Fix bug when too many entity types. Fixes #2800

spacy/syntax/nn_parser.pyx

@@ -747,7 +747,8 @@ cdef class Parser:
 
     def transition_batch(self, states, float[:, ::1] scores):
         cdef StateClass state
-        cdef int[500] is_valid # TODO: Unhack
+        cdef Pool mem = Pool()
+        is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
         cdef float* c_scores = &scores[0, 0]
         for state in states:
             self.moves.set_valid(is_valid, state.c)

spacy/tests/regression/test_issue2800.py

@@ -0,0 +1,34 @@
+'''Test issue that arises when too many labels are added to NER model.'''
+import random
+from ...lang.en import English
+
+def train_model(train_data, entity_types):
+    nlp = English(pipeline=[])
+
+    ner = nlp.create_pipe("ner")
+    nlp.add_pipe(ner)
+
+    for entity_type in list(entity_types):
+        ner.add_label(entity_type)
+
+    optimizer = nlp.begin_training()
+
+    # Start training
+    for i in range(20):
+        losses = {}
+        index = 0
+        random.shuffle(train_data)
+
+        for statement, entities in train_data:
+            nlp.update([statement], [entities], sgd=optimizer, losses=losses, drop=0.5)
+    return nlp
+
+
+def test_train_with_many_entity_types():
+    train_data = []
+    train_data.extend([("One sentence", {"entities": []})])
+    entity_types = [str(i) for i in range(1000)]
+
+    model = train_model(train_data, entity_types)
+
+