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

spacy/cli/train.py

@@ -9,6 +9,7 @@ from timeit import default_timer as timer
 import shutil
 import srsly
 from wasabi import Printer
+from thinc.rates import slanted_triangular
 
 from .._ml import create_default_optimizer
 from ..attrs import PROB, IS_OOV, CLUSTER, LANG
@@ -118,7 +119,7 @@ def train(
     )
     batch_sizes = util.compounding(
         util.env_opt("batch_from", 100.0),
-        util.env_opt("batch_to", 1000.0),
+        util.env_opt("batch_to", 2000.0),
         util.env_opt("batch_compound", 1.001),
     )
 
@@ -180,6 +181,8 @@ def train(
         # Start with a blank model, call begin_training
         optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
 
+    optimizer.b1_decay = 0.0001
+    optimizer.b2_decay = 0.0001
     nlp._optimizer = None
 
     # Load in pre-trained weights

spacy/syntax/ner.pyx

@@ -207,15 +207,16 @@ cdef class BiluoPushDown(TransitionSystem):
         else:
             label_id = label_name
         if action == OUT and label_id != 0:
-            return
+            return None
         if action == MISSING or action == ISNT:
-            return
+            return None
         # Check we're not creating a move we already have, so that this is
         # idempotent
         for trans in self.c[:self.n_moves]:
             if trans.move == action and trans.label == label_id:
                 return 0
         if self.n_moves >= self._size:
+            self._size = self.n_moves
             self._size *= 2
             self.c = <Transition*>self.mem.realloc(self.c, self._size * sizeof(self.c[0]))
         self.c[self.n_moves] = self.init_transition(self.n_moves, action, label_id)