Merge branch 'master' of https://github.com/explosion/spaCy

spacy/about.py

@@ -3,13 +3,13 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy'
-__version__ = '2.0.11.dev0'
+__version__ = '2.0.11'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Explosion AI'
 __email__ = 'contact@explosion.ai'
 __license__ = 'MIT'
-__release__ = False
+__release__ = True
 
 __download_url__ = 'https://github.com/explosion/spacy-models/releases/download'
 __compatibility__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json'

spacy/lang/it/lemmatizer.py

@@ -333680,7 +333680,7 @@ LOOKUP = {
     "zurliniane": "zurliniano",
     "zurliniani": "zurliniano",
     "àncore": "àncora",
-    "sono": "essere"
+    "sono": "essere",
     "è": "essere",
     "èlites": "èlite",
     "ère": "èra",

spacy/language.py

@@ -636,11 +636,11 @@ class Language(object):
         """
         path = util.ensure_path(path)
         deserializers = OrderedDict((
-            ('vocab', lambda p: self.vocab.from_disk(p)),
+            ('meta.json', lambda p: self.meta.update(util.read_json(p))),
+            ('vocab', lambda p: (
+                self.vocab.from_disk(p) and _fix_pretrained_vectors_name(self))),
             ('tokenizer', lambda p: self.tokenizer.from_disk(p, vocab=False)),
-            ('meta.json', lambda p: self.meta.update(util.read_json(p)))
         ))
-        _fix_pretrained_vectors_name(self)
         for name, proc in self.pipeline:
             if name in disable:
                 continue
@@ -682,11 +682,11 @@ class Language(object):
         RETURNS (Language): The `Language` object.
         """
         deserializers = OrderedDict((
-            ('vocab', lambda b: self.vocab.from_bytes(b)),
+            ('meta', lambda b: self.meta.update(ujson.loads(b))),
+            ('vocab', lambda b: (
+                self.vocab.from_bytes(b) and _fix_pretrained_vectors_name(self))),
             ('tokenizer', lambda b: self.tokenizer.from_bytes(b, vocab=False)),
-            ('meta', lambda b: self.meta.update(ujson.loads(b)))
         ))
-        _fix_pretrained_vectors_name(self)
         for i, (name, proc) in enumerate(self.pipeline):
             if name in disable:
                 continue
@@ -696,6 +696,7 @@ class Language(object):
         msg = util.from_bytes(bytes_data, deserializers, {})
         return self
 
+
 def _fix_pretrained_vectors_name(nlp):
     # TODO: Replace this once we handle vectors consistently as static
     # data
@@ -708,12 +709,13 @@ def _fix_pretrained_vectors_name(nlp):
         nlp.vocab.vectors.name = vectors_name
     else:
         raise ValueError(Errors.E092)
+    if nlp.vocab.vectors.size != 0:
+        link_vectors_to_models(nlp.vocab)
     for name, proc in nlp.pipeline:
         if not hasattr(proc, 'cfg'):
             continue
-        if proc.cfg.get('pretrained_dims'):
-            assert nlp.vocab.vectors.name
-            proc.cfg['pretrained_vectors'] = nlp.vocab.vectors.name
+        proc.cfg.setdefault('deprecation_fixes', {})
+        proc.cfg['deprecation_fixes']['vectors_name'] = nlp.vocab.vectors.name
 
 
 class DisabledPipes(list):

spacy/syntax/nn_parser.pyx

@@ -546,7 +546,10 @@ cdef class Parser:
         if len(docs) != len(golds):
             raise ValueError(Errors.E077.format(value='update', n_docs=len(docs),
                                                 n_golds=len(golds)))
-        if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.0:
+        # The probability we use beam update, instead of falling back to
+        # a greedy update
+        beam_update_prob = 1-self.cfg.get('beam_update_prob', 0.5)
+        if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= beam_update_prob:
             return self.update_beam(docs, golds,
                     self.cfg['beam_width'], self.cfg['beam_density'],
                     drop=drop, sgd=sgd, losses=losses)