the model is part of.
         """
         if self.model is True:
+            self.cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model = self.Model(**self.cfg)
             link_vectors_to_models(self.vocab)
 
@@ -398,6 +399,7 @@ class NeuralTagger(BaseThincComponent):
                                           vocab.morphology.lemmatizer,
                                           exc=vocab.morphology.exc)
         if self.model is True:
+            self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
             self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
             link_vectors_to_models(self.vocab)
 
@@ -486,6 +488,7 @@ class NeuralLabeller(NeuralTagger):
         self.model = model
         self.cfg = dict(cfg)
         self.cfg.setdefault('cnn_maxout_pieces', 2)
+        self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
 
     @property
     def labels(self):
@@ -508,8 +511,8 @@ class NeuralLabeller(NeuralTagger):
                         self.labels[dep] = len(self.labels)
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(len(self.labels), token_vector_width=token_vector_width,
-                                    pretrained_dims=self.vocab.vectors_length)
+            self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
+            self.model = self.Model(len(self.labels), **self.cfg)
             link_vectors_to_models(self.vocab)
 
     @classmethod