diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 1b2e913c3..e7e6dcdfc 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -80,7 +80,7 @@ class BaseThincComponent(object):
     def get_loss(self, docs, golds, scores):
         raise NotImplementedError
 
-    def begin_training(self, gold_tuples, pipeline=None):
+    def begin_training(self, gold_tuples=tuple(), pipeline=None):
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
             self.model = self.Model(1, token_vector_width)
@@ -223,7 +223,7 @@ class TokenVectorEncoder(BaseThincComponent):
         # TODO: implement
         raise NotImplementedError
 
-    def begin_training(self, gold_tuples, pipeline=None):
+    def begin_training(self, gold_tuples=tuple(), pipeline=None):
         """Allocate models, pre-process training data and acquire a trainer and
         optimizer.
 
@@ -311,7 +311,7 @@ class NeuralTagger(BaseThincComponent):
         d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
         return float(loss), d_scores
 
-    def begin_training(self, gold_tuples, pipeline=None):
+    def begin_training(self, gold_tuples=tuple(), pipeline=None):
         orig_tag_map = dict(self.vocab.morphology.tag_map)
         new_tag_map = {}
         for raw_text, annots_brackets in gold_tuples:
@@ -420,7 +420,7 @@ class NeuralLabeller(NeuralTagger):
     def set_annotations(self, docs, dep_ids):
         pass
 
-    def begin_training(self, gold_tuples, pipeline=None):
+    def begin_training(self, gold_tuples=tuple(), pipeline=None):
         gold_tuples = nonproj.preprocess_training_data(gold_tuples)
         for raw_text, annots_brackets in gold_tuples:
             for annots, brackets in annots_brackets:
@@ -502,7 +502,7 @@ class SimilarityHook(BaseThincComponent):
 
         return d_tensor1s, d_tensor2s
 
-    def begin_training(self, _, pipeline=None):
+    def begin_training(self, _=tuple(), pipeline=None):
         """
         Allocate model, using width from tensorizer in pipeline.
 
@@ -517,7 +517,7 @@ class TextCategorizer(BaseThincComponent):
     name = 'textcat'
 
     @classmethod
-    def Model(cls, nr_class, width=64, **cfg):
+    def Model(cls, nr_class=1, width=64, **cfg):
         return build_text_classifier(nr_class, width, **cfg)
 
     def __init__(self, vocab, model=True, **cfg):
@@ -544,7 +544,7 @@ class TextCategorizer(BaseThincComponent):
 
     def set_annotations(self, docs, scores):
         for i, doc in enumerate(docs):
-            for j, label in self.labels:
+            for j, label in enumerate(self.labels):
                 doc.cats[label] = float(scores[i, j])
 
     def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
@@ -567,8 +567,11 @@ class TextCategorizer(BaseThincComponent):
         mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
         return mean_square_error, d_scores
 
-    def begin_training(self, gold_tuples, pipeline=None):
-        token_vector_width = pipeline[0].model.nO
+    def begin_training(self, gold_tuples=tuple(), pipeline=None):
+        if pipeline:
+            token_vector_width = pipeline[0].model.nO
+        else:
+            token_vector_width = 64
         if self.model is True:
             self.model = self.Model(len(self.labels), token_vector_width)